PIONEER: Prediction of Duration of Mechanical Ventilation in ARDS

Sponsor

Dr. Negrin University Hospital (Other)

Overall Status

Active, not recruiting

CT.gov ID

NCT05993377

Collaborator

Unity Health Toronto (Other), Cardiff University (Other), Leiden University Medical Center (Other)

1,303

Enrollment

Locations

3.6

Anticipated Duration (Months)

65.2

Patients Per Site

18.2

Patients Per Site Per Month

Study Details

Study Description

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.

Condition or Disease	Intervention/Treatment	Phase
Acute Respiratory Distress Syndrome	Other: Logistic regression Cross validation Area under the RIC curves Machine learning analysis. .

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.

Study Design

Study Type:

Observational

Actual Enrollment :

1303 participants

Observational Model:

Cohort

Time Perspective:

Prospective

Official Title:

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

Actual Study Start Date :

Aug 14, 2023

Anticipated Primary Completion Date :

Dec 1, 2023

Anticipated Study Completion Date :

Dec 1, 2023

Arms and Interventions

Arm	Intervention/Treatment
Derivation and testing cohort It will contain 1000 ARDS patients	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.
Confirmatory cohort It will contain 303 patients (for external validation)	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.

Arm

Intervention/Treatment

Derivation and testing cohort

It will contain 1000 ARDS patients

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.

Confirmatory cohort

It will contain 303 patients (for external validation)

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.

Outcome Measures

Primary Outcome Measures

Days on mechanical ventilation [from diagnosis to extubation]
Duration of mechanical ventilation

Secondary Outcome Measures

ICU mortality [up to 24 weeks]
mortality in the intensive care unit

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years to 100 Years

Sexes Eligible for Study:

All

Inclusion Criteria:

Berlin criteria for moderate to severe acute respiratory distress syndrome

Exclusion Criteria:

Postoperative patients ventilated <24h
brain death patients

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Hospital Universitario Dr. Negrin	Las Palmas De Gran Canaria	Las Palmas	Spain	35019
2	Hospital Universitario Puerta de Hierro (ICU)	Majadahonda	Madrid	Spain	28222
3	Hospital Universitario NS de Candelaria	Santa Cruz de Tenerife	Tenerife	Spain
4	Hospital NS del Prado	Talavera de la Reina	Toledo	Spain
5	Complejo Hospitalario Universitario de Albacete (ICU)	Albacete		Spain	02006
6	Complejo Hospitalario de Albacete	Albacete		Spain
7	Department of Anesthesia, Hospital Clinic	Barcelona		Spain	08036
8	Hospital General de Ciudad Real (ICU)	Ciudad Real		Spain	13005
9	Hospital Virgen de La Luz	Cuenca		Spain
10	Hospital Universitario de A Coruña (ICU)	La Coruña		Spain	15006
11	Complejo Hospitalario Universitario de León	León		Spain
12	Hospital Universitario Ramón y Cajal (Anesthesia)	Madrid		Spain	28034
13	Hospital Universitario La Paz (ICU)	Madrid		Spain	28046
14	Hospital Fundación Jiménez Díaz	Madrid		Spain
15	Hospital Universitario Virgen de Arrixaca (ICU)	Murcia		Spain	30120
16	Hospital Universitario Regional de Malaga Carlos Haya (ICU)	Málaga		Spain	29010
17	Hospital Universitario Carlos Haya	Málaga		Spain
18	Hospital Universitario Río Hortega (ICU)	Valladolid		Spain	47012
19	Hospital Virgen de la Concha (ICU)	Zamora		Spain	49022
20	Cardiff University	Cardiff		United Kingdom