Prediction of Duration of Mechanical Ventilation in ARDS

Study Overview
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Brief Summary
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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
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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.

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

Conditions
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Acute Respiratory Distress Syndrome

Intervention / Treatment
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* Other: Logistic regression Cross validation Area under the RIC curves Machine learning analysis. .


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Berlin criteria for moderate to severe acute respiratory distress syndrome Exclusion Criteria: Postoperative patients ventilated <24h brain death patients

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 100 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Derivation and testing cohort<br>It will contain 1000 ARDS patients | Other: Logistic regression Cross validation Area under the RIC curves Machine learning analysis. .<br>* we will use robust machine learning approaches, such as Random Forest and XGBoost.<br>|
| Confirmatory cohort<br>It will contain 303 patients (for external validation) | Other: Logistic regression Cross validation Area under the RIC curves Machine learning analysis. .<br>* we will use robust machine learning approaches, such as Random Forest and XGBoost.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Days on mechanical ventilation | Duration of mechanical ventilation | from diagnosis to extubation | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| ICU mortality | mortality in the intensive care unit | up to 24 weeks | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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mechanical ventilation, intensive care unit, duration of ventilation, machine learning.