Machine Learning Prediction of Mortality After Prone Positioning in ARDS

Fudan University

Status: Not yet enrolling

Conditions: ICU; Machine Learning; Acute Respiratory Distress Syndrome (ARDS); ARDS; Prone Position Ventilation

Treatments: Other: Prone Position Ventilation

Study type: Observational

Funder types: Other

Identifiers

NCT07445061

B2026-019

Details and patient eligibility

About

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.

Enrollment: 377 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Diagnosis of ARDS according to the Berlin definition [15];
• Receipt of at least one session of prone position ventilation (PPV) during hospitalization;
• Requirement for mechanical ventilation.

Exclusion criteria

• Age <18 years;
• PPV duration <6 hours;
• ICU length of stay <24 hours;
• Pregnancy;
• Missing key clinical data.

Trial design

377 participants in 1 patient group

ARDS Patients Receiving Prone Position Ventilation

Description:

Adult patients diagnosed with acute respiratory distress syndrome (ARDS) who received prone position ventilation during intensive care unit (ICU) admission. Clinical data from electronic medical records will be collected retrospectively for the development and validation of machine learning models to predict ICU mortality.

Treatment:

Other: Prone Position Ventilation