Prediction of Duration of Mechanical Ventilation in ARDS
PIONEER
Predicting Length of Mechanical Ventilation in Moderate-to-severe Acute Respiratory Distress Syndrome Using Machine Learning
1 other identifier
observational
1,303
2 countries
20
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, NCT022288949, NCT02836444, NCT03145974), aimed to characterize the best early scenario during the first three days of diagnosis to predict duration of mechanical ventilation in the intensive care unit (ICU) using supervised machine learning (ML) approaches.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Aug 2023
Shorter than P25 for all trials
20 active sites
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
August 4, 2023
CompletedStudy Start
First participant enrolled
August 14, 2023
CompletedFirst Posted
Study publicly available on registry
August 15, 2023
CompletedPrimary Completion
Last participant's last visit for primary outcome
February 2, 2024
CompletedStudy Completion
Last participant's last visit for all outcomes
February 2, 2024
CompletedMarch 20, 2024
February 1, 2024
6 months
August 4, 2023
March 19, 2024
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Days on mechanical ventilation
Duration of mechanical ventilation
from diagnosis to extubation
Secondary Outcomes (1)
ICU mortality
up to 24 weeks
Study Arms (2)
Derivation and testing cohort
It will contain 1000 ARDS patients
Confirmatory cohort
It will contain 303 patients (for external validation)
Interventions
we will use robust machine learning approaches, such as Random Forest and XGBoost.
Eligibility Criteria
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 acute respiratory distress syndrome
You may not qualify if:
- Postoperative patients ventilated \<24h
- brain death patients
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Dr. Negrin University Hospitallead
- Unity Health Torontocollaborator
- Cardiff Universitycollaborator
- Leiden University Medical Centercollaborator
Study Sites (20)
Hospital Universitario Dr. Negrin
Las Palmas de Gran Canaria, Las Palmas, 35019, Spain
Hospital Universitario Puerta de Hierro (ICU)
Majadahonda, Madrid, 28222, Spain
Hospital Universitario NS de Candelaria
Santa Cruz de Tenerife, Tenerife, Spain
Hospital NS del Prado
Talavera de la Reina, Toledo, Spain
Hospital Universitario de A Coruña (ICU)
A Coruña, 15006, Spain
Complejo Hospitalario Universitario de Albacete (ICU)
Albacete, 02006, Spain
Complejo Hospitalario de Albacete
Albacete, Spain
Department of Anesthesia, Hospital Clinic
Barcelona, 08036, Spain
Hospital General de Ciudad Real (ICU)
Ciudad Real, 13005, Spain
Hospital Virgen de La Luz
Cuenca, Spain
Complejo Hospitalario Universitario de León
León, Spain
Hospital Universitario Ramón y Cajal (Anesthesia)
Madrid, 28034, Spain
Hospital Universitario La Paz (ICU)
Madrid, 28046, Spain
Hospital Fundación Jiménez Díaz
Madrid, Spain
Hospital Universitario Regional de Malaga Carlos Haya (ICU)
Málaga, 29010, Spain
Hospital Universitario Carlos Haya
Málaga, Spain
Hospital Universitario Virgen de Arrixaca (ICU)
Murcia, 30120, Spain
Hospital Universitario Río Hortega (ICU)
Valladolid, 47012, Spain
Hospital Virgen de la Concha (ICU)
Zamora, 49022, Spain
Cardiff University
Cardiff, United Kingdom
Related Publications (3)
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: 30624279RESULTFigueroa-Casas JB, Dwivedi AK, Connery SM, Quansah R, Ellerbrook L, Galvis J. Predictive models of prolonged mechanical ventilation yield moderate accuracy. J Crit Care. 2015 Jun;30(3):502-5. doi: 10.1016/j.jcrc.2015.01.020. Epub 2015 Jan 30.
PMID: 25682346RESULTVillar J, Gonzalez-Martin JM, Fernandez C, Soler JA, Ambros A, Pita-Garcia L, Fernandez L, Ferrando C, Arocas B, Gonzalez-Vaquero M, Anon JM, Gonzalez-Higueras E, Parrilla D, Vidal A, Fernandez MM, Rodriguez-Suarez P, Fernandez RL, Gomez-Bentolila E, Burns KEA, Szakmany T, Steyerberg EW, The PredictION Of Duration Of mEchanical vEntilation In Ards Pioneer Network. Predicting the Length of Mechanical Ventilation in Acute Respiratory Disease Syndrome Using Machine Learning: The PIONEER Study. J Clin Med. 2024 Mar 21;13(6):1811. doi: 10.3390/jcm13061811.
PMID: 38542033DERIVED
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Jesús Villar
Hospital Universitario D. Negrin
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
August 4, 2023
First Posted
August 15, 2023
Study Start
August 14, 2023
Primary Completion
February 2, 2024
Study Completion
February 2, 2024
Last Updated
March 20, 2024
Record last verified: 2024-02
Data Sharing
- IPD Sharing
- Will not share