Citations

ABSTRACT Background A novel individualized antitachycardia pacing (IATP) algorithm using the post‐pacing interval for real‐time control has been introduced. Performance information is limited to a small safety and feasibility study with additional single‐center and case studies. A larger‐scale analysis is needed to better understand algorithm performance. Methods Deidentified remote monitoring transmissions from devices with the IATP therapy applied were randomly selected. Rhythms were classified and effects of the novel algorithm were assessed. For monomorphic ventricular tachycardias (MVTs) proportions of successful therapy, shock‐free episodes, and acceleration were calculated using generalized estimating equations to correct for multiple episodes and compute statistics of the algorithm's performance. Results There were 2259 MVT episodes in 336 patients. IATP succeeded in 87.1% of MVT episodes with 89.9% of MVT episodes ultimately free of shock therapy. Based on multivariate analysis, significant factors in therapy success were programming of at least the recommended number of sequences (90% at least recommended vs 73%, p = 0.00088) and female sex (95% for females vs 86%, p = 0.002). A trend to higher success was found for MVT with a cycle length of 320 ms or greater (90% vs. 83%, p = 0.10). The IATP accelerated 3.6% of MVT episodes. None of the available factors was significantly associated with acceleration in the multivariate analysis. Conclusions The IATP algorithm succeeded in a large proportion of MVT episodes and with low acceleration in patients randomly selected from remote monitoring transmissions. Using at least the recommended number of sequences had the strongest association with successful therapy.

Abstract Aims Artificial intelligence (AI) has the potential to transform cardiac electrophysiology (EP), particularly in arrhythmia detection, procedural optimization, and patient outcome prediction. However, a standardized approach to reporting and understanding AI-related research in EP is lacking. This scientific statement aims to develop and apply a checklist for AI-related research reporting in EP to enhance transparency, reproducibility, and understandability in the field. Methods and results An AI checklist specific to EP was developed with expert input from the writing group and voted on using a modified Delphi process, leading to the development of a 29-item checklist. The checklist was subsequently applied to assess reporting practices to identify areas where improvements could be made and provide an overview of the state of the art in AI-related EP research in three domains from May 2021 until May 2024: atrial fibrillation (AF) management, sudden cardiac death (SCD), and EP lab applications. The EHRA AI checklist was applied to 31 studies in AF management, 18 studies in SCD, and 6 studies in EP lab applications. Results differed between the different domains, but in no domain reporting of a specific item exceeded 55% of included papers. Key areas such as trial registration, participant details, data handling, and training performance were underreported (<20%). The checklist application highlighted areas where reporting practices could be improved to promote clearer, more comprehensive AI research in EP. Conclusion The EHRA AI checklist provides a structured framework for reporting AI research in EP. Its use can improve understanding but also enhance the reproducibility and transparency of AI studies, fostering more robust and reliable integration of AI into clinical EP practice.

Abstract Background Antitachycardia pacing (ATP) found in most implantable cardioverter defibrillators (ICDs) is an important therapy for painless termination of ventricular tachycardias (VT). In ICDs programmed to guideline-recommended detection and therapy settings typical ATP success rates are 50-70%.[1-3] The first closed-loop, automated ATP (AATP) algorithm, introduced in 2021, applies an S1 train derived from the detected VT, followed by an S2 derived initially from a refractory period estimator and then adjusted using analysis of the post-pacing interval if ATP fails. The termination efficacy of AATP in the real-world population is unknown. Purpose To assess the effectiveness AATP in a large real-world population. Methods A database of de-identified ICD transmissions from subjects in the United States, Australia, Canada, and New Zealand was queried for AATP treated episodes. ICDs were randomly sampled, biased toward ICDs with multiple treated VT episodes. For rhythms classified as monomorphic ventricular tachycardia, each AATP pulse train was reviewed for: rhythm at delivery, effect of ATP, and any non-capturing pulses. Acceleration was defined as a post-ATP decrease in MVT cycle length (VTCL) of at least 10% and at least 30ms. Acceleration to polymorphic VT or ventricular fibrillation (PVT/VF) was assessed. Rates of termination and acceleration were calculated for all MVT and dichotomized using a 320ms VTCL cutoff. The method of generalized estimating equations (GEE) was used to correct event rates for multiple episodes from a single patient. Results There were 2281 episodes of MVT in 340 patients; 63% of patients had 2 or more episodes. There were 724 episodes with initial VTCL <320ms and 1557 episodes with VTCL ≥320ms. The rate of termination of MVT by AATP was 78% for all VTCL [95% CI: 74%-81%]. In 300 patients (88%) AATP terminated at least one MVT. For MVT with VTCL ≥320ms the termination rate was 80% [95% CI: 75%-83%] and for VTCL <320ms the termination rate was 75% [95% CI: 69%-80%]. The rate of acceleration was 3.6% [95% CI: 2.7%-4.9%], with a 0.8% rate of acceleration to PVT/VF [95% CI: 0.4%-1.8%]. Acceleration led to shock therapy in 63% of accelerated episodes. All MVT accelerated to PVT/VF either spontaneously terminated (4/14) or were successfully converted by a single shock therapy (10/14). All accelerations that remained MVT were successfully treated by the device with either subsequent ATP (35%) or shock therapy (61%), or spontaneously terminated (4%). Conclusion In this large, real-world cohort analysis, automated ATP’s termination rate exceeded the typical success rate of burst ATP, particularly for MVT <320ms. Acceleration to PVT/VF was low and effective first-shock defibrillation was consistent. Subsequent ATP for accelerated MVT had incremental benefit, leading to successful termination one-third of the time. Additional data are currently being prospectively collected in a separate post-approval study.

AbstractCardiac physiologic pacing (CPP), encompassing cardiac resynchronization therapy (CRT) and conduction system pacing (CSP), has emerged as a pacing therapy strategy that may mitigate or prevent the development of heart failure (HF) in patients with ventricular dyssynchrony or pacing‐induced cardiomyopathy. This clinical practice guideline is intended to provide guidance on indications for CRT for HF therapy and CPP in patients with pacemaker indications or HF, patient selection, pre‐procedure evaluation and preparation, implant procedure management, follow‐up evaluation and optimization of CPP response, and use in pediatric populations. Gaps in knowledge, pointing to new directions for future research, are also identified.

Abstract Digital technology is now an integral part of medicine. Tools for detecting, screening, diagnosis, and monitoring health-related parameters have improved patient care and enabled individuals to identify issues leading to better management of their own health. Wearable technologies have integrated sensors and can measure physical activity, heart rate and rhythm, and glucose and electrolytes. For individuals at risk, wearables or other devices may be useful for early detection of atrial fibrillation or sub-clinical states of cardiovascular disease, disease management of cardiovascular diseases such as hypertension and heart failure, and lifestyle modification. Health data are available from a multitude of sources, namely clinical, laboratory and imaging data, genetic profiles, wearables, implantable devices, patient-generated measurements, and social and environmental data. Artificial intelligence is needed to efficiently extract value from this constantly increasing volume and variety of data and to help in its interpretation. Indeed, it is not the acquisition of digital information, but rather the smart handling and analysis that is challenging. There are multiple stakeholder groups involved in the development and effective implementation of digital tools. While the needs of these groups may vary, they also have many commonalities, including the following: a desire for data privacy and security; the need for understandable, trustworthy, and transparent systems; standardized processes for regulatory and reimbursement assessments; and better ways of rapidly assessing value.

Cardiac sarcoidosis is a component of an often multiorgan granulomatous disease of still uncertain cause. It is being recognized with increasing frequency, mainly as the result of heightened awareness and new diagnostic tests, specifically cardiac magnetic resonance imaging and 18 F-fluorodeoxyglucose positron emission tomography scans. The purpose of this case-based review is to highlight the potentially life-saving importance of making the early diagnosis of cardiac sarcoidosis using these new tools and to provide a framework for the optimal care of patients with this disease. We will review disease mechanisms as currently understood, associated arrhythmias including conduction abnormalities, and atrial and ventricular tachyarrhythmias, guideline-directed diagnostic criteria, screening of patients with extracardiac sarcoidosis, and the use of pacemakers and defibrillators in this setting. Treatment options, including those related to heart failure, and those which may help clarify disease mechanisms are included.

Abstract Introduction Chronic right ventricular pacing (RVP) has been associated with dyssynchrony, leading to increased mortality. However, there have been discrepancies in previous reports in the effect of RVP levels. Objective To sub-stratify mortality risk by age for different RVP level groups within a large real-world ICD cohort. Methods Optum® de-identified electronic health records were linked to the Medtronic Carelink data to identify dual chamber ICD recipients (2007–2017). RVP level was based on median daily pacing during the first 90 days post-implant and categorized either into groups with a cutoff of 40%, or with groups of 0–9%, 10–19%, 20–29%, 30–39%, 40–49%, and 50–100%. The endpoint was death more than 90 days post-implant. Kaplan-Meier survival curves, log-rank tests, and Cox regression were used to analyze the relationship between RVP and risk of death. Results Among 14,832 ICD patients (median age 67; 74.0% male), there were 2,602 deaths within 10 years after implant. In unadjusted comparisons, high RVP (>40%) increased the risk of death relative to low RVP (≤40%) (p<0.001). This effect remained significant in older cohort (≥67 years old at implant) (p<0.001), but not in younger cohort (<67 years old) (p=0.955) (Figure). After controlling for age, gender, pacing mode, MI, SCA, HF hospitalization, diabetes, and renal dysfunction, similar or increased risk was associated with higher pacing groups relative to the 0–9% pacing group in the older cohort, but not in the younger cohort. Conclusions Our data from a large contemporaneous real-world source suggests that older age or characteristics associated with age make patients more sensitive to chronic RVP effects. These results help reconcile differences observed in prior studies. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Medtronic, Inc.

AbstractBackgroundThe AdaptivCRT (aCRT) algorithm continuously adjusts cardiac resynchronization therapy (CRT) according to intrinsic atrioventricular conduction, providing synchronized left ventricular pacing in patients with normal PR interval and adaptive BiV pacing in patients with prolonged PR interval. Previous analyses demonstrated an association between aCRT and clinical benefit. We evaluated the incidence of patient mortality and atrial fibrillation (AF) with aCRT compared with standard CRT in a real‐world population.Methods and ResultsPatients enrolled in the Medtronic Personalized CRT Registry and implanted with a CRT from 2013‐2018 were divided into aCRT ON or standard CRT groups based upon device‐stored data. A Frailty survival model was used to evaluate the potential survival benefit of aCRT, accounting for patient heterogeneity and center variability. Daily AF burden and first device‐detected AF episodes of various durations were recorded by the device during follow‐up.A total of 1814 CRT patients with no reported long‐standing AF history at implant were included. Mean follow‐up time was 26.1 ± 16.5 months and 1162 patients (64.1%) had aCRT ON. Patient survival probability at 36 months was 88.3% for aCRT ON and 83.7% for standard CRT (covariate‐adjusted hazard ratio [HR] = 0.71, 95% CI: 0.53‐0.96, P = .028). Mean AF burden during follow‐up was consistently lower in aCRT ON patients compared with standard CRT. At 36 months, the probability of AF was lower in patients with aCRT ON, regardless of which AF definition threshold was applied (6 minutes‐30 days, all P < .001).ConclusionUse of the AdaptivCRT algorithm was associated with improved patient survival and lower incidence of AF in a real‐world, prospective, nonrandomized registry.

Background: Despite the clear association between atrial fibrillation and risk for thromboembolic events (TEs), the clinical significance of new-onset device-detected atrial tachyarrhythmia (DDAT) and TE remains disputed. We aimed to determine the risk of TE in patients with new-onset DDAT. Methods and Results: The OVID Medline, Cochrane, and Scopus databases (inception to November 2016) were searched. Randomized controlled trials, prospective, or retrospective studies of pacemaker or defibrillator patients reporting incidence of DDAT were selected. Summary statistics were used for analysis. Of 4893 reports identified, 28 studies following 24 984 patients were included: average age 69.9 years, 34.7% women, mean study duration 21.8±18.6 months. New-onset DDAT was observed in 23% of patients. Among 9 studies (n=8181) reporting TE, the absolute incidence was 2.1%. TE risk was significantly greater among patients with new-onset DDAT (relative risk [RR], 2.88; 95% CI, 1.79–4.64; P <0.001). TE risk was correlated with DDAT duration, with an increased risk associated with DDAT ≥5 minutes (RR, 3.86; 95% CI, 2.04–7.30; P <0.001) compared with <1 minute (RR, 1.77; 95% CI, 1.15–2.74; P =0.01). Notably, the risk of TE was also increased in patients with adjudicated atrial electrograms (RR, 3.60; 95% CI, 2.06–6.30; P <0.001) compared with nonadjudicated electrograms (RR, 2.05; 95% CI, 1.06–3.97; P =0.03), even when lower mean thresholds for detection were used. Conclusions: New-onset DDAT is common, affecting close to one quarter of all patients with implanted pacemakers or defibrillators. Adjudication of atrial electrograms further identifies at-risk patients, even when relatively short detection thresholds are used.