1A.08: GENETIC MARKERS IN CARDIAC RESYNCHRONIZATION THERAPY TREATMENT SUCCESS. (June 2015) - Schmitz, B.; De Maria, R.; Gatsios, D.; Chrysanthakopoulou, T.; Landolina, M.; Gasparini, M.; Campolo, J.; Parolini, M.; Sanzo, A.; Galimberti, P.; Lenders, M.; Brand, E.; et al.
Abstract : Objective: Cardiac resynchronization therapy (CRT) can improve ventricular size, shape and mass and reduce mitral regurgitation by reverse remodelling of the failing ventricle. CRT combines right atrial and ventricular pacing with pacing of the left ventricular free wall by a third lead to resynchronize contraction between and within ventricles. About 30% of patients do not respond to this therapy for unknown reasons. In the present study, we aimed at the identification and classification of CRT responder by the use of genetic variants and clinical parameters. Design and method: Out of 1, 421 CRT patients, 207 subjects were consecutively selected and CRT responder and non-responder were matched for their baseline parameters before CRT. Treatment success was defined as decrease in left ventricular end systolic volume (LVESV) >15% at follow-up echocardiography compared to baseline LVESV. An association study was performed to identify genetic variants associated with CRT success. For the classification of CRT patients into responder and non-responder, machine learning algorithms were applied using combinations of clinical parameters and the identified genetic variants. Results: Significant differences, resulting from the defined remodelling phenotypes, were found between CRT responder and non-responder for volume (p < 0.001) and function (p < 0.001) changes. In CRT responder patients, LVEDV decreased by 22 ml [-37 to -16 ml] and LVEF improved by 11% [6 to 16%], whereas changes in LV volume (deltaLVEDV 2 ml [-4 to +10 ml]) and LVEF (deltaLVEF 2.5% [-2 to +5%]) were slight in CRT non-responders. We 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). By application of the classifiers "Clinical & Genotypes" and "Clinical & Alleles" in the machine learning process, the rule-based methods C4.5 and PART were identified to exceed 82.5% accuracy. Conclusions: We demonstrate that rule induction algorithms can successfully be applied for the classification of heart failure patients in CRT responder and non-responder status using clinical and genetic parameters. Our analysis included information on alleles and genotypes of 4 genetic loci, pathophysiologically associated with remodelling of the failing ventricle.