Definition and management of arrhythmia-induced cardiomyopathy: findings from the European Heart Rhythm Association survey.

AIMS: Arrhythmia-induced cardiomyopathy (AiCM) represents a subtype of acute heart failure (HF) in the context of sustained arrhythmia. Clear definitions and management recommendations for AiCM are lacking. The European Heart Rhythm Association Scientific Initiatives Committee (EHRA SIC) conducted a survey to explore the current definitions and management of patients with AiCM among European and non-European electrophysiologists. METHODS AND RESULTS: A 25-item online questionnaire was developed and distributed among EP specialists on the EHRA SIC website and on social media between 4 September and 5 October 2023. Of the 206 respondents, 16% were female and 61% were between 30 and 49 years old. Most of the respondents were EP specialists (81%) working at university hospitals (47%). While most participants (67%) agreed that AiCM should be defined as a left ventricular ejection fraction (LVEF) impairment after new onset of an arrhythmia, only 35% identified a specific LVEF drop to diagnose AiCM with a wide range of values (5-20% LVEF drop). Most respondents considered all available therapies: catheter ablation (93%), electrical cardioversion (83%), antiarrhythmic drugs (76%), and adjuvant HF treatment (76%). A total of 83% of respondents indicated that adjuvant HF treatment should be started at first HF diagnosis prior to antiarrhythmic treatment, and 84% agreed it should be stopped within six months after LVEF normalization. Responses for the optimal time point for the first LVEF reassessment during follow-up varied markedly (1 day-6 months after antiarrhythmic treatment). CONCLUSION: This EHRA Survey reveals varying practices regarding AiCM among physicians, highlighting a lack of consensus and heterogenous care of these patients.

Biomarkers to predict improvement of left ventricular ejection fraction after atrial fibrillation ablation.

BACKGROUND: Atrial fibrillation (AF) and heart failure frequently coexist. Prediction of left ventricular ejection fraction (LVEF) recovery after catheter ablation (CA) for AF remains difficult. OBJECTIVE: The purpose of this study was to evaluate the value of biomarkers, alone and in combination with the Antwerp score, to predict LVEF recovery after CA for AF. METHODS: Patients undergoing CA for AF with depressed LVEF (<50%) were included. Plasma levels of 13 biomarkers were measured immediately before CA. Patients were categorized into "responders" and "nonresponders" in a similar fashion to the Antwerp score performance derivation and validation cohorts. The predictive power of the biomarkers alone and combined in outcome prediction was evaluated. RESULTS: A total of 208 patients with depressed LVEF were included (median age 63 years; 39-19% female; median indexed left atrial volume 42 (33-52) mL/m2; median LVEF 43 (38-46)%). At a median follow-up time of 30 (20-34) months, 161 (77%) were responders and 47 (23%) were nonresponders. Of 13 biomarkers, -4-angiopoietin 2 (ANG2), growth differentiation factor 15 (GDF15), fibroblast growth factor 23, and myosin binding protein C3-were significantly different between responders and nonresponders (P ≤ .001) and their combination could predict the end point with an area under the curve of 0.72 (95% confidence interval [CI] 0.64-0.81) overall, 0.69 (95% CI 0.59-0.78) in heart failure with mildly reduced ejection fraction, and 0.88 (95% CI 0.77-0.98) in heart failure with reduced ejection fraction. Only ANG2 and GDF15 remained significantly associated with LVEF recovery after adjustment for age, sex, and Antwerp score and significantly improved the accuracy of the Antwerp score predictions (P < .001). The area under the curve of the Antwerp score in the outcome prediction improved from 0.75 (95% CI 0.67-0.83) to 0.78 (95% CI 0.70-0.86). CONCLUSION: A biomarker panel (ANG2 and GDF15) significantly improved the accuracy of the Antwerp score.

Repeat catheter ablation after very late recurrence of atrial fibrillation after pulmonary vein isolation.

AIMS: Atrial fibrillation (AF) recurs in about one-third of patients after catheter ablation (CA), mostly in the first year. Little is known about the electrophysiological findings and the effect of re-ablation in very late AF recurrences (VLR) after more than 1 year. The aim of this study was to determine the characteristics and outcomes of the first repeat CA after VLR of AF after index CA. METHODS AND RESULTS: We analysed patients from a prospective Swiss registry that underwent a first repeat ablation procedure. Patients were stratified depending on the time to recurrence after index procedure: early recurrence (ER) for recurrences within the first year and late recurrence (LR) if the recurrence was later. The primary endpoint was freedom from AF in the first year after repeat ablation. Out of 1864 patients included in the registry, 426 patients undergoing a repeat ablation were included in the analysis (28% female, age 63 ± 9.8 years, 46% persistent AF). Two hundred and ninety-one patients (68%) were stratified in the ER group and 135 patients (32%) in the LR group. Pulmonary vein reconnections were a common finding in both groups, with 93% in the ER group compared to 86% in the LR group (P = 0.052). In the LR group, 40 of 135 patients (30%) had a recurrence of AF compared to 90 of 291 patients (31%) in the ER group (log-rank P = 0.72). CONCLUSION: There was no association between the time to recurrence of AF after initial CA and the characteristics and outcomes of the repeat procedure.

