Prediction of Duration of Mechanical Ventilation in ARDS

Acute Respiratory Distress Syndrome Clinical Trial

— PIONEER  

Official title:

Predicting Length of Mechanical Ventilation in Moderate-to-severe Acute Respiratory Distress Syndrome Using Machine Learning

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05993377
• Other study ID #: PIFIISC21-36
• Status: Completed
• Start date: August 14, 2023
• Est. completion date: February 2, 2024

Study information

• Verified date: February 2024
• Source: Dr. Negrin University Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 1303
• Est. completion date: February 2, 2024
• Est. primary completion date: February 2, 2024
• Gender: All
• Age group: 18 Years to 100 Years

Eligibility

Inclusion Criteria:

• Berlin criteria for moderate to severe acute respiratory distress syndrome

Exclusion Criteria:

• Postoperative patients ventilated <24h
• brain death patients

Related Conditions & MeSH terms

• Acute Lung Injury
• Acute Respiratory Distress Syndrome
• Respiratory Distress Syndrome
• Respiratory Distress Syndrome, Newborn
• Syndrome

Intervention

Other:
• 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.

Locations

Country	Name	City	State
Spain	Complejo Hospitalario de Albacete	Albacete
Spain	Complejo Hospitalario Universitario de Albacete (ICU)	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	Hospital Universitario de A Coruña (ICU)	La Coruña
Spain	Hospital Universitario Dr. Negrin	Las Palmas De Gran Canaria	Las Palmas
Spain	Complejo Hospitalario Universitario de León	León
Spain	Hospital Fundación Jiménez Díaz	Madrid
Spain	Hospital Universitario La Paz (ICU)	Madrid
Spain	Hospital Universitario Ramón y Cajal (Anesthesia)	Madrid
Spain	Hospital Universitario Puerta de Hierro (ICU)	Majadahonda	Madrid
Spain	Hospital Universitario Carlos Haya	Málaga
Spain	Hospital Universitario Regional de Malaga Carlos Haya (ICU)	Málaga
Spain	Hospital Universitario Virgen de Arrixaca (ICU)	Murcia
Spain	Hospital Universitario NS de Candelaria	Santa Cruz de Tenerife	Tenerife
Spain	Hospital NS del Prado	Talavera de la Reina	Toledo
Spain	Hospital Universitario Río Hortega (ICU)	Valladolid
Spain	Hospital Virgen de la Concha (ICU)	Zamora
United Kingdom	Cardiff University	Cardiff

Sponsors (4)

Lead Sponsor	Collaborator
Dr. Negrin University Hospital	Cardiff University, Leiden University Medical Center, Unity Health Toronto

Countries where clinical trial is conducted

Spain,  United Kingdom, 

References & Publications (2)

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. — View Citation

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-Feijo — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Days on mechanical ventilation	Duration of mechanical ventilation	from diagnosis to extubation
Secondary	ICU mortality	mortality in the intensive care unit	up to 24 weeks