Prediction of Duration of Mechanical Ventilation in ARDS

Completed

• Conditions: Acute Respiratory Distress Syndrome

• Registration Number: NCT05993377
• Lead Sponsor: Dr. Negrin University Hospital

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.

Study Timeline

• Study First Submitted: 2023-08-04
• Study Start: 2023-08-14
• Study First Submitted QC: 2023-08-14
• Study First Posted: 2023-08-15
• Status Verified: 2024-02
• Primary Completion: 2024-02-02
• Study Completion: 2024-02-02
• Last Update Submitted: 2024-03-19
• Last Update Posted: 2024-03-20

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 1303

Inclusion Criteria

• Berlin criteria for moderate to severe acute respiratory distress syndrome

Exclusion Criteria

• Postoperative patients ventilated <24h
• brain death patients

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Days on mechanical ventilation	from diagnosis to extubation	Duration of mechanical ventilation

Secondary Outcome Measures

Name	Time	Method
ICU mortality	up to 24 weeks	mortality in the intensive care unit

Trial Locations

Locations (20)

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

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

Hospital Universitario de A Coruña (ICU); 🇪🇸 La Coruña, Spain

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