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Completed
NCT06333002
Machine Learning Model to Predict Outcome in Acute Hypoxemic Respiratory Failure
Conditions: Acute Hypoxemic Respiratory Failure
Sex: All
Ages: 18 Years – 100 Years
Healthy volunteers: No
Enrollment: 1241
Sponsor: Hospital Universitario de Gran Canaria Doctor Negrín
Location: Hospital General Universitario de Ciudad Real Ciudad Real
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.
Eligibility Criteria
Inclusion Criteria:
* endotracheal intubation plus mechanical ventilation (MV)
* PaO2/FiO2 ratio ≤300 mmHg under MV with positive end-expiratory pressure (PEEP) ≥5 cmH2O and FiO2 ≥0.3.
Exclusion Criteria:
* Post-operative patients ventilated \
Source: ClinicalTrials.gov (NCT06333002). StuddyBuddy aggregates publicly available trial information.
