Prediction of red blood cell transfusion after orthopedic surgery using an interpretable machine learning framework.

ABSTRACT:

Objective

Postoperative red blood cell (RBC) transfusion is widely used during the perioperative period but is often associated with a high risk of infection and complications. However, prediction models for RBC transfusion in patients with orthopedic surgery have not yet been developed. We aimed to identify predictors and constructed prediction models for RBC transfusion after orthopedic surgery using interpretable machine learning algorithms.

Methods

This retrospective cohort study reviewed a total of 59,605 patients undergoing orthopedic surgery from June 2013 to January 2019 across 7 tertiary hospitals in China. Patients were randomly split into training (80%) and test subsets (20%). The feature selection method of recursive feature elimination (RFE) was used to identify

SUBMITTER: Chen Y 

PROVIDER: S-EPMC10017874 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature