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Prediction of Duration of Mechanical Ventilation in Acute Hypoxemic Respiratoty Failure
Sponsor: Jesus Villar
Summary
Acute hypoxemic respiratory failure (AHRF) is a common cause of admission in intensive care units (ICUs) worldwide. We will assess machine learning (ML) techniques for prediction of prolonged duration (\> or = to 7 days) of mechanical ventilation (MV) in 1,241 patients enrolled in the PANDORA study in Spain. The study was registered with ClinalTrials.gov (NCT03145974). Our aim is to identify a model with the minimum number of variables that predict duration of prolonged ventilation in AHRF patients using data as early as from the first 48 hours with machine learning algorithms.
Official title: Prediction of Duration of Mechanical Venylation in Patients Wit Acute Hypoxemic Respiratory Failure Usinf Machine Learning Approaches
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
1241
Start Date
2025-02-02
Completion Date
2026-06-01
Last Updated
2025-03-11
Healthy Volunteers
No
Conditions
Interventions
Machine learning and logistic regression for the training/testing cohort and validation cohort
Machine learning and logistic regression for the validation cohort
Locations (2)
Hospital Dr. Negrin
Las Palmas de Gran Canaria, Las Palmas, Spain
Hospital Universitario La Paz
Madrid, Spain