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Machine Learning Model to Predict Outcome in Acute Hypoxemic Respiratory Failure

NCT06333002 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 1241

Last updated 2025-02-11

No results posted yet for this study

Summary

Acute hypoxemic respiratory failure (AHRF) is the most common cause of admission in the intensive care units (UCIs) worldwide. We will assess the value of machine learning (ML) techniques for early prediction of ICU death in 1,241 patients enrolled in the PANDORA (Prevalence AND Outcome of acute Respiratory fAilure) Study in Spain. The study was registered with ClinicalTrials.gov (NCT03145974). Our aim is to evaluate the minimum number of variables models using logistic regression and four supervised ML algorithms: Random Forest, Extreme Gradient Boosting, Support Vector Machine and Multilayer Perceptron.

Conditions

  • Acute Hypoxemic Respiratory Failure

Interventions

OTHER

machine learning analysis

We will use robust machine learning approaches, such as Random Forest, Extreme Gradient Boosting, Support Vector Machine and Multilayer Perceptron.

Sponsors & Collaborators

  • Dr. Negrin University Hospital

    lead OTHER

Principal Investigators

  • Jesus Villar, MD, PhD · Fundación Canaria Instituto de Investigación Sanitaria de Canarias

Eligibility

Min Age
18 Years
Max Age
100 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-03-19
Primary Completion
2026-05-30
Completion
2026-05-30

Countries

  • Spain

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 NCT06333002 on ClinicalTrials.gov