Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal.,The BMJ

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Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal.
The BMJ ( IF 105.7 ) Pub Date : 2020-04-07 , DOI: 10.1136/bmj.m1328
Laure Wynants _{1,

2} , Ben Van Calster _{2,

3} , Gary S Collins _{4,

5} , Richard D Riley ₆ , Georg Heinze ₇ , Ewoud Schuit _{8,

9} , Marc M J Bonten _{8,

10} , Darren L Dahly _{11,

12} , Johanna A A Damen _{8,

9} , Thomas P A Debray _{8,

9} , Valentijn M T de Jong _{8,

9} , Maarten De Vos _{2,

13} , Paul Dhiman _{4,

5} , Maria C Haller _{7,

14} , Michael O Harhay _{15,

16} , Liesbet Henckaerts _{17,

18} , Pauline Heus _{8,

9} , Michael Kammer _{7,

19} , Nina Kreuzberger ₂₀ , Anna Lohmann ₂₁ , Kim Luijken ₂₁ , Jie Ma ₅ , Glen P Martin ₂₂ , David J McLernon ₂₃ , Constanza L Andaur Navarro _{8,

9} , Johannes B Reitsma _{8,

9} , Jamie C Sergeant _{24,

25} , Chunhu Shi ₂₆ , Nicole Skoetz ₁₉ , Luc J M Smits ₁ , Kym I E Snell ₆ , Matthew Sperrin ₂₇ , René Spijker _{8,

9,

28} , Ewout W Steyerberg ₃ , Toshihiko Takada ₈ , Ioanna Tzoulaki _{29,

30} , Sander M J van Kuijk ₃₁ , Bas van Bussel _{1,

32} , Iwan C C van der Horst ₃₂ , Florien S van Royen ₈ , Jan Y Verbakel _{33,

34} , Christine Wallisch _{7,

35,

36} , Jack Wilkinson ₂₂ , Robert Wolff ₃₇ , Lotty Hooft _{8,

9} , Karel G M Moons _{8,

9} , Maarten van Smeden ₈

Affiliation

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.
Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands.
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
Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
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
Department of Medical Microbiology, University Medical Centre Utrecht, Utrecht, Netherlands
HRB Clinical Research Facility, Cork, Ireland
School of Public Health, University College Cork, Cork, Ireland
Department of Electrical Engineering, ESAT Stadius, KU Leuven, Leuven, Belgium
Ordensklinikum Linz, Hospital Elisabethinen, Department of Nephrology, Linz, Austria
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
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
Department of Nephrology, Medical University of Vienna, Vienna, Austria
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
Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
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
Division of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester, Manchester, UK
Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Medical Library, Netherlands
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
Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, Netherlands
Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
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
Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
Berlin Institute of Health, Berlin, Germany
Kleijnen Systematic Reviews, York, UK

OBJECTIVE To 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 risk of being admitted to hospital for covid-19 pneumonia. DESIGN Rapid systematic review and critical appraisal. DATA SOURCES PubMed and Embase through Ovid, Arxiv, medRxiv, and bioRxiv up to 24 March 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 2696 titles were screened, and 27 studies describing 31 prediction models were included. Three models were identified for predicting hospital admission from pneumonia and other events (as proxy outcomes for covid-19 pneumonia) in the general population; 18 diagnostic models for detecting covid-19 infection (13 were machine learning based on computed tomography scans); and 10 prognostic models for predicting mortality risk, progression to severe disease, or length of hospital stay. Only one study used patient data from outside of China. The most reported predictors of presence of covid-19 in patients with suspected disease included age, body temperature, and signs and symptoms. The most reported predictors of severe prognosis in patients with covid-19 included age, sex, features derived from computed tomography scans, C reactive protein, lactic dehydrogenase, and lymphocyte count. C index estimates ranged from 0.73 to 0.81 in prediction models for the general population (reported for all three models), from 0.81 to more than 0.99 in diagnostic models (reported for 13 of the 18 models), and from 0.85 to 0.98 in prognostic models (reported for six of the 10 models). All studies 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, and high risk of model overfitting. Reporting quality varied substantially between studies. Most reports did not include a description of the study population or intended use of the models, and calibration of predictions was rarely assessed. 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 proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic. Immediate sharing of well documented individual participant data from covid-19 studies is needed for collaborative efforts to develop more rigorous prediction models and validate existing ones. The predictors identified in included studies could 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 REGISTRATION Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

更新日期：2020-04-08

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