Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

BMJ, The - Trang m1328
Laure Wynants1,2, Ben Van Calster3,1, Gary S. Collins4,5, Richard D Riley6, Georg Heinze7, Ewoud Schuit8,9, Elena Albu1, Banafsheh Arshi2, Vanesa Bellou10, Marc J. M. Bonten11,9, Darren Dahly12,13, Johanna AAG Damen8,9, Thomas P. A. Debray9,14, Valentijn M. T. de Jong8,9, Maarten De Vos1,15, Paula Dhiman4,5, Joie Ensor6, Shan Gao1, Maria Haller16,7, Michael O. Harhay17,18, Liesbet Henckaerts19,20, Pauline Heus8,9, Jeroen Hoogland9, Mohammed T Hudda21, Kevin Jenniskens8,9, Michael Kammer22,7, Nina Kreuzberger23, Anna Lohmann24, Kim Luijken24, Jie Ma5, Glen P. Martin25, David J. McLernon26, Constanza L. Andaur Navarro8,9, Johannes B. Reitsma8,9, Jamie C. Sergeant27,28, Chunhu Shi29, Nicole Skoetz22, Luc Smits2, Kym I E Snell6, Matthew Sperrin30, René Spijker31,8,9, Ewout W. Steyerberg3, Toshihiko Takada32,9, Ioanna Tzoulaki33,10, Sander M. J. van Kuijk34, Bas C. T. van Bussel2,35, Iwan C. C. van der Horst35, Kelly Reeve36, Florien S. van Royen9, Jan Y Verbakel37,38, Christine Wallisch39,40,7, Jack Wilkinson24, Robert Wolff41, Lotty Hooft8,9, Karel G.M. Moons8,9, Maarten van Smeden9
1Department of Development and Regeneration, KU Leuven, Leuven, Belgium
2Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
3Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
4Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
5NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
6Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
7Section for Clinical Biometrics, Centre for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
8Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
9Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
10Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
11Department of Medical Microbiology, University Medical Centre Utrecht, Utrecht, Netherlands
12HRB Clinical Research Facility, Cork, Ireland
13School of Public Health, University College Cork, Cork, Ireland
14Smart Data Analysis and Statistics BV, Utrecht, Netherlands
15Department of Electrical Engineering (ESAT/STADIUS), KU Leuven, Leuven, Belgium
16Ordensklinikum Linz, Hospital Elisabethinen, Department of Nephrology, Linz, Austria
17Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
18Palliative 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
19Department of General Internal Medicine, KU Leuven-University Hospitals Leuven, Leuven, Belgium
20Department of Microbiology, Immunology and Transplantation, KU Leuven-University of Leuven, Leuven, Belgium
21Population Health Research Institute, St George’s University of London, Cranmer Terrace, London, UK
22Department of Nephrology, Medical University of Vienna, Vienna, Austria
23Evidence-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
24Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
25Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
26Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
27Centre for Biostatistics, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
28Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
29Division of Nursing, Midwifery and Social work, School of Health Sciences, University of Manchester, Manchester, UK
30Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
31Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Medical Library, Netherlands
32Department of General Medicine, Shirakawa Satellite for Teaching And Research, Fukushima Medical University, Fukushima, Japan
33Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, UK
34Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre, Maastricht, Netherlands
35Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
36Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, CH
37EPI-Centre, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
38NIHR Community Healthcare Medtech and IVD Cooperative, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
39Berlin Institute of Health, Berlin, Germany
40Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
41Kleijnen Systematic Reviews, York, UK

Tóm tắt

Abstract Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models 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 or dying 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 17 February 2021, 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 126 978 titles were screened, and 412 studies describing 731 new prediction models or validations were included. Of these 731, 125 were diagnostic models (including 75 based on medical imaging) and the remaining 606 were prognostic models for either identifying those at risk of covid-19 in the general population (13 models) or predicting diverse outcomes in those individuals with confirmed covid-19 (593 models). Owing to the widespread availability of diagnostic testing capacity after the summer of 2020, this living review has now focused on the prognostic models. Of these, 29 had low risk of bias, 32 had unclear risk of bias, and 545 had high risk of bias. The most common causes for high risk of bias were inadequate sample sizes (n=408, 67%) and inappropriate or incomplete evaluation of model performance (n=338, 56%). 381 models were newly developed, and 225 were external validations of existing models. The reported C indexes varied between 0.77 and 0.93 in development studies with low risk of bias, and between 0.56 and 0.78 in external validations with low risk of bias. The Qcovid models, the PRIEST score, Carr’s model, the ISARIC4C Deterioration model, and the Xie model showed adequate predictive performance in studies at low risk of bias. Details on all reviewed models are publicly available at https://www.covprecise.org/ . Conclusion Prediction models for covid-19 entered the academic literature to support medical decision making at unprecedented speed and in large numbers. Most published prediction model studies were poorly reported and at high risk of bias such that their reported predictive performances are probably optimistic. Models with low risk of bias should be validated before clinical implementation, preferably through collaborative efforts to also allow an investigation of the heterogeneity in their performance across various populations and settings. 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 modellers 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 the final version of a living systematic review that has been updated over the past two years to reflect emerging evidence. This version is update 4 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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