Background Atrial fibrillation (AF) is the most common cause of arrhythmia-induced cardiomyopathy. Effective management strategies include medical therapy for rate and rhythm control, catheter ablation (CA), and goal directed medical therapy. Sodium-glucose co-transporter 2 inhibitors (SGLT2i), a novel class of anti-diabetic drugs, have shown a promising impact in reducing cardiovascular events in diabetic and non-diabetic heart failure (HF) patients. It is unclear what impact SGLT2i use may have on AF recurrence following catheter ablation. Aim To evaluate the effects of SGLT2i on preventing AF recurrence following CA and impact on other cardiovascular outcomes. Methods and results We performed a comprehensive literature search through multiple search engines (Pubmed, Scopus, Web of Science, and Cochrane) to include eligible studies using the appropriate keywords until 10th April 2024. Our search yielded nine eligible studies with 16,857 patients. Our analysis reveals a significant reduction in AF recurrence after catheter ablation among patients receiving SGLT2i compared to non-SGLT2i medications (RR = 0.72, 95% CI [0.67, 0.78], P < 0.00001). Additionally, SGLT2i therapy was associated with decreased all-cause hospitalizations and reduced risk of ischemic stroke. However, no significant difference in all-cause mortality was observed between SGLT2i and non-SGLT2i groups. Conclusion Our study found that SGLT2 inhibitors significantly reduced AF recurrence post-CA in diabetic patients. Moreover, SGLT2i use was associated with lowered hospitalization and ischemic stroke risk. Though no significant difference in mortality was noted, the decrease in hospitalization suggests a possible favorable effect on cardiovascular events.

Background: Premature ventricular contractions (PVCs) are frequently observed with left ventricular (LV) systolic dysfunction, although the prevalence of these associated conditions in the general population remains unknown. Objective: We sought to understand the prevalence of frequent PVCs (defined PVCs>5%) and high burden PVCs (defined PVCs>10%) and LV systolic dysfunction in patients receiving ambulatory Holter monitors (HM). Methods: A prospective multicenter (8 US medical centers) cross-sectional study collected demographic and PVC burden data from consecutive patients undergoing 24-hour, 48-hour, and 14-day HM (July 2018-June 2020). Left ventricle ejection fraction (LVEF) data was collected if obtained within 6 months of HM. Four PVC burden groups were analyzed (<1%, 1-5%, 5.1-10% and >10% burden) and stratified by normal LVEF (≥50%) or presence LVEF<50%. Results: The prevalence of PVC burden of 5.1-10% and >10% was 4% and 5%, respectively in the population undergoing HM (n=6,529). Age was significantly different between PVC groups (p<0.001). In those with LVEF assessment (n=3,713), the prevalence of LVEF<50% and both LVEF<50% and PVC>5% was 16.4% and 4.2%, respectively. The prevalence of PVC>5% and PVC>10% in patients with LVEF<50% was 26% and 16%, respectively. PVC>5% were more prevalent in older, male, and Caucasians (p<0.001). Females had a lower prevalence of PVC>5% than males (6% vs. 11%; p<0.001), but not among those with LVEF<50% (24% vs. 26%, p=0.10). Conclusion: PVC>5% and PVC>10% and LVEF<50% are prevalent in patients undergoing HM. PVC>5% are associated with older age. Females have a lower prevalence of PVC>5% than males but similar combined PVC>5% and LVEF<50%.

Introduction—We aimed to apply machine learning (ML) to develop a prediction model for cardiac resynchronization therapy (CRT) response. Methods and Results—Participants from the SmartDelay Determined AV Optimization (SMART-AV) trial (n=741; age, 66 ±11 yrs; 33% female; 100% NYHA III-IV; 100% EF≤35%) were randomly split into training & testing (80%; n=593), and validation (20%; n=148) samples. Baseline clinical, ECG, echocardiographic and biomarker characteristics, and left ventricular (LV) lead position (43 variables) were included in 6 ML models (random forests, convolutional neural network, lasso, adaptive lasso, plugin lasso, elastic net, ridge, and logistic regression). A composite of freedom from death and heart failure hospitalization and a >15% reduction in LV end-systolic volume index at 6-months post-CRT was the endpoint. The primary endpoint was met by 337 patients (45.5%). The adaptive lasso model was more accurate than class I ACC/AHA guidelines criteria (AUC 0.759; 95%CI 0.678-0.840 versus 0.639; 95%CI 0.554-0.722; P<0.0001), well-calibrated, and parsimonious (19 predictors; nearly half are potentially modifiable). The model predicted CRT response with 70% accuracy, 70% sensitivity, and 70% specificity, and should be further validated in prospective studies. Conclusions—ML predicts short-term CRT response and thus may help with CRT procedure planning.

A document by Domenico Grieco. Click on the document to view its contents.