Discriminating Acute Respiratory Distress Syndrome from other forms of respiratory failure via iterative machine learning.

ABSTRACT: Acute Respiratory Distress Syndrome (ARDS) is associated with high morbidity and mortality. Identification of ARDS enables lung protective strategies, quality improvement interventions, and clinical trial enrolment, but remains challenging particularly in the first 24 hours of mechanical ventilation. To address this we built an algorithm capable of discriminating ARDS from other similarly presenting disorders immediately following mechanical ventilation. Specifically, a clinical team examined medical records from 1263 ICU-admitted, mechanically ventilated patients, retrospectively assigning each patient a diagnosis of "ARDS" or "non-ARDS" (e.g., pulmonary edema). Exploiting data readily available in the clinical setting, including patient demographics, laboratory test results from before t

SUBMITTER: Afshin-Pour B 

PROVIDER: S-EPMC9812471 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature