Clinical trial NCT05611177: Predicting ICU Mortality in ARDS Patients
Official title: Predicting Mortality in Patients With the Acute Respiratory Distress Syndrome Using Machine Learning
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Acute Respiratory Distress Syndrome
Keywords: outcome
mechanical ventilation
intensive care unit
machine learning
Lead sponsor: Dr. Negrin University Hospital
Sponsor class: OTHER
Collaborators: name: Unity Health Toronto
First posted: 2022-11-09
Enrollment: 1303
Sex: ALL
Minimum age (years): 18
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: De-identified dataset including 1,303 patients with moderate/severe ARDS admitted consecutively in a network of Spanish ICUs.

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.

Eligibility criteria: Inclusion Criteria:

* Berlin criteria for moderate to severe ARDS

Exclusion Criteria:

* Postoperative patients ventilated \<24h; brain death patients.

Primary outcomes:
measure: ICU mortality
timeFrame: up to 6 months
Secondary outcomes:
measure: MV duration
timeFrame: from ARDS diagnosis to extubation
Other outcomes:
Locations:
city: Las Palmas De Gran Canaria
country: Spain
facility: Hospital Universitario Dr. Negrin
state: Las Palmas
city: Barcelona
country: Spain
facility: Department of Anesthesia, Hospital Clinic
city: Madrid
country: Spain
facility: Hospital Universitario La Paz (ICU)
Study officials:
affiliation: Hospital Universitario D. Negrin
name: Jesús Villar, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.