An interpretable machine learning tool for predicting perioperative cardiac events in patients scheduled for hip fracture surgery: insights from the multicenter LUSHIP study.

ABSTRACT:

Background

Elderly patients undergoing surgery for hip fractures are at high risk for perioperative Major Adverse Cardiac Events (MACE), which can markedly compromise postoperative outcomes. This study aims to develop a machine learning (ML) based, interpretable tool to predict MACE using clinical and ultrasound-based variables in this population.

Methods

We analyzed data from 877 patients in the multicenter LUSHIP study, incorporating demographics, Revised Cardiac Risk Index (RCRI), functional status, and preoperative lung ultrasound (LUS) scores. Multiple ML models were trained and validated using bootstrap resampling. The final ensemble meta-model combined GBM (Gradient Boosting Machine) and GLMNET (Elastic-Net Regularized Generalized Linear Models).

Results

The en

SUBMITTER: Azzolina D 

PROVIDER: S-EPMC12560482 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature