Siontis, Konstantinos C.

Abstract Background Atrial fibrillation (AF) is common in hypertrophic cardiomyopathy (HCM). There is limited data regarding the outcomes of AF catheter ablation in HCM patients. In this study, we aimed to synthesize all available evidence on the effectiveness of ablation of AF in patients with HCM compared to those without HCM. Methods and results We systematically reviewed bibliographic databases to identify studies published through February 2023. We included cohort studies with available quantitative information on rates of recurrent atrial arrhythmias, anti-arrhythmic drug (AAD) therapy, and repeat ablation procedures after initial AF ablation in patients with vs without HCM. Estimates were combined using random-effects meta-analysis models and reported as risk ratios (RR) and 95% confidence intervals (CI). Eight studies were included in quantitative synthesis (262 HCM and 642 non-HCM patients). During median follow-up 13–54 months across studies, AF recurrence rates ranged from 13.3% to 92.9% in HCM and 7.6% to 58.8% in non-HCM patients. The pooled RR for recurrent atrial arrhythmia after the first AF ablation in HCM patients compared to non-HCM controls was 1.498 (95% CI = 1.305–1.720; P < 0.001). During follow-up, HCM patients more often required AAD therapy (RR = 2.844; 95% CI = 1.713–4.856; P < 0.001) and repeat AF ablation (RR = 1.544; 95% CI = 1.070–2.228; P = 0.02). The pooled RR for recurrent atrial arrhythmias after the last AF ablation was higher in patients with HCM than those without HCM (RR = 1.607; 95% CI = 1.235–2.090; P < 0.001). Conclusions Compared to non-HCM patients, those with HCM had higher rates of recurrent atrial arrhythmias, AAD use, and need for repeat AF ablation after initial ablation of AF.

Background: An electrocardiogram (ECG)-based artificial intelligence (AI) algorithm has shown good performance in detecting hypertrophic cardiomyopathy (HCM AI-score). However, its clinical application may be challenging due to the low disease prevalence and potentially high false-positive rate when screening unenriched cohorts. Aims: To identify clinical characteristics associated with false-positive HCM AI-score results to improve screening tool application. Methods: From a cohort of 20,677 patients with a digital 12-lead ECG in routine clinical practice in 2021, we performed a detailed chart review of the 200 patients with highest HCM AI-score to determine their HCM status and collect clinical data. Stepwise forward logistic regression was used to create a clinical variable-based “candidacy for HCM screening” score (CHS score), differentiating true- and likely true-positive from likely false-positive HCM AI-score results. We then validated the CHS score in 200 patients with the highest HCM AI-scores from an independent cohort of 15,147 patients with an ECG in 2022. Results: Among the 200 patients in 2021 (median age 71 years, 48% female), 96 (48%) were likely false-positive HCM AI-score results, 71 (36%) definite or likely true-positive results, and 33 (16%) indeterminate. The CHS score included a history of surgical septal myectomy, coronary artery bypass graft surgery, cardiac valve replacement, and pacemaker, and had an AUC of 0.88 (95% CI 0.83-0.92) for the differentiation of true- versus false-positive AI results. In the 2022 cohort, the CHS score had an AUC of 0.82 (95% CI 0.76 - 0.88). After limiting the cohort to HCM candidates identified by the CHS score, the false-positive results rate was reduced to 27/114 (24%). Conclusions: By applying the CHS score to patients with positive HCM AI-screening results, the false-positive rate decreased from 49% to 24%. Utilizing a clinical model used in tandem with an AI model improved diagnostic yield.

Introduction: Deep-learning artificial intelligence (AI) algorithms can identify hypertrophic cardiomyopathy (HCM) from the electrocardiogram (ECG). Preliminary data indicate that AI-ECG may also track treatment response in HCM patients (pts) receiving mavacamten (Mava). In this placebo-controlled analysis, we investigated the performance of AI-ECG in HCM detection and its changes during Mava treatment compared to placebo in the phase 3, EXPLORER-HCM trial. Methods: Pts with non-paced ECGs enrolled in EXPLORER-HCM, evaluating Mava over 30 weeks of treatment, were included. All pts had a 12-lead ECG pre-treatment, at each study visit, and 8 weeks after treatment end. Two independently developed AI-ECG convolutional neural network algorithms from the Mayo Clinic and the University of California, San Francisco (UCSF) were applied to all ECGs suitable for analysis. Serial AI-ECG-predicted scores, which correlate with HCM likelihood (range 0-1), were examined between Mava and placebo groups. Results: A total of 101 Mava and 110 placebo pts were included (mean age 58.6, female 40%). At baseline, the mean Mayo and UCSF AI-ECG scores for HCM were 0.82 and 0.65, with 94% and 88% of pts exceeding the respective diagnostic thresholds of 0.11 (Mayo) and 0.4 (UCSF). During 30 weeks of follow-up in the Mava group, the algorithms showed a mean AI-ECG score reduction of 0.32 (Mayo) and 0.17 (UCSF) in all pts between start and end of treatment. In comparison, AI-ECG scores decreased by 0.05 (Mayo) and increased by 0.02 (UCSF) in the placebo group (Mava vs placebo, t-test p-values <0.001 for both algorithms; Figure ) Conclusion: In the EXPLORER-HCM trial population, two independent deep-learning AI-ECG algorithms correctly identified most HCM pts and demonstrated significant reductions in predicted HCM scores after treatment with Mava when compared to placebo. This relative normalization of the ECG suggests disease-modifying properties of Mava, deserving further investigation.

Introduction: Cardiac resynchronization therapy (CRT) has known benefit for heart failure (HF) with reduced left ventricular ejection fraction (LVEF) and dyssynchrony. However, very little is known about its benefit in infiltrative cardiomyopathies such as cardiac amyloidosis (CA) due to relative rarity of these conditions. Methods: This retrospective case series (2003-2021) comprised patients with HF due to transthyretin wild-type CA with (case) or without (control) CRT implant. Only controls with an indication for CRT or LVEF <40% were included. To further match disease severity, cases were 1:1 matched (propensity score tolerance 0.04) to controls for age, NYHA class, and CA stage (UK Staging). Primary outcome was MACE (major adverse cardiac events) which included stroke, myocardial infarction, HF hospitalization and cardiac mortality during a 3-year follow-up. Loss to follow-up was censored as per last visit date. LVEF and NYHA were noted closest to 1-year follow-up. Survival analysis for MACE was performed with Kaplan-Meier (KM) and multivariable cox regression models. Results: Study Cohort (n=160) had a mean age of 75.5 (SD 7.8) years and 97.5% were males. A majority had NYHA class 2 or 3 (92.3%) and CA stage 2 (42.2.%). Notably, median LVEF at baseline was lower in controls (33.5 vs 36.5; P = .001) and 77.5% of controls had an indication for CRT device. A total of 79 MACE events occurred, most frequent being HF hospitalization (n=45). KM curves were comparable for MACE events in the two groups (log-rank test; P = .059). Even after adjusting, as in table, CRT device was not associated with MACE events (adjusted HR 0.66; 95% CI 0.40-1.09; P = .106). Lastly, change in LVEF ( P = .84) and NYHA ( P = .86) at follow-up was similar in cases vs controls. Conclusions: The findings of this largest yet observational study to examine this question suggest that CRT in patients with transthyretin wild-type CA does not favorably impact outcomes for MACE events, improve LVEF or NYHA functional class.

Background: The communities in which people live affect their health. We aimed to determine whether neighborhood characteristics are associated with outcomes in patients with atrial fibrillation (AF) in a Midwest community. Methods: Patients with incident AF from 2013-2017 from a 27-county region who survived >90 days after their diagnosis were identified (N=14,725). Patient addresses were linked to publicly-available data on population density (rural-urban commuting area; RUCA) and neighborhood socioeconomic status (area deprivation index; ADI). Patients were followed for ischemic stroke or transient ischemic attack (TIA), heart failure (HF), dementia/delirium, and all-cause death through 2021. Cox regression determined risk of outcomes by RUCA category (urban, micropolitan, small rural, isolated) and ADI quartile after adjustment for age, sex, race/ethnicity, and CHA2DS2-VASc score. Results: After excluding persons missing race/ethnicity (n=46), RUCA (n=1,465), and ADI (n=1,640), 13,233 patients with AF (mean age 73.8 years, 57% men) remained. After a median follow-up of 4.7 years, 5,989 patients died; 582 experienced a stroke/TIA, 4,122 (of 9,156 patients at risk) developed HF, and 1,758 (of 12,154 patients at risk) developed dementia/delirium. Persons living in isolated areas (HR 1.14, 95% CI 1.05-1.24) and the highest ADI quartile (HR 1.59, 95% CI 1.37-1.85) experienced the greatest risk of death (Figure). A trend toward an increased risk of HF was observed for the 2 highest ADI quartiles (p-trend <0.01), although the estimates did not reach statistical significance. The risk of other outcomes did not differ by RUCA or ADI category. Conclusion: Patients with AF residing in isolated areas and neighborhoods with greater area deprivation experience increased risks of death. Additional research is needed to better understand how neighborhood characteristics influence treatment, co-morbid conditions, and other outcomes in patients with AF.

Introduction: Myocardial inflammation likely contributes to the pathogenesis of arrhythmogenic cardiomyopathy (ACM), a clinically and genetically heterogenous disorder characterized by ventricular dysfunction and potentially life-threatening ventricular arrhythmias. Due to phenotypic overlap, some patients with genetic ACM may have been evaluated for an underlying inflammatory cardiomyopathy, namely cardiac sarcoidosis. However, the cardiac fludeoxyglucose (FDG) positron emission tomography (PET) findings in ACM patients have not been assessed systematically. Methods: All genotype-positive patients in the Mayo Clinic ACM registry who received a cardiac FDG PET were included in this study. The cardiac FDG PET scan consisted of a resting myocardial perfusion scan with N13-ammonia as well as a F18-FDG scan after a high-fat diet to suppress physiologic FDG uptake. Pertinent demographic, genetic, imaging, and outcome data were extracted from the medical record. Results: Collectively, 12 (75% female) genotype-positive ACM patients received a cardiac PET FDG scan as part of their clinical evaluation (median age at scan 45.3 years [IQR 38.9-62.1]). Amongst these patients, pathogenic/likely pathogenic variants were detected in LMNA (n=7), DSP (n=3), FLNC (n=1), and PLN (n=1). Of note, 6/12 (50%) had abnormal myocardial FDG uptake, including diffuse (entire myocardium) uptake in 2/6 (33%), focal (1-2 segments) uptake in 2/6 (33%) and patchy (3+ segments) in 2/6 (33%). Median myocardial standardized uptake value ratio was 2.1 (IQR 1.5-2.6). Interestingly, LMNA -positive patients accounted for 3 out of 6 (50%) positive studies (diffuse uptake in 2 and focal uptake in 1). Conclusion: Myocardial FDG uptake is common in genetic ACM patients undergoing cardiac FDG PET. This finding further supports the role of myocardial inflammation in genetic ACM and demonstrates the ability of cardiac FDG PET to readily detect this phenomenon. Further investigation is needed to determine (i) the role of cardiac FDG PET in the evaluation of genetic ACM, (ii) if FDG uptake in genetic ACM has prognostic value and (iii) whether the attenuation of myocardial inflammation with immunosuppressive agents can impact the clinical course of inflammatory genetic ACMs.

Introduction: Prior AF screening trials demonstrated low yield, highlighting the need for more targeted approaches. An AI algorithm was developed to identify ECG signatures of AF risk during normal sinus rhythm, which has been validated in diverse external populations. Hypothesis: An AI-guided, targeted screening approach could improve the diagnosis of AF. Methods We conducted a pragmatic decentralized trial to prospectively recruit patients with stroke risk factors but no prior AF. The AI algorithm was applied to the ECGs performed in routine practice and divided patients into high- or low-risk groups. The primary endpoint was AF lasting ≥ 30 seconds on a subsequent 30-day continuous cardiac rhythm monitor. In a secondary analysis, trial participants were 1:1 propensity score-matched to real-world controls derived from the eligible but unenrolled population. Results A total of 1,003 patients from 40 U.S. states completed the study, with a mean age of 74 [SD 8.8] years. Over a mean of 22.3 days of continuous monitoring, AF was detected in 6 (1.6%) of low-risk patients and 48 (7.6%) of high-risk patients (OR 4.98 [2.11-11.75], p<0.001). Compared to usual care, AI-guided AF screening was associated with increased detection of AF (high-risk group: 4.2% vs. 11.1%, p<0.001; low-risk group: 0.9% vs. 2.4%, p=0.12) over a median follow-up of 10 months. Conclusions A prospective pragmatic study found that the AI a can risk-stratify a relatively uniform population (i.e., older adults at risk for stroke) to detect AF during short-term cardiac monitoring. Furthermore, when compared with usual care, AI-guided cardiac monitoring was associated with increased AF detection. As such, an AI-guided AF screening approach, leveraging existing EHR data and infrastructure, could be effective, patient-centered, and massively scalable, thereby reducing unnecessary health utilization and diagnosis-related anxiety (Clinicaltrials.gov: NCT04208971).

Abstract Hypertrophic Cardiomyopathy (HCM) is an inheritable heart disease with the highest rate of sudden cardiac death (SCD) in young adults. Implantable Cardioverter Defibrillator (ICD) therapy is recommended for HCM patients at high risk for SCD. Reviewing the recent clinical literature revealed the potential to improve the selection of candidates for ICD implantation. The current study uses information extracted from echocardiography reports to evaluate HCM patients with ICD and aims to provide a comparative insight into patients who benefited the most from the device therapy, including shock and anti-tachycardia pacing (ATP). The proposed interpretable machine learning approach has used the XGboost algorithm. The model’s performance was considered satisfactory, as evidenced by an accuracy score of 81% and an area under the receiver operating characteristic curve (AUC) value of 69% and SHapley Additive exPlanations (SHAP) identified common properties of HCM patients in each category and provided high-level reasoning and foundation for a clinical decision support tool.

AbstractIntroductionData regarding ventricular tachycardia (VT) or premature ventricular complex (PVC) ablation in patients with aortic valve (AV) intervention (AVI) is limited. Catheter ablation (CA) can be challenging given perivalvular substrate in the setting of prosthetic valves. We sought to investigate the characteristics, safety, and outcomes of CA in patients with prior AVI and ventricular arrhythmias (VA).MethodsWe identified consecutive patients with prior AVI (replacement or repair) who underwent CA for VT or PVC between 2013 and 2018. We investigated the mechanism of arrhythmia, ablation approach, perioperative complications, and outcomes.ResultsWe included 34 patients (88% men, mean age 64 ± 10.4 years, left ventricular (LV) ejection fraction 35.2 ± 15.0%) with prior AVI who underwent CA (22 VT; 12 PVC). LV access was obtained through trans‐septal approach in all patients except one patient who had percutaneous transapical access. One patient had combined retrograde aortic and trans‐septal approach. Scar‐related reentry was the dominant mechanism of induced VTs. Two patients had bundle branch reentry VTs. In the VT group, substrate mapping demonstrated heterogeneous scar that involved the peri‐AV area in 95%. Despite that, the site of successful ablation included the periaortic region only in 6 (27%) patients. In the PVC group, signal abnormalities consistent with scar in the periaortic area were noted in 4 (33%) patients. In 8 (67%) patients, the successful site of ablation was unrelated to the periaortic area. No procedure‐related complications occurred. The survival and recurrence‐free survival rate at 1 year tended to be lower in VT group than in PVC group (p = .06 and p = .05, respectively) with a 1‐year recurrence‐free survival rate of 52.8% and 91.7%, respectively. No arrhythmia‐related death was documented on long‐term follow‐up.ConclusionCA of VAs can be performed safely and effectively in patients with prior AVI.