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

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 increased risk of becoming infected with covid-19 or being admitted to hospital with the disease. DESIGN Living systematic review and critical appraisal. DATA SOURCES PubMed and Embase through Ovid, Arxiv, medRxiv, and bioRxiv up to 7 April 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 4909 titles were screened, and 51 studies describing 66 prediction models were included. The review identified three models for predicting hospital admission from pneumonia and other events (as proxy outcomes for covid-19 pneumonia) in the general population; 47 diagnostic models for detecting covid-19 (34 were based on medical imaging); and 16 prognostic models for predicting mortality risk, progression to severe disease, or length of hospital stay. The most frequently reported predictors of presence of covid-19 included age, body temperature, signs and symptoms, sex, blood pressure, and creatinine. The most frequently reported predictors of severe prognosis in patients with covid-19 included age and features derived from computed tomography scans. 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 0.85 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 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. 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. Hence, we do not recommend any of these reported prediction models to be used 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