Background: Cardiac resynchronization therapy (CRT) can improve ventricular size, shape, and mass and reduce mitral regurgitation by reverse remodeling of the failing ventricle. About 30% of patients do not respond to this therapy for unknown reasons. In this study, we aimed at the identification and classification of CRT responder by the use of genetic variants and clinical parameters.
Methods and results: Of 1421 CRT patients, 207 subjects were consecutively selected, and CRT responder and nonresponder were matched for their baseline parameters before CRT. Treatment success of CRT was defined as a decrease in left ventricular end-systolic volume >15% at follow-up echocardiography compared with left ventricular end-systolic volume at baseline. All other changes classified the patient as CRT nonresponder. A genetic association study was performed, which identified 4 genetic variants to be associated with the CRT responder phenotype at the allelic (P<0.035) and genotypic (P<0.031) level: rs3766031 (ATPIB1), rs5443 (GNB3), rs5522 (NR3C2), and rs7325635 (TNFSF11). Machine learning algorithms were used for the classification of CRT patients into responder and nonresponder status, including combinations of the identified genetic variants and clinical parameters.
Conclusions: We demonstrated that rule induction algorithms can successfully be applied for the classification of heart failure patients in CRT responder and nonresponder status using clinical and genetic parameters. Our analysis included information on alleles and genotypes of 4 genetic loci, rs3766031 (ATPIB1), rs5443 (GNB3), rs5522 (NR3C2), and rs7325635 (TNFSF11), pathophysiologically associated with remodeling of the failing ventricle.
Keywords: artificial intelligence; cardiac resynchronization therapy; cardiovascular disease; data mining; heart failure; risk factors.
© 2014 American Heart Association, Inc.