Individualised treatment effects of enhanced early mobilisation in mechanically ventilated patients: a secondary analysis of the TEAM trial.

ABSTRACT:

Purpose

Benefit or harm from early mobilisation (EM) in mechanically ventilated patients may vary by individual patient characteristics. We used machine learning to predict individualised treatment effects (ITEs) in the "Early Active Mobilization during Mechanical Ventilation in the ICU" (TEAM) trial.

Methods

This was a secondary analysis of the TEAM trial using a causal inference approach to estimate ITEs, which compared enhanced EM to usual care EM. Baseline variables in the original publication were used as predictor variables. The primary outcome was death by day 180. The dataset was randomly split into two halves (train and test) by site. In the training data, fivefold cross-validation was used to compare six candidate machine learning algorithms. The best-performing mo

SUBMITTER: Hodgson CL 

PROVIDER: S-EPMC12852203 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature