Prediction of Duration of Mechanical Ventilation in Acute Hypoxemic Respiratoty Failure

NCT ID: NCT06815523

Last Updated: 2025-03-11

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Total Enrollment: 1241 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-02-02
• Study Completion Date: 2026-06-01

Brief 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.

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.

Conditions

Acute Hypoxemic Respiratory Failure

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

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.

Machine learning and logistic regression for the training/testing cohort and validation cohort

Intervention Type: OTHER

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.

Machine learning and logistic regression for the training/testing cohort and validation cohort

Intervention Type: OTHER

Machine learning and logistic regression for the validation cohort

Interventions

Machine learning and logistic regression for the training/testing cohort and validation cohort

Machine learning and logistic regression for the validation cohort

Intervention Type: OTHER

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Hospital Universitario Dr. Negrín (Las Palmas de Gran Canaria)

UNKNOWN

Sponsor Role: collaborator

Iinstituto de Salud Carlos III

UNKNOWN

Sponsor Role: collaborator

Jesus Villar

OTHER

Sponsor Role: lead

Responsible Party

Jesus Villar

Scientific Advisor

Responsibility Role: SPONSOR_INVESTIGATOR

Principal Investigators

Jesus Villar

Role: STUDY_DIRECTOR

Fundacion Canaria Instituto de Investigación Sanitaria de Canarias

Locations

Hospital Dr. Negrin

Las Palmas de Gran Canaria, Las Palmas, Spain

Hospital Universitario La Paz

Madrid, , Spain

Countries

Spain

Other Identifiers

PIFIISC24

Identifier Type: -

Identifier Source: org_study_id