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Machine Learning Prediction of Mortality After Prone Positioning in ARDS

NCT07445061 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 358

Last updated 2026-08-14

No results posted yet for this study

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 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.

Conditions

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

Interventions

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.

Sponsors & Collaborators

  • Shanghai Zhongshan Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-03-01
Primary Completion
2026-04-01
Completion
2026-05-01

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07445061 on ClinicalTrials.gov