Deshmukh, A J

Abstract Introduction Atrial fibrillation (AF) is a major contributor to stroke risk, yet many cases remain undiagnosed due to its silent nature. Deep learning-based electrocardiogram (DL-ECG) analysis can streamline AF risk stratification, providing a more reliable and accessible method to stratify risk using only a standard resting ECG. However, this tool has been validated only on AF detected clinically or with short ECG event monitors. Purpose This study aimed to validate the predictive accuracy of a DL-ECG model for identifying AF in normal sinus rhythm (NSR) ECGs among patients undergoing continuous cardiac monitoring with implantable loop recorders (ILRs). Methods We conducted a historical cohort study across three major U.S. academic medical centers, enrolling patients with an ILR and no prior diagnosis or documented ECG evidence of AF. Pre-implantation 10-second, 12-lead ECGs in NSR were analyzed using the DL-ECG model. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with the CHARGE-AF score. Kaplan-Meier survival analysis and Cox proportional hazards models assessed the association between DL-ECG risk categories and incident AF, defined as any episode lasting ≥6 minutes. Results We included 2,523 patients (mean age 66 ± 17 years, 52% male). AF was detected in 658 (26%) over a median follow-up of 19 months (IQR 8-37), Table 1. The DL-ECG model classified 1,634 patients (65%) as at low-risk and 889 (35%) as at high-risk. AF developed in 242 (14.8%) of the low-risk group and 416 (46.8%) of the high-risk group, yielding an AUC of 0.73 (95% CI 0.70-0.75), surpassing the CHARGE-AF AUC of 0.69 (95% CI 0.66-0.71). A combined DL-ECG and CHARGE-AF model yielded an AUC of 0.74 (95% CI 0.72-0.76), Figure 1 presents the AUC, Net Reclassification Index, and DeLong test comparisons among the three models. High-risk patients had a significantly higher incidence of AF, which remained significant after adjusting for CHARGE-AF variables, Figure 2. AF incidence increased as DL-ECG quartiles increased, with 61% of patients at the top quartile developing AF during follow-up, Figure 3. False negatives (patients initially classified as low-risk but in whom later AF was detected) had a longer median time to AF detection (127 days [IQR 28-425] vs. 101 days [IQR 20-318] for true positives, p = 0.01), and where found to have at least one positive ECG during follow-up in 36%, compared to 32% of true negatives, indicating eventual risk reclassification. Conclusions Among patients undergoing ILR monitoring, the DL-ECG model demonstrated superior predictive performance compared to CHARGE-AF, offering a promising and accessible alternative for early detection of silent AF. This approach could aid early AF detection, particularly in resource-limited settings where access to specialized rhythm monitoring services is limited.Table 1 and Figure 1 Figure 2 and 3

Abstract Background Left ventricular diastolic function (LVDF) and left bundle branch block (LBBB) are both linked with increased risk of LV systolic dysfunction, heart failure, and mortality. Assessing LVDF, which is typically done through echocardiography, becomes challenging in the presence of LBBB due to the intraventricular conduction delay that disrupts the reliability of tissue Doppler imaging. Recently, we validated an artificial intelligence-enabled electrocardiogram (AI-ECG) model to assess LVDF and filling pressures. Methods We performed a retrospective study among patients with ECG-confirmed LBBB who also underwent comprehensive echocardiography within 14 days of the index ECG between September 2001 and June 2022. Using multivariable survival models, we assessed the risk of all-cause mortality across AI-ECG-derived LVDF grades. Results Of 2,554 patients with LBBB (mean age 72.0+12.6, 54.6% women), 252 (9.8%), 656 (25.7%), and 1,436 (56.2%), and 210 (8.2%) had normal LVDF, G1 LVDF, G2 LVDF, and G3 LVDF, respectively. The median LV ejection fraction (LVEF) was 54% and 49.3% of the patients had heart failure. Worse LVDF was associated with older age, lower LVEF, and higher rates of diabetes, hypertension, chronic pulmonary disease, renal failure, cardiovascular disease, and heart failure (p-of-trend<0.001 for all). During a median follow-up of 2.6 years (interquartile range 0.5-4.0 years), 847 (33.2%) patients died. In multivariable survival analysis, adjusted to multiple risk factors including baseline LVEF and heart failure, worse LVDF was independently associated with a higher risk of death [G1 LVDF: adjusted hazard ratio (aHR) 2.15 (95% confidence interval (95%CI) 0.75-1.77), G2 LVDF: aHR 2.25 (95%CI 1.50-3.38) G3 LVDF: aHR 2.96 (95%CI 1.88-4.65); p<0.001 for both)]. These findings were consistent also in a subgroup of patients with baseline LVEF > 50%. Conclusion Among patients with LBBB, worse LVDF, as derived by a dedicated AI-ECG algorithm is independently associated with all-cause mortality beyond baseline LVEF and clinical heart failure.survival across AI-ECG LVDF grades

Abstract Background Artificial intelligence (AI) models trained on 12-lead ECGs effectively detect left ventricular systolic dysfunction (LVSD; left ventricular ejection fraction [LVEF] <=40%). Continuous ECG monitoring via Holter recordings provides an opportunity for opportunistic screening for structural heart disease beyond rhythm disorders. We hypothesized that a lead-invariant version of the 12-lead AI model would enable a Holter monitor to screen for both arrhythmias and ventricular dysfunction. Methods We retrospectively analyzed continuous Holter ECGs from 17,665 patients who underwent a Holter and transthoracic echocardiogram (TTE) within 30 days of each other at Mayo Clinic. From each Holter, a random 20-minute of valid (non-flatline/lead disconnect) ECG segment was extracted and analyzed for LVSD detection using the adapted lead-invariant AI model. To evaluate stability, we examined model performance across different time points of the day, presenting results as area under the receiver operating characteristic curve (AUC) over time. Moreover, we illustrated the model’s robustness to noisy data by comparing its performance on raw ECG signals with that on bandpass-filtered inputs. Results Among 17,665 patients (mean age 59 years, 48.57% female), 4.96% had an LVEF <=40%. The AI model demonstrated strong predictive performance (20-minute segment AUC 0.90, mean prediction of 24-hour AUC 0.92). Analysis of results over time (Figure) revealed temporal patterns in predictive accuracy, with specific time periods showing greater stability. Despite modest variability, model performance remained consistently high throughout the day, confirming robustness across different physiological states. The predictions remained robust with noisy input. We did not observe performance improvement when the baseline wander and high frequency noise are removed by the bandpass filter. Conclusion Applying a 12-lead AI ECG model with a lead-invariant framework to a continuous Holter ECG enables effective screening for left ventricular dysfunction. This suggests that AI-based analysis of Holter-monitors can facilitate opportunistic screening of ventricular dysfunction and may enable assessment of an arrhythmia’s impact on LVEF, as well as the relationship between arrhythmia burden and LVEF.Figure 1.Mean prediction AUC of the day Figure 2.AUC for different time point

Abstract Background Implantable cardioverter-defibrillator (ICD) is a well-established therapeutic option associated with decreased mortality in patients with ischemic and non-ischemic cardiomyopathies. However, the indications for patients with chronic Chagas cardiomyopathy (CCC) are not well established since these patients were underrepresented in the trials validating ICD use. Purpose This systematic review and single-arm meta-analysis aims to synthetize the available evidence in the literature on the role and outcomes of ICD in CCC. Methods A systematic search of PubMed, Web of Science, Cochrane, and LILACS registries was performed following PRISMA guidelines. Studies evaluating the response to ICD in CCC were included. There were no language restrictions. Data were collected and synthesized using a meta-analysis of proportions with Freeman-Tukey double-arcsine transformation as needed. Results We included 14 observational studies and 1 randomized clinical trial (RCT), comprising 1315 patients with CCC. Mean age at ICD implantation was 58.2 ± 11.8, 65% were male, and 77% received an ICD for secondary prevention. During a mean follow up of 37.3 ± 37.0 months, the all-cause death rate was 22% (95% CI 15 to 29%; Figure 1A), the cardiovascular death rate was 16% (95% CI 11 to 22%; Figure 1B), the sudden cardiac death rate was 3% (95% CI 2 to 5%), and the non-cardiovascular death rate was 7% (95% CI 2 to 5%). In addition, 55% (95% CI 43 to 67%; Figure 1C) of patients received ≥1 appropriate ICD therapy (shocks or antitachycardia pacing), 8% (95% CI 5 to 12%) received inappropriate ICD shocks, and 23% (95% CI 11 to 37%) experienced electrical storms. Conclusion Despite ICD, the mortality rate in patients with CCC remains unacceptably high, primarily driven by cardiovascular causes, with frequent appropriate ICD therapies and electrical storms. Further studies are needed to better delineate the benefits and risks of ICD therapy in patients with CCC.Figure 1

Abstract Background Elevated left ventricular filling pressure (LVFP) is associated with an increased risk of heart failure and mortality. Pulmonary artery wedge pressure (PAWP) is considered the gold standard for assessing LVFP. Recently, we validated an artificial intelligence-enabled electrocardiogram (AI-ECG) to assess LV diastolic function (LVDF) and LVFP. Herein, we compare AI-ECG with PAWP in predicting long-term mortality. Methods We included adults who had comprehensive echocardiography with available ECG within 14-days and right-heart-catheterization (RHC) within 30-days of echocardiography. Elevated-LVFP was defined as PAWP>15 mmHg by RHC, grades 2-3 LVDF by echocardiography, or as adjudicated by the AI-ECG algorithm. Prognosis by AI-ECG was compared to that by echocardiography and PAWP. Results Among 4,168 patients (median age 64 years, 47.7% female), 1,841 had normal-LVFP, and 2,327 had elevated-LVFP by RHC. Echocardiography classified 1,502 patients as normal-LVFP, 1,643 as elevated-LVFP, and 1,023 as indeterminate-LVFP. AI-ECG demonstrated an area-under-the-curve of 0.879 (95%-confidence-interval 0.867-0.891) for predicting elevated-LVFP as determined by echocardiography and 0.742 (95%-confidence-interval 0.724-0.759) for predicting elevated-LVFP as determined by RHC. Over a median follow-up of 7.7 years, 2,735 (34.4%) patients died. In multivariable models, elevated-LVFP by any modality was associated with an increased risk of death (AI-ECG: adjusted-hazard-ratio 1.80, 95% confidence-interval 1.55-2.09; echocardiography: adjusted-hazard-ratio 1.74, 95% confidence-interval 1.51-2.02; PAWP: adjusted-hazard-ratio 1.26, 95% confidence-interval 1.11-1.43). AI-ECG-derived LVFP provided incremental prognostic discrimination compared to echocardiography- or RHC-derived LVFP. Conclusions AI-ECG-derived LVFP offers an accessible, affordable, and noninvasive alternative for detecting elevated LVFP, with potential independent prognostic value beyond traditional echocardiographic and invasive assessments.Survival across LVFP concordance

Abstract Background Moderate-severe mitral annulus calcification (MAC) and left ventricular (LV) diastolic dysfunction are both linked to increased risks of heart failure and mortality. Assessing LV diastolic function (LVDF) in moderate-severe-MAC by echocardiography is frequently challenging and unreliable. We recently validated an AI-ECG model for LVDF. We aimed to determine if this tool can predict long-term mortality in patients with moderate-severe-MAC and whether it can help establish MAC-specific echocardiographic LVDF parameter cutoffs. Methods This retrospective study included all patients from the AI-ECG LVDF study test cohort who underwent comprehensive transthoracic echocardiography confirming moderate-severe-MAC and ECG within fourteen days of each other at Mayo Clinic between 09/2001 and 06/2022. AI-ECG LVDF was determined based on the index ECG. Patients were categorized based on AI-ECG into groups representing normal/grade-1 LVDF, grade-2 LVDF, or grade-3 LVDG (DF-1, DF-2, and DF-3, respectively). Results Of 2,506 patients with moderate-severe-MAC [mean age 77, 64.1% females], 709 (28.3%), 1,510 (60.3%), and 287 (11.5%) were classified as DF-1, DF-2, and DF-3, respectively. Worse LVDF grade was associated with older age, male sex, lower LV ejection fraction, and higher rates of noncardiovascular and cardiovascular comorbidities. Over a median follow-up of 2.8 years, 1,059 (42.3%) patients died. In multivariable survival analysis, DF-2 [adjusted hazard ratio (aHR) 1.43, 95% confidence interval (95%CI) 1.21-1.68] and DF-3 (aHR 1.54, 95%CI 1.22-1.93) were independently associated with a higher risk of death (figure). Findings were also consistent after adjusting to mitral inflow gradients. When comparing LVDF echocardiographic parameters in controls without MAC matched by age, sex, left ventricular ejection fraction, and AI-ECG DF grade, significant differences were observed. Patients with moderate to severe MAC generally exhibited lower e' velocities, higher TR velocities, elevated E/e' ratios, and increased LAVi. Interestingly, the E/A ratio between MAC patients and their matched controls across AI-ECG LVDF grades remained consistent. Conclusion AI-ECG LVDF predicts mortality risk in patients with moderate-severe-MAC, regardless of mitral inflow gradients. Utilizing AI-ECG LVDF might aid in the risk stratification of patients with significant MAC.

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 (>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 (>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< 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

Abstract Background Catheter ablation of ventricular tachycardia (VT) may be performed under general anesthesia (GA) or monitored anesthesia care (MAC). There is a paucity of data studying these two sedation strategies with respect to intraprocedural characteristics, and outcomes of VT ablation. Purpose The purpose of this study was to assess the effects of anesthesia techniques on VT inducibility and outcomes. Methods A single-center, retrospective study of patients undergoing catheter ablation for scar-related VT, from 2016-2022, was conducted. Data collected included demographics, comorbidities, medications, relevant laboratory abnormalities, electrocardiograms, echocardiograms, detailed procedural characteristics, and outcomes. Results One hundred sixty-eight patients who underwent a total of 200 VT ablation procedures were included. The patients undergoing GA had a greater burden of comorbidities including congestive heart failure (77.2% vs. 60.9%), hypertension (66.9% vs. 60.0%), atrial arrhythmias (50.7% vs. 43.8%), diabetes mellitus (21.3% vs. 9.4%), chronic kidney disease (44.1% vs. 31.3%), prior stroke (11.8% vs. 10.9%), myocardial infarction (31.6% vs. 29.7%), pre-existing implantable cardioverter-defibrillator (86.8% vs. 71.9%) and had a higher median number of antiarrhythmic drugs at baseline (2 vs. 1) [Figure 1]. There was a greater percentage of patients with non-ischemic cardiomyopathy (58.1% vs. 51.6%) in the GA cohort. The mean left ventricular ejection fraction was not different among the two groups (39.3 ± 13.9 vs. 42.5 ± 15.3). The GA group had more hemodynamic instability at baseline (p < 0.01), and during VT induction (p < 0.01). There was no significant difference in the number of VT induced, or tachycardia cycle length, in each group. Despite higher radiofrequency energy delivery time in the GA group as compared to the MAC group, the GA group had higher VT inducibility post-procedure and were more likely to be discharged on antiarrhythmic drugs. Even though the recurrence of VT was higher in the GA group compared to the MAC group (41.9% vs. 31.3%), the VT-free survival curves were not significantly different by Kaplan-Meier analysis (p=0.19) [Figure 2]. In a multivariate regression analysis, the sedation strategy was not an independent predictor of VT recurrence (p=NS). However, baseline hemodynamic instability was an independent predictor of VT recurrence after accounting for all intraprocedural characteristics (p=0.008). Conclusion Patients undergoing VT ablation under GA have more comorbidities and tend to be on more antiarrhythmic drugs. The GA group had more hemodynamic instability during the procedure and had greater overall VT recurrence, however, not significant in a time-based analysis. The greater overall recurrence in the GA group could be because they represent a sicker population, or the hemodynamics during the procedure preclude adequate ablation of the VT circuit elements.Figure 1Figure 2

Abstract Background Catheter ablation of ventricular tachycardia (VT) has emerged as the mainstay of management for these sick patients. There is a lack of long-term data studying the characteristics and outcomes of VT ablation in females and the comparison with males. Purpose The purpose of the study was to assess the impact of sex on outcomes of catheter ablation for ventricular tachycardia. Methods A single-center retrospective study of 200 procedures in 168 patients undergoing catheter ablation for scar-related VT under GA or MAC sedation was conducted. Data collected included demographics, comorbidities, medications, relevant laboratory abnormalities, electrocardiograms, structural data including echocardiograms and cardiac magnetic resonance imaging, detailed procedural characteristics, and outcomes. Results Females undergoing VT ablation were younger (49.4 ± 15.6 vs. 65.5 ± 12.2 years) [Figure 1]. Females had a lower burden of comorbidities, including congestive heart failure (30.0 % vs. 82.5%), hypertension (27.5% vs. 74.4%), atrial arrhythmias (20.0% vs. 55.6%), chronic kidney disease (25.0% vs. 43.8%), prior myocardial infarction (15.0% & 35.0%), and coronary artery bypass graft surgery (0.0% & 23.8%). Females were on fewer antiarrhythmic drugs at baseline. During the procedure, males had a greater likelihood of undergoing general anesthesia (72.5% vs. 50.0%) and associated hemodynamic instability at baseline (30.0% vs. 12.5%). It was more likely for VT to be induced in males, and females required more induction medications. There was no difference in complication rates between males and females. A greater proportion of males were discharged and maintained on antiarrhythmic drugs after the procedure. There was no significant difference in VT recurrence or long-term mortality. Using Kaplan-Meier analysis, no difference was seen in long-term VT-free survival by sex (p=0.20) [Figure 2]. In a multivariate regression model, female sex had no bearing on the VT-free survival. Conclusion Females undergoing catheter ablation for VT are younger and have fewer comorbidities. They tend to be on fewer antiarrhythmic drugs at baseline, and after ablation. Interestingly, despite a healthier baseline profile, females do not experience less VT recurrence or mortality compared to males. Further study would be needed to investigate this association.Figure 1Figure 2