1
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The hypoxia-age-shock index at triage to predict the outcomes of Covid-19 patients. Am J Emerg Med 2023; 65:65-70. [PMID: 36586224 PMCID: PMC9773782 DOI: 10.1016/j.ajem.2022.12.034] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 12/05/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022] Open
Abstract
STUDY OBJECTIVE The coronavirus disease 2019 (COVID-19) outbreak has caused a severe burden on medical professionals, as the rapid disposition of patients is important. Therefore, we aimed to develop a new clinical assessment tool based on the shock index (SI) and age-shock index (ASI). We proposed the hypoxia-age-shock index (HASI) and determined the usability of triage for COVID-19 infected patients in the first scene. METHODS The predictive power for three indexes on mortality, intensive care unit (ICU) admission, and endotracheal intubation rate was evaluated using the receiver operating curve (ROC). We used DeLong's method for comparing the ROCs. RESULTS The area under the curve (AUC) for ROC on mortality for SI, ASI, and HASI were 0.546, 0.771, and 0.773, respectively. The AUC on ICU admission mortality for SI, ASI, and HASI were 0.581, 0.700, and 0.743, respectively. The AUC for intubation for SI, ASI, and HASI were 0.592, 0.708, and 0.757, respectively. The AUC differences between HASI and SI showed statistically significant (P = 0.001) results on mortality, ICU admission, and intubation. Additionally, statistically significant results were found for the AUC difference between the HASI and ASI on ICU admission and intubation (P = 0.001 and P = 0.004, respectively). CONCLUSION HASI can provide a better prediction compared to ASI on ICU admission and endotracheal intubation. HASI was more sensitive in mortality, ICU admission, and intubation prediction than the ASI.
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Gasulla Ó, Ledesma-Carbayo MJ, Borrell LN, Fortuny-Profitós J, Mazaira-Font FA, Barbero Allende JM, Alonso-Menchén D, García-Bennett J, Del Río-Carrrero B, Jofré-Grimaldo H, Seguí A, Monserrat J, Teixidó-Román M, Torrent A, Ortega MÁ, Álvarez-Mon M, Asúnsolo A. Enhancing physicians' radiology diagnostics of COVID-19's effects on lung health by leveraging artificial intelligence. Front Bioeng Biotechnol 2023; 11:1010679. [PMID: 37152658 PMCID: PMC10157246 DOI: 10.3389/fbioe.2023.1010679] [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: 08/03/2022] [Accepted: 03/14/2023] [Indexed: 05/09/2023] Open
Abstract
Introduction: This study aimed to develop an individualized artificial intelligence model to help radiologists assess the severity of COVID-19's effects on patients' lung health. Methods: Data was collected from medical records of 1103 patients diagnosed with COVID-19 using RT- qPCR between March and June 2020, in Hospital Madrid-Group (HM-Group, Spain). By using Convolutional Neural Networks, we determine the effects of COVID-19 in terms of lung area, opacities, and pulmonary air density. We then combine these variables with age and sex in a regression model to assess the severity of these conditions with respect to fatality risk (death or ICU). Results: Our model can predict high effect with an AUC of 0.736. Finally, we compare the performance of the model with respect to six physicians' diagnosis, and test for improvements on physicians' performance when using the prediction algorithm. Discussion: We find that the algorithm outperforms physicians (39.5% less error), and thus, physicians can significantly benefit from the information provided by the algorithm by reducing error by almost 30%.
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Affiliation(s)
- Óscar Gasulla
- Hospital Universitari de Bellvitge-Universitat de Barcelona, L´Hospitalet de Llobregat, Spain
- Department of Surgery, Medical and Social Sciences, Faculty of Medicine and Health Sciences, University of Alcalá, Alcala de Henares, Spain
| | - Maria J. Ledesma-Carbayo
- Biomedical Image Technologies, Universidad Politécnica de Madrid & CIBER BBN, ISCIII, Madrid, Spain
| | - Luisa N. Borrell
- Department of Surgery, Medical and Social Sciences, Faculty of Medicine and Health Sciences, University of Alcalá, Alcala de Henares, Spain
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, University of New York, New York, NY, United States
| | | | - Ferran A. Mazaira-Font
- Departament d'Econometria, Estadística i Economia Aplicada-Universitat de Barcelona, Barcelona, Spain
| | - Jose María Barbero Allende
- Department of Medicine and Medical Specialities, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, Spain
| | - David Alonso-Menchén
- Department of Medicine and Medical Specialities, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, Spain
| | - Josep García-Bennett
- Hospital Universitari de Bellvitge-Universitat de Barcelona, L´Hospitalet de Llobregat, Spain
| | - Belen Del Río-Carrrero
- Hospital Universitari de Bellvitge-Universitat de Barcelona, L´Hospitalet de Llobregat, Spain
| | - Hector Jofré-Grimaldo
- Hospital Universitari de Bellvitge-Universitat de Barcelona, L´Hospitalet de Llobregat, Spain
| | - Aleix Seguí
- Campus Nord, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Jorge Monserrat
- Department of Medicine and Medical Specialities, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, Spain
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), Madrid, Spain
| | - Miguel Teixidó-Román
- Departament d'Econometria, Estadística i Economia Aplicada-Universitat de Barcelona, Barcelona, Spain
| | - Adrià Torrent
- Departament d'Econometria, Estadística i Economia Aplicada-Universitat de Barcelona, Barcelona, Spain
| | - Miguel Ángel Ortega
- Department of Medicine and Medical Specialities, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, Spain
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), Madrid, Spain
- *Correspondence: Angel Asúnsolo, ; Melchor Álvarez-Mon, ; Miguel Ángel Ortega,
| | - Melchor Álvarez-Mon
- Department of Medicine and Medical Specialities, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, Spain
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), Madrid, Spain
- Service of Internal Medicine and Immune System Diseases-Rheumatology, University Hospital Príncipe de Asturias, (CIBEREHD), Alcalá de Henares, Spain
- *Correspondence: Angel Asúnsolo, ; Melchor Álvarez-Mon, ; Miguel Ángel Ortega,
| | - Angel Asúnsolo
- Department of Surgery, Medical and Social Sciences, Faculty of Medicine and Health Sciences, University of Alcalá, Alcala de Henares, Spain
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, University of New York, New York, NY, United States
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), Madrid, Spain
- *Correspondence: Angel Asúnsolo, ; Melchor Álvarez-Mon, ; Miguel Ángel Ortega,
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3
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Wu JTY, de la Hoz MÁA, Kuo PC, Paguio JA, Yao JS, Dee EC, Yeung W, Jurado J, Moulick A, Milazzo C, Peinado P, Villares P, Cubillo A, Varona JF, Lee HC, Estirado A, Castellano JM, Celi LA. Developing and Validating Multi-Modal Models for Mortality Prediction in COVID-19 Patients: a Multi-center Retrospective Study. J Digit Imaging 2022; 35:1514-1529. [PMID: 35789446 PMCID: PMC9255527 DOI: 10.1007/s10278-022-00674-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 05/15/2022] [Accepted: 06/08/2022] [Indexed: 01/07/2023] Open
Abstract
The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2 infections with the goal of helping health systems allocate resources. Machine learning models, in particular, hold promise for their ability to leverage patient clinical information and medical images for prediction. However, most of the published COVID-19 prediction models thus far have little clinical utility due to methodological flaws and lack of appropriate validation. In this paper, we describe our methodology to develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. The models for COVID-19 mortality prediction were developed using retrospective data from Madrid, Spain (N = 2547) and were externally validated in patient cohorts from a community hospital in New Jersey, USA (N = 242) and an academic center in Seoul, Republic of Korea (N = 336). The models we developed performed differently across various clinical settings, underscoring the need for a guided strategy when employing machine learning for clinical decision-making. We demonstrated that using features from both the structured electronic health records and chest X-ray imaging data resulted in better 30-day mortality prediction performance across all three datasets (areas under the receiver operating characteristic curves: 0.85 (95% confidence interval: 0.83-0.87), 0.76 (0.70-0.82), and 0.95 (0.92-0.98)). We discuss the rationale for the decisions made at every step in developing the models and have made our code available to the research community. We employed the best machine learning practices for clinical model development. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification, and/or optimization.
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Affiliation(s)
- Joy Tzung-Yu Wu
- Department of Radiology and Nuclear Medicine, Stanford University, Palo Alto, CA, USA
| | - Miguel Ángel Armengol de la Hoz
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Big Data Department, Fundacion Progreso Y Salud, Regional Ministry of Health of Andalucia, Andalucia, Spain
| | - Po-Chih Kuo
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan.
| | - Joseph Alexander Paguio
- Albert Einstein Medical Center, Philadelphia, PA, USA
- Hoboken University Medical Center-CarePoint Health, Hoboken, NJ, USA
| | - Jasper Seth Yao
- Albert Einstein Medical Center, Philadelphia, PA, USA
- Hoboken University Medical Center-CarePoint Health, Hoboken, NJ, USA
| | - Edward Christopher Dee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Wesley Yeung
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- National University Heart Center, National University Hospital, Singapore, Singapore
| | - Jerry Jurado
- Hoboken University Medical Center-CarePoint Health, Hoboken, NJ, USA
| | - Achintya Moulick
- Hoboken University Medical Center-CarePoint Health, Hoboken, NJ, USA
| | - Carmelo Milazzo
- Hoboken University Medical Center-CarePoint Health, Hoboken, NJ, USA
| | - Paloma Peinado
- Centro Integral de Enfermedades Cardiovasculares, Hospital Universitario Monteprincipe, Grupo HM Hospitales, Madrid, Spain
| | - Paula Villares
- Centro Integral de Enfermedades Cardiovasculares, Hospital Universitario Monteprincipe, Grupo HM Hospitales, Madrid, Spain
| | - Antonio Cubillo
- Centro Integral de Enfermedades Cardiovasculares, Hospital Universitario Monteprincipe, Grupo HM Hospitales, Madrid, Spain
| | - José Felipe Varona
- Centro Integral de Enfermedades Cardiovasculares, Hospital Universitario Monteprincipe, Grupo HM Hospitales, Madrid, Spain
| | - Hyung-Chul Lee
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Alberto Estirado
- Centro Integral de Enfermedades Cardiovasculares, Hospital Universitario Monteprincipe, Grupo HM Hospitales, Madrid, Spain
| | - José Maria Castellano
- Centro Integral de Enfermedades Cardiovasculares, Hospital Universitario Monteprincipe, Grupo HM Hospitales, Madrid, Spain
- Centro Nacional de Investigaciones Cardiovasculares, Instituto de Salud Carlos III, Madrid, Spain
| | - Leo Anthony Celi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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4
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Huang SC, Chaudhari AS, Langlotz CP, Shah N, Yeung S, Lungren MP. Developing medical imaging AI for emerging infectious diseases. Nat Commun 2022; 13:7060. [PMID: 36400764 PMCID: PMC9672573 DOI: 10.1038/s41467-022-34234-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 10/19/2022] [Indexed: 11/19/2022] Open
Abstract
Very few of the COVID-19 ML models were fit for deployment in real-world settings. In this Comment, Huang et al. discuss the main steps required to develop clinically useful models in the context of an emerging infectious disease.
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Affiliation(s)
- Shih-Cheng Huang
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA.
| | - Akshay S Chaudhari
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Curtis P Langlotz
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Nigam Shah
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Serena Yeung
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Matthew P Lungren
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
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5
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Carbonell G, Del Valle DM, Gonzalez-Kozlova E, Marinelli B, Klein E, El Homsi M, Stocker D, Chung M, Bernheim A, Simons NW, Xiang J, Nirenberg S, Kovatch P, Lewis S, Merad M, Gnjatic S, Taouli B. Quantitative chest computed tomography combined with plasma cytokines predict outcomes in COVID-19 patients. Heliyon 2022; 8:e10166. [PMID: 35958514 PMCID: PMC9356575 DOI: 10.1016/j.heliyon.2022.e10166] [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: 10/13/2021] [Revised: 03/08/2022] [Accepted: 07/27/2022] [Indexed: 01/29/2023] Open
Abstract
Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest computed tomography (CT) in combination with plasma cytokines using a machine learning and k-fold cross-validation approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n = 152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within five days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α), were collected from the electronic medical record. We found that CT quantitative alone was better at predicting severity (AUC 0.81) than death (AUC 0.70), while cytokine measurements alone better-predicted death (AUC 0.70) compared to severity (AUC 0.66). When combined, chest CT and plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82). Finally, we provide a simple scoring system (nomogram) using plasma IL-6, IL-8, TNF-α, ground-glass opacities (GGO) to aerated lung ratio and age as new metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19.
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Affiliation(s)
- Guillermo Carbonell
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA,BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Department of Radiology, Universidad de Murcia, Spain,Instituto Murciano de Investigación Biosanitaria, Spain
| | - Diane Marie Del Valle
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Gonzalez-Kozlova
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brett Marinelli
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Emma Klein
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Maria El Homsi
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Daniel Stocker
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Switzerland
| | - Michael Chung
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Adam Bernheim
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nicole W. Simons
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jiani Xiang
- Scientific Computing; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sharon Nirenberg
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Scientific Computing; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Patricia Kovatch
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Scientific Computing; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sara Lewis
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA,BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Miriam Merad
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sacha Gnjatic
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Oncological Sciences; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bachir Taouli
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA,BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Corresponding author.
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6
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Park SH, Han K. How to Clearly and Accurately Report Odds Ratio and Hazard Ratio in Diagnostic Research Studies? Korean J Radiol 2022; 23:777-784. [PMID: 35695319 PMCID: PMC9340231 DOI: 10.3348/kjr.2022.0249] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 04/11/2022] [Indexed: 01/17/2023] Open
Affiliation(s)
- Seong Ho Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
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7
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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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8
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Jin KN, Do KH, Nam BD, Hwang SH, Choi M, Yong HS. [Korean Clinical Imaging Guidelines for Justification of Diagnostic Imaging Study for COVID-19]. TAEHAN YONGSANG UIHAKHOE CHI 2022; 83:265-283. [PMID: 36237918 PMCID: PMC9514447 DOI: 10.3348/jksr.2021.0117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 09/10/2021] [Accepted: 09/17/2021] [Indexed: 06/16/2023]
Abstract
To develop Korean coronavirus disease (COVID-19) chest imaging justification guidelines, eight key questions were selected and the following recommendations were made with the evidence-based clinical imaging guideline adaptation methodology. It is appropriate not to use chest imaging tests (chest radiograph or CT) for the diagnosis of COVID-19 in asymptomatic patients. If reverse transcription-polymerase chain reaction testing is not available or if results are delayed or are initially negative in the presence of symptoms suggestive of COVID-19, chest imaging tests may be considered. In addition to clinical evaluations and laboratory tests, chest imaging may be contemplated to determine hospital admission for asymptomatic or mildly symptomatic unhospitalized patients with confirmed COVID-19. In hospitalized patients with confirmed COVID-19, chest imaging may be advised to determine or modify treatment alternatives. CT angiography may be considered if hemoptysis or pulmonary embolism is clinically suspected in a patient with confirmed COVID-19. For COVID-19 patients with improved symptoms, chest imaging is not recommended to make decisions regarding hospital discharge. For patients with functional impairment after recovery from COVID-19, chest imaging may be considered to distinguish a potentially treatable disease.
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9
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Miller JL, Tada M, Goto M, Chen H, Dang E, Mohr NM, Lee S. Prediction models for severe manifestations and mortality due to COVID-19: A systematic review. Acad Emerg Med 2022; 29:206-216. [PMID: 35064988 DOI: 10.1111/acem.14447] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 12/21/2021] [Accepted: 12/29/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND Throughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available. OBJECTIVE This systematic review aims to evaluate published reports of prediction models for severe illnesses caused COVID-19. METHODS Searches were developed by the primary author and a medical librarian using an iterative process of gathering and evaluating terms. Comprehensive strategies, including both index and keyword methods, were devised for PubMed and EMBASE. The data of confirmed COVID-19 patients from randomized control studies, cohort studies, and case-control studies published between January 2020 and May 2021 were retrieved. Studies were independently assessed for risk of bias and applicability using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). We collected study type, setting, sample size, type of validation, and outcome including intubation, ventilation, any other type of organ support, or death. The combination of the prediction model, scoring system, performance of predictive models, and geographic locations were summarized. RESULTS A primary review found 445 articles relevant based on title and abstract. After further review, 366 were excluded based on the defined inclusion and exclusion criteria. Seventy-nine articles were included in the qualitative analysis. Inter observer agreement on inclusion 0.84 (95%CI 0.78-0.89). When the PROBAST tool was applied, 70 of the 79 articles were identified to have high or unclear risk of bias, or high or unclear concern for applicability. Nine studies reported prediction models that were rated as low risk of bias and low concerns for applicability. CONCLUSION Several prognostic models for COVID-19 were identified, with varying clinical score performance. Nine studies that had a low risk of bias and low concern for applicability, one from a general public population and hospital setting. The most promising and well-validated scores include Clift et al.,15 and Knight et al.,18 which seem to have accurate prediction models that clinicians can use in the public health and emergency department setting.
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Affiliation(s)
- Jamie L. Miller
- University of Iowa Carver College of Medicine Iowa City Iowa USA
| | - Masafumi Tada
- Department of Health Promotion and Human Behavior School of Public Health, Kyoto University Graduate School of Medicine Kyoto Japan
| | - Michihiko Goto
- Division of Infectious Diseases, Department of Internal Medicine University of Iowa Carver College of Medicine Iowa City Iowa USA
| | - Hao Chen
- University of Iowa Iowa City Iowa USA
| | | | - Nicholas M. Mohr
- Department of Emergency Medicine, Department of Anesthesia, Department of Epidemiology University of Iowa Carver College of Medicine Iowa City Iowa USA
| | - Sangil Lee
- Department of Emergency Medicine The University of Iowa Carver College of Medicine Iowa City Iowa USA
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10
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Carbonell G, Del Valle DM, Gonzalez-kozlova E, Marinelli B, Klein E, Homsi ME, Stocker D, Chung M, Bernheim A, Simons NW, Xiang J, Nirenberg S, Kovatch P, Lewis S, Merad M, Gnjatic S, Taouli B. Quantitative chest CT combined with plasma cytokines predict outcomes in COVID-19 patients.. [PMID: 34671777 PMCID: PMC8528085 DOI: 10.1101/2021.10.11.21264709] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest CT in combination with plasma cytokines using a machine learning approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n=152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within 5 days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α) were collected from the electronic medical record. We found that chest CT combined with plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82), whereas CT quantitative was better at predicting severity (AUC 0.81 vs 0.70) while cytokine measurements better predicted death (AUC 0.70 vs 0.66). Finally, we provide a simple scoring system using plasma IL-6, IL-8, TNF-α, GGO to aerated lung ratio and age as novel metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19.
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11
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Zhao Y, Wang D, Mei N, Yin B, Li X, Zheng Y, Xiao A, Yu X, Qiu X, Lu Y, Liu L. Longitudinal Radiological Findings in Patients With COVID-19 With Different Severities: From Onset to Long-Term Follow-Up After Discharge. Front Med (Lausanne) 2021; 8:711435. [PMID: 34621760 PMCID: PMC8490620 DOI: 10.3389/fmed.2021.711435] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 08/20/2021] [Indexed: 12/18/2022] Open
Abstract
Objective: This study aimed to investigate the evolution of radiological findings in the patients with coronavirus disease 2019 (COVID-19) pneumonia with different severities from onset to 1-year follow-up and identify the predictive factors for different pulmonary lesion absorption status in the patients infected with COVID-19. Methods: A retrospective study was performed on the clinical and radiological features of 175 patients with COVID-19 pneumonia hospitalized at three institutions from January 21 to March 20, 2020. All the chest CT scans during hospitalization and follow-ups after discharge were collected. The clinical and radiological features from the chest CT scans both at the peak stage and before discharge from the hospital were used to predict whether the pulmonary lesions would be fully absorbed after discharge by Cox regression. Then, these patients were stratified into two groups with different risks of pulmonary lesion absorption, and an optimal timepoint for the first CT follow-up was selected accordingly. Results: A total of 132 (75.4%) patients were classified into the non-severe group, and 43 (24.6%) patients were classified into the severe group, according to the WHO guidelines. The opacification in both the groups changed from ground-glass opacity (GGO) to consolidation and then from consolidation to GGO. Among the 175 participants, 135 (112 non-severe and 23 severe patients with COVID-19) underwent follow-up CT scans after discharge. Pulmonary residuals could be observed in nearly half of the patients (67/135) with the presentation of opacities and parenchymal bands. The parenchymal bands in nine discharged patients got fully absorbed during the follow-up periods. The age of patient [hazard ratio (HR) = 0.95, 95% CI, 0.95–0.99], level of lactate dehydrogenase (LDH) (HR = 0.99; 95% CI, 0.99–1.00), level of procalcitonin (HR = 8.72; 95% CI, 1.04–73.03), existence of diffuse lesions (HR = 0.28; 95% CI, 0.09–0.92), subpleural distribution of lesions (HR = 2.15; 95% CI, 1.17–3.92), morphology of residuals (linear lesion: HR = 4.58, 95% CI, 1.22–17.11; nodular lesion: HR = 33.07, 95% CI, 3.58–305.74), and pleural traction (HR = 0.41; 95% CI, 0.22–0.78) from the last scan before discharge were independent factors to predict the absorption status of COVID-19-related pulmonary abnormalities after discharge. According to a Kaplan–Meier analysis, the probability of patients of the low-risk group to have pulmonary lesions fully absorbed within 90 days reached 91.7%. Conclusion: The development of COVID-19 lesions followed the trend from GGO to consolidation and then from consolidation to GGO. The CT manifestations and clinical and laboratory variables before discharge could help predict the absorption status of pulmonary lesions after discharge. The parenchymal bands could be fully absorbed in some COVID-19 cases. In this study, a Cox regression analysis indicated that a timepoint of 3 months since onset was optimal for the radiological follow-up of discharged patients.
