KoKo, Nway

Introduction: Studies on leadless pacemakers (LP) are very limited and they often fail to reflect real population outcomes. Hypothesis: We sought to evaluate the effect of leadless pacemaker implantation on the echocardiographic parameters including ventricular function and valvular competence. Methods: A retrospective review including 162 patients (81 Micra Leadless pacemaker implant and 81 dual-chamber transvenous pacemakers) from 3 centers were included. Follow-up clinical, echocardiographic, and pacing characteristics were obtained over a median of 14+/-2 months. Results: baseline demographics and clinical characteristics were comparable between the 2 groups including age (78.6+/-9.9, Range 35-95) and gender (49 males, 69.5%). Compared to conventional pacemakers, LP patients had worsening tricuspid regurgitation noted on follow-up echo in 59.3% of patients compared to baseline(P<0.001). This difference was independent of the RV pacing percentage and device location. In addition, LP implantation resulted in a reduction of RV function (P=0.003) and left ventricular ejection fraction (P=0.03). The RV Pacing burden in LP patient patients was not associated with a change in LVEF during the follow-up period (95% CI -0.24, 0.20, p = 0.846). Conclusions: LP therapy is associated with worsening tricuspid valve regurgitation and biventricular dysfunction over a mean 1 year of follow-up. Additional prospective randomized studies are warranted to further evaluate these findings.

Introduction: Syncope was reported to be associated with sudden cardiac death in heart failure patients regardless of the etiology of syncope. We attempted to use unsupervised machine learning to identify the prognostically distinct phenotypes in a large population of heart failure patients presenting with syncope. Hypothesis: Unsupervised machine learning can identify prognostically distinct phenotypes in a large population of heart failure patients presenting with syncope. Methods: Patients with a diagnosis of systolic or diastolic heart failure and syncope at Mayo Clinic sites were identified for baseline and follow-up data. Variables with ≥ 20% missing data were excluded; a total of 57 variables were used for k-means clustering analysis. Patients were grouped according to 4 distinct k-means determined clusters for statistical analysis. Results: A total of 3,557 patients with proper follow-up and echocardiography data were included in the final analysis. The median follow-up duration was 36.3 months, and 2908 (81.7%) patients died. Cluster 2 (n= 680) was identified as the cluster with the highest mortality (92%), which contained patients with high proportions of cardiovascular comorbidities, especially chronic kidney disease. The Kaplan-Meier survival curves are shown in Figure 1. In multivariate Cox regression analysis, the presence of CKD independently predicts mortality (HR 1.62, 95%CI 1.50-1.76, p <0.0001) after adjusting for age and sex. Conclusions: Unsupervised machine learning identified patients with CKD, CAD and DM are high-risk phenotypes in heart failure patients with syncope, and can be used to guide future studies in this highly heterogeneous, unexplored population.