Clinical trial NCT06333002: Machine Learning Model to Predict Outcome in Acute Hypoxemic Respiratory Failure
Official title: Developing an Optimal Machine Learning Model to Predict ICU Outcome in Patients With Acute Hypoxemic Respiratory Failure
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Acute Hypoxemic Respiratory Failure
Keywords: outcome
mechanical ventilation
machine learning
Lead sponsor: Dr. Negrin University Hospital
Sponsor class: OTHER
First posted: 2024-03-27
Enrollment: 1241
Sex: ALL
Minimum age (years): 18
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: De-identified dataset inclusing 1,241 mechanically ventilated patients with acute hypoxemic respiratory failure admitted consecutively in a network of Spanish ICUs.

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.

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 \<24 h
* Brain death patients.

Primary outcomes:
measure: ICU mortality
timeFrame: up to 100 weeks (from inclusion to death or diascharge from intensive care unit
Secondary outcomes:
measure: MV duration
timeFrame: up to 100 weeks (from inclusion to extubation)
Other outcomes:
Locations:
city: Ciudad Real
country: Spain
facility: Hospital General Universitario de Ciudad Real
city: Cuenca
country: Spain
facility: Hospital Virgen de La Luz
city: Madrid
country: Spain
facility: Hospital Universitario La Paz
city: Madrid
country: Spain
facility: Hospital Universitario Puerta de Hierro
city: Murcia
country: Spain
facility: Hospital Universitario Virgen de Arrixaca
city: Santa Cruz De Tenerife
country: Spain
facility: Hospital Universitario NS de Candelaria
city: Valencia
country: Spain
facility: Hospital Cinico de Valencia
city: Valladolid
country: Spain
facility: Hospital Universitario Rio Hortega
Study officials:
affiliation: Fundación Canaria Instituto de Investigación Sanitaria de Canarias
name: Jesus Villar, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: