Early Prediction of Massive Transfusion for Patients With Traumatic Hemorrhage: Development of a Multivariable Machine Learning Model.

ABSTRACT:

Objective

Develop a novel machine learning (ML) model to rapidly identify trauma patients with severe hemorrhage at risk of early mortality.

Background

The critical administration threshold (CAT, 3 or more units of red blood cells in a 60-minute period) indicates severe hemorrhage and predicts mortality, whereas early identification of such patients improves survival.

Methods

Patients from the PRospective, Observational, Multicenter, Major Trauma Transfusion and Pragmatic, Randomized Optimal Platelet, and Plasma Ratio studies were identified as either CAT+ or CAT-. Candidate variables were separated into 4 tiers based on the anticipated time of availability during the patient's assessment. ML models were created with the stepwise addition of variables and compared wit

SUBMITTER: Benjamin AJ 

PROVIDER: S-EPMC10513183 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature