Development and multicentric external validation of a prognostic COVID-19 severity model based on thoracic CT.

ABSTRACT:

Background

Risk stratification of COVID-19 patients can support therapeutic decisions, planning and resource allocation in the hospital. In times of high incidence, a prognostic model based on data efficiently retrieved from one source can enable fast decision support.

Methods

A model was developed to identify patients at risk of developing severe COVID-19 within one month based on their age, sex and imaging features extracted from the thoracic computed tomography (CT). The model was trained on publicly available data from the Study of Thoracic CT in COVID-19 (STOIC) challenge and validated on unseen data from the same study and an external, multicentric dataset. The model, trained on data acquired before any variant of concern dominated, was assessed separately on data coll

SUBMITTER: Dirks I 

PROVIDER: S-EPMC11963321 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature