Risk prediction of arrhythmia after percutaneous coronary intervention in patients with acute coronary syndrome: A systematic review and meta-analysis.

Abstract

The purpose of study was to evaluate the predictive performance of models for the development of arrhythmias in patients with acute coronary syndrome after percutaneous coronary intervention. Two researchers screened the literature according to CHARMS, and assessed the risk of bias and applicability based on PROBAST. A total of 44 studies were included in the review, comprising 62 models, of which 30 models identified as having a low risk of bias, and only 7 studies combined other machine learning algorithms. A meta-analysis of some of the studies combined gave an AUC of 0.813 (95 % CI 0.791 to 0.835), and a meta-analysis of the models with low bias among them gave an AUC of 0.803 (95 % CI 0.768 to 0.837). The performance of the integrated models was satisfactory overall, but the modelling approach was homogeneous. The external validation of the existing models should be incorporated to enhance their extrapolation.