Subphenotyping prone position responders with machine learning.

ABSTRACT:

Background

Acute respiratory distress syndrome (ARDS) is a heterogeneous condition with varying response to prone positioning. We aimed to identify subphenotypes of ARDS patients undergoing prone positioning using machine learning and assess their association with mortality and response to prone positioning.

Methods

In this retrospective observational study, we enrolled 353 mechanically ventilated ARDS patients who underwent at least one prone positioning cycle. Unsupervised machine learning was used to identify subphenotypes based on respiratory mechanics, oxygenation parameters, and demographic variables collected in supine position. The primary outcome was 28-day mortality. Secondary outcomes included response to prone positioning in terms of respiratory system compliance

SUBMITTER: Fosset M 

PROVIDER: S-EPMC11909901 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature