Machine Learning Prediction of Mortality After Prone Positioning in ARDS

Acute respiratory distress syndrome (ARDS) is a life-threatening condition with high mortality. Prone position ventilation (PPV) is an evidence-based therapy that improves oxygenation and survival in patients with moderate to severe ARDS; however, outcomes remain heterogeneous. Early identification of patients at high risk of mortality after PPV may improve clinical decision-making and individualized management. This retrospective observational study developed and validated a machine learning model to predict intensive care unit (ICU) mortality in patients with ARDS receiving prone position ventilation. Clinical, laboratory, and treatment variables obtained from ICU electronic medical records were used to construct prediction models using multiple machine learning algorithms. The performance of these models was evaluated and compared to identify the optimal model for mortality prediction.

NCT Number	NCT07445061
Lead Sponsor	Shanghai Zhongshan Hospital
Conditions	Acute Respiratory Distress Syndrome (ARDS), Prone Position Ventilation, Machine Learning, ICU, ARDS
Enrollment	358 participants
Start Date	2026-03-01
Primary Completion	2026-04-01 (estimated)
Study Completion	2026-05-01 (estimated)
Updated on ClinicalTrials.gov	2026-08-14