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Affiliation(s)
- Yajing Zhao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Dongdong Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Nan Mei
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Bo Yin
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xuanxuan Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yingyan Zheng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Anling Xiao
- Department of Radiology, Fuyang Second People's Hospital, Fuyang, China
| | - Xiangrong Yu
- Department of Radiology, Zhuhai People's Hospital, Zhuhai, China
| | - Xiaohui Qiu
- Department of Radiology, Bozhou People's Hospital, Bozhou, China
| | - Yiping Lu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Li Liu
- Department of Radiology, Shanghai Cancer Center, Fudan University, Shanghai, China
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12
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Park SH, Han K, Park SY. Mistakes to Avoid for Accurate and Transparent Reporting of Survival Analysis in Imaging Research. Korean J Radiol 2021; 22:1587-1593. [PMID: 34431251 PMCID: PMC8484160 DOI: 10.3348/kjr.2021.0579] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 07/15/2021] [Indexed: 02/07/2023] Open
Affiliation(s)
- Seong Ho Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Seo Young Park
- Department of Statistics and Data Science, Korea National Open University, Seoul, Korea
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13
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Huang D, Yang H, Yu H, Wang T, Chen Z, Liang Z, Yao R. Blood Urea Nitrogen to Serum Albumin Ratio (BAR) Predicts Critical Illness in Patients with Coronavirus Disease 2019 (COVID-19). Int J Gen Med 2021; 14:4711-4721. [PMID: 34456583 PMCID: PMC8387643 DOI: 10.2147/ijgm.s326204] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 08/06/2021] [Indexed: 02/05/2023] Open
Abstract
Purpose We sought to explore the prognostic value of blood urea nitrogen (BUN) to serum albumin ratio (BAR) and further develop a prediction model for critical illness in COVID-19 patients. Patients and Methods This was a retrospective, multicenter, observational study on adult hospitalized COVID-19 patients from three provinces in China between January 14 and March 9, 2020. Primary outcome was critical illness, including admission to the intensive care unit (ICU), need for invasive mechanical ventilation (IMV), or death. Clinical data were collected within 24 hours after admission to hospitals. The predictive performance of BAR was tested by multivariate logistic regression analysis and receiver operating characteristic (ROC) curve and then a nomogram was developed. Results A total of 1370 patients with COVID-19 were included and 113 (8.2%) patients eventually developed critical illness in the study. Baseline age (OR: 1.031, 95% CI: 1.014, 1.049), respiratory rate (OR: 1.063, 95% CI: 1.009, 1.120), unconsciousness (OR: 40.078, 95% CI: 5.992, 268.061), lymphocyte counts (OR: 0.352, 95% CI: 0.204, 0.607), total bilirubin (OR: 1.030, 95% CI: 1.001, 1.060) and BAR (OR: 1.319, 95% CI: 1.183, 1.471) were independent risk factors for critical illness. The predictive AUC of BAR was 0.821 (95% CI: 0.784, 0.858; P<0.01) and the optimal cut-off value of BAR was 3.7887 mg/g (sensitivity: 0.690, specificity: 0.786; positive predictive value: 0.225, negative predictive value: 0.966; positive likelihood ratio: 3.226, negative likelihood ratio: 0.394). The C index of nomogram including above six predictors was 0.9031125 (95% CI: 0.8720542, 0.9341708). Conclusion Elevated BAR at admission is an independent risk factor for critical illness of COVID-19. The novel predictive nomogram including BAR has superior predictive performance.
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Affiliation(s)
- Dong Huang
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Huan Yang
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - He Yu
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Ting Wang
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Zhu Chen
- Department of Infectional Inpatient Ward Two, Chengdu Public Health Clinical Medical Center, Chengdu, Sichuan, People's Republic of China
| | - Zongan Liang
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Rong Yao
- Department of Emergency Medicine, Emergency Medical Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
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14
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Romanov A, Bach M, Yang S, Franzeck FC, Sommer G, Anastasopoulos C, Bremerich J, Stieltjes B, Weikert T, Sauter AW. Automated CT Lung Density Analysis of Viral Pneumonia and Healthy Lungs Using Deep Learning-Based Segmentation, Histograms and HU Thresholds. Diagnostics (Basel) 2021; 11:diagnostics11050738. [PMID: 33919094 PMCID: PMC8143124 DOI: 10.3390/diagnostics11050738] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 04/16/2021] [Accepted: 04/17/2021] [Indexed: 02/06/2023] Open
Abstract
CT patterns of viral pneumonia are usually only qualitatively described in radiology reports. Artificial intelligence enables automated and reliable segmentation of lungs with chest CT. Based on this, the purpose of this study was to derive meaningful imaging biomarkers reflecting CT patterns of viral pneumonia and assess their potential to discriminate between healthy lungs and lungs with viral pneumonia. This study used non-enhanced and CT pulmonary angiograms (CTPAs) of healthy lungs and viral pneumonia (SARS-CoV-2, influenza A/B) identified by radiology reports and RT-PCR results. After deep learning segmentation of the lungs, histogram-based and threshold-based analyses of lung attenuation were performed and compared. The derived imaging biomarkers were correlated with parameters of clinical and biochemical severity (modified WHO severity scale; c-reactive protein). For non-enhanced CTs (n = 526), all imaging biomarkers significantly differed between healthy lungs and lungs with viral pneumonia (all p < 0.001), a finding that was not reproduced for CTPAs (n = 504). Standard deviation (histogram-derived) and relative high attenuation area [600-0 HU] (HU-thresholding) differed most. The strongest correlation with disease severity was found for absolute high attenuation area [600-0 HU] (r = 0.56, 95% CI = 0.46-0.64). Deep-learning segmentation-based histogram and HU threshold analysis could be deployed in chest CT evaluation for the differentiating of healthy lungs from AP lungs.
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Affiliation(s)
- Andrej Romanov
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland; (A.R.); (G.S.); (J.B.); (T.W.); (A.W.S.)
| | - Michael Bach
- Department of Research & Analytic Services, University Hospital Basel, University of Basel, Spitalstrasse 8, 4031 Basel, Switzerland; (M.B.); (S.Y.); (F.C.F.); (B.S.)
| | - Shan Yang
- Department of Research & Analytic Services, University Hospital Basel, University of Basel, Spitalstrasse 8, 4031 Basel, Switzerland; (M.B.); (S.Y.); (F.C.F.); (B.S.)
| | - Fabian C. Franzeck
- Department of Research & Analytic Services, University Hospital Basel, University of Basel, Spitalstrasse 8, 4031 Basel, Switzerland; (M.B.); (S.Y.); (F.C.F.); (B.S.)
| | - Gregor Sommer
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland; (A.R.); (G.S.); (J.B.); (T.W.); (A.W.S.)
| | - Constantin Anastasopoulos
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland; (A.R.); (G.S.); (J.B.); (T.W.); (A.W.S.)
- Correspondence:
| | - Jens Bremerich
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland; (A.R.); (G.S.); (J.B.); (T.W.); (A.W.S.)
| | - Bram Stieltjes
- Department of Research & Analytic Services, University Hospital Basel, University of Basel, Spitalstrasse 8, 4031 Basel, Switzerland; (M.B.); (S.Y.); (F.C.F.); (B.S.)
| | - Thomas Weikert
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland; (A.R.); (G.S.); (J.B.); (T.W.); (A.W.S.)
- Department of Research & Analytic Services, University Hospital Basel, University of Basel, Spitalstrasse 8, 4031 Basel, Switzerland; (M.B.); (S.Y.); (F.C.F.); (B.S.)
| | - Alexander Walter Sauter
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland; (A.R.); (G.S.); (J.B.); (T.W.); (A.W.S.)
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15
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Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-021-00307-0] [Citation(s) in RCA: 298] [Impact Index Per Article: 99.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
AbstractMachine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.
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16
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Purkayastha S, Xiao Y, Jiao Z, Thepumnoeysuk R, Halsey K, Wu J, Tran TML, Hsieh B, Choi JW, Wang D, Vallières M, Wang R, Collins S, Feng X, Feldman M, Zhang PJ, Atalay M, Sebro R, Yang L, Fan Y, Liao WH, Bai HX. Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data. Korean J Radiol 2021; 22:1213-1224. [PMID: 33739635 PMCID: PMC8236359 DOI: 10.3348/kjr.2020.1104] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 01/04/2021] [Accepted: 01/06/2021] [Indexed: 01/08/2023] Open
Abstract
Objective To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.
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Affiliation(s)
| | - Yanhe Xiao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Zhicheng Jiao
- Department of Radiology, Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Kasey Halsey
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.,Warren Alpert Medical School at Brown University, Providence, RI, USA
| | - Jing Wu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Thi My Linh Tran
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.,Warren Alpert Medical School at Brown University, Providence, RI, USA
| | - Ben Hsieh
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.,Warren Alpert Medical School at Brown University, Providence, RI, USA
| | - Ji Whae Choi
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.,Warren Alpert Medical School at Brown University, Providence, RI, USA
| | - Dongcui Wang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Canada
| | - Robin Wang
- Department of Radiology, Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Scott Collins
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA
| | - Xue Feng
- Carina Medical, Lexington, KY, USA
| | - Michael Feldman
- Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Paul J Zhang
- Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Atalay
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA
| | - Ronnie Sebro
- Department of Radiology, Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Yang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Yong Fan
- Department of Radiology, Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Wei Hua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.
| | - Harrison X Bai
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.,Warren Alpert Medical School at Brown University, Providence, RI, USA.
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17
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Weikert T, Rapaka S, Grbic S, Re T, Chaganti S, Winkel DJ, Anastasopoulos C, Niemann T, Wiggli BJ, Bremerich J, Twerenbold R, Sommer G, Comaniciu D, Sauter AW. Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings. Korean J Radiol 2021; 22:994-1004. [PMID: 33686818 PMCID: PMC8154782 DOI: 10.3348/kjr.2020.0994] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 12/21/2020] [Accepted: 12/23/2020] [Indexed: 11/15/2022] Open
Abstract
Objective To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. Materials and Methods All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. Results While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79–0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77–0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85–0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66–0.88). Conclusion Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.
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Affiliation(s)
- Thomas Weikert
- Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland.
| | | | - Sasa Grbic
- Siemens Healthineers, Princeton, NJ, USA
| | - Thomas Re
- Siemens Healthineers, Princeton, NJ, USA
| | | | - David J Winkel
- Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland
| | | | - Tilo Niemann
- Department of Radiology, Kantonsspital Baden, Baden, Switzerland
| | - Benedikt J Wiggli
- Department of Infectious Diseases & Infection Control, Kantonsspital Baden, Baden, Switzerland
| | - Jens Bremerich
- Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Raphael Twerenbold
- Department of Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Gregor Sommer
- Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland
| | | | - Alexander W Sauter
- Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland
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18
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Chen Y, Zhou X, Yan H, Huang H, Li S, Jiang Z, Zhao J, Meng Z. CANPT Score: A Tool to Predict Severe COVID-19 on Admission. Front Med (Lausanne) 2021; 8:608107. [PMID: 33681245 PMCID: PMC7930838 DOI: 10.3389/fmed.2021.608107] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 01/14/2021] [Indexed: 12/19/2022] Open
Abstract
Background and Aims: Patients with critical coronavirus disease 2019 (COVID-19) have a mortality rate higher than 50%. The purpose of this study was to establish a model for the prediction of the risk of severe disease and/or death in patients with COVID-19 on admission. Materials and Methods: Patients diagnosed with COVID-19 in four hospitals in China from January 22, 2020 to April 15, 2020 were retrospectively enrolled. The demographic, laboratory, and clinical data of the patients with COVID-19 were collected. The independent risk factors related to the severity of and death due to COVID-19 were identified with a multivariate logistic regression; a nomogram and prediction model were established. The area under the receiver operating characteristic curve (AUROC) and predictive accuracy were used to evaluate the model's effectiveness. Results: In total, 582 patients with COVID-19, including 116 patients with severe disease, were enrolled. Their comorbidities, body temperature, neutrophil-to-lymphocyte ratio (NLR), platelet (PLT) count, and levels of total bilirubin (Tbil), creatinine (Cr), creatine kinase (CK), and albumin (Alb) were independent risk factors for severe disease. A nomogram was generated based on these eight variables with a predictive accuracy of 85.9% and an AUROC of 0.858 (95% CI, 0.823–0.893). Based on the nomogram, the CANPT score was established with cut-off values of 12 and 16. The percentages of patients with severe disease in the groups with CANPT scores <12, ≥12, and <16, and ≥16 were 4.15, 27.43, and 69.64%, respectively. Seventeen patients died. NLR, Cr, CK, and Alb were independent risk factors for mortality, and the CAN score was established to predict mortality. With a cut-off value of 15, the predictive accuracy was 97.4%, and the AUROC was 0.903 (95% CI 0.832, 0.974). Conclusions: The CANPT and CAN scores can predict the risk of severe disease and mortality in COVID-19 patients on admission.
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Affiliation(s)
- Yuanyuan Chen
- Department of Infectious Diseases, Taihe Hospital, Hubei University of Medicine, Shiyan, China.,Institute of Biomedical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Xiaolin Zhou
- Department of Liver Diseases, Yichang Central People's Hospital, China Three Gorges University, Yichang, China
| | - Huadong Yan
- Department of Liver Diseases, HwaMei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Huihong Huang
- Department of Infectious Diseases, Ankang Central Hospital, Hubei University of Medicine, Ankang, China
| | - Shengjun Li
- Department of Infectious Diseases, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Zicheng Jiang
- Department of Infectious Diseases, Ankang Central Hospital, Hubei University of Medicine, Ankang, China
| | - Jun Zhao
- Department of Infectious Diseases, Taihe Hospital, Hubei University of Medicine, Shiyan, China.,School of Public Health, Hubei University of Medicine, Shiyan, China
| | - Zhongji Meng
- Department of Infectious Diseases, Taihe Hospital, Hubei University of Medicine, Shiyan, China.,Institute of Biomedical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, China.,Hubei Clinical Research Center for Precise Diagnosis and Treatment of Liver Cancer, Shiyan, China
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19
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Chen Y, Zhou X, Yan H, Huang H, Li S, Jiang Z, Zhao J, Meng Z. CANPT Score: A Tool to Predict Severe COVID-19 on Admission. Front Med (Lausanne) 2021; 8:608107. [PMID: 33681245 PMCID: PMC7930838 DOI: 10.3389/fmed.2021.608107/full 10.3389/fmed.2021.608107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Background and Aims: Patients with critical coronavirus disease 2019 (COVID-19) have a mortality rate higher than 50%. The purpose of this study was to establish a model for the prediction of the risk of severe disease and/or death in patients with COVID-19 on admission. Materials and Methods: Patients diagnosed with COVID-19 in four hospitals in China from January 22, 2020 to April 15, 2020 were retrospectively enrolled. The demographic, laboratory, and clinical data of the patients with COVID-19 were collected. The independent risk factors related to the severity of and death due to COVID-19 were identified with a multivariate logistic regression; a nomogram and prediction model were established. The area under the receiver operating characteristic curve (AUROC) and predictive accuracy were used to evaluate the model's effectiveness. Results: In total, 582 patients with COVID-19, including 116 patients with severe disease, were enrolled. Their comorbidities, body temperature, neutrophil-to-lymphocyte ratio (NLR), platelet (PLT) count, and levels of total bilirubin (Tbil), creatinine (Cr), creatine kinase (CK), and albumin (Alb) were independent risk factors for severe disease. A nomogram was generated based on these eight variables with a predictive accuracy of 85.9% and an AUROC of 0.858 (95% CI, 0.823-0.893). Based on the nomogram, the CANPT score was established with cut-off values of 12 and 16. The percentages of patients with severe disease in the groups with CANPT scores <12, ≥12, and <16, and ≥16 were 4.15, 27.43, and 69.64%, respectively. Seventeen patients died. NLR, Cr, CK, and Alb were independent risk factors for mortality, and the CAN score was established to predict mortality. With a cut-off value of 15, the predictive accuracy was 97.4%, and the AUROC was 0.903 (95% CI 0.832, 0.974). Conclusions: The CANPT and CAN scores can predict the risk of severe disease and mortality in COVID-19 patients on admission.
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Affiliation(s)
- Yuanyuan Chen
- Department of Infectious Diseases, Taihe Hospital, Hubei University of Medicine, Shiyan, China,Institute of Biomedical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Xiaolin Zhou
- Department of Liver Diseases, Yichang Central People's Hospital, China Three Gorges University, Yichang, China
| | - Huadong Yan
- Department of Liver Diseases, HwaMei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Huihong Huang
- Department of Infectious Diseases, Ankang Central Hospital, Hubei University of Medicine, Ankang, China
| | - Shengjun Li
- Department of Infectious Diseases, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Zicheng Jiang
- Department of Infectious Diseases, Ankang Central Hospital, Hubei University of Medicine, Ankang, China
| | - Jun Zhao
- Department of Infectious Diseases, Taihe Hospital, Hubei University of Medicine, Shiyan, China,School of Public Health, Hubei University of Medicine, Shiyan, China,Jun Zhao
| | - Zhongji Meng
- Department of Infectious Diseases, Taihe Hospital, Hubei University of Medicine, Shiyan, China,Institute of Biomedical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, China,Hubei Clinical Research Center for Precise Diagnosis and Treatment of Liver Cancer, Shiyan, China,*Correspondence: Zhongji Meng
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20
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An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 Patients. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11031238] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The ongoing COVID-19 pandemic currently involves millions of people worldwide. Radiology plays an important role in the diagnosis and management of patients, and chest computed tomography (CT) is the most widely used imaging modality. An automatic method to characterize the lungs of COVID-19 patients based on individually optimized Hounsfield unit (HU) thresholds was developed and implemented. Lungs were considered as composed of three components—aerated, intermediate, and consolidated. Three methods based on analytic fit (Gaussian) and maximum gradient search (using polynomial and original data fits) were implemented. The methods were applied to a population of 166 patients scanned during the first wave of the pandemic. Preliminarily, the impact of the inter-scanner variability of the HU-density calibration curve was investigated. Results showed that inter-scanner variability was negligible. The median values of individual thresholds th1 (between aerated and intermediate components) were −768, −780, and −798 HU for the three methods, respectively. A significantly lower median value for th2 (between intermediate and consolidated components) was found for the maximum gradient on the data (−34 HU) compared to the other two methods (−114 and −87 HU). The maximum gradient on the data method was applied to quantify the three components in our population—the aerated, intermediate, and consolidation components showed median values of 793 ± 499 cc, 914 ± 291 cc, and 126 ± 111 cc, respectively, while the median value of the first peak was −853 ± 56 HU.
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21
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Park SH. What's New in the Korean Journal of Radiology in 2021. Korean J Radiol 2021; 22:1-4. [PMID: 33369290 PMCID: PMC7772385 DOI: 10.3348/kjr.2020.1429] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 12/06/2020] [Accepted: 12/06/2020] [Indexed: 01/07/2023] Open
Affiliation(s)
- Seong Ho Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
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22
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Mudatsir M, Fajar JK, Wulandari L, Soegiarto G, Ilmawan M, Purnamasari Y, Mahdi BA, Jayanto GD, Suhendra S, Setianingsih YA, Hamdani R, Suseno DA, Agustina K, Naim HY, Muchlas M, Alluza HHD, Rosida NA, Mayasari M, Mustofa M, Hartono A, Aditya R, Prastiwi F, Meku FX, Sitio M, Azmy A, Santoso AS, Nugroho RA, Gersom C, Rabaan AA, Masyeni S, Nainu F, Wagner AL, Dhama K, Harapan H. Predictors of COVID-19 severity: a systematic review and meta-analysis. F1000Res 2020; 9:1107. [PMID: 33163160 PMCID: PMC7607482 DOI: 10.12688/f1000research.26186.2] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/04/2020] [Indexed: 01/08/2023] Open
Abstract
Background: The unpredictability of the progression of coronavirus disease 2019 (COVID-19) may be attributed to the low precision of the tools used to predict the prognosis of this disease. Objective: To identify the predictors associated with poor clinical outcomes in patients with COVID-19. Methods: Relevant articles from PubMed, Embase, Cochrane, and Web of Science were searched as of April 5, 2020. The quality of the included papers was appraised using the Newcastle-Ottawa scale (NOS). Data of interest were collected and evaluated for their compatibility for the meta-analysis. Cumulative calculations to determine the correlation and effect estimates were performed using the Z test. Results: In total, 19 papers recording 1,934 mild and 1,644 severe cases of COVID-19 were included. Based on the initial evaluation, 62 potential risk factors were identified for the meta-analysis. Several comorbidities, including chronic respiratory disease, cardiovascular disease, diabetes mellitus, and hypertension were observed more frequent among patients with severe COVID-19 than with the mild ones. Compared to the mild form, severe COVID-19 was associated with symptoms such as dyspnea, anorexia, fatigue, increased respiratory rate, and high systolic blood pressure. Lower levels of lymphocytes and hemoglobin; elevated levels of leukocytes, aspartate aminotransferase, alanine aminotransferase, blood creatinine, blood urea nitrogen, high-sensitivity troponin, creatine kinase, high-sensitivity C-reactive protein, interleukin 6, D-dimer, ferritin, lactate dehydrogenase, and procalcitonin; and a high erythrocyte sedimentation rate were also associated with severe COVID-19. Conclusion: More than 30 risk factors are associated with a higher risk of severe COVID-19. These may serve as useful baseline parameters in the development of prediction tools for COVID-19 prognosis.
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Affiliation(s)
- Mudatsir Mudatsir
- Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Aceh, 23111, Indonesia
| | - Jonny Karunia Fajar
- Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Aceh, 23111, Indonesia
- Brawijaya Internal Medicine Research Center, Department of Internal Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Laksmi Wulandari
- Department of Pulmonology and Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, East Java, 60286, Indonesia
| | - Gatot Soegiarto
- Department of Internal Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, Easy Java, 60286, Indonesia
| | - Muhammad Ilmawan
- Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Yeni Purnamasari
- Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Bagus Aulia Mahdi
- Department of Internal Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, Easy Java, 60286, Indonesia
| | - Galih Dwi Jayanto
- Brawijaya Internal Medicine Research Center, Department of Internal Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Suhendra Suhendra
- Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Yennie Ayu Setianingsih
- Department of Urology, Faculty of Medicine, Universitas Airlangga, Surabaya, East Java, 60285, Indonesia
| | - Romi Hamdani
- Department of Orthopedic Surgery, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Daniel Alexander Suseno
- Department of Obstetry and Gynecology, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Kartika Agustina
- Department of Neurology, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Hamdan Yuwafi Naim
- Department of Urology, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Muchamad Muchlas
- Faculty of Animal Science, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | | | - Nikma Alfi Rosida
- Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Mayasari Mayasari
- Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Mustofa Mustofa
- Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Adam Hartono
- Faculty of Medicine, Universitas Negeri Sebelas Maret, Surakarta, Surakarta, 57126, Indonesia
| | - Richi Aditya
- Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Firman Prastiwi
- Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | | | - Monika Sitio
- Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Abdullah Azmy
- Department of Orthopedic Surgery, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Anita Surya Santoso
- Department of Cardiology and Vascular Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | | | - Camoya Gersom
- Brawijaya Internal Medicine Research Center, Department of Internal Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Ali A. Rabaan
- Molecular Diagnostic Laboratory, Johns Hopkins Aramco Healthcare, Dhahran, Dhahran, 31311, Saudi Arabia
| | - Sri Masyeni
- Department of Internal Medicine, Faculty of Medicine and Health Science, Universitas Warmadewa, Denpasar, Bali, 80235, Indonesia
| | - Firzan Nainu
- Faculty of Pharmacy, Hasanuddin University, Makassar, Makassar, 90245, Indonesia
| | - Abram L. Wagner
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kuldeep Dhama
- Division of Pathology, Indian Veterinary Research Institute, Izatnagar, Uttar Pradesh, 243 122, India
| | - Harapan Harapan
- Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Aceh, 23111, Indonesia
- Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Aceh, 23111, Indonesia
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23
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Mudatsir M, Fajar JK, Wulandari L, Soegiarto G, Ilmawan M, Purnamasari Y, Mahdi BA, Jayanto GD, Suhendra S, Setianingsih YA, Hamdani R, Suseno DA, Agustina K, Naim HY, Muchlas M, Alluza HHD, Rosida NA, Mayasari M, Mustofa M, Hartono A, Aditya R, Prastiwi F, Meku FX, Sitio M, Azmy A, Santoso AS, Nugroho RA, Gersom C, Rabaan AA, Masyeni S, Nainu F, Wagner AL, Dhama K, Harapan H. Predictors of COVID-19 severity: a systematic review and meta-analysis. F1000Res 2020; 9:1107. [PMID: 33163160 PMCID: PMC7607482 DOI: 10.12688/f1000research.26186.1] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/01/2020] [Indexed: 12/15/2022] Open
Abstract
Background: The unpredictability of the progression of coronavirus disease 2019 (COVID-19) may be attributed to the low precision of the tools used to predict the prognosis of this disease. Objective: To identify the predictors associated with poor clinical outcomes in patients with COVID-19. Methods: Relevant articles from PubMed, Embase, Cochrane, and Web of Science were searched and extracted as of April 5, 2020. Data of interest were collected and evaluated for their compatibility for the meta-analysis. Cumulative calculations to determine the correlation and effect estimates were performed using the Z test. Results: In total, 19 papers recording 1,934 mild and 1,644 severe cases of COVID-19 were included. Based on the initial evaluation, 62 potential risk factors were identified for the meta-analysis. Several comorbidities, including chronic respiratory disease, cardiovascular disease, diabetes mellitus, and hypertension were observed more frequent among patients with severe COVID-19 than with the mild ones. Compared to the mild form, severe COVID-19 was associated with symptoms such as dyspnea, anorexia, fatigue, increased respiratory rate, and high systolic blood pressure. Lower levels of lymphocytes and hemoglobin; elevated levels of leukocytes, aspartate aminotransferase, alanine aminotransferase, blood creatinine, blood urea nitrogen, high-sensitivity troponin, creatine kinase, high-sensitivity C-reactive protein, interleukin 6, D-dimer, ferritin, lactate dehydrogenase, and procalcitonin; and a high erythrocyte sedimentation rate were also associated with severe COVID-19. Conclusion: More than 30 risk factors are associated with a higher risk of severe COVID-19. These may serve as useful baseline parameters in the development of prediction tools for COVID-19 prognosis.
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Affiliation(s)
- Mudatsir Mudatsir
- Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Aceh, 23111, Indonesia
| | - Jonny Karunia Fajar
- Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Aceh, 23111, Indonesia
- Brawijaya Internal Medicine Research Center, Department of Internal Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Laksmi Wulandari
- Department of Pulmonology and Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, East Java, 60286, Indonesia
| | - Gatot Soegiarto
- Department of Internal Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, Easy Java, 60286, Indonesia
| | - Muhammad Ilmawan
- Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Yeni Purnamasari
- Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Bagus Aulia Mahdi
- Department of Internal Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, Easy Java, 60286, Indonesia
| | - Galih Dwi Jayanto
- Brawijaya Internal Medicine Research Center, Department of Internal Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Suhendra Suhendra
- Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Yennie Ayu Setianingsih
- Department of Urology, Faculty of Medicine, Universitas Airlangga, Surabaya, East Java, 60285, Indonesia
| | - Romi Hamdani
- Department of Orthopedic Surgery, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Daniel Alexander Suseno
- Department of Obstetry and Gynecology, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Kartika Agustina
- Department of Neurology, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Hamdan Yuwafi Naim
- Department of Urology, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Muchamad Muchlas
- Faculty of Animal Science, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | | | - Nikma Alfi Rosida
- Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Mayasari Mayasari
- Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Mustofa Mustofa
- Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Adam Hartono
- Faculty of Medicine, Universitas Negeri Sebelas Maret, Surakarta, Surakarta, 57126, Indonesia
| | - Richi Aditya
- Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Firman Prastiwi
- Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | | | - Monika Sitio
- Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Abdullah Azmy
- Department of Orthopedic Surgery, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Anita Surya Santoso
- Department of Cardiology and Vascular Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | | | - Camoya Gersom
- Brawijaya Internal Medicine Research Center, Department of Internal Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Ali A. Rabaan
- Molecular Diagnostic Laboratory, Johns Hopkins Aramco Healthcare, Dhahran, Dhahran, 31311, Saudi Arabia
| | - Sri Masyeni
- Department of Internal Medicine, Faculty of Medicine and Health Science, Universitas Warmadewa, Denpasar, Bali, 80235, Indonesia
| | - Firzan Nainu
- Faculty of Pharmacy, Hasanuddin University, Makassar, Makassar, 90245, Indonesia
| | - Abram L. Wagner
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kuldeep Dhama
- Division of Pathology, Indian Veterinary Research Institute, Izatnagar, Uttar Pradesh, 243 122, India
| | - Harapan Harapan
- Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Aceh, 23111, Indonesia
- Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Aceh, 23111, Indonesia
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24
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Lu Y, Li X, Geng D, Mei N, Wu PY, Huang CC, Jia T, Zhao Y, Wang D, Xiao A, Yin B. Cerebral Micro-Structural Changes in COVID-19 Patients - An MRI-based 3-month Follow-up Study. EClinicalMedicine 2020; 25:100484. [PMID: 32838240 PMCID: PMC7396952 DOI: 10.1016/j.eclinm.2020.100484] [Citation(s) in RCA: 356] [Impact Index Per Article: 89.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 07/15/2020] [Accepted: 07/16/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Increasing evidence supported the possible neuro-invasion potential of SARS-CoV-2. However, no studies were conducted to explore the existence of the micro-structural changes in the central nervous system after infection. We aimed to identify the existence of potential brain micro-structural changes related to SARS-CoV-2. METHODS In this prospective study, diffusion tensor imaging (DTI) and 3D high-resolution T1WI sequences were acquired in 60 recovered COVID-19 patients (56.67% male; age: 44.10 ± 16.00) and 39 age- and sex-matched non-COVID-19 controls (56.41% male; age: 45.88 ± 13.90). Registered fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were quantified for DTI, and an index score system was introduced. Regional volumes derived from Voxel-based Morphometry (VBM) and DTI metrics were compared using analysis of covariance (ANCOVA). Two sample t-test and Spearman correlation were conducted to assess the relationships among imaging indices, index scores and clinical information. FINDINGS In this follow-up stage, neurological symptoms were presented in 55% COVID-19 patients. COVID-19 patients had statistically significantly higher bilateral gray matter volumes (GMV) in olfactory cortices, hippocampi, insulas, left Rolandic operculum, left Heschl's gyrus and right cingulate gyrus and a general decline of MD, AD, RD accompanied with an increase of FA in white matter, especially AD in the right CR, EC and SFF, and MD in SFF compared with non-COVID-19 volunteers (corrected p value <0.05). Global GMV, GMVs in left Rolandic operculum, right cingulate, bilateral hippocampi, left Heschl's gyrus, and Global MD of WM were found to correlate with memory loss (p value <0.05). GMVs in the right cingulate gyrus and left hippocampus were related to smell loss (p value <0.05). MD-GM score, global GMV, and GMV in right cingulate gyrus were correlated with LDH level (p value <0.05). INTERPRETATION Study findings revealed possible disruption to micro-structural and functional brain integrity in the recovery stages of COVID-19, suggesting the long-term consequences of SARS-CoV-2. FUNDING Shanghai Natural Science Foundation, Youth Program of National Natural Science Foundation of China, Shanghai Sailing Program, Shanghai Science and Technology Development, Shanghai Municipal Science and Technology Major Project and ZJ Lab.
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Key Words
- 3D-T1WI, 3 Dimensional T1-weighted Images
- AAL-3, Automated Anatomical Labelling Atlas-3
- ACE-2, Angiotensin Converting Enzyme-2
- AD, Axial Diffusivity
- CNS, Central Nervous System
- COVID-19
- COVID-19, Coronavirus Disease
- CR, Corona Radiata
- CSF, Cerebral Spinal Fluid
- Central Nervous System Diseases
- DICOM, Digital Imaging and Communications in Medicine
- DTI, Diffusion Tensor Imaging
- Diffusion Tensor Imaging
- EC, External Capsule
- FA, Fractional Anisotropy
- FOV, Field of View
- GM, Gray Matter
- GMV, Gray Matter Volume
- HIV, Human Immunodeficiency Virus
- HSV, Herpes Simplex Virus
- JEV, Japanese Encephalitis Virus
- LDH, Lactate Dehydrogenase
- MD, Mean Diffusivity
- MPRAGE, Magnetization Prepared Rapid Gradient Echo
- Neuroimaging
- OB, Olfactory Bulb
- PCR, Polymerase Chain Reaction
- Prospective studies
- RD, Radial Diffusivity
- SARS-CoV, Severe Acute Respiratory Syndrome Coronavirus
- SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus-2
- SFF, Superior Frontal-occipital Fasciculus
- TBSS, Track-based Spatial Statistics
- TE, Echo Time
- TR, Repetition Time
- UF, Uncinate Fasciculus
- URTI, Upper Respiratory Tract Infection
- VBM, Voxel-based Morphometry
- WBC, White Blood Cell
- WHO, World Health Organization
- WM, White Matter
- WMV, White Matter Volume
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Affiliation(s)
- Yiping Lu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China (Y Lu, X Li, D Geng, N Mei, Y Zhao, D Wang, B Yin)
| | - Xuanxuan Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China (Y Lu, X Li, D Geng, N Mei, Y Zhao, D Wang, B Yin)
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China (Y Lu, X Li, D Geng, N Mei, Y Zhao, D Wang, B Yin)
| | - Nan Mei
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China (Y Lu, X Li, D Geng, N Mei, Y Zhao, D Wang, B Yin)
| | - Pu-Yeh Wu
- GE Healthcare, MR Research China, Beijing, China (P Wu)
| | - Chu-Chung Huang
- Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China (C Huang)
| | - Tianye Jia
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, England (T Jia)
| | - Yajing Zhao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China (Y Lu, X Li, D Geng, N Mei, Y Zhao, D Wang, B Yin)
| | - Dongdong Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China (Y Lu, X Li, D Geng, N Mei, Y Zhao, D Wang, B Yin)
| | - Anling Xiao
- Department of Radiology, Fu Yang No.2 Hospital, Anhui, China (A Xiao)
| | - Bo Yin
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China (Y Lu, X Li, D Geng, N Mei, Y Zhao, D Wang, B Yin)
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Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Dahly DL, Damen JAA, Debray TPA, de Jong VMT, De Vos M, Dhiman P, Haller MC, Harhay MO, Henckaerts L, Heus P, Kammer M, Kreuzberger N, Lohmann A, Luijken K, Ma J, Martin GP, McLernon DJ, Andaur Navarro CL, Reitsma JB, Sergeant JC, Shi C, Skoetz N, Smits LJM, Snell KIE, Sperrin M, Spijker R, Steyerberg EW, Takada T, Tzoulaki I, van Kuijk SMJ, van Bussel B, van der Horst ICC, van Royen FS, Verbakel JY, Wallisch C, Wilkinson J, Wolff R, Hooft L, Moons KGM, van Smeden M. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020; 369:m1328. [PMID: 32265220 PMCID: PMC7222643 DOI: 10.1136/bmj.m1328] [Citation(s) in RCA: 1651] [Impact Index Per Article: 412.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/31/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. DESIGN Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. DATA SOURCES PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. STUDY SELECTION Studies that developed or validated a multivariable covid-19 related prediction model. DATA EXTRACTION At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). RESULTS 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. CONCLUSION Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. SYSTEMATIC REVIEW REGISTRATION Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. READERS' NOTE This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.
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Affiliation(s)
- Laure Wynants
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Georg Heinze
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Ewoud Schuit
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Marc M J Bonten
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Medical Microbiology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Darren L Dahly
- HRB Clinical Research Facility, Cork, Ireland
- School of Public Health, University College Cork, Cork, Ireland
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Valentijn M T de Jong
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten De Vos
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT Stadius, KU Leuven, Leuven, Belgium
| | - Paul Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Maria C Haller
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Ordensklinikum Linz, Hospital Elisabethinen, Department of Nephrology, Linz, Austria
| | - Michael O Harhay
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Palliative and Advanced Illness Research Center and Division of Pulmonary and Critical Care Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Liesbet Henckaerts
- Department of Microbiology, Immunology and Transplantation, KU Leuven-University of Leuven, Leuven, Belgium
- Department of General Internal Medicine, KU Leuven-University Hospitals Leuven, Leuven, Belgium
| | - Pauline Heus
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Michael Kammer
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Nina Kreuzberger
- Evidence-Based Oncology, Department I of Internal Medicine and Centre for Integrated Oncology Aachen Bonn Cologne Dusseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Anna Lohmann
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Kim Luijken
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Jie Ma
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Glen P Martin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - David J McLernon
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jamie C Sergeant
- Centre for Biostatistics, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Chunhu Shi
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester, Manchester, UK
| | - Nicole Skoetz
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Luc J M Smits
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
| | - Kym I E Snell
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Matthew Sperrin
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - René Spijker
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Medical Library, Netherlands
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Toshihiko Takada
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Sander M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Bas van Bussel
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Iwan C C van der Horst
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Florien S van Royen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jan Y Verbakel
- EPI-Centre, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Christine Wallisch
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Jack Wilkinson
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | | | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
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