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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT05993377
Other study ID # PIFIISC21-36
Secondary ID
Status Completed
Phase
First received
Last updated
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
Health authority
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
Accepts healthy volunteers
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

Study Design


Related Conditions & MeSH terms


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
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