• Laure Wynants, Ben Van Calster, Gary S Collins, Richard D Riley, Georg Heinze, Ewoud Schuit, Bonten Marc M J MMJ 0000-0002-9095-9201 Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utr, Johanna A A Damen, Debray Thomas P A TPA 0000-0002-1790-2719 Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, U, Maarten De Vos, Paul Dhiman, Maria C Haller, Michael O Harhay, Liesbet Henckaerts, Nina Kreuzberger, Anna Lohman, Kim Luijken, Jie Ma, Constanza L Andaur, Johannes B Reitsma, Jamie C Sergeant, Chunhu Shi, Nicole Skoetz, Smits Luc J M LJM 0000-0003-0785-1345 Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debye, Snell Kym I E KIE 0000-0001-9373-6591 Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK, Matthew Sperrin, René Spijker, Ewout W Steyerberg, Toshihiko Takada, van Kuijk Sander M J SMJ Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, Netherlands, Florien S van Royen, Christine Wallisch, Lotty Hooft, Moons Karel G M KGM Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands , and Maarten van Smeden.
• Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands laure.wynants@maastrichtuniversity.nl.
• BMJ. 2020 Apr 7; 369: m1328.

ObjectiveTo review and critically appraise 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 becoming infected with covid-19 or being admitted to hospital with the disease.DesignLiving 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 SourcesPubMed and Embase through Ovid, arXiv, medRxiv, and bioRxiv up to 5 May 2020.Study SelectionStudies that developed or validated a multivariable covid-19 related prediction model.Data ExtractionAt 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).Results14 217 titles were screened, and 107 studies describing 145 prediction models were included. The review identified four models for identifying people at risk in the general population; 91 diagnostic models for detecting covid-19 (60 were based on medical imaging, nine to diagnose disease severity); and 50 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequently reported predictors of diagnosis and prognosis of covid-19 are age, body temperature, lymphocyte count, and lung imaging features. Flu-like symptoms and neutrophil count are frequently predictive in diagnostic models, while comorbidities, sex, C reactive protein, and creatinine are frequent prognostic factors. C index estimates ranged from 0.73 to 0.81 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.68 to 0.99 in prognostic models. All models were rated at high 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 vague reporting. Most reports did not include any description of the study population or intended use of the models, and calibration of the model predictions was rarely assessed.ConclusionPrediction 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 proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic. Hence, we do not recommend any of these reported prediction models for use in current practice. Immediate sharing of well documented individual participant data from covid-19 studies and collaboration are urgently needed to develop more rigorous prediction models, and validate promising ones. The predictors identified in included models should be considered as candidate predictors for new models. Methodological guidance should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, studies should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.Systematic Review RegistrationProtocol https://osf.io/ehc47/, registration https://osf.io/wy245.Readers' NoteThis 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 2 of the original article published on 7 April 2020 (BMJ 2020;369:m1328), and previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp).© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. No commercial re-use. See rights and permissions. Published by BMJ.

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