Publications

BACKGROUND: Remote monitoring of cardiac implantable electronic devices (CIEDs) offers practical and clinical benefits juxtaposed against burdens associated with high transmission volume.

METHODS: We identified patients receiving de novo pacemakers (PPMs) and implantable cardiac defibrillators (ICDs) at a single academic medical center (January 2016-December 2019) with at least 1 year of follow-up device care. We collected patient- and device-specific data at time of implant and assessed all remote and in-person interrogation reports for clinically actionable findings based on pre-specified criteria.

RESULTS: Among 963 patients (mean age of 71 (± 14) years, 37% female), 655 (68%) underwent PPM, and 308 (32%) underwent ICD implant. Median follow-up was 874 (627-1221) days, during which time patients underwent a mean of 13 (10-16) total interrogations; remote interrogations comprised 53% of all device evaluations; and of these, 96% were scheduled transmissions. Overall, 22% of all CIED interrogations yielded significant findings with a slightly higher rate in the PPM than in the ICD group (23% vs. 20%, p < 0.01). Only 8% of remote interrogations produced clinically meaningful results, compared with 38% of in-person ones. In adjusted models, routine, remote transmissions were least likely to be useful for both PPM and ICD patients (p < 0.001), whereas time from initial device implant was inversely associated with probability of obtaining a useful interrogation (p < 0.001).

CONCLUSIONS: Routine remote interrogations constitute the majority of device evaluations performed, but uncommonly identify clinically actionable findings.

BACKGROUND: Shared decision-making is mandated for patients receiving primary prevention implantable cardioverter defibrillators (ICDs). Less attention has been paid to generator exchange decisions, although at the time of generator exchange, patients' risk of sudden cardiac death, risk of procedural complications, quality of life, or prognosis may have changed. This study was designed to explore how patients make ICD generator exchange decisions.

METHODS: Emory Healthcare patients with primary prevention ICDs implanted from 2013 to 2021 were recruited to complete in-depth interviews exploring perspectives regarding generator exchanges. Interviews were conducted in 2021. Transcribed interviews were qualitatively coded using multilevel template analytic methods. To investigate benefit thresholds for pursuing generator exchanges, patients were presented standard-gamble type hypothetical scenarios where their ICD battery was depleted but their 5-year risk of sudden cardiac death at that time varied (10%, 5%, and 1%).

RESULTS: Fifty patients were interviewed; 18 had a prior generator exchange, 16 had received ICD therapy, and 17 had improved left ventricular ejection fraction. As sudden cardiac death risk decreased from 10% to 5% to 1%, the number of participants willing to undergo a generator exchange decreased from 48 to 42 to 33, respectively. Responses suggest that doctor's recommendations are likely to substantially impact patients' decision-making. Other drivers of decision-making included past experiences with ICD therapy and device implantation, as well as risk aversion. Therapeutic inertia and misconceptions about ICD therapy were common and represent substantive barriers to effective shared decision-making in this context.

CONCLUSIONS: Strong defaults may exist to continue therapy and exchange ICD generators. Updated risk stratification may facilitate shared decision-making and reduce generator exchanges in very low-risk patients, especially if these interventions are directed toward clinicians. Interventions targeting phenomena such as therapeutic inertia may be more impactful and warrant exploration in randomized trials.

BACKGROUND: Ablation for persistent atrial fibrillation (PsAF) has been performed for over 20 years, although success rates have remained modest. Several adjunctive lesion sets have been studied but none have become standard of practice. We sought to describe how the efficacy of ablation for PsAF has evolved in this time period with a focus on the effect of adjunctive ablation strategies.

METHODS: Databases were searched for prospective studies of PsAF ablation. We performed meta-regression and trial sequential analysis.

RESULTS: A total of 99 studies (15 424 patients) were included. Ablation for PsAF achieved the primary outcome (freedom of atrial fibrillation/atrial tachycardia rate at 12 months follow-up) in 48.2% (5% CI, 44.0-52.3). Meta-regression showed freedom from atrial arrhythmia at 12 months has improved over time, while procedure time and fluoroscopy time have significantly reduced. Through the use of cumulative meta-analyses and trial sequential analysis, we show that some ablation strategies may initially seem promising, but after several randomized controlled trials may be found to be ineffective. Trial sequential analysis showed that complex fractionated atrial electrogram ablation is ineffective and further study of this treatment would be futile, while posterior wall isolation currently does not have sufficient evidence for routine use in PsAF ablation.

CONCLUSIONS: Overall success rates from PsAF ablation and procedure/fluoroscopy times have improved over time. However, no adjunctive lesion set, in addition to pulmonary vein isolation, has been conclusively demonstrated to be beneficial. Through the use of trial sequential analysis, we highlight the importance of adequately powered randomized controlled trials, to avoid reaching premature conclusions, before widespread adoption of novel therapies.

BACKGROUND: The incidence and clinical impact of lead-related venous obstruction (LRVO) among patients with cardiovascular implantable electronic devices (CIEDs) is poorly defined.

OBJECTIVES: The objectives of this study were to determine the incidence of symptomatic LRVO after CIED implant; describe patterns in CIED extraction and revascularization; and quantify LRVO-related health care utilization based on each type of intervention.

METHODS: LRVO status was defined among Medicare beneficiaries after CIED implant from October 1, 2015, to December 31, 2020. Cumulative incidence functions of LRVO were estimated by Fine-Gray methods. LRVO predictors were identified using Cox regression. Incidence rates for LRVO-related health care visits were calculated with Poisson models.

RESULTS: Among 649,524 patients who underwent CIED implant, 28,214 developed LRVO, with 5.0% cumulative incidence at maximum follow-up of 5.2 years. Independent predictors of LRVO included CIEDs with >1 lead (HR: 1.09; 95% CI: 1.07-1.15), chronic kidney disease (HR: 1.17; 95% CI: 1.14-1.20), and malignancies (HR: 1.23; 95% CI: 1.20-1.27). Most patients with LRVO (85.2%) were managed conservatively. Among 4,186 (14.8%) patients undergoing intervention, 74.0% underwent CIED extraction and 26.0% percutaneous revascularization. Notably, 90% of the patients did not receive another CIED after extraction, with low use (2.2%) of leadless pacemakers. In adjusted models, extraction was associated with significant reductions in LRVO-related health care utilization (adjusted rate ratio: 0.58; 95% CI: 0.52-0.66) compared with conservative management.

CONCLUSIONS: In a large nationwide sample, the incidence of LRVO was substantial, affecting 1 of every 20 patients with CIEDs. Device extraction was the most common intervention and was associated with long-term reduction in recurrent health care utilization.

AIMS: Frailty is associated with increased morbidity and mortality in patients undergoing left atrial appendage closure (LAAC). This study aimed to compare the performance of two claims-based frailty measures in predicting adverse outcomes following LAAC.

METHODS: We identified patients 66 years and older who underwent LAAC between October 1, 2016, and December 31, 2019, in Medicare fee-for-service claims. Frailty was assessed using the previously validated Hospital Frailty Risk Score (HFRS) and Kim Claims-based Frailty Index (CFI). Patients were identified as frail based on HFRS ≥5 and CFI ≥0.25.

RESULTS: Of the 21,787 patients who underwent LAAC, frailty was identified in 45.6% by HFRS and 15.4% by CFI. There was modest agreement between the two frailty measures (kappa 0.25, Pearson's correlation 0.62). After adjusting for age, sex, and comorbidities, frailty was associated with higher risk of 30-day mortality, 1-year mortality, 30-day readmission, long hospital stay, and reduced days at home (p < .01 for all) regardless of the frailty measure used. The addition of frailty to standard comorbidities significantly improved model performance to predict 1-year mortality, long hospital stay, and reduced days at home (Delong p-value < .001).

CONCLUSION: Despite significant variation in frailty detection and modest agreement between the two frailty measures, frailty status remained highly predictive of mortality, readmissions, long hospital stay, and reduced days at home among patients undergoing LAAC. Measuring frailty in clinical practice, regardless of the method used, may provide prognostic information useful for patients being considered for LAAC, and may inform shared decision-making in this population.

There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are currently only in paper format, which are not suitable for NN training. We developed a fully-automated online ECG digitisation tool to convert scanned paper ECGs into digital signals. Using automated horizontal and vertical anchor point detection, the algorithm automatically segments the ECG image into separate images for the 12 leads and a dynamical morphological algorithm is then applied to extract the signal of interest. We then validated the performance of the algorithm on 515 digital ECGs, of which 45 were printed, scanned and redigitised. The automated digitisation tool achieved 99.0% correlation between the digitised signals and the ground truth ECG (n = 515 standard 3-by-4 ECGs) after excluding ECGs with overlap of lead signals. Without exclusion, the performance of average correlation was from 90 to 97% across the leads on all 3-by-4 ECGs. There was a 97% correlation for 12-by-1 and 3-by-1 ECG formats after excluding ECGs with overlap of lead signals. Without exclusion, the average correlation of some leads in 12-by-1 ECGs was 60-70% and the average correlation of 3-by-1 ECGs achieved 80-90%. ECGs that were printed, scanned, and redigitised, our tool achieved 96% correlation with the original signals. We have developed and validated a fully-automated, user-friendly, online ECG digitisation tool. Unlike other available tools, this does not require any manual segmentation of ECG signals. Our tool can facilitate the rapid and automated digitisation of large repositories of paper ECGs to allow them to be used for deep learning projects.

AIMS: Accurately determining atrial arrhythmia mechanisms from a 12-lead electrocardiogram (ECG) can be challenging. Given the high success rate of cavotricuspid isthmus (CTI) ablation, identification of CTI-dependent typical atrial flutter (AFL) is important for treatment decisions and procedure planning. We sought to train a convolutional neural network (CNN) to classify CTI-dependent AFL vs. non-CTI dependent atrial tachycardia (AT), using data from the invasive electrophysiology (EP) study as the gold standard.

METHODS AND RESULTS: We trained a CNN on data from 231 patients undergoing EP studies for atrial tachyarrhythmia. A total of 13 500 five-second 12-lead ECG segments were used for training. Each case was labelled CTI-dependent AFL or non-CTI-dependent AT based on the findings of the EP study. The model performance was evaluated against a test set of 57 patients. A survey of electrophysiologists in Europe was undertaken on the same 57 ECGs. The model had an accuracy of 86% (95% CI 0.77-0.95) compared to median expert electrophysiologist accuracy of 79% (range 70-84%). In the two thirds of test set cases (38/57) where both the model and electrophysiologist consensus were in agreement, the prediction accuracy was 100%. Saliency mapping demonstrated atrial activation was the most important segment of the ECG for determining model output.

CONCLUSION: We describe the first CNN trained to differentiate CTI-dependent AFL from other AT using the ECG. Our model matched and complemented expert electrophysiologist performance. Automated artificial intelligence-enhanced ECG analysis could help guide treatment decisions and plan ablation procedures for patients with organized atrial arrhythmias.