Unraveling relevant cross-waves pattern drifts in patient-hospital risk factors among hospitalized COVID-19 patients using explainable machine learning methods.

ABSTRACT:

Background

Several studies explored factors related to adverse clinical outcomes among COVID-19 patients but lacked analysis of the impact of the temporal data shifts on the strength of association between different predictors and adverse outcomes. This study aims to evaluate factors related to patients and hospitals in the prediction of in-hospital mortality, need for invasive mechanical ventilation (IMV), and intensive care unit (ICU) transfer throughout the pandemic waves.

Methods

This multicenter retrospective cohort included COVID-19 patients from 39 hospitals, from March/2020 to August/2022. The pandemic was divided into waves: 10/03/2020-14/11/2020 (first), 15/11/2020-25/12/2021 (second), 26/12/2021-03/08/2022 (third). Patient-related factors included clinical, demogr

SUBMITTER: Lana FCB 

PROVIDER: S-EPMC12001466 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature