NCT07445061

Machine Learning Prediction of Mortality After Prone Positioning in ARDS

A Machine Learning Model to Predict Mortality in Patients With Acute Respiratory Distress Syndrome After Prone Positioning

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 377 (estimated)
• Sponsor: Shanghai Zhongshan Hospital · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

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 aims to develop and validate a machine learning model to predict intensive care unit (ICU) mortality in ARDS patients receiving prone position ventilation. Clinical, laboratory, and treatment variables collected from ICU electronic medical records will be used to construct prediction models using multiple machine learning algorithms. The performance of these models will be evaluated and compared to identify the optimal model for mortality prediction.

Conditions

• Acute Respiratory Distress Syndrome (ARDS)
• Prone Position Ventilation
• Machine Learning
• ICU
• ARDS

Interventions

Type	Name	Description
OTHER	Prone Position Ventilation	Prone position ventilation applied as part of routine clinical care for patients with acute respiratory distress syndrome. No experimental intervention was assigned in this observational study.

Timeline

• Start date: 2026-03-01
• Primary completion: 2026-04-01
• Completion: 2026-05-01
• First posted: 2026-03-03
• Last updated: 2026-03-03