Predicting ICU Mortality in ARDS Patients

NCT ID: NCT05611177

Last Updated: 2023-08-21

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 1303 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-11-14
• Study Completion Date: 2023-08-01

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.

Conditions

Acute Respiratory Distress Syndrome

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Derivation cohort

It will contain 700 patients (70% of 1000 ARDS patients)

machine learning analysis

Intervention Type: OTHER

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

Validation cohort

It will contain 300 patients (30% of 1000 ARDS patients)

machine learning analysis

Intervention Type: OTHER

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

Confirmatory cohort

It will contain 303 patients (for external validation)

machine learning analysis

Intervention Type: OTHER

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

Interventions

machine learning analysis

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

Intervention Type: OTHER

Other Intervention Names

Logistic regression; cross-validation; are aunder the ROC curves

Eligibility Criteria

Inclusion Criteria

• Berlin criteria for moderate to severe ARDS

Exclusion Criteria

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

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 100 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Unity Health Toronto

OTHER

Sponsor Role: collaborator

Dr. Negrin University Hospital

OTHER

Sponsor Role: lead

Responsible Party

Jesus Villar

principal investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Jesús Villar, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Hospital Universitario D. Negrin

Locations

Hospital Universitario Dr. Negrin

Las Palmas de Gran Canaria, Las Palmas, Spain

Department of Anesthesia, Hospital Clinic

Barcelona, , Spain

Hospital Universitario La Paz (ICU)

Madrid, , Spain

Countries

Spain

References

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.

Reference Type: BACKGROUND

PMID: 30624279 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 34268407 (View on PubMed)

Other Identifiers

7/2021

Identifier Type: -

Identifier Source: org_study_id