Kapa, Suraj

Introduction: Multiple arrhythmias including ventricular tachycardia (VT) and atrial fibrillation (AF) have been reported in patients with left ventricular noncompaction (LVNC). We sought to determine the frequency of rhythm abnormalities on Holter monitoring, and their association with all-cause mortality, in patients with LVNC. Methods: Imaging databases from 2000-2016 were reviewed to identify patients with LVNC. On transthoracic echocardiography (TTE), LVNC was defined as: end systolic noncompacted: compacted (NC:C) ratio>2 and end diastolic epicardium to trough: epicardium to peak trabeculation (X:Y) ratio<0.5. Holter monitor reports from the medical record were evaluated. VT was defined as ≥3 consecutive beats of ventricular origin. All-cause mortality was obtained from a web-based research and location service (Accurint). We used a proportional hazards model to determine associations of Holter variables with all-cause mortality. Results: Among 340 LVNC patients, Holter reports were available for 177 patients (median age 45 years, interquartile range [IQR] 33-61; 45% females). On TTE, the median NC:C ratio was 2.7 (IQR 2.4-3.2) and X:Y ratio was 0.25 (IQR 0.20-0.28). The median ejection fraction (EF) was 48% (IQR 33-58). 48 patients (27%) had at least one episode of VT (median 4 episodes, IQR 2-4). The median longest duration of VT was 5 beats (IQR 4-9) and maximum rate was 148 beats per minute (IQR: 125-169). PVC burden >5% and 20% was present in 13% and 5% of patients respectively. Patients with EF<50% were more likely to have VT (43% vs 16%, p=0.0003) and PVC burden >5% (19% vs 7%, p=0.03). AF was infrequent (N=5, all persistent). All-cause mortality was 12% over median 6.9 years of follow up (IQR: 3.6-11.3 years). Presence of VT and PVC burden on Holter monitoring were not associated with all-cause mortality (p>0.05). Among patients with VT, the number, longest duration, and fastest rate of VT episodes were not associated with all-cause mortality. Conclusions: Ventricular tachycardia on Holter monitoring is common among patients with LVNC, especially those with a reduced EF. However, the clinical significance of this finding is uncertain, and presence of VT was not associated with long term all-cause mortality.

Introduction: Electro-anatomic mapping (EAM) has been studied as a modality to improve the sensitivity and reduce false negative rates with endomyocardial biopsy (EMB). We systematically reviewed the literature describing EAM guided EMB. Methods: An experienced librarian developed search strategies for Ovid MEDLINE, Ovid Embase, Ovid CDR, Cochrane Central, Scopus and Web of Science. There were no restrictions on language or publication date. Animal studies were excluded. Search strategy included MESH headings and keywords including endomyocardial biopsy and electroanatomic mapping. Results: Out of 139 studies initially identified, 16 (9 case series, 7 case reports) were included in this systematic review. EAM guided EMB was performed in 152 patients and results of 241 biopsies were available for analysis. The most common indications for EAM guided EMB were suspected arrhythmogenic right ventricular cardiomyopathy (ARVC), myocarditis and cardiac sarcoidosis (CS). While fluoroscopic visualization of the bioptome close to mapping catheter was the most commonly utilized biopsy technique (n=7 studies), direct visualization of bioptome on CARTO (n=4) and on NavX (n=2) were used as well. Low voltage areas had abnormal myocardial histology more commonly (range 60-100%) than normal voltage areas (range 11-52%). Sensitivity of low voltage areas for abnormal myocardium ranged from 60% to 82%. EAM guided EMB appears safe and adverse events were noted in only one study (remote hemopericardium). Conclusions: EAM guided EMB is a safe and efficacious method to improve the sensitivity and reduce the false negative rate of conventional fluoroscopy guided biopsy.

Background: Early diagnosis of heart failure with reduced ejection fraction (HFrEF) can lead to reduced healthcare costs and improved outcomes. In this study, we evaluated the economic effectiveness of an AI algorithm for detecting low ejection fraction using 12-lead ECG data. Aims: The primary aim was to estimate the health economic impact of the AI-ECG algorithm intervention to detect low ejection fraction. Methods: The study population consisted of 113,719 individuals without a prior history of HFrEF who visited a multistate health system between 2010-2018 with 12-lead ECG data and an echocardiogram within 1 year. The AI-ECG algorithm was run retrospectively, and projected costs were estimated assuming each patient incurred the average cost based on their age group and AI-detected heart failure stage. Hospitalization costs were estimated using Medicare reimbursement rates, and economic benefits from increased survival were estimated using a willingness to pay of $50,000 per quality-adjusted life year (QALY) gained. Results: At the optimal model threshold, the total cost savings per patient (including benefits from QALYs gained) for the AI-ECG algorithm vs. standard-of-care on the overall study population were projected to be $79.11 (95% CI: [$53.77, $104.44]) and $124.93 (95% CI: [$83.99, $165.88]) for the 2 and 4 year periods, respectively. For patients over 50 years old, the projected total cost savings were $85.56 (95% CI: [$56.35, $114.78]) and $135.28 (95% CI: [$88.18, $182.38]) per patient for the 2 and 4 year periods, respectively. See Figure. Conclusions: The AI-ECG algorithm to detect low ejection fraction is a cost-effective early screening tool for HFrEF.

Introduction: The combination of local impedance (LI), a direct measure of electrical resistance at the catheter tip, and contact force (CF) may facilitate treatment of atrial fibrillation (AF). This trial is designed to examine the safety and effectiveness of a novel RF catheter that allows measurement of both LI and CF. Hypothesis: Demonstrate the safety and effectiveness of a novel radiofrequency catheter with LI and CF for treatment of drug refractory, recurrent, symptomatic Paroxysmal AF (PAF). Methods: NEwTON AF is a multi-center, global, prospective, single arm study. Patients received index treatment for PAF with the study catheter. The present 6-month endpoint analysis was prospectively planned. Results: The study treated 299 patients at 45 sites in North America, Europe, and Asia. Patients were 39.5% female with a mean age of 62yrs (±12), and had a mean LVEF of 59% and an LA diameter of 3.9cm. Procedure time, LA dwell, and fluoroscopy times were 166, 136, and 15 minutes, respectively. High-power short-duration (HSPD, 45-50W) was exclusively used in 210 cases, which had notably shorter procedure times than the 82 cases that used conventional power, 148 vs 208 min, respectively. Acute procedural success was observed in 98.3% of cases. As of 6 months, freedom from documented recurrence of any atrial arrhythmia was 79.9% (Figure). Recurrence in relation to LI shows that pts with a 20Ω LI drop (in at least 60% of lesions) had better 6m outcomes (84.2% vs 73.3, p=0.04). The freedom from safety event rate at 30d was 96.0%; events included pericarditis (6), access complications (2), pulmonary edema (2), embolism (2), cerebrovascular accident (1), tamponade (1). Pericarditis was higher in pts with a history of COVID at enrollment, 16.3% vs 5.9%, p=0.025. Conclusions: This data suggests that patient outcomes and safety for this novel catheter are promising. Further data from this study, including 12m outcomes, and detailed analysis of local impedance data are forthcoming.

AbstractAimsAn artificial intelligence algorithm detecting age from 12-lead electrocardiogram (ECG) has been suggested to reflect ‘physiological age’. An increased physiological age has been associated with a higher risk of cardiac mortality in the non-transplant population. We aimed to investigate the utility of this algorithm in patients who underwent heart transplantation (HTx).Methods and resultsA total of 540 patients were studied. The average ECG ages within 1 year before and after HTx were used to represent pre- and post-HTx ECG ages. Major adverse cardiovascular event (MACE) was defined as any coronary revascularization, heart failure hospitalization, re-transplantation, and mortality. Recipient pre-transplant ECG age (mean 63 ± 11 years) correlated significantly with recipient chronological age (mean 49 ± 14 years, R = 0.63, P &lt; 0.0001), while post-transplant ECG age (mean 54 ± 10 years) correlated with both the donor (mean 32 ± 13 years, R = 0.45, P &lt; 0.0001) and the recipient ages (R = 0.38, P &lt; 0.0001). During a median follow-up of 8.8 years, 307 patients experienced MACE. Patients with an increase in ECG age post-transplant showed an increased risk of MACE [hazard ratio (HR): 1.58, 95% confidence interval (CI): (1.24, 2.01), P = 0.0002], even after adjusting for potential confounders [HR: 1.58, 95% CI: (1.19, 2.10), P = 0.002].ConclusionElectrocardiogram age-derived cardiac ageing after transplantation is associated with a higher risk of MACE. This study suggests that physiological age change of the heart might be an important determinant of MACE risk post-HTx.

Background: Ablation of ventricular tachycardia (VT) is limited by the inability to create penetrating lesions to reach intramyocardial origins. Intramural needle ablation using in-catheter, heated saline-enhanced radio frequency (SERF) energy uses convective heating to increase heat transfer and produce deeper, controllable lesions at intramural targets. This first-in-human trial was designed to evaluate the safety and efficacy of SERF needle ablation in patients with refractory VT. Methods: Thirty-two subjects from 6 centers underwent needle electrode ablation. Each had recurrent drug-refractory monomorphic VT after implantable cardioverter defibrillator implantation and prior standard ablation. During the SERF study procedure, one or more VTs were induced and mapped. The SERF needle catheter was used to create intramural lesions at targeted VT site(s). Acute procedural success was defined as noninducibility of the clinical VT after the procedure. Patients underwent follow-up at 30 days, and 3 and 6 months, with implantable cardioverter defibrillator interrogation at follow-up to determine VT recurrence. Results: These refractory VT patients (91% male, 66±10 years, ejection fraction 35±11%; 56% ischemic, and 44% nonischemic) had a median of 45 device therapies (shock/antitachycardia pacing) for VT in the 3 to 6 months pre-SERF ablation. The study catheter was used to deliver an average of 10±5 lesions per case, with an average of 430±295 seconds of radiofrequency time, 122±65 minute of catheter use time, and a procedural duration of 4.3±1.3 hours. Acute procedural success was 97% for eliminating the clinical VT. At average follow-up of 5 months (n=32), device therapies were reduced by 89%. Complications included 2 periprocedural deaths: an embolic mesenteric infarct and cardiogenic shock, 2 mild strokes, and a pericardial effusion treated with pericardiocentesis (n=1). Conclusions: Intramural heated saline needle ablation showed complete acute and satisfactory mid-term control of difficult VTs failing 1 to 5 prior ablations and drug therapy. Further study is warranted to define safety and longer-term efficacy. Registration: URL: https://www.clinicaltrials.gov ; Unique Identifier: NCT03628534 and NCT02994446.

Abstract Aims Electrocardiogram (ECG)-enabled stethoscope (ECG-Scope) acquires a single-lead ECGs during cardiac auscultation and may facilitate real-time screening for pathologies not routinely identified by cardiac auscultation alone. We previously demonstrated an artificial intelligence (AI) algorithm can identify left ventricular dysfunction (LVSD) [defined as ejection fraction (EF) ≤ 40%] with an area under the curve (AUC) of 0.91 using a 12-lead ECG. Methods and results One hundred patients referred for clinically indicated echocardiography were prospectively recruited. ECG-Scope recordings with the patient supine and sitting were obtained in multiple electrode locations at the time of the echocardiogram. The AI algorithm for the detection of LVSD was retrained using single leads from ECG-12 and validated against ECG-Scope to determine accuracy for low EF detection (≤35%, &lt;40%, or &lt;50%). We evaluated the algorithm with respect to body position and lead location. Amongst 100 patients (aged 61.3 ± 13.8; 61% male, BMI: 30.0 ± 5.4), eight had EF≤40%, and six had EF 40–50%. The best single recording position was V2 with the patient supine [AUC: 0.88 (CI: 0.80–0.97) for EF≤35%, 0.85 (CI: 0.75–0.95) for EF≤40%, and 0.81 (CI: 0.71–0.90) for EF &lt; 50%]. When using an AI model to select the recording automatically, AUC was 0.91 (CI: 0.84–0.97) for EF≤35%, 0.89 (CI: 0.83–0.96) for EF≤40%, and 0.84 (CI: 0.73–0.94) for EF &lt; 50%. Conclusion An AI algorithm applied to an ECG-enabled stethoscope recording in standard auscultation positions reliably detected the presence of a low EF in this prospective study of patients referred for echocardiography. The ability to screen patients with a possible low EF during routine physical examination may facilitate rapid detection of LVSD.

Abstract Aims Some artificial intelligence models applied in medical practice require ongoing retraining, introduce unintended racial bias, or have variable performance among different subgroups of patients. We assessed the real-world performance of the artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction with respect to multiple patient and electrocardiogram variables to determine the algorithm’s long-term efficacy and potential bias in the absence of retraining. Methods and results Electrocardiograms acquired in 2019 at Mayo Clinic in Minnesota, Arizona, and Florida with an echocardiogram performed within 14 days were analyzed (n = 44 986 unique patients). The area under the curve (AUC) was calculated to evaluate performance of the algorithm among age groups, racial and ethnic groups, patient encounter location, electrocardiogram features, and over time. The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction had an AUC of 0.903 for the total cohort. Time series analysis of the model validated its temporal stability. Areas under the curve were similar for all racial and ethnic groups (0.90–0.92) with minimal performance difference between sexes. Patients with a ‘normal sinus rhythm’ electrocardiogram (n = 37 047) exhibited an AUC of 0.91. All other electrocardiogram features had areas under the curve between 0.79 and 0.91, with the lowest performance occurring in the left bundle branch block group (0.79). Conclusion The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction is stable over time in the absence of retraining and robust with respect to multiple variables including time, patient race, and electrocardiogram features.

Abstract Aims Artificial intelligence (AI) enabled electrocardiography (ECG) can detect latent atrial fibrillation (AF) in patients with sinus rhythm (SR). However, the change of AI-ECG probability before and after the first AF episode is not well characterized. We sought to characterize the temporal trend of AI-ECG AF probability around the first episode of AF. Methods and results We retrospectively studied adults who had at least one ECG in SR prior to an ECG that documented AF. An AI network calculated the AF probability from ECGs during SR (positive defined &gt;8.7%, based on optimal sensitivity and specificity). The AI-ECG probability was reported prior to and after the first episode of AF and stratified by age and CHA2DS2-VASc score. Mixed effect models were used to assess the rate of change between time points. A total of 59 212 patients with 544 330 ECGs prior to AF and 413 486 ECGs after AF were included. The mean time between the first positive AI-ECG and first AF was 5.4 ± 5.7 years. The mean AI-ECG probability was 19.8% 2–5 years prior to AF, 23.6% 1–2 years prior to AF, 34.0% 0–3 months prior to AF, 40.9% 0–3 months after AF, 35.2% 1–2 years after AF, and 42.2% 2–5 years after AF (P &lt; 0.001). The rate of increase prior to AF was higher for age &gt;50 years CHA2DS2-VASc score ≥4. Conclusion The AI-ECG probability progressively increases with time prior to the first AF episode, transiently decreases 1–2 years following AF and continues to increase thereafter.