Prediction of Duration of Mechanical Ventilation in Acute Hypoxemic Respiratoty Failure (PREMIER)

Prediction of Duration of Mechanical Venylation in Patients Wit Acute Hypoxemic Respiratory Failure Usinf Machine Learning Approaches

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Acute Hypoxemic Respiratory Failure
• Intervention / Treatment: Other: Machine learning and logistic regression for the training/testing cohort and validation cohort

Detailed Description

Acute hypoxemic respiratory failure (AHRF) is the most common cause of admission in intensive care units (ICUs) worldwide. The investigators will assess the value of machine learning (ML) techniques for prediction of prolonged duration (> or equeal to 7 days) of mechanical ventilation (MV) in 1,241 patients enrolled in the PANDORA study in Spain. Few studies have investigated the prediction of prolonged MV in patients with AHRF.

For model training and testing, the investigators will extract data from random pateints from the first 2 days after diagnosis of AHRF. The investigators had a database with 2,000,000 anonymized and dissociated demographics and clinically relevant data from 1,241 patients with AHRF from 22 hospitals in Spain. The investigators will follow the TRIPOD guidelines for prediction models. The investigators will screen relevant collected variables using a genetic algorithm variable selection to achieve parsimony. We will use 5-fold corss-validation in the data set of patients with data at T0, T24 and T48. We will use 25% of patients randomly selected for evaluation of the model.

• Study Type: Observational
• Enrollment (Actual): 1241

Contacts and Locations

Study Locations

• Spain
  • Madrid, Spain
    • Hospital Universitario La Paz
  • Las Palmas
    • Las Palmas de Gran Canaria, Las Palmas, Spain, 35019
      • Hospital Dr. Negrín

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

De-identified dataset inclusing 1241 ventilated patients with AHRF admitted in a network of Spainisg ICUs.

Description

Inclusion Criteria:

• enotracheal intubation puls mechanical ventilation
• PaO2/FiO2 ratio <or = 300 mmHg under MV with PEEP >or =5 and FiO2 >or = 0.3

Exclusion Criteria:

• Brain death patients

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Derivation/testing cohort; The investigators will use a chort of 75% of patients, randomly selected, with data at T0, T24 and T48 after diagnosis of acute hypoxemic respiratory failure (AHRF). We will apply machine learning approaches.	Other: Machine learning and logistic regression for the training/testing cohort and validation cohort; Machine learning and logistic regression for the validation cohort
Validation hohort; we will use 25% of unseen patients, randomly selected, with data at T0, T24 and T48 after diagnosis of AHRF.	Other: Machine learning and logistic regression for the training/testing cohort and validation cohort; Machine learning and logistic regression for the validation cohort

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
MV duration	duration of mechanical ventilation	up to 100 weeks

Collaborators and Investigators

• Sponsor: Jesus Villar
• Collaborators: Hospital Universitario Dr. Negrín (Las Palmas de Gran Canaria); Iinstituto de Salud Carlos III
• Investigators: Study Director: Jesus Villar, Fundación Canaria Instituto de Investigación Sanitaria de Canarias

Study record dates

Study Major Dates

• Study Start (Actual): February 2, 2025
• Primary Completion (Estimated): May 1, 2026
• Study Completion (Estimated): June 1, 2026

Study Registration Dates

• First Submitted: February 3, 2025
• First Submitted That Met QC Criteria: February 3, 2025
• First Posted (Actual): March 25, 2025

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: March 7, 2025
• Last Verified: March 1, 2025

More Information

Terms related to this study

• Keywords: mechanical ventilation; machine learning; outcome; prediction of prolonged mechanical ventilation
• Additional Relevant MeSH Terms: Respiratory Tract Diseases; Respiration Disorders; Respiratory Insufficiency
• Other Study ID Numbers: PIFIISC24

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Ethical reasons

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No