Predicting ICU Mortality in ARDS Patients

Completed

• Conditions: Acute Respiratory Distress Syndrome
• Interventions: Other: machine learning analysis

• Registration Number: NCT05611177
• 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, NCT02288949, NCT02836444, NCT03145974), aimed to characterize the best early model to predict duration of mechanical ventilation and mortality in the intensive care unit (ICU) after ARDS diagnosis using machine learning approaches.

Detailed Description

The acute respiratory distress syndrome (ARDS) is a severe form of acute hypoxemic respiratory failure in Critical Care Units worldwide. Most ARDS patients requiere mechanical ventilation (MV). Few studies have investigated the prediction of MV duration and mortality of ARDS.

For model description, the investigators will extract data from the first two ICU days after diagnosis of moderate-to-severe ARDS from patients included in the de-identified database, which includes 1,303 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 tecniques will be implemented (Random Forest, XGBoost, Logistic regression analysis, and/or neural networks) for development of the prediction model, and the accuracy will be compared to those of existing scoring systems for assessing ICU severity (APACHE II, SOFA) and the PaO2/FiO2 ratio. 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 calculating the respective confusion matrices and several statistics such as sensitivity, specificity, positive predictive value, and negative predictive value for mortality and duration of MV. Investigators will select the best probabilistic model with a minimum number of clinical variables.

Study Timeline

• Study First Submitted: 2022-11-01
• Study First Submitted QC: 2022-11-08
• Study First Posted: 2022-11-09
• Study Start: 2022-11-14
• Status Verified: 2023-08
• Primary Completion: 2023-08-01
• Study Completion: 2023-08-01
• Last Update Submitted: 2023-08-17
• Last Update Posted: 2023-08-21

Recruitment & Eligibility

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

Inclusion Criteria

• Berlin criteria for moderate to severe ARDS

Exclusion Criteria

• Postoperative patients ventilated <24h; brain death patients.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Validation cohort	machine learning analysis	It will contain 300 patients (30% of 1000 ARDS patients)
Derivation cohort	machine learning analysis	It will contain 700 patients (70% of 1000 ARDS patients)
Confirmatory cohort	machine learning analysis	It will contain 303 patients (for external validation)

Primary Outcome Measures

Name	Time	Method
ICU mortality	up to 6 months	mortality in the intensive care unit

Secondary Outcome Measures

Name	Time	Method
MV duration	from ARDS diagnosis to extubation	Duration of mechanical ventilation

Trial Locations

Locations (3)

Hospital Universitario La Paz (ICU); 🇪🇸 Madrid, Spain

Hospital Universitario Dr. Negrin; 🇪🇸 Las Palmas De Gran Canaria, Las Palmas, Spain

Department of Anesthesia, Hospital Clinic; 🇪🇸 Barcelona, Spain