Clinical Epidemiology and Ageing

Analysis of COVID-19 inpatients in France during first lockdown of 2020 using explainability methods.

Excoffier J-B, Salaün-Penquer N, Ortala M, Raphaël-Rousseau M, Chouaid C, Jung C Med Biol Eng Comput. 2022;60(6):1647-1658.

The COVID-19 pandemic rapidly puts a heavy pressure on hospital centers, especially on intensive care units. There was an urgent need for tools to understand typology of COVID-19 patients and identify those most at risk of aggravation during their hospital stay. Data included more than 400 patients hospitalized due to COVID-19 during the first wave in France (spring of 2020) with clinical and biological features. Machine learning and explainability methods were used to construct an aggravation risk score and analyzed feature effects. The model had a robust AUC ROC Score of 81%. Most important features were age, chest CT Severity and biological variables such as CRP, O2 Saturation and Eosinophils. Several features showed strong non-linear effects, especially for CT Severity. Interaction effects were also detected between age and gender as well as age and Eosinophils. Clustering techniques stratified inpatients in three main subgroups (low aggravation risk with no risk factor, medium risk due to their high age, and high risk mainly due to high CT Severity and abnormal biological values). This in-depth analysis determined significantly distinct typologies of inpatients, which facilitated definition of medical protocols to deliver the most appropriate cares for each profile. Graphical Abstract Graphical abstract represents main methods used and results found with a focus on feature impact on aggravation risk and identified groups of patients.

MeSH terms: Communicable Disease Control; COVID-19; Humans; Inpatients; Pandemics; Retrospective Studies; SARS-CoV-2
DOI: 10.1007/s11517-022-02540-0