Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| Unity Health Toronto | OTHER |
| Cardiff University | OTHER |
| Leiden University Medical Center | OTHER |
Not provided
Not provided
Not provided
Not provided
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.
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.](streamdown:incomplete-link)
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Derivation and testing cohort | It will contain 1000 ARDS patients |
| |
| Confirmatory cohort | It will contain 303 patients (for external validation) |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Logistic regression Cross validation Area under the RIC curves Machine learning analysis. . | Other | we will use robust machine learning approaches, such as Random Forest and XGBoost. |
| Measure | Description | Time Frame |
|---|---|---|
| Days on mechanical ventilation | Duration of mechanical ventilation | from diagnosis to extubation |
| Measure | Description | Time Frame |
|---|---|---|
| ICU mortality | mortality in the intensive care unit | up to 24 weeks |
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Not provided
De-identified dataset including 1,303 patients with moderate/severe ARDS admitted consecutively in a network of Spanish ICUs.
Not provided
| Name | Affiliation | Role |
|---|---|---|
| Jesús Villar | 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 | ||
| Hospital Universitario Puerta de Hierro (ICU) |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 30624279 | Result | 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. | |
| 25682346 |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Majadahonda |
| Madrid |
| 28222 |
| Spain |
| Hospital Universitario NS de Candelaria | Santa Cruz de Tenerife | Tenerife | Spain |
| Hospital NS del Prado | Talavera de la Reina | Toledo | Spain |
| Hospital Universitario de A Coruña (ICU) | A Coruña | 15006 | Spain |
| Complejo Hospitalario Universitario de Albacete (ICU) | Albacete | 02006 | Spain |
| Complejo Hospitalario de Albacete | Albacete | Spain |
| Department of Anesthesia, Hospital Clinic | Barcelona | 08036 | Spain |
| Hospital General de Ciudad Real (ICU) | Ciudad Real | 13005 | Spain |
| Hospital Virgen de La Luz | Cuenca | Spain |
| Complejo Hospitalario Universitario de León | León | Spain |
| Hospital Universitario Ramón y Cajal (Anesthesia) | Madrid | 28034 | Spain |
| Hospital Universitario La Paz (ICU) | Madrid | 28046 | Spain |
| Hospital Fundación Jiménez Díaz | Madrid | Spain |
| Hospital Universitario Regional de Malaga Carlos Haya (ICU) | Málaga | 29010 | Spain |
| Hospital Universitario Carlos Haya | Málaga | Spain |
| Hospital Universitario Virgen de Arrixaca (ICU) | Murcia | 30120 | Spain |
| Hospital Universitario Río Hortega (ICU) | Valladolid | 47012 | Spain |
| Hospital Virgen de la Concha (ICU) | Zamora | 49022 | Spain |
| Cardiff University | Cardiff | United Kingdom |
| Result |
| Figueroa-Casas JB, Dwivedi AK, Connery SM, Quansah R, Ellerbrook L, Galvis J. Predictive models of prolonged mechanical ventilation yield moderate accuracy. J Crit Care. 2015 Jun;30(3):502-5. doi: 10.1016/j.jcrc.2015.01.020. Epub 2015 Jan 30. |
| 38542033 | Derived | Villar J, Gonzalez-Martin JM, Fernandez C, Soler JA, Ambros A, Pita-Garcia L, Fernandez L, Ferrando C, Arocas B, Gonzalez-Vaquero M, Anon JM, Gonzalez-Higueras E, Parrilla D, Vidal A, Fernandez MM, Rodriguez-Suarez P, Fernandez RL, Gomez-Bentolila E, Burns KEA, Szakmany T, Steyerberg EW, The PredictION Of Duration Of mEchanical vEntilation In Ards Pioneer Network. Predicting the Length of Mechanical Ventilation in Acute Respiratory Disease Syndrome Using Machine Learning: The PIONEER Study. J Clin Med. 2024 Mar 21;13(6):1811. doi: 10.3390/jcm13061811. |
| ID | Term |
|---|---|
| D012128 | Respiratory Distress Syndrome |
| ID | Term |
|---|---|
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D012120 | Respiration Disorders |
Not provided
Not provided