Machine learning-based identification of efficient and restrictive physiological subphenotypes in acute respiratory distress syndrome.

ABSTRACT:

Introduction

Acute respiratory distress syndrome (ARDS) is a severe condition with high morbidity and mortality, characterized by significant clinical heterogeneity. This heterogeneity complicates treatment selection and patient inclusion in clinical trials. Therefore, the objective of this study is to identify physiological subphenotypes of ARDS using machine learning, and to determine ventilatory variables that can effectively discriminate between these subphenotypes in a bedside setting with high performance, highlighting potential utility for future clinical stratification approaches.

Methodology

A retrospective cohort study was conducted using data from our ICU, covering admissions from 2017 to 2021. The study included 224 patients over 18 years of age diagnosed with AR

SUBMITTER: Meza-Fuentes G 

PROVIDER: S-EPMC11872963 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature