Use of Artificial Intelligence to Predict Intensive Care Admission or Death in Patients Hospitalised for COVID-19: The PREDICT-COVID Study
Author(s): Michel Ducher, Christelle Elias, Nans Florens, Maelys Granal, Mitra Saadatian-Elahi, Laetitia Henaff, Philippe Vanhems, Jean-Pierre Fauvel
Purpose: Propose a carefully developed prediction clinical tool to predict unfavourable outcome at admission of a SARS-CoV2–infected patient.
Methods: This study is a post-hoc analysis of the NOSO-COR study, a multicentre prospective, observational study. All patients infected by SARSCoV2 hospitalised in the Lyon-University hospitals from 8-March-2020 to 2-June-2020 were included. The database was split into a learning dataset (80%) and a validation dataset (20%). The primary composite outcome was the need for mechanical ventilation and/or transfer to intensive care unit and/or death within 21 days of admission. The PREDICT-COVID risk prediction tool was developed using a Bayesian network.
Results: Data from 823 patients were analysed: age 70.6±16.9 years; BMI 26.7±5.4 kg/m2. Out of the 44 recorded variables, 11 that were the most linked to the primary outcome criteria were retained to develop the risk prediction tool. The primary composite endpoint was met by 36.5% of patients and 15.9% of patients died. The 5 most informative predictors were: C-Reactive-Protein, neutrophil-to-lymphocyte ratio, aspartate transaminase, shortness of breath, and prothrombin time. The final optimised models that used 11 variables had a mean±SD area under the receiver operating characteristic curve of 0.76±0.06, sensitivity of 55.5±7.0%, specificity of 78.6±4.6%, for the prediction of the primary outcome in patients hospitalised for COVID-19. The performance of the PREDICT-COVID prediction tool to predict the primary outcome of the validation dataset had accuracy of 77.6%.
Conclusions: The PREDICT-COVID prediction tool that uses 11 routinely determined variables to predict an unfavourable course at admission for COVID-19 had satisfactory performance.