Predicting an Unfavourable Course in Patients Hospitalised for COVID-19: The PREDICT-COVID Study

Abstract

Background. Clinical decision tools that have been proposed to predict the clinical course of patients admitted to hospital with COVID-19 are poorly presented and are at high risk of selection bias. The aim of the study was to propose a prediction clinical tool to predict an unfavourable outcome at the admission of a SARS-CoV2 infected patient that was carefully developed using a large learning database and that was developed from models derived from artificial intelligence.Methods. The PREDICT-COVID study is a post hoc analysis of the Noso-Cor study, a multicenter prospective, observational study. All patients infected by SARS-CoV2 hospitalized in one of the 11 Lyon-University hospitals since 8-March-2020 have been included. The PREDICT-COVID database was split in two separate datasets: the learning dataset (80%) was used for the development of the model and the validation dataset (20%) for internal validation. The primary composite outcome was the need for mechanical ventilation or admission into an intensive care unit, or death within 21 days of admission.Results. Data from 823 patients were analysed: age 70.6±16·9 years; body mass index 26.7±5·4 kg/m2 and median number of comorbidities was 2. Out of the 44 recorded variables, 11 that were the most linked to the primary outcome criteria were retained to develop the optimised risk prediction tool. At admission the 5 most informative predictors were, in descending order: C-Reactive Protein, neutrophil-to-lymphocyte ratio, aspartate transaminase, shortness of breath, and prothrombin time. The ten-fold cross validation of the optimised model had an area under the ROC curve of 0.76±0.06. The performance of the developed Bayesian model to predict the primary outcome of the validation dataset had a mean area under the ROC curve of 0.78, sensitivity of 60%, and specificity of 77%.Conclusions. The proposed optimised prediction tool that uses 11 routinely determined variables to predict an unfavourable course at admission for COVID-19 had satisfactory performance. For an external validation, the PREDICT-COVID prediction tool is available online at: https://www.hed.cc/?a=covid&n=NETCRIT21J.netaTrial registration: The Noso-Cor study was registered on ClinicalTrials (NCT04290780). The present analysis was registered on ClinicalTrials (NCT04412031) the June 2, 2020.