Predicting ICU Mortality in ARDS Patients (POSTCARDS)
Predicting Mortality in Patients With the Acute Respiratory Distress Syndrome Using Machine Learning
Study Overview
Status
Conditions
Intervention / Treatment
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 Type
Enrollment (Actual)
Contacts and Locations
Study Locations
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Barcelona, Spain, 08036
- Department of Anesthesia, Hospital Clinic
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Madrid, Spain, 28046
- Hospital Universitario La Paz (ICU)
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Las Palmas
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Las Palmas De Gran Canaria, Las Palmas, Spain, 35019
- Hospital Universitario Dr. Negrín
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Berlin criteria for moderate to severe ARDS
Exclusion Criteria:
- Postoperative patients ventilated <24h; brain death patients.
Study Plan
How is the study designed?
Design Details
- Observational Models: Cohort
- Time Perspectives: Prospective
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
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Derivation cohort
It will contain 700 patients (70% of 1000 ARDS patients)
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We will use robust machine learning approaches, such as Random Forest, XGBoost or Neural Networks.
Other Names:
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Validation cohort
It will contain 300 patients (30% of 1000 ARDS patients)
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We will use robust machine learning approaches, such as Random Forest, XGBoost or Neural Networks.
Other Names:
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Confirmatory cohort
It will contain 303 patients (for external validation)
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We will use robust machine learning approaches, such as Random Forest, XGBoost or Neural Networks.
Other Names:
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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ICU mortality
Time Frame: up to 6 months
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mortality in the intensive care unit
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up to 6 months
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Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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MV duration
Time Frame: from ARDS diagnosis to extubation
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Duration of mechanical ventilation
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from ARDS diagnosis to extubation
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Collaborators and Investigators
Sponsor
Collaborators
Investigators
- Principal Investigator: Jesús Villar, MD, PhD, Hospital Universitario D. Negrin
Publications and helpful links
General Publications
- 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-Feijoo J, Martin-Delgado C, Romera MA, Gonzalez-Martin JM, Fernandez RL, Kacmarek RM; Spanish Initiative for Epidemiology, Stratification and Therapies of ARDS (SIESTA) Network. A Prognostic Enrichment Strategy for Selection of Patients With Acute Respiratory Distress Syndrome in Clinical Trials. Crit Care Med. 2019 Mar;47(3):377-385. doi: 10.1097/CCM.0000000000003624.
- Huang B, Liang D, Zou R, Yu X, Dan G, Huang H, Liu H, Liu Y. Mortality prediction for patients with acute respiratory distress syndrome based on machine learning: a population-based study. Ann Transl Med. 2021 May;9(9):794. doi: 10.21037/atm-20-6624.
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- 7/2021
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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