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Prediction of Duration of Mechanical Ventilation in ARDS

NCT05993377 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 1303

Last updated 2024-03-20

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, NCT022288949, NCT02836444, NCT03145974), aimed to characterize the best early scenario during the first three days of diagnosis to predict duration of mechanical ventilation in the intensive care unit (ICU) using supervised machine learning (ML) approaches.

Conditions

  • Acute Respiratory Distress Syndrome

Interventions

OTHER

Logistic regression Cross validation Area under the RIC curves Machine learning analysis. .

we will use robust machine learning approaches, such as Random Forest and XGBoost.

Sponsors & Collaborators

  • Unity Health Toronto

    collaborator OTHER

  • Cardiff University

    collaborator OTHER

  • Leiden University Medical Center

    collaborator OTHER

  • Dr. Negrin University Hospital

    lead OTHER

Principal Investigators

  • Jesús Villar · Hospital Universitario D. Negrin

Eligibility

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

Timeline & Regulatory

Start
2023-08-14
Primary Completion
2024-02-02
Completion
2024-02-02

Countries

  • Spain
  • United Kingdom

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 NCT05993377 on ClinicalTrials.gov