Machine learning for risk stratification in the emergency department (MARS-ED): a randomized controlled trial.

ABSTRACT: Emergency department (ED) crowding necessitates rapid, accurate risk stratification to optimize care and resource allocation. Traditional clinical prediction tools like NEWS, APACHE II, and SOFA score have limited generalizability or rely on extensive inputs, including vitals. We developed RISK^INDEX, a machine-learning tool predicting 31-day mortality using routine laboratory values, age, and sex. To evaluate the clinical impact of a machine learning-based risk score in routine care, we conducted an investigator-initiated, open-label, randomized, non-inferiority trial (MARS-ED) at the Maastricht University Medical Center+ ED. Adult patients ( ≥ 18 years) assessed by an internal medicine specialist and with ≥4 laboratory tests were eligible. Patients were randomized 1:1 using com

SUBMITTER: van Dam PMEL 

PROVIDER: S-EPMC12783782 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature