An interpretable machine learning model for predicting myocardial injury in patients with high cervical spinal cord injury.

ABSTRACT:

Background

High cervical spinal cord injury (HCSCI) is associated with severe autonomic dysfunction and an increased risk of cardiovascular complications, including myocardial injury. However, early identification of myocardial injury remains challenging because of the lack of predictive tools.

Methods

A total of 454 patients with HCSCI were retrospectively enrolled and categorized into myocardial injury (n = 101) and non-injury (n = 353) groups. Univariate and multivariate logistic regression analyses were used to identify independent risk factors. Four machine learning (ML) models-logistic regression, gradient boosting machine (GBM), neural network (NeuralNetwork), and adaptive boosting (AdaBoost)-were constructed to predict myocardial injury, and model performance was eva

SUBMITTER: Li J 

PROVIDER: S-EPMC12358262 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature