Using machine learning to aid treatment decision and risk assessment for severe three-vessel coronary artery disease.

ABSTRACT:

Background

Three-vessel disease (TVD) with a SYNergy between PCI with TAXus and cardiac surgery (SYNTAX) score of ≥ 23 is one of the most severe types of coronary artery disease. We aimed to take advantage of machine learning to help in decision-making and prognostic evaluation in such patients.

Methods

We analyzed 3786 patients who had TVD with a SYNTAX score of ≥ 23, had no history of previous revascularization, and underwent either coronary artery bypass grafting (CABG) or percutaneous coronary intervention (PCI) after enrollment. The patients were randomly assigned to a training group and testing group. The C4.5 decision tree algorithm was applied in the training group, and all-cause death after a median follow-up of 6.6 years was regarded as the class label.

Results<

SUBMITTER: Jie L 

PROVIDER: S-EPMC9170909 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature