Development and validation of a machine learning model for early prediction of intensive care unit acquired weakness.

ABSTRACT:

Background

Early identification of potential high cost and high need patients on the ICU may assist in the development of targeted protocols, which allows proper resource utilization and initialization of preventive care. Weakness acquired in the ICU developed within the first week is an independent predictor of both short and long-term adverse outcomes, nonetheless early prediction is challenging. We aimed to develop and validate a machine learning model for ICU acquired-weakness (ICU-AW), using data readily available within the first 24 h of ICU admission.

Methods

Patients from the EPaNIC trial (NCT00512122, N = 4640) who were assessed for muscle weakness at day 9 (IQR 8-13), after ICU-admission, using the Medical Research Council (MRC) sum. Patients are diagnosed with ICU

SUBMITTER: Nakano FK 

PROVIDER: S-EPMC12484466 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature