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NCT Number: NCT07445061

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.

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Key information

Conditions

Acute Respiratory Distress Syndrome (ARDS) ARDS ICU Lung Diseases Machine Learning Prone Position Ventilation Respiration Disorders Respiratory Distress Syndrome Respiratory Tract Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Zhongshan Hospital, Fudan University

Shanghai, Shanghai Municipality, 200032, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Diagnosis of ARDS according to the Berlin definition;
  • 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 <2 days;
  • Pregnancy;
  • Receipt of awake prone positioning.
  • Missing key clinical data.

Treatment and study plan

Prone Position Ventilation

Other

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.

Primary outcomes

  1. ICU Mortality

    Time frame: Up to 90 days.

    Death from any cause during the intensive care unit (ICU) stay among patients with acute respiratory distress syndrome receiving prone position ventilation.

Other outcomes

  1. ARDS subphenotype classification based on machine learning model.

    Time frame: Baseline (at initiation of prone position ventilation).

    Number of patients classified into different ARDS subphenotypes using a machine learning model based on clinical and physiological variables collected at baseline.

Sponsors and collaborators

Lead sponsor

Shanghai Zhongshan Hospital

Other

Registry information

Official study title

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

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Mar 3, 2026
Registry last updated
Aug 14, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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