TCT-607 Machine Learning for Predicting Major Adverse Cardiac Events in Percutaneous Coronary Intervention of Coronary Artery Chronic Total Occlusion
Recommended Citation
Karacsonyi J, Stanberry L, Bergstedt S, Rempakos A, Kostantinis S, Simsek B, Alexandrou M, Allana S, Al-Ogaili A, Alaswad K, Krestyaninov O, Karmpaliotis D, Kirtane A, McEntegart M, Khatri J, Jaffer F, Poommipanit P, Choi J, Gorgulu S, Jaber W, Rinfret S, ElGuindy A, Abi Rafeh N, Goktekin O, Ungi I, Azzalini L, Rangan B, Mastrodemos O, Sandoval Y, Burke MN, Brilakis E. TCT-607 Machine Learning for Predicting Major Adverse Cardiac Events in Percutaneous Coronary Intervention of Coronary Artery Chronic Total Occlusion. J Am Coll Cardiol 2023; 82(17 Suppl):B244.
Document Type
Conference Proceeding
Publication Date
10-24-2023
Publication Title
J Am Coll Cardiol
Keywords
aged, artificial neural network, averaging, bootstrapping, chronic total occlusion, complication, conference abstract, female, human, machine learning, major adverse cardiac event, major clinical study, male, multicenter study, multilayer perceptron, percutaneous coronary intervention, prediction, prospective study, random forest, receiver operating characteristic, risk assessment, support vector machine
Abstract
Background: Predicting major adverse cardiac events (MACE) in chronic total occlusion (CTO) percutaneous coronary intervention (PCI) can assist with decision making and procedural planning. Methods: We analyzed 4,681 CTO PCIs from the PROGRESS-CTO (Prospective Global Registry for the Study of CTO intervention) registry performed between 2012 and 2022 at 42 centers. 8 machine learning (ML) methods were applied based on 32 parameters: multilayer perceptron (MLP) with decay model; support vector machine model; generalized additive model; MLP; extreme gradient boosting model; random forest model; Bayes GLM: Bayesian generalized linear model; and AvNNet: Neural Networks Using Model Averaging. The intervals were estimated using a bootstrap approach. The performance of the models was assessed using the receiver-operating characteristic curve. Results: The overall MACE rate was 1.9%. The median age of the patients was 65 years (57, 71 years). The data were divided into testing (n = 936) and training (n = 3,745) sets. The performance of the ML models on the testing set was as follows: MLP with decay model AUC 0.60; support vector machine model: 0.67; generalized additive model: 0.71; MLP: 0.65; extreme gradient boosting model: 0.44; random forest model: 0.60; Bayes GLM 0.72; AvNNet: 0.70. The AUC of the PROGRESS-CTO Complication logistic regression model was 0.675. The AUC of ML models and the Bayes-GLM method was found to have 50% of AUCs between 0.72 and 0.81, whereas the model built using linear logistic method had 50% of AUCs between 0.66 and 0.74 (Figure 1). [Formula presented] Conclusion: A machine learning-based model may improve the prediction of MACE in CTO PCI. Categories: CORONARY: Complex and Higher Risk Procedures for indicated Patients (CHIP)
Volume
82
Issue
17 Suppl
First Page
B244
