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Wang N, Dong G, Qiao R, Yin X, Lin S. Bringing Artificial Intelligence (AI) into Environmental Toxicology Studies: A Perspective of AI-Enabled Zebrafish High-Throughput Screening. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:9487-9499. [PMID: 38691763 DOI: 10.1021/acs.est.4c00480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
The booming development of artificial intelligence (AI) has brought excitement to many research fields that could benefit from its big data analysis capability for causative relationship establishment and knowledge generation. In toxicology studies using zebrafish, the microscopic images and videos that illustrate the developmental stages, phenotypic morphologies, and animal behaviors possess great potential to facilitate rapid hazard assessment and dissection of the toxicity mechanism of environmental pollutants. However, the traditional manual observation approach is both labor-intensive and time-consuming. In this Perspective, we aim to summarize the current AI-enabled image and video analysis tools to realize the full potential of AI. For image analysis, AI-based tools allow fast and objective determination of morphological features and extraction of quantitative information from images of various sorts. The advantages of providing accurate and reproducible results while avoiding human intervention play a critical role in speeding up the screening process. For video analysis, AI-based tools enable the tracking of dynamic changes in both microscopic cellular events and macroscopic animal behaviors. The subtle changes revealed by video analysis could serve as sensitive indicators of adverse outcomes. With AI-based toxicity analysis in its infancy, exciting developments and applications are expected to appear in the years to come.
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Affiliation(s)
- Nan Wang
- College of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai East Hospital, Tongji University, Shanghai 200092, People's Republic of China
- Key Laboratory of Yangtze River Water Environment, Ministry of Education; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, People's Republic of China
| | - Gongqing Dong
- College of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai East Hospital, Tongji University, Shanghai 200092, People's Republic of China
- Key Laboratory of Yangtze River Water Environment, Ministry of Education; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, People's Republic of China
| | - Ruxia Qiao
- College of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai East Hospital, Tongji University, Shanghai 200092, People's Republic of China
- Key Laboratory of Yangtze River Water Environment, Ministry of Education; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, People's Republic of China
| | - Xiang Yin
- College of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai East Hospital, Tongji University, Shanghai 200092, People's Republic of China
- Key Laboratory of Yangtze River Water Environment, Ministry of Education; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, People's Republic of China
| | - Sijie Lin
- College of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai East Hospital, Tongji University, Shanghai 200092, People's Republic of China
- Key Laboratory of Yangtze River Water Environment, Ministry of Education; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, People's Republic of China
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Ye J, Huang Y, Chu C, Li J, Liu G, Li W, Gao C. Association Between Artificial Intelligence Based Chest Computed Tomography and Clinical/Laboratory Characteristics with Severity and Mortality in COVID-19 Hospitalized Patients. J Inflamm Res 2024; 17:2977-2989. [PMID: 38764494 PMCID: PMC11102184 DOI: 10.2147/jir.s456440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 04/23/2024] [Indexed: 05/21/2024] Open
Abstract
Background Some patients with COVID-19 rapidly develop respiratory failure or mortality, underscoring the necessity for early identification of those prone to severe illness. Numerous studies focus on clinical and lab traits, but only few attend to chest computed tomography. The current study seeks to numerically quantify pulmonary lesions using early-phase CT scans calculated through artificial intelligence algorithms in conjunction with clinical and laboratory helps clinicians to early identify the development of severe illness and death in a group of COVID-19 patients. Methods From December 15, 2022, to January 30, 2023, 191 confirmed COVID-19 patients admitted to Xinhua Hospital Affiliated with Shanghai Jiao Tong University School of Medicine were consecutively enrolled. All patients underwent chest CT scans and serum tests within 48 hours prior to admission. Variables significantly linked to critical illness or mortality in univariate analysis were subjected to multivariate logistic regression models post collinearity assessment. Adjusted odds ratio, 95% confidence intervals, sensitivity, specificity, Youden index, receiver-operator-characteristics (ROC) curves, and area under the curve (AUC) were computed for predicting severity and in-hospital mortality. Results Multivariate logistic analysis revealed that myoglobin (OR = 1.003, 95% CI 1.001-1.005), APACHE II score (OR = 1.387, 95% CI 1.216-1.583), and the infected CT region percentage (OR = 113.897, 95% CI 4.939-2626.496) independently correlated with in-hospital COVID-19 mortality. Prealbumin stood as an independent safeguarding factor (OR = 0.965, 95% CI 0.947-0.984). Neutrophil counts (OR = 1.529, 95% CI 1.131-2.068), urea nitrogen (OR = 1.587, 95% CI 1.222-2.062), SOFA score(OR = 3.333, 95% CI 1.476-7.522), qSOFA score(OR = 15.197, 95% CI 3.281-70.384), PSI score(OR = 1.053, 95% CI 1.018-1.090), and the infected CT region percentage (OR = 548.221, 95% CI 2.615-114,953.586) independently linked to COVID-19 patient severity.
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Affiliation(s)
- Jiawei Ye
- Department of Emergency Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People’s Republic of China
| | - Yingying Huang
- Dementia Research Centre, Faculty of Medicine, Health and Human Sciences, Macquarie UniversitySydney, Australia
| | - Caiting Chu
- Department of Radiology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People’s Republic of China
| | - Juan Li
- Department of Emergency Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People’s Republic of China
| | - Guoxiang Liu
- Department of Emergency Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People’s Republic of China
| | - Wenjie Li
- Department of Emergency Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People’s Republic of China
| | - Chengjin Gao
- Department of Emergency Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People’s Republic of China
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Chen L, Li M, Wu Z, Liu S, Huang Y. A nomogram to predict severe COVID-19 patients with increased pulmonary lesions in early days. Front Med (Lausanne) 2024; 11:1343661. [PMID: 38737763 PMCID: PMC11082326 DOI: 10.3389/fmed.2024.1343661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 03/25/2024] [Indexed: 05/14/2024] Open
Abstract
Objectives This study aimed to predict severe coronavirus disease 2019 (COVID-19) progression in patients with increased pneumonia lesions in the early days. A simplified nomogram was developed utilizing artificial intelligence (AI)-based quantified computed tomography (CT). Methods From 17 December 2019 to 20 February 2020, a total of 246 patients were confirmed COVID-19 infected in Jingzhou Central Hospital, Hubei Province, China. Of these patients, 93 were mildly ill and had follow-up examinations in 7 days, and 61 of them had enlarged lesions on CT scans. We collected the neutrophil-to-lymphocyte ratio (NLR) and three quantitative CT features from two examinations within 7 days. The three quantitative CT features of pneumonia lesions, including ground-glass opacity volume (GV), semi-consolidation volume (SV), and consolidation volume (CV), were automatically calculated using AI. Additionally, the variation volumes of the lesions were also computed. Finally, a nomogram was developed using a multivariable logistic regression model. To simplify the model, we classified all the lesion volumes based on quartiles and curve fitting results. Results Among the 93 patients, 61 patients showed enlarged lesions on CT within 7 days, of whom 19 (31.1%) developed any severe illness. The multivariable logistic regression model included age, NLR on the second time, an increase in lesion volume, and changes in SV and CV in 7 days. The personalized prediction nomogram demonstrated strong discrimination in the sample, with an area under curve (AUC) and the receiver operating characteristic curve (ROC) of 0.961 and a 95% confidence interval (CI) of 0.917-1.000. Decision curve analysis illustrated that a nomogram based on quantitative AI was clinically useful. Conclusion The integration of CT quantitative changes, NLR, and age in this model exhibits promising performance in predicting the progression to severe illness in COVID-19 patients with early-stage pneumonia lesions. This comprehensive approach holds the potential to assist clinical decision-making.
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Affiliation(s)
- Lina Chen
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei Province, China
| | - Min Li
- Department of Radiology, Jingzhou Hospital of Traditional Chinese Medicine, Jingzhou, Hubei Province, China
| | - Zhenghong Wu
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei Province, China
| | - Sibin Liu
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei Province, China
| | - Yuanyi Huang
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei Province, China
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Maleki Varnosfaderani S, Forouzanfar M. The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering (Basel) 2024; 11:337. [PMID: 38671759 PMCID: PMC11047988 DOI: 10.3390/bioengineering11040337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.
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Affiliation(s)
| | - Mohamad Forouzanfar
- Département de Génie des Systèmes, École de Technologie Supérieure (ÉTS), Université du Québec, Montréal, QC H3C 1K3, Canada
- Centre de Recherche de L’institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC H3W 1W5, Canada
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Santos HO, Delpino FM, Veloso OM, Freire JMR, Gomes ESN, Pereira CGM. Elevated neutrophil-lymphocyte ratio is associated with high rates of ICU mortality, length of stay, and invasive mechanical ventilation in critically ill patients with COVID-19 : NRL and severe COVID-19. Immunol Res 2024; 72:147-154. [PMID: 37768500 DOI: 10.1007/s12026-023-09424-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 09/12/2023] [Indexed: 09/29/2023]
Abstract
Neutrophil and lymphocyte ratio (NLR) has emerged as a complementary marker in intensive care. This study aimed to associate high NLR values with mortality as the primary outcome, and length of stay and need for invasive mechanical ventilation as secondary outcomes, in critically ill patients with COVID-19. A cross-sectional study encompassing 189 critically ill patients with COVID-19 was performed. The receiver operating characteristic curve was used to identify the best NLR cutoff value for ICU mortality (≥ 10.6). An NLR ≥ 10.6, compared with an NLR < 10.6, was associated with higher odds of ICU mortality (odds ratio [OR], 2.77; 95% confidence interval [CI], 1.24-6.18), ICU length of stay ≥ 14 days (OR, 3.56; 95% CI, 1.01-12.5), and need for invasive mechanical ventilation (OR, 5.39; 95% CI, 1.96-14.81) in the fully adjusted model (age, sex, kidney dysfunction, diabetes, obesity, hypertension, deep vein thrombosis, antibiotics, anticoagulants, antivirals, corticoids, neuromuscular blockers, and vasoactive drugs). In conclusion, elevated NLR is associated with high rates of mortality, length of stay, and need for invasive mechanical ventilation in critically ill patients with COVID-19.
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Affiliation(s)
- Heitor O Santos
- School of Medicine, Federal University of Uberlandia (UFU), Para Street, 1720, Umuarama. Block 2H, Uberlandia, 38400-902, MG, Brazil.
| | - Felipe M Delpino
- Postgraduate in Nursing, Federal University of Pelotas (UFPel), Pelotas, Rio Grande do Sul, Brazil
| | - Octavio M Veloso
- Department of Medicine, Federal University of Sergipe (UFS), Sergipe. Augusto Franco Avenue, 3500. Unit 134. Aracaju - Sergipe, Aracaju, Sergipe, Brazil
| | | | | | - Cristina G M Pereira
- Department of Medicine, Federal University of Sergipe (UFS), Sergipe. Augusto Franco Avenue, 3500. Unit 134. Aracaju - Sergipe, Aracaju, Sergipe, Brazil
- São Lucas Hospital - Rede D'OR (HSL), Aracaju, Sergipe, Brazil
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Mohammedain SA, Badran S, Elzouki AY, Salim H, Chalaby A, Siddiqui MYA, Hussein YY, Rahim HA, Thalib L, Alam MF, Al-Badriyeh D, Al-Maadeed S, Doi SAR. Validation of a risk prediction model for COVID-19: the PERIL prospective cohort study. Future Virol 2023:10.2217/fvl-2023-0036. [PMID: 37970094 PMCID: PMC10630949 DOI: 10.2217/fvl-2023-0036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 10/03/2023] [Indexed: 11/17/2023]
Abstract
Aim: This study aims to perform an external validation of a recently developed prognostic model for early prediction of the risk of progression to severe COVID-19. Patients & methods/materials: Patients were recruited at their initial diagnosis at two facilities within Hamad Medical Corporation in Qatar. 356 adults were included for analysis. Predictors for progression of COVID-19 were all measured at disease onset and first contact with the health system. Results: The C statistic was 83% (95% CI: 78%-87%) and the calibration plot showed that the model was well-calibrated. Conclusion: The published prognostic model for the progression of COVID-19 infection showed satisfactory discrimination and calibration and the model is easy to apply in clinical practice.d.
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Affiliation(s)
- Shahd A Mohammedain
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
| | - Saif Badran
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
- Department of Plastic Surgery, Hamad Medical Corporation, Doha, Qatar
| | - AbdelNaser Y Elzouki
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - Halla Salim
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - Ayesha Chalaby
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - MYA Siddiqui
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - Yehia Y Hussein
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
| | - Hanan Abdul Rahim
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Lukman Thalib
- Department of Biostatistics, Faculty of Medicine, Istanbul Aydin University, Istanbul, Turkey
| | - Mohammed Fasihul Alam
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | | | - Sumaya Al-Maadeed
- Department of Computer Science, College of Engineering, Qatar University, Doha, Qatar
| | - Suhail AR Doi
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
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Hori M, Yasuda K, Takahashi H, Aoi T, Mori Y, Tsujita M, Shirasawa Y, Kondo C, Hashimoto T, Koyama H, Morozumi K, Maruyama S. The Impact of Liver Chemistries on Respiratory Failure among Hemodialysis Patients with COVID-19 during the Omicron Wave. Intern Med 2023; 62:2617-2625. [PMID: 37407459 PMCID: PMC10569926 DOI: 10.2169/internalmedicine.2115-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 05/30/2023] [Indexed: 07/07/2023] Open
Abstract
Objective Although the coronavirus disease 2019 (COVID-19) Omicron variant causes less severe symptoms than previous variants, early indicators for respiratory failure are needed in hemodialysis patients, who have a higher mortality rate than the general population. Liver chemistries are known to reflect the severity of COVID-19 in the general population. This study explored the early indicators for worsened respiratory failure based on patient characteristics, including liver chemistries. Methods This retrospective study included 117 patients admitted for COVID-19 during the Omicron wave. Respiratory failure was defined as oxygen requirement during treatment. Information on the symptoms and clinical characteristics, including liver chemistries [aspartate aminotransferase (AST) and alanine aminotransferase (ALT)], at admission was collected. Results Thirty-five patients (29.9%) required oxygen supply during treatment. In the multivariate logistic regression analyses, AST [odds ratio (OR) 1.06, 95% confidence interval (CI) 1.00-1.13, p=0.029], ALT (OR 1.09, 95% CI 1.02-1.18, p=0.009), and moderate COVID-19 illness (Model including AST, OR 6.95, 95% CI 2.23-23.17, p<0.001; Model including ALT, OR 7.19, 95% CI 2.21-25.22, p=0.001) were independent predictors for respiratory failure. Based on the cutoff values determined by the receiver operating characteristic curve, higher AST (≥23 IU/L) and ALT levels (≥14 IU/L) were also independently associated with respiratory failure (higher AST: 64.3% vs. 18.8%, OR 3.44, 95% CI 1.08-11.10, p=0.035; higher ALT: 48.8% vs. 19.7%, OR 4.23, 95% CI 1.34-14.52, p=0.013, respectively). Conclusion The measurement of AST and ALT levels at baseline may help predict oxygen requirement in hemodialysis patients with COVID-19.
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Affiliation(s)
- Mayuko Hori
- Department of Nephrology, Masuko Memorial Hospital, Japan
| | - Kaoru Yasuda
- Department of Nephrology, Masuko Memorial Hospital, Japan
| | - Hiroshi Takahashi
- Department of Nephrology, Fujita Health University School of Medicine, Japan
| | - Tomonori Aoi
- Department of Nephrology, Masuko Memorial Hospital, Japan
| | - Yoshiko Mori
- Department of Nephrology, Masuko Memorial Hospital, Japan
| | - Makoto Tsujita
- Department of Nephrology, Masuko Memorial Hospital, Japan
| | | | - Chika Kondo
- Department of Nephrology, Masuko Memorial Hospital, Japan
| | - Takashi Hashimoto
- Department of General Internal Medicine, Masuko Memorial Hospital, Japan
| | - Hiroichi Koyama
- Department of General Internal Medicine, Masuko Memorial Hospital, Japan
| | - Kunio Morozumi
- Department of Nephrology, Masuko Memorial Hospital, Japan
| | - Shoichi Maruyama
- Department of Nephrology, Nagoya University Graduate School of Medicine, Japan
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Du P, Niu X, Li X, Ying C, Zhou Y, He C, Lv S, Liu X, Du W, Wu W. Automatically transferring supervised targets method for segmenting lung lesion regions with CT imaging. BMC Bioinformatics 2023; 24:332. [PMID: 37667214 PMCID: PMC10478337 DOI: 10.1186/s12859-023-05435-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 08/02/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND To present an approach that autonomously identifies and selects a self-selective optimal target for the purpose of enhancing learning efficiency to segment infected regions of the lung from chest computed tomography images. We designed a semi-supervised dual-branch framework for training, where the training set consisted of limited expert-annotated data and a large amount of coarsely annotated data that was automatically segmented based on Hu values, which were used to train both strong and weak branches. In addition, we employed the Lovasz scoring method to automatically switch the supervision target in the weak branch and select the optimal target as the supervision object for training. This method can use noisy labels for rapid localization during the early stages of training, and gradually use more accurate targets for supervised training as the training progresses. This approach can utilize a large number of samples that do not require manual annotation, and with the iterations of training, the supervised targets containing noise become closer and closer to the fine-annotated data, which significantly improves the accuracy of the final model. RESULTS The proposed dual-branch deep learning network based on semi-supervision together with cost-effective samples achieved 83.56 ± 12.10 and 82.67 ± 8.04 on our internal and external test benchmarks measured by the mean Dice similarity coefficient (DSC). Through experimental comparison, the DSC value of the proposed algorithm was improved by 13.54% and 2.02% on the internal benchmark and 13.37% and 2.13% on the external benchmark compared with U-Net without extra sample assistance and the mean-teacher frontier algorithm, respectively. CONCLUSION The cost-effective pseudolabeled samples assisted the training of DL models and achieved much better results compared with traditional DL models with manually labeled samples only. Furthermore, our method also achieved the best performance compared with other up-to-date dual branch structures.
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Affiliation(s)
- Peng Du
- Hangzhou AiSmartIoT Co., Ltd., Hangzhou, Zhejiang, China
| | - Xiaofeng Niu
- Artificial Intelligence Lab, Hangzhou AiSmartVision Co., Ltd., Hangzhou, Zhejiang, China
| | - Xukun Li
- Artificial Intelligence Lab, Hangzhou AiSmartVision Co., Ltd., Hangzhou, Zhejiang, China
| | - Chiqing Ying
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, 310003, Zhejiang, China
| | - Yukun Zhou
- Artificial Intelligence Lab, Hangzhou AiSmartVision Co., Ltd., Hangzhou, Zhejiang, China
| | - Chang He
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, 310003, Zhejiang, China
| | - Shuangzhi Lv
- Department of Radiology The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaoli Liu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, 310003, Zhejiang, China
| | - Weibo Du
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, 310003, Zhejiang, China.
| | - Wei Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, 310003, Zhejiang, China.
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Huang C, Huang L, Wang Y, Li X, Ren L, Gu X, Kang L, Guo L, Liu M, Zhou X, Luo J, Huang Z, Tu S, Zhao Y, Chen L, Xu D, Li Y, Li C, Peng L, Li Y, Xie W, Cui D, Shang L, Fan G, Xu J, Wang G, Wang Y, Zhong J, Wang C, Wang J, Zhang D, Cao B. 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study. Lancet 2023; 401:e21-e33. [PMID: 37321233 PMCID: PMC10258565 DOI: 10.1016/s0140-6736(23)00810-3] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/02/2023] [Accepted: 04/13/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND The long-term health consequences of COVID-19 remain largely unclear. The aim of this study was to describe the long-term health consequences of patients with COVID-19 who have been discharged from hospital and investigate the associated risk factors, in particular disease severity. METHODS We did an ambidirectional cohort study of patients with confirmed COVID-19 who had been discharged from Jin Yin-tan Hospital (Wuhan, China) between Jan 7 and May 29, 2020. Patients who died before follow-up; patients for whom follow-up would be difficult because of psychotic disorders, dementia, or readmission to hospital; those who were unable to move freely due to concomitant osteoarthropathy or immobile before or after discharge due to diseases such as stroke or pulmonary embolism; those who declined to participate; those who could not be contacted; and those living outside of Wuhan or in nursing or welfare homes were all excluded. All patients were interviewed with a series of questionnaires for evaluation of symptoms and health-related quality of life, underwent physical examinations and a 6-min walking test, and received blood tests. A stratified sampling procedure was used to sample patients according to their highest seven-category scale during their hospital stay as 3, 4, and 5-6, to receive pulmonary function test, high resolution CT of the chest, and ultrasonography. Enrolled patients who had participated in the Lopinavir Trial for Suppression of SARS-CoV-2 in China received SARS-CoV-2 antibody tests. Multivariable adjusted linear or logistic regression models were used to evaluate the association between disease severity and long-term health consequences. FINDINGS In total, 1733 of 2469 discharged patients with COVID-19 were enrolled after 736 were excluded. Patients had a median age of 57·0 years (IQR 47·0-65·0) and 897 (52%) were male and 836 (48%) were female. The follow-up study was done from June 16 to Sept 3, 2020, and the median follow-up time after symptom onset was 186·0 days (175·0-199·0). Fatigue or muscle weakness (52%, 855 of 1654) and sleep difficulties (26%, 437 of 1655) were the most common symptoms. Anxiety or depression was reported among 23% (367 of 1616) of patients. The proportions of 6-min walking distance less than the lower limit of the normal range were 17% for those at severity scale 3, 13% for severity scale 4, and 28% for severity scale 5-6. The corresponding proportions of patients with diffusion impairment were 22% for severity scale 3, 29% for scale 4, and 56% for scale 5-6, and median CT scores were 3·0 (IQR 2·0-5·0) for severity scale 3, 4·0 (3·0-5·0) for scale 4, and 5·0 (4·0-6·0) for scale 5-6. After multivariable adjustment, patients showed an odds ratio (OR) of 1·61 (95% CI 0·80-3·25) for scale 4 versus scale 3 and 4·60 (1·85-11·48) for scale 5-6 versus scale 3 for diffusion impairment; OR 0·88 (0·66-1·17) for scale 4 versus scale 3 and OR 1·76 (1·05-2·96) for scale 5-6 versus scale 3 for anxiety or depression, and OR 0·87 (0·68-1·11) for scale 4 versus scale 3 and 2·75 (1·61-4·69) for scale 5-6 versus scale 3 for fatigue or muscle weakness. Of 94 patients with blood antibodies tested at follow-up, the seropositivity (96·2% vs 58·5%) and median titres (19·0 vs 10·0) of the neutralising antibodies were significantly lower compared with at the acute phase. 107 of 822 participants without acute kidney injury and with an estimated glomerular filtration rate (eGFR) of 90 mL/min per 1·73 m2 or more at acute phase had eGFR less than 90 mL/min per 1·73 m2 at follow-up. INTERPRETATION At 6 months after acute infection, COVID-19 survivors were mainly troubled with fatigue or muscle weakness, sleep difficulties, and anxiety or depression. Patients who were more severely ill during their hospital stay had more severe impaired pulmonary diffusion capacities and abnormal chest imaging manifestations, and are the main target population for intervention of long-term recovery. FUNDING National Natural Science Foundation of China, Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences, National Key Research and Development Program of China, Major Projects of National Science and Technology on New Drug Creation and Development of Pulmonary Tuberculosis, and Peking Union Medical College Foundation.
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Affiliation(s)
- Chaolin Huang
- Medical Department, Jin Yin-tan Hospital, Wuhan, China; Wuhan Research Center for Communicable Disease Diagnosis and Treatment, Chinese Academy of Medical Sciences, Wuhan, China
| | - Lixue Huang
- Department of Pulmonary and Critical Care Medicine, National Center for Respiratory Medicine, Center of Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, China-Japan Friendship Hospital, Beijing, China; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Department of Pulmonary and Critical Care Medicine, Capital Medical University, Beijing, China
| | - Yeming Wang
- Department of Pulmonary and Critical Care Medicine, National Center for Respiratory Medicine, Center of Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, China-Japan Friendship Hospital, Beijing, China; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xia Li
- Department of COVID-19 Re-examination Clinic, Jin Yin-tan Hospital, Wuhan, China; Wuhan Research Center for Communicable Disease Diagnosis and Treatment, Chinese Academy of Medical Sciences, Wuhan, China
| | - Lili Ren
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Merieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoying Gu
- Department of Pulmonary and Critical Care Medicine, National Center for Respiratory Medicine, Center of Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, China-Japan Friendship Hospital, Beijing, China; Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Liang Kang
- Medical Department, Jin Yin-tan Hospital, Wuhan, China; Wuhan Research Center for Communicable Disease Diagnosis and Treatment, Chinese Academy of Medical Sciences, Wuhan, China
| | - Li Guo
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Merieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Min Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xing Zhou
- Department of COVID-19 Re-examination Clinic, Jin Yin-tan Hospital, Wuhan, China; Wuhan Research Center for Communicable Disease Diagnosis and Treatment, Chinese Academy of Medical Sciences, Wuhan, China
| | - Jianfeng Luo
- Department of COVID-19 Re-examination Clinic, Jin Yin-tan Hospital, Wuhan, China; Wuhan Research Center for Communicable Disease Diagnosis and Treatment, Chinese Academy of Medical Sciences, Wuhan, China
| | - Zhenghui Huang
- Department of COVID-19 Re-examination Clinic, Jin Yin-tan Hospital, Wuhan, China; Wuhan Research Center for Communicable Disease Diagnosis and Treatment, Chinese Academy of Medical Sciences, Wuhan, China
| | - Shengjin Tu
- Department of COVID-19 Re-examination Clinic, Jin Yin-tan Hospital, Wuhan, China; Wuhan Research Center for Communicable Disease Diagnosis and Treatment, Chinese Academy of Medical Sciences, Wuhan, China
| | - Yue Zhao
- Department of COVID-19 Re-examination Clinic, Jin Yin-tan Hospital, Wuhan, China
| | - Li Chen
- Department of COVID-19 Re-examination Clinic, Jin Yin-tan Hospital, Wuhan, China
| | - Decui Xu
- Department of COVID-19 Re-examination Clinic, Jin Yin-tan Hospital, Wuhan, China
| | - Yanping Li
- Department of COVID-19 Re-examination Clinic, Jin Yin-tan Hospital, Wuhan, China
| | - Caihong Li
- Department of COVID-19 Re-examination Clinic, Jin Yin-tan Hospital, Wuhan, China
| | - Lu Peng
- Department of COVID-19 Re-examination Clinic, Jin Yin-tan Hospital, Wuhan, China
| | - Yong Li
- Department of Pulmonary and Critical Care Medicine, National Center for Respiratory Medicine, Center of Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, China-Japan Friendship Hospital, Beijing, China; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wuxiang Xie
- Peking University Clinical Research Institute, Beijing, China
| | - Dan Cui
- Department of Pulmonary and Critical Care Medicine, National Center for Respiratory Medicine, Center of Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, China-Japan Friendship Hospital, Beijing, China; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Harbin Medical University, Harbin, China
| | - Lianhan Shang
- Department of Pulmonary and Critical Care Medicine, National Center for Respiratory Medicine, Center of Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, China-Japan Friendship Hospital, Beijing, China; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Beijing University of Chinese Medicine, Beijing, China
| | - Guohui Fan
- Department of Pulmonary and Critical Care Medicine, National Center for Respiratory Medicine, Center of Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, China-Japan Friendship Hospital, Beijing, China; Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiuyang Xu
- Tsinghua University School of Medicine, Beijing, China
| | - Geng Wang
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Merieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Ying Wang
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Merieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jingchuan Zhong
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Merieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chen Wang
- Department of Pulmonary and Critical Care Medicine, National Center for Respiratory Medicine, Center of Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, China-Japan Friendship Hospital, Beijing, China; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Tsinghua University-Peking University Joint Center for Life Sciences, Beijing, China
| | - Jianwei Wang
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Merieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dingyu Zhang
- Medical Department, Jin Yin-tan Hospital, Wuhan, China; Wuhan Research Center for Communicable Disease Diagnosis and Treatment, Chinese Academy of Medical Sciences, Wuhan, China
| | - Bin Cao
- Department of Pulmonary and Critical Care Medicine, National Center for Respiratory Medicine, Center of Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, China-Japan Friendship Hospital, Beijing, China; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Department of Pulmonary and Critical Care Medicine, Capital Medical University, Beijing, China; Tsinghua University-Peking University Joint Center for Life Sciences, Beijing, China.
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Yang D, Ju M, Wang H, Jia Y, Wang X, Fang H, Fan J. Efficacy and safety of proxalutamide (GT0918) in severe or critically ill patients with COVID-19: study protocol for a prospective, open-label, single-arm, single-center exploratory trial. BMC Pharmacol Toxicol 2023; 24:38. [PMID: 37322522 PMCID: PMC10268455 DOI: 10.1186/s40360-023-00678-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 06/01/2023] [Indexed: 06/17/2023] Open
Abstract
BACKGROUND The rapid worldwide spread of COVID-19 has caused a global health challenge with high mortality of severe or critically ill patients with COVID-19. To date, there is no specific efficient therapeutics for severe or critically ill patients with COVID-19. It has been reported that androgen is related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Proxalutamide as an androgen receptor antagonist has shown potential treatment effects on COVID-19 patients. Thus, this trial is designed to investigate the efficacy and safety of proxalutamide in severe or critically ill patients with COVID-19. METHODS This single-arm, open-label, single-center prospective exploratory trial is planned to recruit 64 severe or critically ill patients with COVID-19 in China. Recruitment started on 16 May 2022 and is foreseen to end on 16 May 2023. Patients will be followed-up until 60 days or death, whichever comes first. The primary outcome is the 30-day all-cause mortality. Secondary endpoints included 60-day all-cause mortality, rate of clinical deterioration within 30 days after administration, time to sustain clinical recovery (determined using an 8-point ordinal scale), mean change in the Acute Physiology and Chronic Health Evaluation II scores, change in oxygenation index, changes in chest CT scan, percentage of patients confirmed negative for SARS-CoV-2 by nasopharyngeal swab, change in Ct values of SARS-CoV-2 and safety. Visits will be performed on days 1 (baseline), 15 or 30, 22, and 60. DISCUSSION The trial is the first to investigate the efficacy and safety of proxalutamide in severe or critically ill patients with COVID-19. The findings of this study might lead to the development of better treatment for COVID-19 and provide convincing evidence regarding the efficacy and safety of proxalutamide. TRIAL REGISTRATION This study was registered on 18 June 2022 at the Chinese Clinical Trial Registry (ChiCTR2200061250).
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Affiliation(s)
- Dawei Yang
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital Fudan University, Shanghai, China
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
- Shanghai Engineer & Technology Research Center of Internet of Things for Respiratory Medicine, Shanghai, China
| | - Minjie Ju
- Department of Critical Care Medicine, Zhongshan Hospital Fudan University, Shanghai, China
| | - Hao Wang
- Department of Thoracic Surgery, Zhongshan Hospital Fudan University, Shanghai, China
| | - Yichen Jia
- Department of Urology, Zhongshan Hospital Fudan University, Shanghai, China
| | - Xiaodan Wang
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital Fudan University, Shanghai, China
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
- Shanghai Engineer & Technology Research Center of Internet of Things for Respiratory Medicine, Shanghai, China
| | - Hao Fang
- Department of Anesthesiology, Zhongshan Hospital Fudan University, Shanghai, China.
- Department of Anesthesiology, Minhang Hospital, Fudan University, Shanghai, China.
| | - Jia Fan
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
- Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education，Fudan University, Shanghai, China.
- Key Laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
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Yoo SJ, Kim H, Witanto JN, Inui S, Yoon JH, Lee KD, Choi YW, Goo JM, Yoon SH. Generative adversarial network for automatic quantification of Coronavirus disease 2019 pneumonia on chest radiographs. Eur J Radiol 2023; 164:110858. [PMID: 37209462 DOI: 10.1016/j.ejrad.2023.110858] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 04/10/2023] [Accepted: 04/29/2023] [Indexed: 05/22/2023]
Abstract
PURPOSE To develop a generative adversarial network (GAN) to quantify COVID-19 pneumonia on chest radiographs automatically. MATERIALS AND METHODS This retrospective study included 50,000 consecutive non-COVID-19 chest CT scans in 2015-2017 for training. Anteroposterior virtual chest, lung, and pneumonia radiographs were generated from whole, segmented lung, and pneumonia pixels from each CT scan. Two GANs were sequentially trained to generate lung images from radiographs and to generate pneumonia images from lung images. GAN-driven pneumonia extent (pneumonia area/lung area) was expressed from 0% to 100%. We examined the correlation of GAN-driven pneumonia extent with semi-quantitative Brixia X-ray severity score (one dataset, n = 4707) and quantitative CT-driven pneumonia extent (four datasets, n = 54-375), along with analyzing a measurement difference between the GAN and CT extents. Three datasets (n = 243-1481), where unfavorable outcomes (respiratory failure, intensive care unit admission, and death) occurred in 10%, 38%, and 78%, respectively, were used to examine the predictive power of GAN-driven pneumonia extent. RESULTS GAN-driven radiographic pneumonia was correlated with the severity score (0.611) and CT-driven extent (0.640). 95% limits of agreements between GAN and CT-driven extents were -27.1% to 17.4%. GAN-driven pneumonia extent provided odds ratios of 1.05-1.18 per percent for unfavorable outcomes in the three datasets, with areas under the receiver operating characteristic curve (AUCs) of 0.614-0.842. When combined with demographic information only and with both demographic and laboratory information, the prediction models yielded AUCs of 0.643-0.841 and 0.688-0.877, respectively. CONCLUSION The generative adversarial network automatically quantified COVID-19 pneumonia on chest radiographs and identified patients with unfavorable outcomes.
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Affiliation(s)
- Seung-Jin Yoo
- Department of Radiology, Hanyang University Medical Center, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Hyungjin Kim
- Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Korea
| | | | - Shohei Inui
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Department of Radiology, Japan Self-Defense Forces Central Hospital, Tokyo, Japan
| | - Jeong-Hwa Yoon
- Institute of Health Policy and Management, Medical Research Center, Seoul National University, Seoul, South Korea
| | - Ki-Deok Lee
- Division of Infectious diseases, Department of Internal Medicine, Myongji Hospital, Goyang, Korea
| | - Yo Won Choi
- Department of Radiology, Hanyang University Medical Center, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Jin Mo Goo
- Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Korea; Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Soon Ho Yoon
- Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Korea; MEDICALIP Co. Ltd., Seoul, Korea
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12
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Chrzan R, Wizner B, Sydor W, Wojciechowska W, Popiela T, Bociąga-Jasik M, Olszanecka A, Strach M. Artificial intelligence guided HRCT assessment predicts the severity of COVID-19 pneumonia based on clinical parameters. BMC Infect Dis 2023; 23:314. [PMID: 37165346 PMCID: PMC10170419 DOI: 10.1186/s12879-023-08303-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 05/03/2023] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND The purpose of the study was to compare the results of AI (artificial intelligence) analysis of the extent of pulmonary lesions on HRCT (high resolution computed tomography) images in COVID-19 pneumonia, with clinical data including laboratory markers of inflammation, to verify whether AI HRCT assessment can predict the clinical severity of COVID-19 pneumonia. METHODS The analyzed group consisted of 388 patients with COVID-19 pneumonia, with automatically analyzed HRCT parameters of volume: AIV (absolute inflammation), AGV (absolute ground glass), ACV (absolute consolidation), PIV (percentage inflammation), PGV (percentage ground glass), PCV (percentage consolidation). Clinical data included: age, sex, admission parameters: respiratory rate, oxygen saturation, CRP (C-reactive protein), IL6 (interleukin 6), IG - immature granulocytes, WBC (white blood count), neutrophil count, lymphocyte count, serum ferritin, LDH (lactate dehydrogenase), NIH (National Institute of Health) severity score; parameters of clinical course: in-hospital death, transfer to the ICU (intensive care unit), length of hospital stay. RESULTS The highest correlation coefficients were found for PGV, PIV, with LDH (respectively 0.65, 0.64); PIV, PGV, with oxygen saturation (respectively - 0.53, -0.52); AIV, AGV, with CRP (respectively 0.48, 0.46); AGV, AIV, with ferritin (respectively 0.46, 0.45). Patients with critical pneumonia had significantly lower oxygen saturation, and higher levels of immune-inflammatory biomarkers on admission. The radiological parameters of lung involvement proved to be strong predictors of transfer to the ICU (in particular, PGV ≥ cut-off point 29% with Odds Ratio (OR): 7.53) and in-hospital death (in particular: AIV ≥ cut-off point 831 cm3 with OR: 4.31). CONCLUSIONS Automatic analysis of HRCT images by AI may be a valuable method for predicting the severity of COVID-19 pneumonia. The radiological parameters of lung involvement correlate with laboratory markers of inflammation, and are strong predictors of transfer to the ICU and in-hospital death from COVID-19. TRIAL REGISTRATION National Center for Research and Development CRACoV-HHS project, contract number SZPITALE-JEDNOIMIENNE/18/2020.
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Affiliation(s)
- Robert Chrzan
- Department of Radiology, Jagiellonian University Medical College, Kopernika 19, Krakow, 31-501, Poland.
| | - Barbara Wizner
- Department of Internal Medicine and Gerontology, Jagiellonian University Medical College, Krakow, Poland
| | - Wojciech Sydor
- Department of Rheumatology and Immunology, Jagiellonian University Medical College, Krakow, Poland
| | - Wiktoria Wojciechowska
- 1st Department of Cardiology, Interventional Electrocardiology and Arterial Hypertension, Jagiellonian University Medical College, Krakow, Poland
| | - Tadeusz Popiela
- Department of Radiology, Jagiellonian University Medical College, Kopernika 19, Krakow, 31-501, Poland
| | - Monika Bociąga-Jasik
- Department of Infectious Diseases, Jagiellonian University Medical College, Krakow, Poland
| | - Agnieszka Olszanecka
- 1st Department of Cardiology, Interventional Electrocardiology and Arterial Hypertension, Jagiellonian University Medical College, Krakow, Poland
| | - Magdalena Strach
- Department of Rheumatology and Immunology, Jagiellonian University Medical College, Krakow, Poland
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Dabbagh R, Jamal A, Bhuiyan Masud JH, Titi MA, Amer YS, Khayat A, Alhazmi TS, Hneiny L, Baothman FA, Alkubeyyer M, Khan SA, Temsah MH. Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review. Cureus 2023; 15:e38373. [PMID: 37265897 PMCID: PMC10230599 DOI: 10.7759/cureus.38373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/30/2023] [Indexed: 06/03/2023] Open
Abstract
During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.
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Affiliation(s)
- Rufaidah Dabbagh
- Family & Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Amr Jamal
- Family & Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
- Research Chair for Evidence-Based Health Care and Knowledge Translation, Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | | | - Maher A Titi
- Quality Management Department, King Saud University Medical City, Riyadh, SAU
- Research Chair for Evidence-Based Health Care and Knowledge Translation, Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Yasser S Amer
- Pediatrics, Quality Management Department, King Saud University Medical City, Riyadh, SAU
- Research Chair for Evidence-Based Health Care and Knowledge Translation, Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Afnan Khayat
- Health Information Management Department, Prince Sultan Military College of Health Sciences, Al Dhahran, SAU
| | - Taha S Alhazmi
- Family & Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Layal Hneiny
- Medicine, Wegner Health Sciences Library, University of South Dakota, Vermillion, USA
| | - Fatmah A Baothman
- Department of Information Systems, King Abdulaziz University, Jeddah, SAU
| | | | - Samina A Khan
- School of Computer Sciences, Universiti Sains Malaysia, Penang, MYS
| | - Mohamad-Hani Temsah
- Pediatric Intensive Care Unit, Department of Pediatrics, King Saud University, Riyadh, SAU
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Karbasi Z, Gohari SH, Sabahi A. Bibliometric analysis of the use of artificial intelligence in COVID-19 based on scientific studies. Health Sci Rep 2023; 6:e1244. [PMID: 37152228 PMCID: PMC10158785 DOI: 10.1002/hsr2.1244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/11/2023] [Accepted: 04/16/2023] [Indexed: 05/09/2023] Open
Abstract
Background and Aims One such strategy is citation analysis used by researchers for research planning an article referred to by another article receives a "citation." By using bibliometric analysis, the development of research areas and authors' influence can be investigated. The current study aimed to identify and analyze the characteristics of 100 highly cited articles on the use of artificial intelligence concerning COVID-19. Methods On July 27, 2022, this database was searched using the keywords "artificial intelligence" and "COVID-19" in the topic. After extensive searching, all retrieved articles were sorted by the number of citations, and 100 highly cited articles were included based on the number of citations. The following data were extracted: year of publication, type of study, name of journal, country, number of citations, language, and keywords. Results The average number of citations for 100 highly cited articles was 138.54. The top three cited articles with 745, 596, and 549 citations. The top 100 articles were all in English and were published in 2020 and 2021. China was the most prolific country with 19 articles, followed by the United States with 15 articles and India with 10 articles. Conclusion The current bibliometric analysis demonstrated the significant growth of the use of artificial intelligence for COVID-19. Using these results, research priorities are more clearly defined, and researchers can focus on hot topics.
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Affiliation(s)
- Zahra Karbasi
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
- Department of Health Information Sciences, Faculty of Management and Medical Information SciencesKerman University of Medical SciencesKermanIran
| | - Sadrieh H. Gohari
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Azam Sabahi
- Department of Health Information Technology, Ferdows School of Health and Allied Medical SciencesBirjand University of Medical SciencesBirjandIran
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15
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Chrzan R, Polok K, Antczak J, Siwiec-Koźlik A, Jagiełło W, Popiela T. The value of lung ultrasound in COVID-19 pneumonia, verified by high resolution computed tomography assessed by artificial intelligence. BMC Infect Dis 2023; 23:195. [PMID: 37003997 PMCID: PMC10064611 DOI: 10.1186/s12879-023-08173-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 03/17/2023] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND Lung ultrasound (LUS) is an increasingly popular imaging method in clinical practice. It became particularly important during the COVID-19 pandemic due to its mobility and ease of use compared to high-resolution computed tomography (HRCT). The objective of this study was to assess the value of LUS in quantifying the degree of lung involvement and in discrimination of lesion types in the course of COVID-19 pneumonia as compared to HRCT analyzed by the artificial intelligence (AI). METHODS This was a prospective observational study including adult patients hospitalized due to COVID-19 in whom initial HRCT and LUS were performed with an interval < 72 h. HRCT assessment was performed automatically by AI. We evaluated the correlations between the inflammation volume assessed both in LUS and HRCT, between LUS results and the HRCT structure of inflammation, and between LUS and the laboratory markers of inflammation. Additionally we compared the LUS results in subgroups depending on the respiratory failure throughout the hospitalization. RESULTS Study group comprised 65 patients, median 63 years old. For both lungs, the median LUS score was 19 (IQR-interquartile range 11-24) and the median CT score was 22 (IQR 16-26). Strong correlations were found between LUS and CT scores (for both lungs r = 0.75), and between LUS score and percentage inflammation volume (PIV) (r = 0.69). The correlations remained significant, if weakened, for individual lung lobes. The correlations between LUS score and the value of the percentage consolidation volume (PCV) divided by percentage ground glass volume (PGV), were weak or not significant. We found significant correlation between LUS score and C-reactive protein (r = 0.55), and between LUS score and interleukin 6 (r = 0.39). LUS score was significantly higher in subgroups with more severe respiratory failure. CONCLUSIONS LUS can be regarded as an accurate method to evaluate the extent of COVID-19 pneumonia and as a promising tool to estimate its clinical severity. Evaluation of LUS in the assessment of the structure of inflammation, requires further studies in the course of the disease. TRIAL REGISTRATION The study has been preregistered 13 Aug 2020 on clinicaltrials.gov with the number NCT04513210.
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Affiliation(s)
- Robert Chrzan
- Department of Radiology, Jagiellonian University Medical College, Kopernika 19, 31-501, Krakow, Poland.
| | - Kamil Polok
- Department of Intensive Care and Perioperative Medicine, Jagiellonian University Medical College, Krakow, Poland
| | - Jakub Antczak
- Department of Neurology, Jagiellonian University Medical College, Krakow, Poland
| | - Andżelika Siwiec-Koźlik
- Department of Rheumatology and Immunology, Jagiellonian University Medical College, Krakow, Poland
| | - Wojciech Jagiełło
- Second Department of Internal Medicine, Jagiellonian University Medical College, Krakow, Poland
| | - Tadeusz Popiela
- Department of Radiology, Jagiellonian University Medical College, Kopernika 19, 31-501, Krakow, Poland
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Shukla AK, Seth T, Muhuri PK. Artificial intelligence centric scientific research on COVID-19: an analysis based on scientometrics data. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 82:1-33. [PMID: 37362722 PMCID: PMC9978294 DOI: 10.1007/s11042-023-14642-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 07/01/2022] [Accepted: 02/03/2023] [Indexed: 06/28/2023]
Abstract
With the spread of the deadly coronavirus disease throughout the geographies of the globe, expertise from every field has been sought to fight the impact of the virus. The use of Artificial Intelligence (AI), especially, has been the center of attention due to its capability to produce trustworthy results in a reasonable time. As a result, AI centric based research on coronavirus (or COVID-19) has been receiving growing attention from different domains ranging from medicine, virology, and psychiatry etc. We present this comprehensive study that closely monitors the impact of the pandemic on global research activities related exclusively to AI. In this article, we produce highly informative insights pertaining to publications, such as the best articles, research areas, most productive and influential journals, authors, and institutions. Studies are made on top 50 most cited articles to identify the most influential AI subcategories. We also study the outcome of research from different geographic areas while identifying the research collaborations that have had an impact. This study also compares the outcome of research from the different countries around the globe and produces insights on the same.
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Affiliation(s)
- Amit K. Shukla
- Faculty of Information Technology, University of Jyväskylä, Box 35 (Agora), Jyväskylä, 40014 Finland
| | - Taniya Seth
- Department of Computer Science, South Asian University, Akbar Bhawan, Chanakyapuri, New Delhi 110021 India
| | - Pranab K. Muhuri
- Department of Computer Science, South Asian University, Akbar Bhawan, Chanakyapuri, New Delhi 110021 India
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17
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Admission Predictors of Mortality in Hospitalized COVID-19 Patients-A Serbian Cohort Study. J Clin Med 2022; 11:jcm11206109. [PMID: 36294430 PMCID: PMC9605560 DOI: 10.3390/jcm11206109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/03/2022] [Accepted: 10/06/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Early prediction of COVID-19 patients’ mortality risk may be beneficial in adequate triage and risk assessment. Therefore, we aimed to single out the independent morality predictors of hospitalized COVID-19 patients among parameters available on hospital admission. Methods: An observational, retrospective−prospective cohort study was conducted on 703 consecutive COVID-19 patients hospitalized in the University Clinical Center Kragujevac between September and December 2021. Patients were followed during the hospitalization, and in-hospital mortality was observed as a primary end-point. Within 24 h of admission, patients were sampled for blood gas and laboratory analysis, including complete blood cell count, inflammation biomarkers and other biochemistry, coagulation parameters, and cardiac biomarkers. Socio-demographic and medical history data were obtained using patients’ medical records. Results: The overall prevalence of mortality was 28.4% (n = 199). After performing multiple regression analysis on 20 parameters, according to the initial univariate analysis, only four independent variables gave statistically significant contributions to the model: SaO2 < 88.5 % (aOR 3.075), IL-6 > 74.6 pg/mL (aOR 2.389), LDH > 804.5 U/L (aOR 2.069) and age > 69.5 years (aOR 1.786). The C-index of the predicted probability calculated using this multivariate logistic model was 0.740 (p < 0.001). Conclusions: Parameters available on hospital admission can be beneficial in predicting COVID-19 mortality.
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Zinellu A, Mangoni AA. A systematic review and meta-analysis of the association between the neutrophil, lymphocyte, and platelet count, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio and COVID-19 progression and mortality. Expert Rev Clin Immunol 2022; 18:1187-1202. [PMID: 36047369 DOI: 10.1080/1744666x.2022.2120472] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND AND AIMS Severe manifestations of coronavirus disease 2019 (COVID-19) are associated with alterations in blood cells that regulate immunity, inflammation, and hemostasis. We conducted an updated systematic review and meta-analysis of the association between the neutrophil, lymphocyte, and platelet count, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR), and COVID-19 progression and mortality. METHODS A systematic literature search was conducted in PubMed, Web of Science, and Scopus for studies published between January 2020 and June 2022. RESULTS In 71 studies reporting the investigated parameters within 48 hours of admission, higher NLR (HR 1.21, 95% CI 1.16 to 1.27, p < 0.0001), relative neutrophilia (HR 1.62, 95% CI 1.46 to 1.80, p < 0.0001), relative lymphopenia (HR 1.62, 95% CI 1.27 to 2.08, p < 0.001), and relative thrombocytopenia (HR 1.74, 95% CI 1.36 to 2.22, p < 0.001), but not PLR (p = 0.11), were significantly associated with disease progression and mortality. Between-study heterogeneity was large-to-extreme. The magnitude and direction of the effect size were not modified in sensitivity analysis. CONCLUSIONS NLR and neutrophil, lymphocyte, and platelet count significantly discriminate COVID-19 patients with different progression and survival outcomes. (PROSPERO registration number: CRD42021267875).
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Affiliation(s)
- Angelo Zinellu
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Arduino A Mangoni
- Discipline of Clinical Pharmacology, College of Medicine and Public Health, Flinders University, Adelaide, Australia.,Department of Clinical Pharmacology, Flinders Medical Centre, Southern Adelaide Local Health Network, Adelaide, Australia
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Liu H, Wang J, Geng Y, Li K, Wu H, Chen J, Chai X, Li S, Zheng D. Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10665. [PMID: 36078380 PMCID: PMC9518491 DOI: 10.3390/ijerph191710665] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/21/2022] [Accepted: 08/24/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clinico-radiological features, especially the quantitative computed tomography (CT) image analysis results, may achieve early, accurate, and fine-grained assessment of COVID-19 severity, which is an urgent clinical need. OBJECTIVE To evaluate if machine learning algorithms using CT-based clinico-radiological features could achieve the accurate fine-grained assessment of COVID-19 severity. METHODS The clinico-radiological features were collected from 78 COVID-19 patients with different severities. A neural network was developed to automatically measure the lesion volume from CT images. The severity was clinically diagnosed using two-type (severe and non-severe) and fine-grained four-type (mild, regular, severe, critical) classifications, respectively. To investigate the key features of COVID-19 severity, statistical analyses were performed between patients' clinico-radiological features and severity. Four machine learning algorithms (decision tree, random forest, SVM, and XGBoost) were trained and applied in the assessment of COVID-19 severity using clinico-radiological features. RESULTS The CT imaging features (CTscore and lesion volume) were significantly related with COVID-19 severity (p < 0.05 in statistical analysis for both in two-type and fine-grained four-type classifications). The CT imaging features significantly improved the accuracy of machine learning algorithms in assessing COVID-19 severity in the fine-grained four-type classification. With CT analysis results added, the four-type classification achieved comparable performance to the two-type one. CONCLUSIONS CT-based clinico-radiological features can provide an important reference for the accurate fine-grained assessment of illness severity using machine learning to achieve the early triage of COVID-19 patients.
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Affiliation(s)
- Haipeng Liu
- Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UK
| | - Jiangtao Wang
- Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UK
| | - Yayuan Geng
- Scientific Research Department, HY Medical Technology, B-2 Building, Dongsheng Science Park, Beijing 100192, China
| | - Kunwei Li
- Department of Radiology, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, China
| | - Han Wu
- College of Engineering, Mathematics and Physical Sciences, Streatham Campus, University of Exeter, North Park Road, Exeter EX4 4QF, UK
| | - Jian Chen
- Department of Radiology, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, China
| | - Xiangfei Chai
- Scientific Research Department, HY Medical Technology, B-2 Building, Dongsheng Science Park, Beijing 100192, China
| | - Shaolin Li
- Department of Radiology, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, China
- Guangdong Provincial Key Laboratory of Biomedical Imaging, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, China
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UK
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20
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Ayvat P, Kayhan Omeroglu S. Mortality estimation using APACHE and CT scores with stepwise linear regression method in COVID-19 intensive care unit: A retrospective study. Clin Imaging 2022; 88:4-8. [PMID: 35533542 PMCID: PMC9067018 DOI: 10.1016/j.clinimag.2022.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 04/28/2022] [Accepted: 04/29/2022] [Indexed: 11/17/2022]
Abstract
Background COVID-19 is a disease with high mortality worldwide, and which parameters that affect mortality in intensive care are still being investigated. This study aimed to show the factors affecting mortality in COVID-19 intensive care patients and write a model that can predict mortality. Methods The data of 229 patients in the COVID-19 intensive care unit were scanned. Laboratory tests, APACHE, SOFA, and GCS values were recorded. CT scores were calculated with chest CTs. The effects of these data on mortality were examined. The effects of the variables were modeled using the stepwise regression method. Results While the mean age of female (30.14%) patients was 69.1 ± 12.2, the mean age of male (69.86%) patients was 66.9 ± 11.5. The mortality rate was 69.86%. Age, CRP, D-dimer, creatinine, procalcitonin, APACHE, SOFA, GCS, and CT score were significantly different in the deceased patients than the survival group. When we attempted to create a model using stepwise linear regression analysis, the appropriate model was achieved at the fourth step. Age, CRP, APACHE, and CT score were included in the model, which has the power to predict mortality with 89.9% accuracy. Conclusion Although, when viewed individually, there is a significant difference in parameters such as creatinine, procalcitonin, D-dimer, GCS, and SOFA score, the probability of mortality can be estimated by knowing only the age, CRP, APACHE, and CT scores. These four simple parameters will help clinicians effectively use resources in treatment.
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Affiliation(s)
- Pinar Ayvat
- Izmir Democracy University, School of Medicine, Department of Anesthesiology, Turkey.
| | - Seyda Kayhan Omeroglu
- University of Health Sciences, Izmir Dr. Suat Seren Chest Diseases and Chest Surgery Training and Research Hospital, Anesthesiology Department, Turkey.
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21
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Parthasarathi A, Padukudru S, Arunachal S, Basavaraj CK, Krishna MT, Ganguly K, Upadhyay S, Anand MP. The Role of Neutrophil-to-Lymphocyte Ratio in Risk Stratification and Prognostication of COVID-19: A Systematic Review and Meta-Analysis. Vaccines (Basel) 2022; 10:vaccines10081233. [PMID: 36016121 PMCID: PMC9415708 DOI: 10.3390/vaccines10081233] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 07/27/2022] [Accepted: 07/28/2022] [Indexed: 01/18/2023] Open
Abstract
Several studies have proposed that the neutrophil−lymphocyte ratio (NLR) is one of the various biomarkers that can be useful in assessing COVID-19 disease-related outcomes. Our systematic review analyzes the relationship between on-admission NLR values and COVID-19 severity and mortality. Six different severity criteria were used. A search of the literature in various databases was conducted from 1 January 2020 to 1 May 2021. We calculated the pooled standardized mean difference (SMD) for the collected NLR values. A meta-regression analysis was performed, looking at the length of hospitalization and other probable confounders, such as age, gender, and comorbidities. A total of sixty-four studies were considered, which included a total of 15,683 patients. The meta-analysis showed an SMD of 3.12 (95% CI: 2.64−3.59) in NLR values between severe and non-severe patients. A difference of 3.93 (95% CI: 2.35−5.50) was found between survivors and non-survivors of the disease. Upon summary receiver operating characteristics analysis, NLR showed 80.2% (95% CI: 74.0−85.2%) sensitivity and 75.8% (95% CI: 71.3−79.9%) specificity for the prediction of severity and 78.8% (95% CI: 73.5−83.2%) sensitivity and 73.0% (95% CI: 68.4−77.1%) specificity for mortality, and was not influenced by age, gender, or co-morbid conditions. Conclusion: On admission, NLR predicts both severity and mortality in COVID-19 patients, and an NLR > 6.5 is associated with significantly greater the odds of mortality.
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Affiliation(s)
| | - Sunag Padukudru
- Yenepoya Medical College, Yenepoya University, Mangalore 575018, India;
| | - Sumalata Arunachal
- Department of Respiratory Medicine, JSS Medical College, JSSAHER, Mysore 570015, India; (S.A.); (C.K.B.)
| | - Chetak Kadabasal Basavaraj
- Department of Respiratory Medicine, JSS Medical College, JSSAHER, Mysore 570015, India; (S.A.); (C.K.B.)
| | - Mamidipudi Thirumala Krishna
- University Hospitals Birmingham NHS Foundation Trust, Institute of Immunology Immunotherapy, University of Birmingham, Birmingham B15 2GW, UK;
| | - Koustav Ganguly
- Unit of Integrative Toxicology, Institute of Environmental Medicine (IMM), Karolinska Institutet, 17177 Stockholm, Sweden;
| | - Swapna Upadhyay
- Unit of Integrative Toxicology, Institute of Environmental Medicine (IMM), Karolinska Institutet, 17177 Stockholm, Sweden;
- Correspondence: (S.U.); (M.P.A.)
| | - Mahesh Padukudru Anand
- Department of Respiratory Medicine, JSS Medical College, JSSAHER, Mysore 570015, India; (S.A.); (C.K.B.)
- Correspondence: (S.U.); (M.P.A.)
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22
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The prognostic value of biomarker levels and chest imaging in patients with COVID-19 presenting to the emergency department. Am J Emerg Med 2022; 59:15-23. [PMID: 35772223 PMCID: PMC9233869 DOI: 10.1016/j.ajem.2022.06.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 05/29/2022] [Accepted: 06/18/2022] [Indexed: 12/15/2022] Open
Abstract
Introduction We aimed to compare the prognostic value of a quantitative CT severity score with several laboratory parameters, particularly C-reactive protein, Procalcitonin, Neutrophil to lymphocyte ratio, D-dimer, ferritin, lactate dehydrogenase, lactate, troponin and B-type Natriuretic Peptide in predicting in-hospital mortality. Methods This was a retrospective chart review study of COVID-19 patients who presented to the Emergency Department of a tertiary care center between February and December 2020. All patients ≥18 years old who tested positive for the COVID-19 real-time reverse transcriptase polymerase chain reaction and underwent CT imaging at presentation were included. The primary outcome was the prognostic ability of CT severity score versus biomarkers in predicting in-hospital mortality. Results The AUCs were: D-dimer (AUC: 0.67 95% CI = 0.57–0.77), CT severity score (0.66, 95% CI = 0.55–0.77), LDH (0.66, 95% CI = 0.55–0.77), Pro-BNP (0.65, 95% CI = 0.55–0.76), NLR (0.64, 95% CI = 0.53–0.75) and troponin (0.64, 95% CI = 0.52–0.75). In the stepwise logistic regression, age (OR = 1.07 95% CI = 1.05–1.09), obesity (OR = 2.02 95% CI = 1.25–3.26), neutrophil/lymphocyte ratio (OR = 1.02 95% CI = 1.01–1.04), CRP (OR = 1.01 95% CI = 1.004–1.01), lactate dehydrogenase (OR = 1.003 95% CI = 1.001–1.004) and CT severity score (OR = 1.17 95% CI = 1.12–1.23) were significantly associated with in-hospital mortality. Conclusion In summary, CT severity score outperformed several biomarkers as a prognostic tool for covid related mortality. In COVID-19 patients requiring lung imaging, such as patients requiring ICU admission, patients with abnormal vital signs and those requiring mechanical ventilation, the results suggest obtaining and calculating the CT severity score to use it as a prognostic tool. If a CT was not performed, the results suggest using LDH, CRP or NLR if already done as prognostic tools in COVID-19 as these biomarkers were also found to be prognostic in COVID-19 patients.
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23
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Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients. Diagnostics (Basel) 2022; 12:diagnostics12061501. [PMID: 35741310 PMCID: PMC9222070 DOI: 10.3390/diagnostics12061501] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/15/2022] [Accepted: 06/17/2022] [Indexed: 01/08/2023] Open
Abstract
Background: Chest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients either qualitatively, via visual inspection, or quantitatively, via AI-based software. This study aims to compare the qualitative/quantitative pathological lung extension data on COVID-19 patients. Secondly, the quantitative data obtained were compared to verify their concordance since they were derived from three different lung segmentation software. Methods: This double-center study includes a total of 120 COVID-19 patients (60 from each center) with positive reverse-transcription polymerase chain reaction (RT-PCR) who underwent a chest CT scan from November 2020 to February 2021. CT scans were analyzed retrospectively and independently in each center. Specifically, CT images were examined manually by two different and experienced radiologists for each center, providing the qualitative extent score of lung involvement, whereas the quantitative analysis was performed by one trained radiographer for each center using three different software: 3DSlicer, CT Lung Density Analysis, and CT Pulmo 3D. Results: The agreement between radiologists for visual estimation of pneumonia at CT can be defined as good (ICC 0.79, 95% CI 0.73–0.84). The statistical tests show that 3DSlicer overestimates the measures assessed; however, ICC index returns a value of 0.92 (CI 0.90–0.94), indicating excellent reliability within the three software employed. ICC was also performed between each single software and the median of the visual score provided by the radiologists. This statistical analysis underlines that the best agreement is between 3D Slicer “LungCTAnalyzer” and the median of the visual score (0.75 with a CI 0.67–82 and with a median value of 22% of disease extension for the software and 25% for the visual values). Conclusions: This study provides for the first time a direct comparison between the actual gold standard, which is represented by the qualitative information described by radiologists, and novel quantitative AI-based techniques, here represented by three different commonly used lung segmentation software, underlying the importance of these specific values that in the future could be implemented as consistent prognostic and clinical course parameters.
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24
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Malécot N, Chrusciel J, Sanchez S, Sellès P, Goetz C, Lévêque HP, Parizel E, Pradel J, Almhana M, Bouvier E, Uyttenhove F, Bonnefoy E, Vazquez G, Adib O, Calvo P, Antoine C, Jullien V, Cirille S, Dumas A, Defasque A, Ben Ghorbal Y, Elkadri M, Schertz M, Cavet M. Chest CT Characteristics are Strongly Predictive of Mortality in Patients with COVID-19 Pneumonia: A Multicentric Cohort Study. Acad Radiol 2022; 29:851-860. [PMID: 35282991 PMCID: PMC8769941 DOI: 10.1016/j.acra.2022.01.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/09/2021] [Accepted: 01/13/2022] [Indexed: 12/11/2022]
Abstract
Rationale and Objectives The novel coronavirus (COVID-19) has presented a significant and urgent threat to global health and there has been a need to identify prognostic factors in COVID-19 patients. The aim of this study was to determine whether chest computed tomography (CT) characteristics had any prognostic value in patients with COVID-19. Materials and Methods A retrospective analysis of COVID-19 patients who underwent a chest CT-scan was performed in four medical centers. The prognostic value of chest CT results was assessed using a multivariable survival analysis with the Cox model. The characteristics included in the model were the degree of lung involvement, ground glass opacities, nodular consolidations, linear consolidations, a peripheral topography, a predominantly inferior lung involvement, pleural effusion, and crazy paving. The model was also adjusted on age, sex, and the center in which the patient was hospitalized. The primary endpoint was 30-day in-hospital mortality. A second model used a composite endpoint of admission to an intensive care unit or 30-day in-hospital mortality. Results A total of 515 patients with available follow-up information were included. Advanced age, a degree of pulmonary involvement ≥50% (Hazard Ratio 2.25 [95% CI: 1.378-3.671], p = 0.001), nodular consolidations and pleural effusions were associated with lower 30-day in-hospital survival rates. An exploratory subgroup analysis showed a 60.6% mortality rate in patients over 75 with ≥50% lung involvement on a CT-scan. Conclusion Chest CT findings such as the percentage of pulmonary involvement ≥50%, pleural effusion and nodular consolidation were strongly associated with 30-day mortality in COVID-19 patients. CT examinations are essential for the assessment of severe COVID-19 patients and their results must be considered when making care management decisions.
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25
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Tiwari L, Gupta P, N Y, Banerjee A, Kumar Y, Singh PK, Ranjan A, Singh CM, Singh PK. Clinicodemographic profile and predictors of poor outcome in hospitalised COVID-19 patients: a single-centre, retrospective cohort study from India. BMJ Open 2022; 12:e056464. [PMID: 35649611 PMCID: PMC9160596 DOI: 10.1136/bmjopen-2021-056464] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVES Primary objective was to study the clinicodemographic profile of hospitalised COVID-19 patients at a tertiary-care centre in India. Secondary objective was to identify predictors of poor outcome. SETTING Single centre tertiary-care level. DESIGN Retrospective cohort study. PARTICIPANTS Consecutively hospitalised adults patients with COVID-19. PRIMARY AND SECONDARY OUTCOME MEASURES Primary outcome variable was in-hospital mortality. Covariables were known comorbidities, clinical features, vital signs at the time of admission and on days 3-5 of admission, and initial laboratory investigations. RESULTS Intergroup differences were tested using χ2 or Fischer's exact tests, Student's t-test or Mann-Whitney U test. Predictors of mortality were evaluated using multivariate logistic regression model. Out of 4102 SARS-CoV-2 positive patients admitted during 1-year period, 3268 (79.66%) survived to discharge and 834 (20.33%) died in the hospital. Mortality rates increased with age. Death was more common among males (OR 1.51, 95% CI 1.25 to 1.81). Out of 261 cases analysed in detail, 55.1% were in mild, 32.5% in moderate and 12.2% in severe triage category. Most common clinical presentations in the subgroup were fever (73.2%), cough/coryza (65.5%) and breathlessness (54%). Hypertension (45.2%), diabetes mellitus (41.8%) and chronic kidney disease (CKD; 6.1%) were common comorbidities. Disease severity on admission (adjusted OR 12.53, 95% CI 4.92 to 31.91, p<0.01), coagulation defect (33.21, 3.85-302.1, p<0.01), CKD (5.67, 1.08-29.64, p=0.04), high urea (11.05, 3.9-31.02, p<0.01), high prothrombin time (3.91, 1.59-9.65, p<0.01) and elevated ferritin (1.02, 1.00-1.03, p=0.02) were associated with poor outcome on multivariate regression. A strong predictor of mortality was disease progression on days 3-5 of admission (adjusted OR 13.66 95% CI 3.47 to 53.68). CONCLUSION COVID-19 related mortality in hospitalised adult patients at our center was similar to the developed countries. Progression in disease severity on days 3-5 of admission or days 6-13 of illness onset acts as 'turning point' for timely referral or treatment intensification for optimum use of resources.
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Affiliation(s)
- Lokesh Tiwari
- Pediatrics, All India Institute of Medical Sciences, Patna, Bihar, India
| | - Prakriti Gupta
- Pediatrics, All India Institute of Medical Sciences, Patna, Bihar, India
| | - Yankappa N
- Pediatrics, All India Institute of Medical Sciences, Patna, Bihar, India
| | - Amrita Banerjee
- Pediatrics, All India Institute of Medical Sciences, Patna, Bihar, India
| | - Yogesh Kumar
- Physiology, All India Institute of Medical Sciences, Patna, Bihar, India
| | - Prashant K Singh
- Surgery, All India Institute of Medical Sciences, Patna, Bihar, India
| | - Alok Ranjan
- Community and Family Medicine, All India Institute of Medical Sciences, Patna, Bihar, India
| | - C M Singh
- Community and Family Medicine, All India Institute of Medical Sciences, Patna, Bihar, India
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26
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Kang J, Kang J, Seo WJ, Park SH, Kang HK, Park HK, Song JE, Kwak YG, Chang J, Kim S, Kim KH, Park J, Choe WJ, Lee SS, Koo HK. Quantitative Computed Tomography Parameters in Coronavirus Disease 2019 Patients and Prediction of Respiratory Outcomes Using a Decision Tree. Front Med (Lausanne) 2022; 9:914098. [PMID: 35669915 PMCID: PMC9163736 DOI: 10.3389/fmed.2022.914098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 05/06/2022] [Indexed: 12/15/2022] Open
Abstract
Background Chest computed tomography (CT) scans play an important role in the diagnosis of coronavirus disease 2019 (COVID-19). This study aimed to describe the quantitative CT parameters in COVID-19 patients according to disease severity and build decision trees for predicting respiratory outcomes using the quantitative CT parameters. Methods Patients hospitalized for COVID-19 were classified based on the level of disease severity: (1) no pneumonia or hypoxia, (2) pneumonia without hypoxia, (3) hypoxia without respiratory failure, and (4) respiratory failure. High attenuation area (HAA) was defined as the quantified percentage of imaged lung volume with attenuation values between −600 and −250 Hounsfield units (HU). Decision tree models were built with clinical variables and initial laboratory values (model 1) and including quantitative CT parameters in addition to them (model 2). Results A total of 387 patients were analyzed. The mean age was 57.8 years, and 50.3% were women. HAA increased as the severity of respiratory outcome increased. HAA showed a moderate correlation with lactate dehydrogenases (LDH) and C-reactive protein (CRP). In the decision tree of model 1, the CRP, fibrinogen, LDH, and gene Ct value were chosen as classifiers whereas LDH, HAA, fibrinogen, vaccination status, and neutrophil (%) were chosen in model 2. For predicting respiratory failure, the decision tree built with quantitative CT parameters showed a greater accuracy than the model without CT parameters. Conclusions The decision tree could provide higher accuracy for predicting respiratory failure when quantitative CT parameters were considered in addition to clinical characteristics, PCR Ct value, and blood biomarkers.
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Affiliation(s)
- Jieun Kang
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea
| | - Jiyeon Kang
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea
| | - Woo Jung Seo
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea
| | - So Hee Park
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea
| | - Hyung Koo Kang
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea
| | - Hye Kyeong Park
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea
| | - Je Eun Song
- Division of Infectious Diseases, Department of Internal Medicine, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea
| | - Yee Gyung Kwak
- Division of Infectious Diseases, Department of Internal Medicine, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea
| | - Jeonghyun Chang
- Department of Laboratory Medicine, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea
| | - Sollip Kim
- Department of Laboratory Medicine, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea
| | - Ki Hwan Kim
- Department of Radiology, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea
| | - Junseok Park
- Department of Emergency Medicine, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea
| | - Won Joo Choe
- Department of Anesthesiology and Pain Medicine, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea
| | - Sung-Soon Lee
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea
| | - Hyeon-Kyoung Koo
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea
- *Correspondence: Hyeon-Kyoung Koo,
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27
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Han X, Yu Z, Zhuo Y, Zhao B, Ren Y, Lamm L, Xue X, Feng J, Marr C, Shan F, Peng T, Zhang XY. The value of longitudinal clinical data and paired CT scans in predicting the deterioration of COVID-19 revealed by an artificial intelligence system. iScience 2022; 25:104227. [PMID: 35434542 PMCID: PMC8989658 DOI: 10.1016/j.isci.2022.104227] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 03/10/2022] [Accepted: 04/05/2022] [Indexed: 01/09/2023] Open
Abstract
The respective value of clinical data and CT examinations in predicting COVID-19 progression is unclear, because the CT scans and clinical data previously used are not synchronized in time. To address this issue, we collected 119 COVID-19 patients with 341 longitudinal CT scans and paired clinical data, and we developed an AI system for the prediction of COVID-19 deterioration. By combining features extracted from CT and clinical data with our system, we can predict whether a patient will develop severe symptoms during hospitalization. Complementary to clinical data, CT examinations show significant add-on values for the prediction of COVID-19 progression in the early stage of COVID-19, especially in the 6th to 8th day after the symptom onset, indicating that this is the ideal time window for the introduction of CT examinations. We release our AI system to provide clinicians with additional assistance to optimize CT usage in the clinical workflow. COVID-19 patients with 341 longitudinal CT scans and paired clinical data included A new AI model for the prediction of COVID-19 progression was developed CT scans show significant add-on value over clinical data for the prediction Day 6–8 after the onset of COVID-19 symptoms is an ideal time window for a CT scan
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Affiliation(s)
- Xiaoyang Han
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China
| | - Ziqi Yu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China
| | - Yaoyao Zhuo
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.,Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Botao Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China
| | - Yan Ren
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200433, China
| | - Lorenz Lamm
- Institute of AI for Health, Helmholtz Zentrum München, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany.,Helmholtz AI, Helmholtz Zentrum München, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Xiangyang Xue
- Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China
| | - Carsten Marr
- Institute of AI for Health, Helmholtz Zentrum München, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Tingying Peng
- Helmholtz AI, Helmholtz Zentrum München, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Xiao-Yong Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
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28
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Lu X, Cui Z, Ma X, Pan F, Li L, Wang J, Sun P, Li H, Yang L, Liang B. The association of obesity with the progression and outcome of COVID-19: The insight from an artificial-intelligence-based imaging quantitative analysis on computed tomography. Diabetes Metab Res Rev 2022; 38:e3519. [PMID: 35062046 PMCID: PMC9015278 DOI: 10.1002/dmrr.3519] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 12/13/2021] [Accepted: 12/22/2021] [Indexed: 11/25/2022]
Abstract
AIMS To explore the association of obesity with the progression and outcome of coronavirus disease 2019 (COVID-19) at the acute period and 5-month follow-up from the perspectives of computed tomography (CT) imaging with artificial intelligence (AI)-based quantitative evaluation, which may help to predict the risk of obese COVID-19 patients progressing to severe and critical disease. MATERIALS AND METHODS This retrospective cohort enrolled 213 hospitalized COVID-19 patients. Patients were classified into three groups according to their body mass index (BMI): normal weight (from 18.5 to <24 kg/m2 ), overweight (from 24 to <28 kg/m2 ) and obesity (≥28 kg/m2 ). RESULTS Compared with normal-weight patients, patients with higher BMI were associated with more lung involvements in lung CT examination (lung lesions volume [cm3 ], normal weight vs. overweight vs. obesity; 175.5[34.0-414.9] vs. 261.7[73.3-576.2] vs. 395.8[101.6-1135.6]; p = 0.002), and were more inclined to deterioration at the acute period. At the 5-month follow-up, the lung residual lesion was more serious (residual total lung lesions volume [cm3 ], normal weight vs. overweight vs. obesity; 4.8[0.0-27.4] vs. 10.7[0.0-55.5] vs. 30.1[9.5-91.1]; p = 0.015), and the absorption rates were lower for higher BMI patients (absorption rates of total lung lesions volume [%], normal weight vs. overweight vs. obesity; 99.6[94.0-100.0] vs. 98.9[85.2-100.0] vs. 88.5[66.5-95.2]; p = 0.013). The clinical-plus-AI parameter model was superior to the clinical-only parameter model in the prediction of disease deterioration (areas under the ROC curve, 0.884 vs. 0.794, p < 0.05). CONCLUSIONS Obesity was associated with severe pneumonia lesions on CT and adverse clinical outcomes. The AI-based model with combinational use of clinical and CT parameters had incremental prognostic value over the clinical parameters alone.
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Affiliation(s)
- Xiaoting Lu
- Department of RadiologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
- Hubei Province Key Laboratory of Molecular ImagingWuhanChina
| | - Zhenhai Cui
- Department of EndocrinologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic DisordersWuhanChina
| | - Xiang Ma
- Department of RadiologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
- Hubei Province Key Laboratory of Molecular ImagingWuhanChina
| | - Feng Pan
- Department of RadiologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
- Hubei Province Key Laboratory of Molecular ImagingWuhanChina
| | - Lingli Li
- Department of RadiologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
- Hubei Province Key Laboratory of Molecular ImagingWuhanChina
| | - Jiazheng Wang
- Clinical & Technical SolutionsPhilips HealthcareWuhanChina
| | - Peng Sun
- Clinical & Technical SolutionsPhilips HealthcareWuhanChina
| | - Huiqing Li
- Department of EndocrinologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic DisordersWuhanChina
| | - Lian Yang
- Department of RadiologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
- Hubei Province Key Laboratory of Molecular ImagingWuhanChina
| | - Bo Liang
- Department of RadiologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
- Hubei Province Key Laboratory of Molecular ImagingWuhanChina
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29
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Laino ME, Ammirabile A, Lofino L, Lundon DJ, Chiti A, Francone M, Savevski V. Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence. Emerg Radiol 2022; 29:243-262. [PMID: 35048222 PMCID: PMC8769787 DOI: 10.1007/s10140-021-02008-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 12/03/2021] [Indexed: 01/08/2023]
Abstract
Infection with SARS-CoV-2 has dominated discussion and caused global healthcare and economic crisis over the past 18 months. Coronavirus disease 19 (COVID-19) causes mild-to-moderate symptoms in most individuals. However, rapid deterioration to severe disease with or without acute respiratory distress syndrome (ARDS) can occur within 1-2 weeks from the onset of symptoms in a proportion of patients. Early identification by risk stratifying such patients who are at risk of severe complications of COVID-19 is of great clinical importance. Computed tomography (CT) is widely available and offers the potential for fast triage, robust, rapid, and minimally invasive diagnosis: Ground glass opacities (GGO), crazy-paving pattern (GGO with superimposed septal thickening), and consolidation are the most common chest CT findings in COVID pneumonia. There is growing interest in the prognostic value of baseline chest CT since an early risk stratification of patients with COVID-19 would allow for better resource allocation and could help improve outcomes. Recent studies have demonstrated the utility of baseline chest CT to predict intensive care unit (ICU) admission in patients with COVID-19. Furthermore, developments and progress integrating artificial intelligence (AI) with computer-aided design (CAD) software for diagnostic imaging allow for objective, unbiased, and rapid assessment of CT images.
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Affiliation(s)
- Maria Elena Laino
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Radiology, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Ludovica Lofino
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Radiology, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Dara Joseph Lundon
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Humanitas Clinical and Research Center—IRCCS, Via Manzoni 56, 20089 Rozzano, Italy
| | - Marco Francone
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Radiology, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Victor Savevski
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
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30
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Scharf G, Meiler S, Zeman F, Schaible J, Poschenrieder F, Knobloch C, Kleine H, Scharf SE, Dinkel J, Stroszczynski C, Zorger N, Hamer OW. Combined Model of Quantitative Evaluation of Chest Computed Tomography and Laboratory Values for Assessing the Prognosis of Coronavirus Disease 2019. ROFO-FORTSCHR RONTG 2022; 194:737-746. [PMID: 35272354 DOI: 10.1055/a-1731-7905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
PURPOSE To assess the prognostic power of quantitative analysis of chest CT, laboratory values, and their combination in COVID-19 pneumonia. MATERIALS AND METHODS Retrospective analysis of patients with PCR-confirmed COVID-19 pneumonia and chest CT performed between March 07 and November 13, 2020. Volume and percentage (PO) of lung opacifications and mean HU of the whole lung were quantified using prototype software. 13 laboratory values were collected. Negative outcome was defined as death, ICU admittance, mechanical ventilation, or extracorporeal membrane oxygenation. Positive outcome was defined as care in the regular ward or discharge. Logistic regression was performed to evaluate the prognostic value of CT parameters and laboratory values. Independent predictors were combined to establish a scoring system for prediction of prognosis. This score was validated on a separate validation cohort. RESULTS 89 patients were included for model development between March 07 and April 27, 2020 (mean age: 60.3 years). 38 patients experienced a negative outcome. In univariate regression analysis, all quantitative CT parameters as well as C-reactive protein (CRP), relative lymphocyte count (RLC), troponin, and LDH were associated with a negative outcome. In a multivariate regression analysis, PO, CRP, and RLC were independent predictors of a negative outcome. Combination of these three values showed a strong predictive value with a C-index of 0.87. A scoring system was established which categorized patients into 4 groups with a risk of 7 %, 30 %, 67 %, or 100 % for a negative outcome. The validation cohort consisted of 28 patients between May 5 and November 13, 2020. A negative outcome occurred in 6 % of patients with a score of 0, 50 % with a score of 1, and 100 % with a score of 2 or 3. CONCLUSION The combination of PO, CRP, and RLC showed a high predictive value for a negative outcome. A 4-point scoring system based on these findings allows easy risk stratification in the clinical routine and performed exceptionally in the validation cohort. KEY POINTS · A high PO is associated with an unfavorable outcome in COVID-19. · PO, CRP, and RLC are independent predictors of an unfavorable outcome, and their combination has strong predictive power. · A 4-point scoring system based on these values allows quick risk stratification in a clinical setting. CITATION FORMAT · Scharf G, Meiler S, Zeman F et al. Combined Model of Quantitative Evaluation of Chest Computed Tomography and Laboratory Values for Assessing the Prognosis of Coronavirus Disease 2019. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1731-7905.
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Affiliation(s)
- Gregor Scharf
- Institut für Röntgendiagnostik, Universitätsklinikum Regensburg, Germany
| | - Stefanie Meiler
- Institut für Röntgendiagnostik, Universitätsklinikum Regensburg, Germany
| | - Florian Zeman
- Zentrum für Klinische Studien, Universitätsklinikum Regensburg, Germany
| | - Jan Schaible
- Institut für Röntgendiagnostik, Universitätsklinikum Regensburg, Germany
| | | | - Charlotte Knobloch
- Institut für Röntgendiagnostik, Universitätsklinikum Regensburg, Germany
| | - Henning Kleine
- Klinik für Pneumologie, Krankenhaus Barmherzige Brüder Regensburg, Germany
| | | | - Julien Dinkel
- Klinik und Poliklinik für Radiologie, Klinikum der Universität München, Germany.,Abteilung für Radiologie, Asklepios Fachkliniken München-Gauting, Germany
| | | | - Niels Zorger
- Institut für Radiologie, Krankenhaus Barmherzige Brüder Regensburg, Germany
| | - Okka Wilkea Hamer
- Institut für Röntgendiagnostik, Universitätsklinikum Regensburg, Germany.,Abteilung für Radiologie, Fachklinik Donaustauf, Germany
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31
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Ke Z, Li L, Wang L, Liu H, Lu X, Zeng F, Zha Y. Radiomics analysis enables fatal outcome prediction for hospitalized patients with coronavirus disease 2019 (COVID-19). Acta Radiol 2022; 63:319-327. [PMID: 33601893 DOI: 10.1177/0284185121994695] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND In December 2019, a rare respiratory disease named coronavirus disease 2019 (COVID-19) broke out, leading to great concern around the world. PURPOSE To develop and validate a radiomics nomogram for predicting the fatal outcome of COVID-19 pneumonia. MATERIAL AND METHODS The present study consisted of a training dataset (n = 66) and a validation dataset (n = 30) with COVID-19 from January 2020 to March 2020. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics score (Rad-score) was developed from the training cohort. The radiomics model, clinical model, and integrated model were built to assess the association between radiomics signature/clinical characteristics and the mortality of COVID-19 cases. The radiomics signature combined with the Rad-score and the independent clinical factors and radiomics nomogram were constructed. RESULTS Seven stable radiomics features associated with the mortality of COVID-19 were finally selected. A radiomics nomogram was based on a combined model consisting of the radiomics signature and the clinical risk factors indicating optimal predictive performance for the fatal outcome of patients with COVID-19 with a C-index of 0.912 (95% confidence interval [CI] 0.867-0.957) in the training dataset and 0.907 (95% CI 0.849-0.966) in the validation dataset. The calibration curves indicated optimal consistency between the prediction and the observation in both training and validation cohorts. CONCLUSION The CT-based radiomics nomogram indicated favorable predictive efficacy for the overall survival risk of patients with COVID-19, which could help clinicians intensively follow up high-risk patients and make timely diagnoses.
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Affiliation(s)
- Zan Ke
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, PR China
| | - Liang Li
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, PR China
| | - Li Wang
- Department of Infection Prevention and Control, Renmin Hospital of Wuhan University, Wuhan, PR China
| | - Huan Liu
- GE Healthcare, Shanghai, PR China
| | - Xuefang Lu
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, PR China
| | - Feifei Zeng
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, PR China
| | - Yunfei Zha
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, PR China
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32
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Bartoli A, Fournel J, Maurin A, Marchi B, Habert P, Castelli M, Gaubert JY, Cortaredona S, Lagier JC, Million M, Raoult D, Ghattas B, Jacquier A. Value and prognostic impact of a deep learning segmentation model of COVID-19 lung lesions on low-dose chest CT. RESEARCH IN DIAGNOSTIC AND INTERVENTIONAL IMAGING 2022; 1:100003. [PMID: 37520010 PMCID: PMC8939894 DOI: 10.1016/j.redii.2022.100003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 03/02/2022] [Accepted: 03/09/2022] [Indexed: 12/23/2022]
Abstract
Objectives 1) To develop a deep learning (DL) pipeline allowing quantification of COVID-19 pulmonary lesions on low-dose computed tomography (LDCT). 2) To assess the prognostic value of DL-driven lesion quantification. Methods This monocentric retrospective study included training and test datasets taken from 144 and 30 patients, respectively. The reference was the manual segmentation of 3 labels: normal lung, ground-glass opacity(GGO) and consolidation(Cons). Model performance was evaluated with technical metrics, disease volume and extent. Intra- and interobserver agreement were recorded. The prognostic value of DL-driven disease extent was assessed in 1621 distinct patients using C-statistics. The end point was a combined outcome defined as death, hospitalization>10 days, intensive care unit hospitalization or oxygen therapy. Results The Dice coefficients for lesion (GGO+Cons) segmentations were 0.75±0.08, exceeding the values for human interobserver (0.70±0.08; 0.70±0.10) and intraobserver measures (0.72±0.09). DL-driven lesion quantification had a stronger correlation with the reference than inter- or intraobserver measures. After stepwise selection and adjustment for clinical characteristics, quantification significantly increased the prognostic accuracy of the model (0.82 vs. 0.90; p<0.0001). Conclusions A DL-driven model can provide reproducible and accurate segmentation of COVID-19 lesions on LDCT. Automatic lesion quantification has independent prognostic value for the identification of high-risk patients.
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Key Words
- ACE, angiotensin-converting enzyme
- Artificial intelligence
- BMI, body mass index
- CNN, convolutional neural network
- COVID-19
- COVID-19, coronavirus disease 2019
- CT-SS, chest tomography severity score
- Cons, consolidation
- DL, deep learning
- DSC, Dice similarity coefficient
- Deep learning
- Diagnostic imaging
- GGO, ground-glass opacity
- ICU, intensive care unit
- LDCT, low-dose computed tomography
- MAE, mean absolute error
- MVSF, mean volume similarity fraction
- Multidetector computed tomography
- ROC, receiver operating characteristic
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Affiliation(s)
- Axel Bartoli
- Department of Radiology, Hôpital de la Timone Adultes, AP-HM. 264, rue Saint-Pierre, 13385 Marseille Cedex 05, France
- CRMBM - UMR CNRS 7339, Medical Faculty, Aix-Marseille University, 27, Boulevard Jean Moulin, 13385 Marseille Cedex 05, France
| | - Joris Fournel
- Department of Radiology, Hôpital de la Timone Adultes, AP-HM. 264, rue Saint-Pierre, 13385 Marseille Cedex 05, France
- CRMBM - UMR CNRS 7339, Medical Faculty, Aix-Marseille University, 27, Boulevard Jean Moulin, 13385 Marseille Cedex 05, France
| | - Arnaud Maurin
- Department of Radiology, Hôpital de la Timone Adultes, AP-HM. 264, rue Saint-Pierre, 13385 Marseille Cedex 05, France
| | - Baptiste Marchi
- Department of Radiology, Hôpital de la Timone Adultes, AP-HM. 264, rue Saint-Pierre, 13385 Marseille Cedex 05, France
| | - Paul Habert
- Department of Radiology, Hôpital de la Timone Adultes, AP-HM. 264, rue Saint-Pierre, 13385 Marseille Cedex 05, France
- LIEE, Medical Faculty, Aix-Marseille University, 27, Boulevard Jean Moulin, 13385 Marseille Cedex 05, France
- CERIMED, Medical Faculty, Aix-Marseille University, 27, Boulevard Jean Moulin, 13385 Marseille Cedex 05, France
| | - Maxime Castelli
- Department of Radiology, Hôpital de la Timone Adultes, AP-HM. 264, rue Saint-Pierre, 13385 Marseille Cedex 05, France
| | - Jean-Yves Gaubert
- Department of Radiology, Hôpital de la Timone Adultes, AP-HM. 264, rue Saint-Pierre, 13385 Marseille Cedex 05, France
- LIEE, Medical Faculty, Aix-Marseille University, 27, Boulevard Jean Moulin, 13385 Marseille Cedex 05, France
- CERIMED, Medical Faculty, Aix-Marseille University, 27, Boulevard Jean Moulin, 13385 Marseille Cedex 05, France
| | - Sebastien Cortaredona
- Institut Hospitalo-Universitaire Méditerannée Infection, 19-21 boulevard Jean Moulin, 13005, Marseille, France
- IRD, VITROME, Institut Hospitalo-Universitaire Méditerannée Infection, 19-21 boulevard Jean Moulin, 13005, Marseille, France
| | - Jean-Christophe Lagier
- Institut Hospitalo-Universitaire Méditerannée Infection, 19-21 boulevard Jean Moulin, 13005, Marseille, France
- IRD, MEPHI, Institut Hospitalo-Universitaire Méditerannée Infection, 19-21 boulevard Jean Moulin, 13005, Marseille, France
| | - Matthieu Million
- Institut Hospitalo-Universitaire Méditerannée Infection, 19-21 boulevard Jean Moulin, 13005, Marseille, France
- IRD, MEPHI, Institut Hospitalo-Universitaire Méditerannée Infection, 19-21 boulevard Jean Moulin, 13005, Marseille, France
| | - Didier Raoult
- Institut Hospitalo-Universitaire Méditerannée Infection, 19-21 boulevard Jean Moulin, 13005, Marseille, France
- IRD, MEPHI, Institut Hospitalo-Universitaire Méditerannée Infection, 19-21 boulevard Jean Moulin, 13005, Marseille, France
| | - Badih Ghattas
- I2M - UMR CNRS 7373, Aix-Marseille University. CNRS, Centrale Marseille, 13453 Marseille, France
| | - Alexis Jacquier
- Department of Radiology, Hôpital de la Timone Adultes, AP-HM. 264, rue Saint-Pierre, 13385 Marseille Cedex 05, France
- CRMBM - UMR CNRS 7339, Medical Faculty, Aix-Marseille University, 27, Boulevard Jean Moulin, 13385 Marseille Cedex 05, France
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Usefulness of Selected Peripheral Blood Counts in Predicting Death in Patients with Severe and Critical COVID-19. J Clin Med 2022; 11:jcm11041011. [PMID: 35207281 PMCID: PMC8878821 DOI: 10.3390/jcm11041011] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/05/2022] [Accepted: 02/13/2022] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Immune dysregulation and hypoxemia are two important pathophysiological problems in patients with COVID-19 that affect peripheral blood count parameters. We hypothesized that assessment of the neutrophil-lymphocyte ratio (NLR) and red blood cell distribution width index (RDW-SD) could predict death in patients with severe and critical COVID-19. METHODS Seventy patients admitted to the intensive care unit (ICU) for COVID-19 acute respiratory failure were included in the study. RDW-SD and NLR on the day of ICU admission and peak values during the entire hospitalization were assessed. The primary endpoint was death before ICU discharge. RESULTS Patients who died had higher NLR on admission (20.3, IQR 15.3-30.2 vs. 11.0, IQR 6.8-16.9; p = 0.003) and higher RDW-SD (48.1 fL; IQR 43.1-50.5 vs. 43.9 fL; IQR 40.9-47.3, p = 0.01) than patients discharged from the ICU. NLR and RDW-SD values on ICU admission accurately predicted death in 76% (AUC = 0.76; 95%CI 0.65-0.86; p = 0.001; cut-off > 14.38) and 72% of cases (AUC = 0.72; 95%CI 0.60-0.82; p = 0.003; cut-off > 44.7 fL), respectively. Multivariable analysis confirmed that NLR > 14.38 on the day of ICU admission was associated with a 12-fold increased risk of death (logOR 12.43; 95%CI 1.61-96.29, p = 0.02), independent of other blood counts, clinical and demographic parameters. CONCLUSIONS Neutrophil-lymphocyte ratio determined on the day of ICU admission may be a useful biomarker predicting death in patients with severe and critical COVID-19.
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Chen Y, Liu C, Wang T, Qi J, Jia X, Zeng X, Bai J, Lu W, Deng Y, Zhong B, He W, Xing Y, Lian Z, Zhou H, Yan J, Yang X, Yu H, Zhou J, Zhou D, Qiu L, Zhong N, Wang J. Efficacy and safety of Bufei Huoxue capsules in the management of convalescent patients with COVID-19 infection: A multicentre, double-blind, and randomised controlled trial. JOURNAL OF ETHNOPHARMACOLOGY 2022; 284:114830. [PMID: 34763045 PMCID: PMC8575540 DOI: 10.1016/j.jep.2021.114830] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 09/17/2021] [Accepted: 11/06/2021] [Indexed: 05/28/2023]
Abstract
BACKGROUND As of September 17, 2021, coronavirus disease 2019 (COVID-19) has infected more than 226 million people in a worldwide pandemic, with conservative estimates suggesting that there are more than 204 million convalescent patients with COVID-19. Previous studies have indicated that patients in the recovery phase exhibit decreased function of multiple organs. In China, traditional Chinese medicine (TCM) treatment is recommended in the rehabilitation period of COVID-19; however, the safety and efficacy of such treatment remain to be confirmed. AIM OF STUDY The present study aimed to evaluate the efficacy and safety of Bufei Huoxue (BFHX) in restoring the functional status and exercise tolerance of patients recovering from COVID-19. METHODS A total of 131 patients in the rehabilitation period of COVID-19 infection were randomly divided into a Bufei Huoxue (BFHX) group (n = 66) and a placebo group (n = 65). BFHX or placebo was given orally three times a day (1.4 g/dose) for 90 days. The primary outcomes was to evaluate improvements in exercise tolerance and imaging manifestations on chest computed tomography (CT). RESULTS After the exclusion of two patients who withdrew prior to receiving any medications, 129 patients were recruited, including 64 patients in the BFHX group and 65 patients in the placebo group. After 3 months of treatment, the BFHX group exhibited greater attenuation of pneumonia lesions on chest CT than the placebo group (P<0.05). Improvements in 6-min walk distance (6MWD) relative to baseline were also significantly better in the BFHX group than in the placebo group (P<0.01). Scores on the Fatigue Assessment Inventory (FAI) were lower in the BFHX group than in the placebo group (P<0.05). Although the rate of adverse events was higher in the BFHX group than in the placebo group (9.38% vs. 4.62%), the difference was not significant (P=0.3241). CONCLUSIONS BFHX may exert strong rehabilitative effects on physiological activity in patients recovering from COVID-19, which may in turn attenuate symptoms of fatigue and improve exercise tolerance.
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Affiliation(s)
- Yuqin Chen
- State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, Guangdong-Hong Kong-Macao Joint Laboratory for respiratory infectious disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Chunli Liu
- State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, Guangdong-Hong Kong-Macao Joint Laboratory for respiratory infectious disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Tingping Wang
- Department of Out-patient and Emergency, Wuhan Institute for Tuberculosis Control, Wuhan Pulmonary Hospital, Wuhan, Hubei, China
| | - Jingjing Qi
- Department of Respiratory and Critical Care Medicine, Xiangzhou District People's Hospital, Xiangyang, Hubei, China
| | - Xiaoqing Jia
- Department of Respiratory, Third Hospital of Baotou City, Baotou, Inner Mongolia, China
| | - Xiansheng Zeng
- Department of Respiratory and Critical Care Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Jianling Bai
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wenju Lu
- State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, Guangdong-Hong Kong-Macao Joint Laboratory for respiratory infectious disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Yu Deng
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Bihua Zhong
- State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, Guangdong-Hong Kong-Macao Joint Laboratory for respiratory infectious disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Wenjun He
- State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, Guangdong-Hong Kong-Macao Joint Laboratory for respiratory infectious disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Yue Xing
- State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, Guangdong-Hong Kong-Macao Joint Laboratory for respiratory infectious disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Zhan Lian
- Department of Out-patient and Emergency, Wuhan Institute for Tuberculosis Control, Wuhan Pulmonary Hospital, Wuhan, Hubei, China
| | - Haohao Zhou
- Department of Respiratory and Critical Care Medicine, Xiangzhou District People's Hospital, Xiangyang, Hubei, China
| | - Junping Yan
- Department of Respiratory, Third Hospital of Baotou City, Baotou, Inner Mongolia, China
| | - Xuejiao Yang
- Department of Respiratory and Critical Care Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Hao Yu
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jiawei Zhou
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Dansha Zhou
- State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, Guangdong-Hong Kong-Macao Joint Laboratory for respiratory infectious disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Lixia Qiu
- Hangzhou YITU Healthcare Technology Co., Ltd., Hangzhou, Zhejiang, China
| | - Nanshan Zhong
- State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, Guangdong-Hong Kong-Macao Joint Laboratory for respiratory infectious disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
| | - Jian Wang
- State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangdong Key Laboratory of Vascular Disease, Guangdong-Hong Kong-Macao Joint Laboratory for respiratory infectious disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
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Liao X, Li D, Liu Z, Ma Z, Zhang L, Dong J, Shi Y, Gu X, Zheng G, Huang L, Yuan L, Cao J, Shu D, Yang X, He Q, Li G, Zhang Z, Liu L. Pulmonary Sequelae in Patients After Recovery From Coronavirus Disease 2019: A Follow-Up Study With Chest CT. Front Med (Lausanne) 2022; 8:686878. [PMID: 35096849 PMCID: PMC8794727 DOI: 10.3389/fmed.2021.686878] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 12/20/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: The pulmonary sequelae of coronavirus disease 2019 (COVID-19) have not been comprehensively evaluated. We performed a follow-up study analyzing chest computed tomography (CT) findings of COVID-19 patients at 3 and 6 months after hospital discharge. Methods: Between February 2020 and May 2020, a total of 273 patients with COVID-19 at the Shenzhen Third People's Hospital were recruited and followed for 6 months after discharge. Chest CT scanning was performed with the patient in the supine position at end-inspiration. A total of 957 chest CT scans was obtained at different timepoints. A semi-quantitative score was used to assess the degree of lung involvement. Results: Most chest CT scans showed bilateral lung involvement with peripheral location at 3 and 6 months follow-up. The most common CT findings were ground-glass opacity and parenchymal band, which were found in 136 (55.3%) and 94 (38.2%) of the 246 patients at 3 months follow-up, and 82 (48.2%) and 76 (44.7%) of 170 patients at 6 months follow-up, respectively. The number of lobes involved and the total CT severity score declined over time. The total CT score gradually increased with the increasement of disease severity at both 3 months follow-up (trend test P < 0.001) and 6 months follow-up (trend test P < 0.001). Patients with different disease severity represented diverse CT patterns over time. Conclusions: The most common CT findings were ground-glass opacity and parenchymal bands at the 3 and 6 months follow-up. Patients with different disease severity represent diverse CT manifestations, indicating the necessary for long-term follow-up monitoring of patients with severe and critical conditions.
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Affiliation(s)
- Xuejiao Liao
- Department of Chronic Follow-Up, Shenzhen Third People's Hospital, Shenzhen, China.,National Clinical Research Center for Infectious Disease, Institute of Hepatology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Dapeng Li
- National Clinical Research Center for Infectious Disease, Institute of Hepatology, Shenzhen Third People's Hospital, Shenzhen, China.,School of Medicine, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Zhi Liu
- Department of the Third Pulmonary Disease, Shenzhen Third People's Hospital, Shenzhen, China
| | - Zhenghua Ma
- Department of Chronic Follow-Up, Shenzhen Third People's Hospital, Shenzhen, China
| | - Lina Zhang
- Department of Chronic Follow-Up, Shenzhen Third People's Hospital, Shenzhen, China
| | - Jingke Dong
- Department of Chronic Follow-Up, Shenzhen Third People's Hospital, Shenzhen, China
| | - Yirong Shi
- Department of Chronic Follow-Up, Shenzhen Third People's Hospital, Shenzhen, China
| | - Xiaowen Gu
- Department of Chronic Follow-Up, Shenzhen Third People's Hospital, Shenzhen, China
| | - Guangping Zheng
- Department of Radiology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Ling Huang
- Department of the Third Pulmonary Disease, Shenzhen Third People's Hospital, Shenzhen, China
| | - Lijun Yuan
- School of Medicine, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Jing Cao
- School of Medicine, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Dan Shu
- National Clinical Research Center for Infectious Disease, Institute of Hepatology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Xiangyi Yang
- National Clinical Research Center for Infectious Disease, Institute of Hepatology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Qing He
- School of Medicine, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Guobao Li
- Department of the Third Pulmonary Disease, Shenzhen Third People's Hospital, Shenzhen, China
| | - Zheng Zhang
- National Clinical Research Center for Infectious Disease, Institute of Hepatology, Shenzhen Third People's Hospital, Shenzhen, China.,School of Medicine, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Lei Liu
- School of Medicine, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
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Affiliation(s)
- Yufan Zhang
- State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for Cell Responses, Key Laboratory of Bioactive Materials, Ministry of Education, and College of Life Sciences Nankai University Tianjin China
| | - Dan Ding
- State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for Cell Responses, Key Laboratory of Bioactive Materials, Ministry of Education, and College of Life Sciences Nankai University Tianjin China
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Yuan Y, Sun C, Tang X, Cheng C, Mombaerts L, Wang M, Hu T, Sun C, Guo Y, Li X, Xu H, Ren T, Xiao Y, Xiao Y, Zhu H, Wu H, Li K, Chen C, Liu Y, Liang Z, Cao Z, Zhang HT, Paschaldis IC, Liu Q, Goncalves J, Zhong Q, Yan L. Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China. ENGINEERING (BEIJING, CHINA) 2022; 8:116-121. [PMID: 33282444 PMCID: PMC7695569 DOI: 10.1016/j.eng.2020.10.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/04/2020] [Accepted: 10/11/2020] [Indexed: 05/14/2023]
Abstract
Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People's Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low, intermediate, or high risk, with an area under the curve (AUC) score of 0.9551. In summary, a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); it has also been validated in independent cohorts.
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Affiliation(s)
- Ye Yuan
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Chuan Sun
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xiuchuan Tang
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Cheng Cheng
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Laurent Mombaerts
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval L-4367, Luxembourg
| | - Maolin Wang
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Tao Hu
- Department of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Chenyu Sun
- AMITA Health Saint Joseph Hospital Chicago, Chicago, IL 60657, USA
| | - Yuqi Guo
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xiuting Li
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hui Xu
- Department of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Tongxin Ren
- Huazhong University of Science and Technology-Wuxi Research Institute, Wuxi 214174, China
| | - Yang Xiao
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yaru Xiao
- Department of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Hongling Zhu
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Honghan Wu
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Kezhi Li
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Chuming Chen
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yingxia Liu
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Zhichao Liang
- Department of Infectious Diseases, Shenzhen Key Laboratory of Pathogenic Microbiology and Immunology, National Clinical Research Center for Infectious Disease, The Third People's Hospital of Shenzhen (Second Hospital Affiliated with the Southern University of Science and Technology), Shenzhen 518055, China
| | - Zhiguo Cao
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hai-Tao Zhang
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Ioannis Ch Paschaldis
- Department of Electrical and Computer Engineering & Division of Systems Engineering & Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Quanying Liu
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jorge Goncalves
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval L-4367, Luxembourg
- Department of Plant Sciences, University of Cambridge, Cambridge CB2 1TN, UK
| | - Qiang Zhong
- Department of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Li Yan
- Department of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
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Puhr-Westerheide D, Reich J, Sabel BO, Kunz WG, Fabritius MP, Reidler P, Rübenthaler J, Ingrisch M, Wassilowsky D, Irlbeck M, Ricke J, Gresser E. Sequential Organ Failure Assessment Outperforms Quantitative Chest CT Imaging Parameters for Mortality Prediction in COVID-19 ARDS. Diagnostics (Basel) 2021; 12:diagnostics12010010. [PMID: 35054177 PMCID: PMC8775048 DOI: 10.3390/diagnostics12010010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 12/16/2021] [Accepted: 12/17/2021] [Indexed: 01/28/2023] Open
Abstract
(1) Background: Respiratory insufficiency with acute respiratory distress syndrome (ARDS) and multi-organ dysfunction leads to high mortality in COVID-19 patients. In times of limited intensive care unit (ICU) resources, chest CTs became an important tool for the assessment of lung involvement and for patient triage despite uncertainties about the predictive diagnostic value. This study evaluated chest CT-based imaging parameters for their potential to predict in-hospital mortality compared to clinical scores. (2) Methods: 89 COVID-19 ICU ARDS patients requiring mechanical ventilation or continuous positive airway pressure mask ventilation were included in this single center retrospective study. AI-based lung injury assessment and measurements indicating pulmonary hypertension (PA-to-AA ratio) on admission CT, oxygenation indices, lung compliance and sequential organ failure assessment (SOFA) scores on ICU admission were assessed for their diagnostic performance to predict in-hospital mortality. (3) Results: CT severity scores and PA-to-AA ratios were not significantly associated with in-hospital mortality, whereas the SOFA score showed a significant association (p < 0.001). In ROC analysis, the SOFA score resulted in an area under the curve (AUC) for in-hospital mortality of 0.74 (95%-CI 0.63–0.85), whereas CT severity scores (0.53, 95%-CI 0.40–0.67) and PA-to-AA ratios (0.46, 95%-CI 0.34–0.58) did not yield sufficient AUCs. These results were consistent for the subgroup of more critically ill patients with moderate and severe ARDS on admission (oxygenation index <200, n = 53) with an AUC for SOFA score of 0.77 (95%-CI 0.64–0.89), compared to 0.55 (95%-CI 0.39–0.72) for CT severity scores and 0.51 (95%-CI 0.35–0.67) for PA-to-AA ratios. (4) Conclusions: Severe COVID-19 disease is not limited to lung (vessel) injury but leads to a multi-organ involvement. The findings of this study suggest that risk stratification should not solely be based on chest CT parameters but needs to include multi-organ failure assessment for COVID-19 ICU ARDS patients for optimized future patient management and resource allocation.
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Affiliation(s)
- Daniel Puhr-Westerheide
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany; (J.R.); (B.O.S.); (W.G.K.); (M.P.F.); (P.R.); (J.R.); (M.I.); (J.R.); (E.G.)
- Correspondence: ; Tel.: +49-89-4400-73620
| | - Jakob Reich
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany; (J.R.); (B.O.S.); (W.G.K.); (M.P.F.); (P.R.); (J.R.); (M.I.); (J.R.); (E.G.)
| | - Bastian O. Sabel
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany; (J.R.); (B.O.S.); (W.G.K.); (M.P.F.); (P.R.); (J.R.); (M.I.); (J.R.); (E.G.)
| | - Wolfgang G. Kunz
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany; (J.R.); (B.O.S.); (W.G.K.); (M.P.F.); (P.R.); (J.R.); (M.I.); (J.R.); (E.G.)
| | - Matthias P. Fabritius
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany; (J.R.); (B.O.S.); (W.G.K.); (M.P.F.); (P.R.); (J.R.); (M.I.); (J.R.); (E.G.)
| | - Paul Reidler
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany; (J.R.); (B.O.S.); (W.G.K.); (M.P.F.); (P.R.); (J.R.); (M.I.); (J.R.); (E.G.)
| | - Johannes Rübenthaler
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany; (J.R.); (B.O.S.); (W.G.K.); (M.P.F.); (P.R.); (J.R.); (M.I.); (J.R.); (E.G.)
| | - Michael Ingrisch
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany; (J.R.); (B.O.S.); (W.G.K.); (M.P.F.); (P.R.); (J.R.); (M.I.); (J.R.); (E.G.)
| | - Dietmar Wassilowsky
- Department of Anesthesiology, University Hospital, LMU Munich, 81377 Munich, Germany; (D.W.); (M.I.)
| | - Michael Irlbeck
- Department of Anesthesiology, University Hospital, LMU Munich, 81377 Munich, Germany; (D.W.); (M.I.)
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany; (J.R.); (B.O.S.); (W.G.K.); (M.P.F.); (P.R.); (J.R.); (M.I.); (J.R.); (E.G.)
| | - Eva Gresser
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany; (J.R.); (B.O.S.); (W.G.K.); (M.P.F.); (P.R.); (J.R.); (M.I.); (J.R.); (E.G.)
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Liu CH, Lu CH, Lin LT. Pandemic strategies with computational and structural biology against COVID-19: A retrospective. Comput Struct Biotechnol J 2021; 20:187-192. [PMID: 34900126 PMCID: PMC8650801 DOI: 10.1016/j.csbj.2021.11.040] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 11/26/2021] [Accepted: 11/28/2021] [Indexed: 12/14/2022] Open
Abstract
The emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which is the etiologic agent of the coronavirus disease 2019 (COVID-19) pandemic, has dominated all aspects of life since of 2020. Research studies on the virus and exploration of therapeutic and preventive strategies has been moving at rapid rates to control the pandemic. In the field of bioinformatics or computational and structural biology, recent research strategies have used multiple disciplines to compile large datasets to uncover statistical correlations and significance, visualize and model proteins, perform molecular dynamics simulations, and employ the help of artificial intelligence and machine learning to harness computational processing power to further the research on COVID-19, including drug screening, drug design, vaccine development, prognosis prediction, and outbreak prediction. These recent developments should help us better understand the viral disease and develop the much-needed therapies and strategies for the management of COVID-19.
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Affiliation(s)
- Ching-Hsuan Liu
- Graduate Institute of Medical Sciences, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Microbiology & Immunology, Dalhousie University, Halifax, NS, Canada
| | - Cheng-Hua Lu
- Graduate Institute of Medical Sciences, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Liang-Tzung Lin
- Graduate Institute of Medical Sciences, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Microbiology and Immunology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
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Yousef HA, Moussa EMM, Abdel-Razek MZM, El-Kholy MMSA, Hasan LHS, El-Sayed AEDAM, Saleh MAK, Omar MKM. Automated quantification of COVID-19 pneumonia severity in chest CT using histogram-based multi-level thresholding segmentation. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2021. [PMCID: PMC8656142 DOI: 10.1186/s43055-021-00602-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Background Chest computed tomography (CT) has proven its critical importance in detection, grading, and follow-up of lung affection in COVID-19 pneumonia. There is a close relationship between clinical severity and the extent of lung CT findings in this potentially fatal disease. The extent of lung lesions in CT is an important indicator of risk stratification in COVID-19 pneumonia patients. This study aims to explore automated histogram-based quantification of lung affection in COVID-19 pneumonia in volumetric computed tomography (CT) images in comparison to conventional semi-quantitative severity scoring. This retrospective study enrolled 153 patients with proven COVID-19 pneumonia. Based on the severity of clinical presentation, the patients were divided into three groups: mild, moderate and severe. Based upon the need for oxygenation support, two groups were identified as follows: common group that incorporated mild and moderate severity patients who did not need intubation, and severe illness group that included patients who were intubated. An automated multi-level thresholding histogram-based quantitative analysis technique was used for evaluation of lung affection in CT scans together with the conventional semi-quantitative severity scoring performed by two expert radiologists. The quantitative assessment included volumes, percentages and densities of ground-glass opacities (GGOs) and consolidation in both lungs. The results of the two evaluation methods were compared, and the quantification metrics were correlated. Results The Spearman’s correlation coefficient between the semi-quantitative severity scoring and automated quantification methods was 0.934 (p < 0.0001). Conclusions The automated histogram-based quantification of COVID-19 pneumonia shows good correlation with conventional severity scoring. The quantitative imaging metrics show high correlation with the clinical severity of the disease.
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Varikasuvu SR, Varshney S, Dutt N, Munikumar M, Asfahan S, Kulkarni PP, Gupta P. D-dimer, disease severity, and deaths (3D-study) in patients with COVID-19: a systematic review and meta-analysis of 100 studies. Sci Rep 2021; 11:21888. [PMID: 34750495 PMCID: PMC8576016 DOI: 10.1038/s41598-021-01462-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/22/2021] [Indexed: 12/15/2022] Open
Abstract
Hypercoagulability and the need for prioritizing coagulation markers for prognostic abilities have been highlighted in COVID-19. We aimed to quantify the associations of D-dimer with disease progression in patients with COVID-19. This systematic review and meta-analysis was registered with PROSPERO, CRD42020186661.We included 113 studies in our systematic review, of which 100 records (n = 38,310) with D-dimer data) were considered for meta-analysis. Across 68 unadjusted (n = 26,960) and 39 adjusted studies (n = 15,653) reporting initial D-dimer, a significant association was found in patients with higher D-dimer for the risk of overall disease progression (unadjusted odds ratio (uOR) 3.15; adjusted odds ratio (aOR) 1.64). The time-to-event outcomes were pooled across 19 unadjusted (n = 9743) and 21 adjusted studies (n = 13,287); a strong association was found in patients with higher D-dimers for the risk of overall disease progression (unadjusted hazard ratio (uHR) 1.41; adjusted hazard ratio (aHR) 1.10). The prognostic use of higher D-dimer was found to be promising for predicting overall disease progression (studies 68, area under curve 0.75) in COVID-19. Our study showed that higher D-dimer levels provide prognostic information useful for clinicians to early assess COVID-19 patients at risk for disease progression and mortality outcomes. This study, recommends rapid assessment of D-dimer for predicting adverse outcomes in COVID-19.
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Affiliation(s)
| | | | - Naveen Dutt
- Department of Respiratory Medicine, All India Institute of Medical Sciences, Jodhpur, 342005, India
| | - Manne Munikumar
- Department of Bioinformatics, ICMR-National Institute of Nutrition, Hyderabad, 500007, India
| | - Shahir Asfahan
- Department of Respiratory Medicine, All India Institute of Medical Sciences, Jodhpur, 342005, India
| | - Paresh P Kulkarni
- Department of Biochemistry, Institute of Medical Sciences, Banaras Hindu University, Varanasi, 221005, India
| | - Pratima Gupta
- Department of Microbiology, All India Institute of Medical Sciences, Rishikesh, 249203, India
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Ulloque‐Badaracco JR, Ivan Salas‐Tello W, Al‐kassab‐Córdova A, Alarcón‐Braga EA, Benites‐Zapata VA, Maguiña JL, Hernandez AV. Prognostic value of neutrophil-to-lymphocyte ratio in COVID-19 patients: A systematic review and meta-analysis. Int J Clin Pract 2021; 75:e14596. [PMID: 34228867 PMCID: PMC9614707 DOI: 10.1111/ijcp.14596] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 07/01/2021] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Neutrophil-to-lymphocyte ratio (NLR) is an accessible and widely used biomarker. NLR may be used as an early marker of poor prognosis in patients with COVID-19. OBJECTIVE To evaluate the prognostic value of the NLR in patients diagnosed with COVID-19. METHODS We conducted a systematic review and meta-analysis. Observational studies that reported the association between baseline NLR values (ie, at hospital admission) and severity or all-cause mortality in COVID-19 patients were included. The quality of the studies was assessed using the Newcastle-Ottawa Scale (NOS). Random effects models and inverse variance method were used for meta-analyses. The effects were expressed as odds ratios (ORs) and their 95% confidence intervals (CIs). Small study effects were assessed with the Egger's test. RESULTS We analysed 61 studies (n = 15 522 patients), 58 cohorts, and 3 case-control studies. An increase of one unit of NLR was associated with higher odds of severity (OR 6.22; 95%CI 4.93 to 7.84; P < .001) and higher odds of all-cause mortality (OR 12.6; 95%CI 6.88 to 23.06; P < .001). In our sensitivity analysis, we found that 41 studies with low risk of bias and moderate heterogeneity (I2 = 53% and 58%) maintained strong association between NLR values and both outcomes (severity: OR 5.36; 95% CI 4.45 to 6.45; P < .001; mortality: OR 10.42 95% CI 7.73 to 14.06; P = .005). CONCLUSIONS Higher values of NLR were associated with severity and all-cause mortality in hospitalised COVID-19 patients.
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Affiliation(s)
| | | | | | | | - Vicente A. Benites‐Zapata
- Vicerrectorado de Investigación Unidad de Investigación para la Generación y Síntesis de Evidencias en Salud, Vicerrectorado de InvestigaciónUniversidad San Ignacio de LoyolaLimaPeru
| | - Jorge L. Maguiña
- Escuela de MedicinaUniversidad Peruana de Ciencias AplicadasLimaPeru
- Instituto de Evaluación de Tecnologías en Salud e Investigación — IETSI, EsSaludLimaPeru
| | - Adrian V. Hernandez
- Unidad de Revisiones Sistemáticas y Meta‐análisis, Guías de Práctica Clínica y Evaluaciones de Tecnología Sanitaria, Vicerrectorado de InvestigaciónUniversidad San Ignacio de LoyolaLimaPeru
- Health OutcomesPolicy, and Evidence Synthesis (HOPES) Group, University of Connecticut School of PharmacyMansfieldCTUSA
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Huang X, Shi K, Zhou J, Liang Y, Liu Y, Zhang J, Guo Y, Jin C. Development of a Machine Learning-Assisted Model for the Early Detection of Severe COVID-19 Cases Combining Blood Test and Quantitative Computed Tomography Parameters. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3866] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
<sec> <title>Purpose:</title> This study aimed to identify severe Coronavirus Disease 2019 (COVID-19) cases combining blood test results and imaging parameters based on a machine learning classifier at the initial admission. </sec> <sec> <title>Materials
and methods:</title> Ninety-five non-severe and 22 severe laboratory-confirmed COVID-19 cases treated between January 23, 2020 and March 25, 2020 were examined in this retrospective trial. Blood test results and chest computed tomography (CT) images were obtained at the initial
admission. The lesions on CT images were segmented using an artificial intelligent (AI) tool. Then, quantitative CT (QCT) parameters, including the volume, percentage, ground glass opacity (GGO) percentage and heterogeneity of the lesions were calculated. Correlations of blood test results
and QCT parameters were analyzed by the Pearson test first. Then, discriminative features for detecting severe cases were selected by both the independent samples t test and least absolute shrinkage and selection operator (LASSO) regression. Next, support vector machine (SVM),
Gaussian naïve Bayes (GNB), Knearest neighbor (KNN), decision tree (DT), random forest (RF) and multi-layer perceptron-neural net (MLP-NN) algorithms were used as classifiers, and their accuracies were assessed by 10-fold-cross-validation. </sec> <sec> <title>Results:</title>
Blood test indexes and CT parameters were moderately to medially correlated. Of all selected features, lesion percentage contributed mostly to the classification of the two groups, followed by lesion volume, patient age, lymphocyte count, neutrophil count, GGO percentage and tumor heterogeneity.
RF-assisted identification had the highest accuracy of 91.38%, followed by GNB (87.83%), KNN (87.93%), SVM (86.21%), MLP-NN (85.34%) and DT (84.48%). </sec> <sec> <title>Conclusions:</title> The RF-assisted model combining blood test and QCT parameters is
helpful in the identification of severe COVID-19 cases. </sec>
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Affiliation(s)
- Xiaoqi Huang
- Department of Radiology, The Affiliated Hospital of Yan’an University, Yan’an, 716000, China
| | - Ke Shi
- Department of Radiology, Ankang People’s Hospital, Ankang, 725000, China
| | - Jie Zhou
- Department of Radiology, Xi’an Chest Hospital, Xi’an, 710000, China
| | - Yudong Liang
- Department of CT&MR Imaging Diagnostics, Weinan Central Hospital, Weinan, 714000, China
| | - Yaliang Liu
- Department of Radiology, Hanzhong Central Hospital, Hanzhong, 723000, China
| | - Jinpin Zhang
- Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710000, Shaanxi, China
| | - Youmin Guo
- Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710000, Shaanxi, China
| | - Chenwang Jin
- Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710000, Shaanxi, China
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Can Deep Learning-Based Volumetric Analysis Predict Oxygen Demand Increase in Patients with COVID-19 Pneumonia? Medicina (B Aires) 2021; 57:medicina57111148. [PMID: 34833366 PMCID: PMC8619125 DOI: 10.3390/medicina57111148] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 10/09/2021] [Accepted: 10/19/2021] [Indexed: 11/17/2022] Open
Abstract
Background and Objectives: This study aimed to investigate whether predictive indicators for the deterioration of respiratory status can be derived from the deep learning data analysis of initial chest computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19). Materials and Methods: Out of 117 CT scans of 75 patients with COVID-19 admitted to our hospital between April and June 2020, we retrospectively analyzed 79 CT scans that had a definite time of onset and were performed prior to any medication intervention. Patients were grouped according to the presence or absence of increased oxygen demand after CT scan. Quantitative volume data of lung opacity were measured automatically using a deep learning-based image analysis system. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of the opacity volume data were calculated to evaluate the accuracy of the system in predicting the deterioration of respiratory status. Results: All 79 CT scans were included (median age, 62 years (interquartile range, 46–77 years); 56 (70.9%) were male. The volume of opacity was significantly higher for the increased oxygen demand group than for the nonincreased oxygen demand group (585.3 vs. 132.8 mL, p < 0.001). The sensitivity, specificity, and AUC were 76.5%, 68.2%, and 0.737, respectively, in the prediction of increased oxygen demand. Conclusion: Deep learning-based quantitative analysis of the affected lung volume in the initial CT scans of patients with COVID-19 can predict the deterioration of respiratory status to improve treatment and resource management.
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Sarkar S, Khanna P, Singh AK. The Impact of Neutrophil-Lymphocyte Count Ratio in COVID-19: A Systematic Review and Meta-Analysis. J Intensive Care Med 2021; 37:857-869. [PMID: 34672824 PMCID: PMC9160638 DOI: 10.1177/08850666211045626] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Background: The neutrophil–lymphocyte count ratio (NLR) has emerged
as a potential prognostic tool for different diseases. In the current
coronavirus disease (COVID-19) pandemic, the NLR may be a useful tool for risk
scarification and the optimal utilization of limited healthcare resources.
However, there is no consensus regarding the optimal value of NLR, and the
association with disease severity and mortality. Thus, this study aims to
systematically analyze the current evidence of the utility of baseline NLR as a
predictive tool for mortality, disease severity in COVID-19 patients.
Methods: A compendious screening of electronic databases up to
June 15, 2021, was done after enlisting the protocol in PROSPERO
(CRD42020202659). Studies evaluating the utility of baseline NLR in COVID-19 are
included for this review as per the PRISMA statement. Results: We
retrieved a total of 13112 and 12986 COVID-19 patients for survivability and
severity over 90 studies. The expired and critically sick patients had elevated
baseline NLR on admission, in comparison to survivors and noncritical patients.
(SMD = 3.82; 95% CI: 2.79-4.85; I2 = 100% and SMD = 1.42; 95% CI:
1.22-1.63; I2 = 95%, respectively). The summary receiver operating
curve analysis for mortality (AUC = 0.87; 95% CI: 0.86-0.87;
I2 = 94.7%), and severity (AUC = 0.82; 95% CI: 0.80-0.84;
I2 = 79.7%) were also suggestive of its significant predictive
value. Conclusions: The elevated NLR on admission in COVID-19
patients is associated with poor outcomes.
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Value of the Neutrophil-Lymphocyte Ratio in Predicting COVID-19 Severity: A Meta-analysis. DISEASE MARKERS 2021; 2021:2571912. [PMID: 34650648 PMCID: PMC8510823 DOI: 10.1155/2021/2571912] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 08/12/2021] [Accepted: 09/15/2021] [Indexed: 12/27/2022]
Abstract
Background Coronavirus disease 2019 (COVID-19) is highly contagious and continues to spread rapidly. However, there are no simple and timely laboratory techniques to determine the severity of COVID-19. In this meta-analysis, we assessed the potential of the neutrophil-lymphocyte ratio (NLR) as an indicator of severe versus nonsevere COVID-19 cases. Methods A search for studies on the NLR in severe and nonsevere COVID-19 cases published from January 1, 2020, to July 1, 2021, was conducted on the PubMed, EMBASE, and Cochrane Library databases. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR), and area under the curve (AUC) analyses were done on Stata 14.0 and Meta-disc 1.4 to assess the performance of the NLR. Results Thirty studies, including 5570 patients, were analyzed. Of these, 1603 and 3967 patients had severe and nonsevere COVID-19, respectively. The overall sensitivity and specificity were 0.82 (95% confidence interval (CI), 0.77-0.87) and 0.77 (95% CI, 0.70-0.83), respectively; positive and negative correlation ratios were 3.6 (95% CI, 2.7-4.7) and 0.23 (95% CI, 0.17-0.30), respectively; DOR was 16 (95% CI, 10-24), and the AUC was 0.87 (95% CI, 0.84-0.90). Conclusion The NLR could accurately determine the severity of COVID-19 and can be used to identify patients with severe disease to guide clinical decision-making.
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Zhang S, Huang S, Liu J, Dong X, Meng M, Chen L, Wen Z, Zhang L, Chen Y, Du H, Liu Y, Wang T, Chen D. Identification and validation of prognostic factors in patients with COVID-19: A retrospective study based on artificial intelligence algorithms. JOURNAL OF INTENSIVE MEDICINE 2021; 1:103-109. [PMID: 36943822 PMCID: PMC8142059 DOI: 10.1016/j.jointm.2021.04.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 04/01/2021] [Accepted: 04/08/2021] [Indexed: 01/08/2023]
Abstract
Background Novel coronavirus disease 2019 (COVID-19) is an ongoing global pandemic with high mortality. Although several studies have reported different risk factors for mortality in patients based on traditional analytics, few studies have used artificial intelligence (AI) algorithms. This study investigated prognostic factors for COVID-19 patients using AI methods. Methods COVID-19 patients who were admitted in Wuhan Infectious Diseases Hospital from December 29, 2019 to March 2, 2020 were included. The whole cohort was randomly divided into training and testing sets at a 6:4 ratio. Demographic and clinical data were analyzed to identify predictors of mortality using least absolute shrinkage and selection operator (LASSO) regression and LASSO-based artificial neural network (ANN) models. The predictive performance of the models was evaluated using receiver operating characteristic (ROC) curve analysis. Results A total of 1145 patients (610 male, 53.3%) were included in the study. Of the 1145 patients, 704 were assigned to the training set and 441 were assigned to the testing set. The median age of the patients was 57 years (range: 47-66 years). Severity of illness, age, platelet count, leukocyte count, prealbumin, C-reactive protein (CRP), total bilirubin, Acute Physiology and Chronic Health Evaluation (APACHE) II score, and Sequential Organ Failure Assessment (SOFA) score were identified as independent prognostic factors for mortality. Incorporating these nine factors into the LASSO regression model yielded a correct classification rate of 0.98, with area under the ROC curve (AUC) values of 0.980 and 0.990 in the training and testing cohorts, respectively. Incorporating the same factors into the LASSO-based ANN model yielded a correct classification rate of 0.990, with an AUC of 0.980 in both the training and testing cohorts. Conclusions Both the LASSO regression and LASSO-based ANN model accurately predicted the clinical outcome of patients with COVID-19. Severity of illness, age, platelet count, leukocyte count, prealbumin, CRP, total bilirubin, APACHE II score, and SOFA score were identified as prognostic factors for mortality in patients with COVID-19.
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Affiliation(s)
- Sheng Zhang
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai 200025, China
| | - Sisi Huang
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai 200025, China
| | - Jiao Liu
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai 200025, China
| | - Xuan Dong
- Tuberculosis and Respiratory Department, Wuhan Jinyin-tan Hospital, No. 1 Yintan Road, Wuhan 430023, China
| | - Mei Meng
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai 200025, China
| | - Limin Chen
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai 200025, China
| | - Zhenliang Wen
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai 200025, China
| | - Lidi Zhang
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai 200025, China
| | - Yizhu Chen
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai 200025, China
| | - Hangxiang Du
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai 200025, China
| | - Yongan Liu
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai 200025, China
| | - Tao Wang
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai 200025, China
| | - Dechang Chen
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai 200025, China
- Corresponding author: Dechang Chen, No. 197, Ruijin 2nd Road, Shanghai 200025, China.
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AI-Based Quantitative CT Analysis of Temporal Changes According to Disease Severity in COVID-19 Pneumonia. J Comput Assist Tomogr 2021; 45:970-978. [PMID: 34581706 PMCID: PMC8607923 DOI: 10.1097/rct.0000000000001224] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Objective To quantitatively evaluate computed tomography (CT) parameters of coronavirus disease 2019 (COVID-19) pneumonia an artificial intelligence (AI)-based software in different clinical severity groups during the disease course. Methods From March 11 to April 15, 2020, 51 patients (age, 18–84 years; 28 men) diagnosed and hospitalized with COVID-19 pneumonia with a total of 116 CT scans were enrolled in the study. Patients were divided into mild (n = 12), moderate (n = 31), and severe (n = 8) groups based on clinical severity. An AI-based quantitative CT analysis, including lung volume, opacity score, opacity volume, percentage of opacity, and mean lung density, was performed in initial and follow-up CTs obtained at different time points. Receiver operating characteristic analysis was performed to find the diagnostic ability of quantitative CT parameters for discriminating severe from nonsevere pneumonia. Results In baseline assessment, the severe group had significantly higher opacity score, opacity volume, higher percentage of opacity, and higher mean lung density than the moderate group (all P ≤ 0.001). Through consecutive time points, the severe group had a significant decrease in lung volume (P = 0.006), a significant increase in total opacity score (P = 0.003), and percentage of opacity (P = 0.007). A significant increase in total opacity score was also observed for the mild group (P = 0.011). Residual opacities were observed in all groups. The involvement of more than 4 lobes (sensitivity, 100%; specificity, 65.26%), total opacity score greater than 4 (sensitivity, 100%; specificity, 64.21), total opacity volume greater than 337.4 mL (sensitivity, 80.95%; specificity, 84.21%), percentage of opacity greater than 11% (sensitivity, 80.95%; specificity, 88.42%), total high opacity volume greater than 10.5 mL (sensitivity, 95.24%; specificity, 66.32%), percentage of high opacity greater than 0.8% (sensitivity, 85.71%; specificity, 80.00%) and mean lung density HU greater than −705 HU (sensitivity, 57.14%; specificity, 90.53%) were related to severe pneumonia. Conclusions An AI-based quantitative CT analysis is an objective tool in demonstrating disease severity and can also assist the clinician in follow-up by providing information about the disease course and prognosis according to different clinical severity groups.
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The Cross-Talk between Thrombosis and Inflammatory Storm in Acute and Long-COVID-19: Therapeutic Targets and Clinical Cases. Viruses 2021; 13:v13101904. [PMID: 34696334 PMCID: PMC8540492 DOI: 10.3390/v13101904] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 09/16/2021] [Accepted: 09/20/2021] [Indexed: 01/08/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) commonly complicates with coagulopathy. A syndrome called Long-COVID-19 is emerging recently in COVID-19 survivors, characterized, in addition to the persistence of symptoms typical of the acute phase, by alterations in inflammatory and coagulation parameters due to endothelial damage. The related disseminated intravascular coagulation (DIC) can be associated with high death rates in COVID-19 patients. It is possible to find a prothrombotic state also in Long-COVID-19. Early administration of anticoagulants in COVID-19 was suggested in order to improve patient outcomes, although exact criteria for their application were not well-established. Low-molecular-weight heparin (LMWH) was commonly adopted for counteracting DIC and venous thromboembolism (VTE), due to its pharmacodynamics and anti-inflammatory properties. However, the efficacy of anticoagulant therapy for COVID-19-associated DIC is still a matter of debate. Thrombin and Factor Xa (FXa) are well-known components of the coagulation cascade. The FXa is known to strongly promote inflammation as the consequence of increased cytokine expression. Endothelial cells and mononuclear leucocytes release cytokines, growth factors, and adhesion molecules due to thrombin activation. On the other hand, cytokines can activate coagulation. The cross-talk between coagulation and inflammation is mediated via protease-activated receptors (PARs). These receptors might become potential targets to be considered for counteracting the clinical expressions of COVID-19. SARS-CoV-2 is effectively able to activate local and circulating coagulation factors, thus inducing the generation of disseminated coagula. LMWH may be considered as the new frontier in the treatment of COVID-19 and Long-COVID-19. Indeed, direct oral anticoagulants (DOACs) may be an alternative option for both early and later treatment of COVID-19 patients due to their ability to inhibit PARs. The aim of this report was to evaluate the role of anticoagulants—and DOACs in particular in COVID-19 and Long-COVID-19 patients. We report the case of a COVID-19 patient who, after administration of enoxaparin developed DIC secondary to virosis and positivity for platelet factor 4 (PF4) and a case of Long-COVID with high residual cardiovascular risk and persistence of blood chemistry of inflammation and procoagulative state.
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Geng J, Yu X, Bao H, Feng Z, Yuan X, Zhang J, Chen X, Chen Y, Li C, Yu H. Chronic Diseases as a Predictor for Severity and Mortality of COVID-19: A Systematic Review With Cumulative Meta-Analysis. Front Med (Lausanne) 2021; 8:588013. [PMID: 34540855 PMCID: PMC8440884 DOI: 10.3389/fmed.2021.588013] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 08/05/2021] [Indexed: 01/08/2023] Open
Abstract
Introduction: Given the ongoing coronavirus disease 2019 (COVID-19) pandemic and the consequent global healthcare crisis, there is an urgent need to better understand risk factors for symptom deterioration and mortality among patients with COVID-19. This systematic review aimed to meet the need by determining the predictive value of chronic diseases for COVID-19 severity and mortality. Methods: We searched PubMed, Embase, Web of Science, and Cumulative Index to Nursing and Allied Health Complete to identify studies published between December 1, 2019, and December 31, 2020. Two hundred and seventeen observational studies from 26 countries involving 624,986 patients were included. We assessed the risk of bias of the included studies and performed a cumulative meta-analysis. Results: We found that among COVID-19 patients, hypertension was a very common condition and was associated with higher severity, intensive care unit (ICU) admission, acute respiratory distress syndrome, and mortality. Chronic obstructive pulmonary disease was the strongest predictor for COVID-19 severity, admission to ICU, and mortality, while asthma was associated with a reduced risk of COVID-19 mortality. Patients with obesity were at a higher risk of experiencing severe symptoms of COVID-19 rather than mortality. Patients with cerebrovascular disease, chronic liver disease, chronic renal disease, or cancer were more likely to become severe COVID-19 cases and had a greater probability of mortality. Conclusions: COVID-19 patients with chronic diseases were more likely to experience severe symptoms and ICU admission and faced a higher risk of mortality. Aggressive strategies to combat the COVID-19 pandemic should target patients with chronic diseases as a priority.
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Affiliation(s)
- JinSong Geng
- Department of Medical Informatics, Medical School of Nantong University, Nantong, China
| | - XiaoLan Yu
- Department of Medical Informatics, Medical School of Nantong University, Nantong, China
| | - HaiNi Bao
- Department of Medical Informatics, Medical School of Nantong University, Nantong, China
| | - Zhe Feng
- Department of Medical Informatics, Medical School of Nantong University, Nantong, China
| | - XiaoYu Yuan
- Department of Emergency Medicine, Affiliated Hospital of Nantong University, Nantong, China
| | - JiaYing Zhang
- Department of Medical Informatics, Medical School of Nantong University, Nantong, China
| | - XiaoWei Chen
- Library and Reference Department, Zhejiang University School of Medicine First Affiliated Hospital, Hangzhou, China
| | - YaLan Chen
- Department of Medical Informatics, Medical School of Nantong University, Nantong, China
| | - ChengLong Li
- Department of Medical Informatics, Medical School of Nantong University, Nantong, China
| | - Hao Yu
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, United States
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