Predicting ICU Mortality in ARDS Patients

Acute Respiratory Distress Syndrome Clinical Trial

— POSTCARDS  

Official title:

Predicting Mortality in Patients With the Acute Respiratory Distress Syndrome Using Machine Learning

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05611177
• Other study ID #: 7/2021
• Status: Completed
• Start date: November 14, 2022
• Est. completion date: August 1, 2023

Study information

• Verified date: August 2023
• Source: Dr. Negrin University Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 1303
• Est. completion date: August 1, 2023
• Est. primary completion date: August 1, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 100 Years

Eligibility

Inclusion Criteria:

• Berlin criteria for moderate to severe ARDS

Exclusion Criteria:

• Postoperative patients ventilated <24h; brain death patients.

Related Conditions & MeSH terms

• Acute Lung Injury
• Acute Respiratory Distress Syndrome
• Respiratory Distress Syndrome
• Respiratory Distress Syndrome, Newborn
• Syndrome

Intervention

Other:
• machine learning analysis
We will use robust machine learning approaches, such as Random Forest, XGBoost or Neural Networks.

Locations

Country	Name	City	State
Spain	Department of Anesthesia, Hospital Clinic	Barcelona
Spain	Hospital Universitario Dr. Negrin	Las Palmas De Gran Canaria	Las Palmas
Spain	Hospital Universitario La Paz (ICU)	Madrid

Sponsors (2)

Lead Sponsor	Collaborator
Dr. Negrin University Hospital	Unity Health Toronto

Country where clinical trial is conducted

Spain, 

References & Publications (2)

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. — View 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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	ICU mortality	mortality in the intensive care unit	up to 6 months
Secondary	MV duration	Duration of mechanical ventilation	from ARDS diagnosis to extubation