Machine Learning Model to Predict Outcome in Acute Hypoxemic Respiratory Failure
- Conditions
- Acute Hypoxemic Respiratory Failure
- Interventions
- Other: machine learning analysis
- Registration Number
- NCT06333002
- Lead Sponsor
- Dr. Negrin University Hospital
- Brief 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.
- Detailed Description
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 AHRF patients on mechanical ventilation (MV). Few studies have investigated the prediction of mortality in patients with AHRF.
For model development, the investigators will extract data for the first 2 days after diagnosis of AHRF from patients included in the de-identified database of the PANDORA cohort. We had a database with 2,000,000 anonymized and dissociated demographics and clinical, data from 1,241 patients with AHRF enrolled in our PANDORA cohort (Prevalence AND Outcome of acute Respiratory fAilure) from 22 Spanish hospitals and coordinated by the principal investigator (JV). The investigators will follow the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines for model prediction. We will screen collected variables employing a genetic algorithm variable selection method to achieve parsimony. We evaluated the minimum number of variables models using logistic regression and 4 supervised ML algorithms: Random Forest, Extreme Gradient Boosting, Support Vector Machine and Multilayer Perceptron. We will use a 5-fold cross-validation in the dataset of 1,000 patients selected randomly in training data (80%) and testing data (20%). For external validation, we will use the remaining 241 patients.
Recruitment & Eligibility
- Status
- ACTIVE_NOT_RECRUITING
- Sex
- All
- Target Recruitment
- 1241
- 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.
- Post-operative patients ventilated <24 h
- Brain death patients.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Derivation cohort machine learning analysis It will contain 800 patients randomly selected (80% of 1,000 patients with AHRF) Validation cohort machine learning analysis It will contain 200 patients randomly selected (20% of 1000 patients with AHRF Confirmatory cohort machine learning analysis It will contain the remaining 241 patients randomply selected (por external validation)
- Primary Outcome Measures
Name Time Method ICU mortality up to 100 weeks (from inclusion to death or diascharge from intensive care unit death in the intensive care unit
- Secondary Outcome Measures
Name Time Method MV duration up to 100 weeks (from inclusion to extubation) duration of mechanical ventilation
Trial Locations
- Locations (8)
Hospital Cinico de Valencia
🇪🇸Valencia, Spain
Hospital Universitario La Paz
🇪🇸Madrid, Spain
Hospital General Universitario de Ciudad Real
🇪🇸Ciudad Real, Spain
Hospital Virgen de La Luz
🇪🇸Cuenca, Spain
Hospital Universitario Puerta de Hierro
🇪🇸Madrid, Spain
Hospital Universitario Virgen de Arrixaca
🇪🇸Murcia, Spain
Hospital Universitario NS de Candelaria
🇪🇸Santa Cruz De Tenerife, Spain
Hospital Universitario Rio Hortega
🇪🇸Valladolid, Spain