Kowlgi, Gurukripa N.

Background: Patients undergoing epicardial access for ventricular tachycardia (VT) have a higher rate of complications and VT recurrence post-procedure. Data regarding electrophysiological factors driving the outcomes are scant. Objective: To compare the factors and outcomes associated with epicardial vs endocardial VT ablation. Methods: A single-center, retrospective study of patients undergoing catheter ablation for scar-related VT was conducted. Data collected included demographics, comorbidities, medications, relevant laboratory abnormalities, electrocardiograms, echocardiograms, detailed procedural characteristics, and outcomes. Results: Our cohort of 554 patients had 89 (16.1%) epicardial and 465 (83.9%) endocardial VT ablations. Patients undergoing epicardial ablation had a greater frequency of NICM, and more patients had undergone sympathetic modulation for VT (p < 0.05) but had lesser frequency of valve surgery, and CABG. Epicardial ablation was associated with greater use of both short-term (<3 months) and long-term (>6 months) anti-arrhythmic drugs (AAD) (p<0.01). Epicardial access was obtained by micropuncture or needle-in-needle technique for 22.5% (n=20) of patients. Except for more use of general anesthesia and the lesser usage of drugs for VT induction during epicardial procedures, there were no significant differences. Epicardial ablation had a higher risk of acute complications (<30 days) after the procedure (driven by pericarditis) (25% vs 7.8%, p<0.01). On survival analysis, epicardial access was associated with an increase in VT recurrence and repeat ablation (p<0.01) during a mean follow-up of 1072 days. On multivariate analysis, after adjusting for age, sex and comorbidities, epicardial access was an independent predictor of VT recurrence or repeat ablation [aHR 1.61 (CI: 1.18- 2.2)] Conclusion: Patients undergoing epicardial ablations with fewer comorbidities demonstrated increased AAD usage prior to and after the procedure, and a higher risk of acute complications after the procedure. Epicardial access was an independent predictor of VT recurrence or repeat ablation.

Introduction: Tricuspid regurgitation (TR) is a common complication of right ventricle lead extraction (RVLE) in 5 to 10% of cases. This study aims to identify clinical outcomes such as predictors of heart failure hospitalization, need for tricuspid valve intervention, and death among patients with new severe TR after device lead removal (DLR). Methods: Patients who underwent DLR (pacemaker/ICD) between 3/1997 and 3/2024 were identified. Those who had an echocardiogram before and after RVLE with evidence of new severe TR were included. Cox univariable regression controlling for current age was used to assess association with death. Prentice Williams Peterson total time (PWPTT) Cox univariable regression was used to determine the rate of heart failure hospitalization up to three events per subject. Bivariable cox regression was used to control for ejection fraction for ICD and coil removal. Analysis was conducted using Stata IC16. Results: We identified 2,122 people who underwent RVLE, of whom 90 (4.24%) developed new severe TR and were included in the analysis. There were 68 hospitalizations among 38 people at a median of 2 years following lead extraction (IQR 0.57 to 4.5 years). The rate of hospitalization was 11.60 [9.14 to 14.71] per 100 person-years. Removal of an ICD was associated with increased hospitalization (HR 5.48 p<0.001) but not mortality (HR 1.21, p=0.707). Removal of a single coil ICD lead was associated with increased hospitalization rate (HR 3.01, p=0.031) but not mortality (HR =0.51, p=0.380). Removal of dual coil ICD lead was associated with more than a tenfold increase in hospitalization rate (HR 11.55, p=0.001) and four times the mortality rate (HR 4.05, p=0.031) (Figures 1&2). The presence of an abandoned (pacemaker/ICD) lead was associated with increased hospitalization (HR 5.75, p=0.004) and mortality (HR4.95, p=0.034). There was no change in hospitalization rate following tricuspid valve replacement (HR 1.33, p=0.716, n=18). Conclusion: Among people with new severe TR following DLR, the characteristics of the leads are strongly correlated with morbidity and mortality.

Introduction: Identifying patients at risk for ischemic events after percutaneous coronary intervention (PCI) relies on traditional analysis of limited clinical and imaging variables. Machine learning (ML) has shown promise in effectively predicting cardiovascular risk in population studies. While existing ML models mainly predict mortality and incorporate clinical variables, there is a lack of tools that have utilized genetic data and that predict ischemic events. Aims: This study aims to develop and validate a ML model incorporating genotyping and clinical data to enhance prediction of ischemic outcomes for PCI patients utilizing large prospectively derived diverse datasets. Methods: Patients from the TAILOR-PCI trial (n=5302) were utilized for model development. 50% of the sample was utilized for Boruta feature selection and 50% for training and testing using cross validation. Features included demographics, medical history, medications, PCI characteristics, and genetic data (specifically, CYP2C19 *2, *3, *17 alleles). The primary endpoint was a composite of cardiovascular death, myocardial infarction, stroke, stent thrombosis, and severe recurrent ischemia at 12 months. Multiple ML classification algorithms, including Support Vector Machine (SVM) polynomial, Random Forest, Light gradient boost, XG Boost, among others, were benchmarked for their prediction performance on rare events. The top performing classifiers were externally validated on an independent dataset from the PRECISION PCI study (n=3,745). Results: Mean participant age of the training set was 64.2 ± 11.0 years, with 75.4% being male. During follow-up of 12 months, among 4,572 patients in the entire cohort 343 (7.5%) met the primary outcome. The SVM polynomial model demonstrated the highest area under the curve (AUC) of 0.67 for predicting the primary outcome with test dataset. The sensitivity, specificity, precision, and recall were 0.87, 0.28, 0.07, and 0.87 respectively (Figure). Peripheral arterial disease, body mass index, and age were among the top variables by feature importance. Conclusion: ML models incorporating both clinical and genetic data are feasible and highly promising in predicting major adverse cardiac events that may help guide use of anti-platelet drug therapy. The AUC values are reasonable given imbalances and misclassifications in datasets, and further model optimization with prospective utilization of the model will be paramount.

Introduction: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic disorder characterized by fibrofatty replacement of myocardial tissue, creating areas of electrical heterogeneity and increasing the risk of ventricular arrhythmias (VAs) and sudden cardiac death (SCD). Catheter ablation is commonly performed in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC) for management of ventricular arrhythmias (VAs). Whether catheter ablation affects cardiac function post-procedurally is uncertain. Research Question: Does catheter ablation for ARVC result in acute change in cardiac function? Goals/Aims: To assess the impact of catheter ablation for VAs on cardiac function in the acute post-procedure setting. Methods: This retrospective study included patients with ARVC who underwent catheter ablation for VAs at Mayo Clinic. Cardiac function was assessed on pre- and post-procedure transthoracic echocardiograms (TTEs). Post-procedure TTEs were obtained within 4 weeks after the ablation procedure. Right ventricular (RV) enlargement and systolic dysfunction were evaluated as continuous variables (0=none, 1=mild, 2=mild-moderate, 3=moderate, 4=moderate-severe, 5=severe). Results: The study included 32 patients who underwent a total of 44 ablations. Mean age at the time of ablation was 49±16 years, and 24 (75%) were male. After a mean follow-up duration of 2.6±5.5 days, there was a significant worsening in mean RV dysfunction (1.6±1.5 to 2.0±1.4, P = 0.001). There were no significant changes in mean left ventricular ejection fraction (LVEF), left ventricular (LV) dimensions, and RV size. 6 (14%) patients had a >5% decline in LVEF, and 14 (32%) patients had worsening RV function. Transient inotropic support was required in 4 (9%) patients. Conclusion(s): This study is the first to assess the acute impact of catheter ablation for VAs on cardiac function in ARVC patients. These results underscore the importance of careful post-procedure monitoring and planning of the ablation target in these patients to avoid excessive ablation and therefore impairment in cardiac function.

Background: In recent years, catheter ablation has emerged as a sustainable, first-line therapy for the management of ventricular tachycardia (VT). Given the rise of the “obesity paradox” theory in atrial ablation outcomes, we sought to study the effect of BMI on patients undergoing structural VT ablation. Purpose: To assess the outcomes of VT ablation, specifically VT recurrence, in patients with elevated vs. normal BMI. Methods: Clinical characteristics and demographic data were collected for VT patients who underwent ablation at Mayo Clinic Rochester from 2012-2022. Patient BMI was classified as non-obese (<30 kg/m2), obese (30-40 kg/m2), and morbidly obese (≥40 kg/m2). The endpoint was a composite of initial monomorphic VT recurrence and repeat ablation after adjusting for age, sex, and comorbidities. Results: A total of 399 patients underwent 508 ablations. 51.4% of ablations had a BMI <30, 39.2% were between 30-40, and 9.4% were ≥40. There was a greater prevalence of hypertension and type 2 diabetes mellitus in morbidly obese patients (p <0.01). Morbidly obese individuals also had a significantly increased rate of general anesthesia during ablation and long VT cycle lengths. The Kaplan-Meier curve demonstrates no statistically significant differences in time to VT recurrence among the three BMI groups (p = 0.324). Neither morbid-obesity (aHR= 1.20; CI = 0.94-1.56, p = 0.14) nor nonmorbid-obesity (aHR = 0.92; CI = 0.76-1.10, p = 0.4), compared to non-obesity was a significant independent predictor of VT recurrence or repeat ablation on multivariate regression after adjusting for age, sex and comorbidities. Conclusion: There was no significant association between BMI groups and VT recurrence or repeat ablation, and no difference in complications. We did not find the obesity paradox to be relevant for VT ablation in our patient population. Further study would be needed with larger samples to corroborate these findings.

Abstract Background Radiofrequency ablation (RFA) has been effective in managing premature ventricular contractions (PVCs). Despite improvement in ablation techniques, recurrence remains high for LV summit PVCs. Epicardial ablation here is often precluded due to fat and proximity to coronaries, thus, aggressive but safe endocardial options are needed. Purpose To evaluate the safety and effectiveness of ultra-long ablation (&gt;240 seconds) duration for refractory LV summit PVCs. Methods Patients with deep intramural LV summit PVCs were identified by lack of PVC elimination or late (&gt;60 s suppression) and recurrence post-lesion under the left aortic sinus of Valsalva. We included patients who received the longest RFA of at least 240 seconds (maximum 15 minutes). RFA power was titrated slowly from 20 to 35W, carefully ensuring that the impedance drop did not exceed 12 ohms. Acute procedure success was defined as clinical PVC suppression for ≥60 minutes after the last lesion. Short-term success was defined as symptom improvement, ejection fraction (EF) recovery, and drop in PVC burden at 3 months. Results Twenty patients (59.5 ± 16.4 years, 55% male) were included. Baseline features are shown in Table 1. All RFA were delivered endocardially. No steam pops were noted. PVC burden was significantly lower at 3-month follow-up (P&lt; 0.01), Figure 1A. Most patients had a normal ejection fraction (EF) at baseline; thus, no significant difference change in EF was observed (Figure 1B). One complication was recorded, which was tamponade likely caused by coronary sinus dissection requiring pericardiocentesis. Acute success was achieved in 95.0% of the cases, and all cases followed at 3 months (18/18) achieved short-term success. Conclusion Ultra-long duration RFA with lesions ≥ 240 seconds at 35W per lesion for LV summit PVCs appeared to be safe and effective in achieving acute and short-term success. The lack of acute suppression should not discourage operators, as there seems to be delayed suppression in some cases. Careful monitoring of biophysics is paramount when attempting this technique.

Abstract Background Ventricular tachycardia (VT) is a life-threatening arrhythmia that can complicate structural heart disease. While catheter ablation is effective in treating this condition, the recurrence of VT after the procedure continues to be a concern. Consequently, there is a need for a reliable system to evaluate the likelihood of arrhythmia recurrence following an ablation. Objective To predict recurrence or repeat ablation after a VT catheter ablation procedure using machine learning (ML) models. Methods We conducted a single-center, retrospective study of all patients undergoing catheter ablation for scar-related VT between 2012 and 2022. Data collected included demographics, comorbidities, medications, relevant laboratory abnormalities, electrocardiograms, echocardiograms, detailed procedural characteristics, and outcomes. Python version 3.8.5 for exploratory data analysis (EDA) and visualization. Using six different ML models from the training set (90:10 split), we used 34 variables to predict the primary outcome, including VT recurrence or repeat catheter ablation. The accuracy of these models was compared using ROC curves based on their performance on the test set. Results Out of 508 VT ablation procedures, 261 experienced a recurrence. Support Vector Classifier (SVM) performed the best, having an AUC of 0.73, followed by logistic regression at 0.69 (Figure A). SVM yielded sensitivity, specificity, positive and negative predictive values of 0.73, 0.73, 0.79, and 0.67, respectively. The F1 score was 0.70. Among the factors with the highest feature importance in the model, the most influential variable was the proceduralist’s experience level, followed by QRS width at baseline, LVEF, and patient’s age (Figure B). Conclusion Our model can predict recurrent VT after ablation with reasonable accuracy, outperforming the existing clinical risk scores significantly. Larger training sample sizes will help achieve more robust ML models and improve predictive accuracy further.Figure A: ROC curve depicting the AUROCB. Feature importance graph

Introduction: Atrial fibrillation (AF) is the most prevalent arrhythmia, placing a substantial burden on the healthcare system. Patients with AF are cared for in a variety of settings including primary care and cardiology clinics, while some require cardiac electrophysiology (EP) clinics offering specialized drugs or procedures. Appropriate triage of AF referrals to these specific clinics will lead to timely and appropriate care and reduce inefficiency in the health system. Goal: To develop a machine learning (ML) model to triage AF referrals to a large Cardiology center to either the Cardiology or EP clinic based on likelihood of requiring an invasive EP procedure or initiation of antiarrhythmic drug. Methods: We retrospectively analyzed 6,755 patients who were referred to CV with referral diagnosis of AF (mean age 70 ± 11 years, 66% male). We employed multiple input variables to predict the suitability for EP clinic defined as need for initiating an antiarrhythmic drug or an invasive EP procedure. These encompassed patient demographics, relevant comorbidities, medications, prior procedures, medical notes analyzed using natural language processing (NLP), and Holter monitoring data. Results: For internal referrals, XG boost model performed the best with AUC at 79%, accuracy at 72%, and F1 score at 68%. This model produces true positive (TP) at 68% and true negative (TN) at 74%, which was better in comparison with triaging using NLP only (AUC = 69%, TP = 58%, TN = 61%), Holter only (AUC = 52%, TP = 51%, TN = 77%), and NLP/Holter (AUC = 67%, TP = 60%, TN = 60%). When supplementing ML model with Holter readings on average HR ≥ 100 and maximum HR ≥ 150, the results can be improved to TP at 70% and TN at 73%. Conclusions: Our model effectively identifies patients who are more likely to require specialized EP care, enabling the automation and increased efficiency of the AF referral triage process. Prospective validation studies are paramount in determining the true value of this tool.

Background: Catheter ablation is an effective tool to reduce ventricular tachycardia (VT) burden. There is a lack of robust data studying the characteristics and outcomes of VT ablation in patients with ischemic (ICM) versus non-ischemic cardiomyopathy (NICM). Aims: The aim of this study was to assess the impact of ICM vs NICM on outcomes of VT ablation. Methods: A single-center, retrospective study of patients undergoing catheter ablation for VT was conducted. Results: A total of 168 patients, undergoing 200 VT ablation procedures, were included in this study. NICM group (112 procedures) was compared to ICM group (88 procedures). Patients in the ICM group were older (66.6 ± 13.4 vs 58.9 ± 14.4 years) and had higher rates of prior myocardial infarction (65.9% vs 3.6) and coronary artery bypass graft surgery (40.9% vs 1.8%). Overall, ICM group had more comorbidities like hypertension (80.7% vs 52.7%), diabetes mellitus (23.9% vs 12.5%), chronic kidney disease (45.5% vs 35.7%), stroke (17% vs 7.1%), and lower left ventricular ejection fraction (36.8 ± 13.2% vs 43.1 ± 14.7%). The ICM group had lower incidence of prior cardiac valvular surgery (9.1% vs 29.5%). The inducibility of VT (93% vs 93.5%; p= 1.0) and the number of different VT morphologies induced during the case (1.9 ± 1.5 vs 2.3 ± 2.5; p=0.19) was similar between the two groups. There was no acute mortality in either group. There was a non-significant trend towards greater VT recurrence in the NICM group (43.2% vs 33.3%, p= 0.19). However, patients with NICM had a lower VT-free survival as determined by Kaplan-Meier analysis (p=0.05) [Figure]. Long term mortality was higher in ICM group when compared to NICM group (18.2% vs 8.9%, p<0.01). Conclusion: Patients undergoing VT ablation in the NICM group when compared to the ICM group were noted to be younger, had fewer comorbidities, and lower long-term mortality. However, they had significantly less VT-free survival, likely owing to more challenging substrate responsible for VT.

Introduction: Accurate pre-procedure localization of the premature ventricular contractions (PVCs) is critical in preparing for the ablation. So far, no tools exist for artificial intelligence guided PVC-localization. Hypothesis: Trained deep learning model will be able to predict the site of origin (SOO) of PVCs using 12 lead electrocardiogram (ECG). Methods: We analyzed 707 ECG-ablation pairs from 2403 patients who had at least one ECG flagged for PVCs in the 6 months prior to their procedure. First, we used a cohort of 100,000 ECGs without PVCs and 10,000 ECGs with PVCs to train a convolutional neural network (CNN) to isolate the ECG segments with PVCs in them from a 12-lead ECG (Figure 1). We then trained two models, one utilizing a 10-second 12-lead ECG, and the other with just the isolated PVC beats as input. The models were trained using the Keras framework with TensorFlow (Google). Results: The mean age of the patients in the cohort was 61.2 ± 15.0 years. 66.3 percent of patients underwent left ventricle ablation, 54.6 percent had outflow ablation, and 55.3 percent had ablation of sites with leftward axis (septal location for midline and left ventricle PVCs). The model trained on 10-second ECGs yielded validation and test AUCs of 0.941 and 0.931 for outflow, 0.898 and 0.891 for ventricle, and 0.863 and 0.846 for leftward axis (Figure 1A). The model trained on isolated segments containing PVC beats yielded validation and test AUCs of 0.966 and 0.957 for outflow, 0.879 and 0.907 for ventricle, and 0.846 and 0.829 for SOO for leftward axis (Figure 1B). Conclusions: We have trained an accurate convolutional neural network that can predict SOO using the surface ECG. Further fine-tuning would be needed to refine the model and develop prediction of specific sites.