Sudden cardiac death (SCD) is a major public health problem worldwide. Despite many advances in the care of sudden cardiac arrest (SCA), 90% of individuals with SCA do not survive. A major challenge in SCD prevention is that although the highest incidence of SCD is in known high-risk groups such as patients with previous sustained ventricular arrhythmias and those with heart failure, the absolute number of SCD events is highest among individuals who are not presently known to be at an increased risk of SCD. Another difficulty is that although current guidelines recommend implantable cardioverter-defibrillators for individuals at a high risk of SCD, a large number of these patients never receive a lifesaving therapy from their device. How to identify high-risk patients in low-risk groups and low-risk patients in high-risk groups remains a fundamental challenge. Recognizing the major impact of SCD and SCA on the lives of many individuals and the enormous burden of SCD and SCA, the Heart Rhythm Society convened a think tank meeting of stakeholders, "The Present and The Future of Risk Stratification for and Prevention of SCD." The objectives of the meeting were to (1) improve understanding of the current public health impact of SCD, (2) review the current state of prediction and prevention of SCD, (3) identify gaps in prediction and prevention of SCD, and (4) develop an action plan to address the identified gaps. This manuscript summarizes the proceedings of the meeting and proposes a plan for reducing the burden and impact of SCD.

BACKGROUND:Care pathways for patients with atrial fibrillation (AF) are poorly understood and may influence the likelihood of guideline concordant oral anticoagulation or antiarrhythmic drug therapy and associated outcomes. METHODS:Adult patients in the Optum Clinformatics Database (2015-2023) with incident AF were identified. Care pathways, by treating clinician specialty (primary care, cardiology, electrophysiology) during each AF-related visit in the 1 year post diagnosis were examined. Cox regression models were used to assess associations between an electrophysiologist visit and AF-related treatments including antiarrhythmic drug therapy and oral anticoagulation and outcomes including AF-related hospitalizations, heart failure hospitalizations, and stroke. RESULTS:Of the 37 370 patients included, 7700 (20.6%) had a care pathway including an electrophysiologist visit. Older patients (70-79 and 80+ versus 18-49 years: hazard ratio [HR], 0.71 [95% CI, 0.64-0.78] and HR, 0.57 [95% CI, 0.51-0.63], respectively) and those with a higher CHA2DS2-VASc score (2-3 and ≥4 versus 0-1: HR, 0.79 [95% CI, 0.74-0.85] and HR, 0.76 [95% CI, 0.68-0.84], respectively) were less likely to have an electrophysiologist visit. Electrophysiologist visits were associated with a significant increase in rates of treatment with antiarrhythmic drug and oral anticoagulation. Patients aged ≥80 years with an electrophysiologist visit had significantly lower risks of stroke compared with those without an electrophysiologist visit (HR, 0.63 [95% CI, 0.42-0.95]). CONCLUSIONS:Certain demographic groups including older patients had lesser likelihood of electrophysiologist consultation, but guideline concordant care was more likely associated with electrophysiology care pathways. Electrophysiology care pathways were associated with improved outcomes especially among the groups with greatest stroke risk.

BACKGROUND:Permanent pacemaker (PPM) battery longevity impacts downstream procedures, patient outcomes, and healthcare costs. OBJECTIVES:To measure and predict PPM battery longevity in routine, clinical practice. METHODS:We analyzed a nationwide, multicenter, remote monitoring dataset (PaceMate) to evaluate PPM battery longevity from 2007 to 2023. Multivariable models identified factors associated with replacement interval (RI) across device types and manufacturers. We also compared observed versus expected battery longevity and assessed differences between newer and older generation pacemakers. RESULTS:We included 58,395 devices from 47 sites: 5803 single-chamber transvenous, 1637 single-chamber leadless, 45,293 dual-chamber transvenous, 1759 RV/LV-only CRT pacemakers, and 3903 full CRT-P systems (1888 reaching RI). Median observed battery longevity for the 1493 dual chamber transvenous pacemakers that reached RI ranged from (103 months for BSX to 125 months for MDT and BIO), with similar trends after adjustment (p<0.001). There was variability in median battery longevity across manufacturers for other device types: 105-122 months for single-chamber transvenous, 61-96 months for RV/LV-only CRT pacemakers, and 77-93 months for full CRT-P systems. Compared with older generators, those implanted in 2022-23 demonstrated improved estimated battery longevity for Medtronic devices but decreased for Abbott (no significant change for Boston Scientific). CONCLUSIONS:Observed pacemaker battery longevity, based on nearly 2000 devices reaching ERI, varies substantially by manufacturer and device type, without consistent improvement for newer devices. Device-based estimates of remaining longevity show manufacturer-specific variability and are influenced by programming and utilization, highlighting the need for individualized expectations in clinical practice. CONDENSED ABSTRACT:Permanent pacemaker (PPM) battery longevity is clinically impactful and not well described in routine, clinical practice. We quantified pacemaker battery performance in a nationwide, multicenter, remote monitoring dataset, including 58,395 devices. Pacemaker battery longevity varies by manufacturer and device type, without consistent improvement for newer devices. Manufacturer-specific accuracy of battery longevity estimates and differences in device programming and utilization highlight the need for individualized expectations in clinical practice.

BACKGROUND:Implantable loop recorders (ILRs) frequently generate false-positive alert transmissions, increasing clinical burden. OBJECTIVES:This study sought to evaluate and compare arrhythmia detection accuracy among currently used ILRs: Medtronic (MDT) LINQ II with AccuRhythm AI, Boston Scientific (BSX) LUX-Dx with a Dual-Stage Algorithm, Biotronik (BIO) Biomonitor III with RhythmCheck, and Abbott (ABT) Confirm Rx (SharpSense). METHODS:A multicenter, multivendor database of 437,351 electrocardiogram-verified episodes from 6,756 patients (January 2022 to March 2023, across 25 centers) was used. A random sample of 1,140 patients (∼283 per device) was selected based on a prospectively determined power analysis. All alerts were independently adjudicated by trained physicians. RESULTS:The study cohort (median age: 69.5 years [IQR: 60.1-77.0]; 39% women) generated 25,826 total alerts, with a median of 5 alerts (IQR: 2-14) per patient. Indications for ILR implant included cryptogenic stroke (21.0%), suspected atrial fibrillation (AF) (11.3%), AF management (23.6%), syncope (23.2%), palpitations (14.9%), ventricular tachycardia (2.1%), and unknown (3.8%). Alerts for atrial tachycardia/AF were most frequent (BIO: 70.8%; MDT: 68.0%; ABT: 55.7%; BSX: 35.7%). After adjudication, BSX had the highest positive predictive values (PPVs), followed by ABT and MDT, which had similar and moderate PPVs, whereas BIO had the lowest (BSX: 0.73; MDT: 0.56; ABT: 0.51; BIO: 0.23). False-positive atrial tachycardia/AF alerts with BIO devices were due to ectopy (75.8%), combined ectopy with oversensing/noise (5.3%), isolated oversensing (5.2%), sinus arrhythmias (3.9%), indeterminate reason (3.7%), noise (3.6%), and bradycardia (2.5%). For bradycardia detection, BSX demonstrated the highest PPV (0.96), whereas MDT had the lowest PPV (0.36; primarily due to undersensing). Pause detection accuracy was lowest for ABT (PPV: 0.01; 79.8% due to undersensing) and highest for BSX (PPV: 0.70). Tachycardia alerts had moderate PPVs for BIO (0.62) and BSX (0.61), with substantially lower PPVs for MDT (0.29) and ABT (0.27). CONCLUSIONS:ILRs demonstrate variable accuracy in arrhythmia detection, with a substantial burden of false-positive alerts across all vendors despite artificial intelligence-based and other algorithmic enhancements. Further improvement of alert algorithms to reduce clinic burdens is needed.

BACKGROUND:Atrial fibrillation (AF) can lead to significant cardiovascular events and health care utilization. Continuous monitoring via insertable cardiac monitors (ICMs) and data analytics have the potential to improve care delivery. OBJECTIVE:The DEFINE Atrial Fibrillation (DEFINE AFib) study was designed to develop and evaluate novel algorithms using ICM data to predict AF-associated clinical actions (AFCAs) and guide AF management. METHODS:DEFINE AFib enrolled patients with an ICM (Reveal LINQ/LINQ II; Medtronic) and a history of AF. An Apple iPhone application collected AF-related quality of life (AFEQT) and EQ-5D data. ICM daily AF burden and Apple Watch (AW) irregular rhythm notification (IRN) data were also collected. AFCA was defined as an AF-related procedure or initiation of rate/rhythm control medication. Mutivariable logistic regression was used to identify ICM features in the last 30 days associated with first occurrence of AFCA in the next 30 days in a train and test approach (70%/30%). RESULTS:Among 864 patients (mean 69 ± 10 years; 56% male) meeting inclusion criteria, there were 8963 30-day evaluation windows that included 151 AFCAs. Area under the receiver operating characteristic curve (AUC) was 76% (train) and 70% (test). The model placed participants into high- vs low-risk AFCA groups. At the patient-level, 21% of participants crossing the high-risk threshold for their first time experienced an AFCA at a mean time of 195 ± 164 days compared with 5% in the low-risk group (AUC 65%). Increasing daily mean AF burden was associated with lower quality of life: <6 minutes (reference), 6 minutes to 5.5 hours (AFEQT -7.69; P < .001), and 5.5-12 hours (AFETQ -13.97, P < .001; ≥5.5 hours EQ-5D -0.03, P = .007). In a subanalysis of individuals with smart watch data (n = 53), the ICM model signaled high risk before AFCAs 85% of the time (AUC 57%) compared with 23% for AW IRN (P = .005; AUC 55%). CONCLUSION:DEFINE AFib transformed ICM diagnostic data to predict risk of AFCA with good discrimination, particularly compared with wearable data. These results highlight the potential advantages of ICM-based continuous monitoring for AF management and the utility of ICM prediction models that could help inform pre-emptive therapeutic strategies.

Purpose Atrial fibrillation (AF) care has shifted dramatically, with a focus on early rhythm control to reduce AF-related morbidity and mortality and improve quality of life. However, clinical trials for AF rely on historical definitions of treatment failure, including freedom from recurrence of ≥30 seconds of AF/flutter/tachycardia, which is a poor predictor of AF severity, or traditional clinical endpoints (ie, stroke, heart failure, death) which have low incidence in contemporary AF populations. Therefore, a directly measurable and clinically meaningful measure for these clinical endpoints has the potential to accelerate clinical trials of rhythm control in AF while reducing overall trial overhead. Results The Cardiovascular Sciences Research Consortium hosted a Think Tank comprising scientists, clinicians, regulators, and industry representatives to develop a roadmap to establish AF burden as a valid surrogate clinical endpoint. This document reviews currently available data to support the use of AF burden as a surrogate endpoint, provides standards for measuring AF burden across measurement modalities and devices, and establishes a practical roadmap for a collaborative approach to validating the use of AF burden. Conclusion Moving beyond historical definitions of AF treatment success and failure, AF burden has the potential to be a patient-centric endpoint that can leverage contemporary monitoring technologies while serving as an early signifier of AF-related risk.