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Predicting ICU Mortality in ARDS Patients

NCT05611177 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 1303

Last updated 2023-08-21

No results posted yet for this study

Summary

The investigators are planning to perform a secondary analysis of an academic dataset of 1,303 patients with moderate-to-severe acute respiratory distress syndrome (ARDS) included in several published cohorts (NCT00736892, NCT02288949, NCT02836444, NCT03145974), aimed to characterize the best early model to predict duration of mechanical ventilation and mortality in the intensive care unit (ICU) after ARDS diagnosis using machine learning approaches.

Conditions

  • Acute Respiratory Distress Syndrome

Interventions

OTHER

machine learning analysis

We will use robust machine learning approaches, such as Random Forest, XGBoost or Neural Networks.

Sponsors & Collaborators

  • Unity Health Toronto

    collaborator OTHER

  • Dr. Negrin University Hospital

    lead OTHER

Principal Investigators

  • Jesús Villar, MD, PhD · Hospital Universitario D. Negrin

Eligibility

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

Timeline & Regulatory

Start
2022-11-14
Primary Completion
2023-08-01
Completion
2023-08-01

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 NCT05611177 on ClinicalTrials.gov