Tree-based survival analysis improves mortality prediction in cardiac surgery.

ABSTRACT:

Objectives

Machine learning (ML) classification tools are known to accurately predict many cardiac surgical outcomes. A novel approach, ML-based survival analysis, remains unstudied for predicting mortality after cardiac surgery. We aimed to benchmark performance, as measured by the concordance index (C-index), of tree-based survival models against Cox proportional hazards (CPH) modeling and explore risk factors using the best-performing model.

Methods

144,536 patients with 147,301 surgery events from the Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) national database were used to train and validate models. Univariate analysis was performed using Student's T-test for continuous variables, Chi-squared test for categorical variables, and stratif

SUBMITTER: Penny-Dimri JC 

PROVIDER: S-EPMC10365268 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature