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.

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.

Adult patients diagnosed with acute respiratory distress syndrome (ARDS) who received prone position ventilation in the intensive care unit at Zhongshan Hospital, Fudan University. Clinical data were collected retrospectively from electronic medical records.

Individual participant data will not be publicly shared due to institutional and ethical restrictions.