NCT05611177

Brief Summary

The investigators are planning to perform a secondary analysis of an academic dataset of 1,303 patients with moderate-to-severe acute respiratory distress syndrome (ARDS) included in several published cohorts (NCT00736892, NCT02288949, NCT02836444, NCT03145974), aimed to characterize the best early model to predict duration of mechanical ventilation and mortality in the intensive care unit (ICU) after ARDS diagnosis using machine learning approaches.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
1,303

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Nov 2022

Shorter than P25 for all trials

Geographic Reach
1 country

3 active sites

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

November 1, 2022

Completed
8 days until next milestone

First Posted

Study publicly available on registry

November 9, 2022

Completed
5 days until next milestone

Study Start

First participant enrolled

November 14, 2022

Completed
9 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

August 1, 2023

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

August 1, 2023

Completed
Last Updated

August 21, 2023

Status Verified

August 1, 2023

Enrollment Period

9 months

First QC Date

November 1, 2022

Last Update Submit

August 17, 2023

Conditions

Acute Respiratory Distress Syndrome

Keywords

outcomemechanical ventilationintensive care unitmachine learning

Outcome Measures

Primary Outcomes (1)

  • ICU mortality

    mortality in the intensive care unit

    up to 6 months

Secondary Outcomes (1)

  • MV duration

    from ARDS diagnosis to extubation

Study Arms (3)

Derivation cohort

It will contain 700 patients (70% of 1000 ARDS patients)

Other: machine learning analysis

Validation cohort

It will contain 300 patients (30% of 1000 ARDS patients)

Other: machine learning analysis

Confirmatory cohort

It will contain 303 patients (for external validation)

Other: machine learning analysis

Interventions

machine learning analysisOTHER

We will use robust machine learning approaches, such as Random Forest, XGBoost or Neural Networks.

Also known as: Logistic regression, cross-validation, are aunder the ROC curves
Confirmatory cohortDerivation cohortValidation cohort

Eligibility Criteria

Age18 Years - 100 Years
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

De-identified dataset including 1,303 patients with moderate/severe ARDS admitted consecutively in a network of Spanish ICUs.

You may qualify if:

  • Berlin criteria for moderate to severe ARDS

You may not qualify if:

  • Postoperative patients ventilated \<24h; brain death patients.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (3)

Hospital Universitario Dr. Negrin

Las Palmas de Gran Canaria, Las Palmas, 35019, Spain

Location

Department of Anesthesia, Hospital Clinic

Barcelona, 08036, Spain

Location

Hospital Universitario La Paz (ICU)

Madrid, 28046, Spain

Location

Related Publications (2)

  • Villar J, Ambros A, Mosteiro F, Martinez D, Fernandez L, Ferrando C, Carriedo D, Soler JA, Parrilla D, Hernandez M, Andaluz-Ojeda D, Anon JM, Vidal A, Gonzalez-Higueras E, Martin-Rodriguez C, Diaz-Lamas AM, Blanco J, Belda J, Diaz-Dominguez FJ, Rico-Feijoo J, Martin-Delgado C, Romera MA, Gonzalez-Martin JM, Fernandez RL, Kacmarek RM; Spanish Initiative for Epidemiology, Stratification and Therapies of ARDS (SIESTA) Network. A Prognostic Enrichment Strategy for Selection of Patients With Acute Respiratory Distress Syndrome in Clinical Trials. Crit Care Med. 2019 Mar;47(3):377-385. doi: 10.1097/CCM.0000000000003624.

    PMID: 30624279BACKGROUND

  • Huang B, Liang D, Zou R, Yu X, Dan G, Huang H, Liu H, Liu Y. Mortality prediction for patients with acute respiratory distress syndrome based on machine learning: a population-based study. Ann Transl Med. 2021 May;9(9):794. doi: 10.21037/atm-20-6624.

    PMID: 34268407BACKGROUND

MeSH Terms

Conditions

Respiratory Distress Syndrome

Interventions

Logistic Models

Condition Hierarchy (Ancestors)

Lung DiseasesRespiratory Tract DiseasesRespiration Disorders

Intervention Hierarchy (Ancestors)

Models, StatisticalStatistics as TopicEpidemiologic MethodsInvestigative TechniquesRiskProbabilityRegression AnalysisModels, TheoreticalHealth Care Evaluation MechanismsQuality of Health CareHealth Care Quality, Access, and EvaluationPublic HealthEnvironment and Public Health

Study Officials

  • Jesús Villar, MD, PhD

    Hospital Universitario D. Negrin

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
principal investigator

Study Record Dates

First Submitted

November 1, 2022

First Posted

November 9, 2022

Study Start

November 14, 2022

Primary Completion

August 1, 2023

Study Completion

August 1, 2023

Last Updated

August 21, 2023

Record last verified: 2023-08

Data Sharing

IPD Sharing
Will not share

Locations

ES(3)