Prediction of Duration of Mechanical Ventilation in ARDS

NCT ID: NCT05993377

Last Updated: 2024-03-20

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 1303 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-08-14
• Study Completion Date: 2024-02-02

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.

Detailed Description

The acute respiratory distress syndrome (ARDS) is an important cause of morbidity, mortality, and costs in intensive care units (ICUs) worldwide. Most ARDS patients require mechanical ventilation (MV). Few studies have investigated the prediction of MV duration of ARDS.

For model description and testing, the investigators will extract data from he first three ICU days after diagnosis of moderate-to-severe ARDS from patients included in the de-identified database, which includes 1,000 mechanically ventilated patients enrolled in several observational cohorts in Spain, coordinated by the principal investigator (JV), and funded by the Instituto de Salud Carlos III (ISCIII). The investigators will follow the TRIPOD guidelines and machine learning techniques will be implemented [Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Logistic regression analysis) for the development and accuracy of prediction models. Disease progression will be tracked along those 3 ICU days to assess lung severity according to Berlin criteria. For external validation, the investigators will use 303 patients enrolled in a contemporary observational study (NCT03145974). The investigators will evaluate the accuracy of prediction models by calculation several statistics, such as sensitivity, specificity, positive predictive value, negative value for each model. The investigators will select the best early prediction model with data captured on the 1st, 2nd, or 3rd day.

Conditions

Acute Respiratory Distress Syndrome

Study Design

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

Study Groups

Derivation and testing cohort

It will contain 1000 ARDS patients

Logistic regression Cross validation Area under the RIC curves Machine learning analysis. .

Intervention Type: OTHER

we will use robust machine learning approaches, such as Random Forest and XGBoost.

Confirmatory cohort

It will contain 303 patients (for external validation)

Logistic regression Cross validation Area under the RIC curves Machine learning analysis. .

Intervention Type: OTHER

we will use robust machine learning approaches, such as Random Forest and XGBoost.

Interventions

Logistic regression Cross validation Area under the RIC curves Machine learning analysis. .

we will use robust machine learning approaches, such as Random Forest and XGBoost.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Berlin criteria for moderate to severe acute respiratory distress syndrome

Exclusion Criteria

• Postoperative patients ventilated <24h
• brain death patients

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 100 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Unity Health Toronto

OTHER

Sponsor Role: collaborator

Cardiff University

OTHER

Sponsor Role: collaborator

Leiden University Medical Center

OTHER

Sponsor Role: collaborator

Dr. Negrin University Hospital

OTHER

Sponsor Role: lead

Responsible Party

Jesus Villar

principal investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Jesús Villar

Role: PRINCIPAL_INVESTIGATOR

Hospital Universitario D. Negrin

Locations

Hospital Universitario Dr. Negrin

Las Palmas de Gran Canaria, Las Palmas, Spain

Hospital Universitario Puerta de Hierro (ICU)

Majadahonda, Madrid, 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, , Spain

Complejo Hospitalario Universitario de Albacete (ICU)

Albacete, , Spain

Complejo Hospitalario de Albacete

Albacete, , Spain

Department of Anesthesia, Hospital Clinic

Barcelona, , Spain

Hospital General de Ciudad Real (ICU)

Ciudad Real, , Spain

Hospital Virgen de La Luz

Cuenca, , Spain

Complejo Hospitalario Universitario de León

León, , Spain

Hospital Universitario Ramón y Cajal (Anesthesia)

Madrid, , Spain

Hospital Universitario La Paz (ICU)

Madrid, , Spain

Hospital Fundación Jiménez Díaz

Madrid, , Spain

Hospital Universitario Regional de Malaga Carlos Haya (ICU)

Málaga, , Spain

Hospital Universitario Carlos Haya

Málaga, , Spain

Hospital Universitario Virgen de Arrixaca (ICU)

Murcia, , Spain

Hospital Universitario Río Hortega (ICU)

Valladolid, , Spain

Hospital Virgen de la Concha (ICU)

Zamora, , Spain

Cardiff University

Cardiff, , United Kingdom

Countries

Spain; United Kingdom

References

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.

Reference Type: RESULT

PMID: 30624279 (View on PubMed)

Figueroa-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.

Reference Type: RESULT

PMID: 25682346 (View on PubMed)

Villar 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.

Reference Type: DERIVED

PMID: 38542033 (View on PubMed)

Other Identifiers

PIFIISC21-36

Identifier Type: -

Identifier Source: org_study_id