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| Name | Class |
|---|---|
| Unity Health Toronto | OTHER |
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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, 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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Derivation cohort | It will contain 700 patients (70% of 1000 ARDS patients) |
| |
| Validation cohort | It will contain 300 patients (30% of 1000 ARDS patients) |
| |
| Confirmatory cohort | It will contain 303 patients (for external validation) |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| machine learning analysis | Other | We will use robust machine learning approaches, such as Random Forest, XGBoost or Neural Networks. |
|
| Measure | Description | Time Frame |
|---|---|---|
| ICU mortality | mortality in the intensive care unit | up to 6 months |
| Measure | Description | Time Frame |
|---|---|---|
| MV duration | Duration of mechanical ventilation | from ARDS diagnosis to extubation |
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Inclusion Criteria:
Exclusion Criteria:
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De-identified dataset including 1,303 patients with moderate/severe ARDS admitted consecutively in a network of Spanish ICUs.
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| Name | Affiliation | Role |
|---|---|---|
| Jesús Villar, MD, PhD | Hospital Universitario D. Negrin | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Hospital Universitario Dr. Negrin | Las Palmas de Gran Canaria | Las Palmas | 35019 | Spain | ||
| Department of Anesthesia, Hospital Clinic |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 30624279 | Background | 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. | |
| 34268407 |
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| ID | Term |
|---|---|
| D012128 | Respiratory Distress Syndrome |
| ID | Term |
|---|---|
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D012120 | Respiration Disorders |
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| ID | Term |
|---|---|
| D016015 | Logistic Models |
| ID | Term |
|---|---|
| D015233 | Models, Statistical |
| D013223 | Statistics as Topic |
| D004812 | Epidemiologic Methods |
| D008919 | Investigative Techniques |
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|
| Barcelona |
| 08036 |
| Spain |
| Hospital Universitario La Paz (ICU) | Madrid | 28046 | Spain |
| Background |
| 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. |
| D012306 |
| Risk |
| D011336 | Probability |
| D012044 | Regression Analysis |
| D008962 | Models, Theoretical |
| D017531 | Health Care Evaluation Mechanisms |
| D011787 | Quality of Health Care |
| D017530 | Health Care Quality, Access, and Evaluation |
| D011634 | Public Health |
| D004778 | Environment and Public Health |