Impact of implantation depth and calcium burden on infranodal conduction delay after transcatheter aortic valve replacement.

Background: Infranodal conduction disorders are common after transcatheter aortic valve replacement (TAVR). Risk factors are incompletely understood. Objective: The purpose of this study was to assess the impact of valve implantation depth and calcium burden of the device landing zone on infranodal conduction intraprocedure pre- and post-TAVR. Methods: In all patients undergoing TAVR between June 2020 and June 2021, the His-ventricle (HV) interval was measured pre- and post-valve deployment. The difference between the 2 measurements defined delta HV, whereas infranodal conduction delay was defined as HV interval >55 ms. Valve implantation depth was measured as the distance between the aortic annular plane and the ventricular prosthesis end. Calcium burden was quantified as the volume of calcium in 6 regions of interest: the non-, right, and left coronary cusps (NCC, RCC, and LCC, respectively) and the corresponding regions of the left ventricular outflow tract (LVOT) underlying each cusp (LVOTNCC, LVOTRCC, LVOTLCC, respectively). Results: Of 101 patients (mean age 81 ± 5.7 years; 47% women), 37 demonstrated infranodal conduction delay intraprocedure post-TAVR. Overall, mean implantation depth was 5 ± 3.1 mm, median calcium volume was 2080 mm3 [interquartile range 632-2400]. Delta HV showed no correlation with implantation depth or calcium burden (r = -0.08 and r = 0.12, respectively). However, LVOTNCC calcification was a significant predictor for infranodal conduction delay post-valve deployment in a multivariable logistic regression model (odds ratio 1.62 per 100-mm3 increase (95% confidence interval 1.06-2.69; P = .04). Conclusion: Assessment of LVOTNCC calcification may identify patients at risk for infranodal conduction delay after TAVR, whereas implantation depth did not predict infranodal conduction delay.

Reducing the burden of inconclusive smart device single-lead ECG tracings via a novel artificial intelligence algorithm.

Background: Multiple smart devices capable of automatically detecting atrial fibrillation (AF) based on single-lead electrocardiograms (SL-ECG) are presently available. The rate of inconclusive tracings by manufacturers' algorithms is currently too high to be clinically useful. Method: This is a prospective, observational study enrolling patients presenting to a cardiology service at a tertiary referral center. We assessed the clinical value of applying a smart device artificial intelligence (AI)-based algorithm for detecting AF from 4 commercially available smart devices (AliveCor KardiaMobile, Apple Watch 6, Fitbit Sense, and Samsung Galaxy Watch3). Patients underwent a nearly simultaneous 12-lead ECG and 4 smart device SL-ECGs. The novel AI algorithm (PulseAI, Belfast, United Kingdom) was compared with each manufacturer's algorithm. Results: We enrolled 206 patients (31% female, median age 64 years). AF was present in 60 patients (29%). Sensitivity and specificity for the detection of AF by the novel AI algorithm vs manufacturer algorithm were 88% vs 81% (P = .34) and 97% vs 77% (P < .001) for the AliveCor KardiaMobile, 86% vs 81% (P = .45) and 95% vs 83% (P < .001) for the Apple Watch 6, 91% vs 67% (P < .01) and 94% vs 82% (P < .001) for the Fitbit Sense, and 86% vs 82% (P = .63) and 94% vs 80% (P < .001) for the Samsung Galaxy Watch3, respectively. In addition, the proportion of SL-ECGs with an inconclusive diagnosis (1.2%) was significantly lower for all smart devices using the AI-based algorithm compared to manufacturer's algorithms (14%-17%), P < .001. Conclusion: A novel AI algorithm reduced the rate of inconclusive SL-ECG diagnosis massively while maintaining sensitivity and improving the specificity compared to the manufacturers' algorithms.

Durability of pulmonary vein isolation for atrial fibrillation: a meta-analysis and systematic review.

AIMS: Pulmonary vein isolation (PVI) plays a central role in the interventional treatment of atrial fibrillation (AF). Uncertainties remain about the durability of ablation lesions from different energy sources. We aimed to systematically review the durability of ablation lesions associated with various PVI-techniques using different energy sources for the treatment of AF. METHODS AND RESULTS: Structured systematic database search for articles published between January 2010 and January 2023 reporting PVI-lesion durability as evaluated in the overall cohort through repeat invasive remapping during follow-up. Studies evaluating only a proportion of the initial cohort in redo procedures were excluded. A total of 19 studies investigating 1050 patients (mean age 60 years, 31% women, time to remap 2-7 months) were included. In a pooled analysis, 99.7% of the PVs and 99.4% of patients were successfully ablated at baseline and 75.5% of the PVs remained isolated and 51% of the patients had all PVs persistently isolated at follow-up across all energy sources. In a pooled analysis of the percentages of PVs durably isolated during follow-up, the estimates of RFA were the lowest of all energy sources at 71% (95% CI 69-73, 11 studies), but comparable with cryoballoon (79%, 95%CI 74-83, 3 studies). Higher durability percentages were reported in PVs ablated with laser-balloon (84%, 95%CI 78-89, one study) and PFA (87%, 95%CI 84-90, 2 studies). CONCLUSION: We observed no significant difference in the durability of the ablation lesions of the four evaluated energies after adjusting for procedural and baseline populational characteristics.

Accuracy in detecting atrial fibrillation in single-lead ECGs: an online survey comparing the influence of clinical expertise and smart devices.

BACKGROUND: Manual interpretation of single-lead ECGs (SL-ECGs) is often required to confirm a diagnosis of atrial fibrillation. However accuracy in detecting atrial fibrillation via SL-ECGs may vary according to clinical expertise and choice of smart device. AIMS: To compare the accuracy of cardiologists, internal medicine residents and medical students in detecting atrial fibrillation via SL-ECGs from five different smart devices (Apple Watch, Fitbit Sense, KardiaMobile, Samsung Galaxy Watch, Withings ScanWatch). Participants were also asked to assess the quality and readability of SL-ECGs. METHODS: In this prospective study (BaselWearableStudy, NCT04809922), electronic invitations to participate in an online survey were sent to physicians at major Swiss hospitals and to medical students at Swiss universities. Participants were asked to classify up to 50 SL-ECGs (from ten patients and five devices) into three categories: sinus rhythm, atrial fibrillation or inconclusive. This classification was compared to the diagnosis via a near-simultaneous 12-lead ECG recording interpreted by two independent cardiologists. In addition, participants were asked their preference of each manufacturer's SL-ECG. RESULTS: Overall, 450 participants interpreted 10,865 SL-ECGs. Sensitivity and specificity for the detection of atrial fibrillation via SL-ECG were 72% and 92% for cardiologists, 68% and 86% for internal medicine residents, 54% and 65% for medical students in year 4-6 and 44% and 58% for medical students in year 1-3; p <0.001. Participants who stated prior experience in interpreting SL-ECGs demonstrated a sensitivity and specificity of 63% and 81% compared to a sensitivity and specificity of 54% and 67% for participants with no prior experience in interpreting SL-ECGs (p <0.001). Of all participants, 107 interpreted all 50 SL-ECGs. Diagnostic accuracy for the first five interpreted SL-ECGs was 60% (IQR 40-80%) and diagnostic accuracy for the last five interpreted SL-ECGs was 80% (IQR 60-90%); p <0.001. No significant difference in the accuracy of atrial fibrillation detection was seen between the five smart devices; p = 0.33. SL-ECGs from the Apple Watch were considered as having the best quality and readability by 203 (45%) and 226 (50%) participants, respectively. CONCLUSION: SL-ECGs can be challenging to interpret. Accuracy in correctly identifying atrial fibrillation depends on clinical expertise, while the choice of smart device seems to have no impact.

Ventricular pacing burden in patients with left bundle branch block after transcatheter aortic valve replacement therapy.

INTRODUCTION: Electrophysiological testing has been proposed in the latest European Society of Cardiology (ESC) guidelines for cardiac pacing to identify left bundle branch block (LBBB) patients with infrahisian conduction delay (IHCD) after transcatheter aortic valve replacement (TAVR). While in general IHCD is defined by a His-ventricular (HV) interval of >55 ms, a cut-off of ≥70 ms to trigger pacemaker (PM) implantation has been proposed in the latest ESC guidelines. The ventricular pacing (VP) burden during follow-up in such patients is largely unknown. As such, we aimed to assess the VP burden during follow-up of patients receiving PM therapy for LBBB after TAVR based on an HV interval > 55 ms and ≥70 ms. METHODS: All patients with new-onset or pre-existing LBBB after undergoing TAVR at a tertiary referral center underwent EP testing the day after TAVR. In patients with a prolonged HV interval (>55 ms), PM implantation was performed by a trained electrophysiologist in a standardized fashion. All devices were programmed to avoid unnecessary VP by specific algorithms (e.g., AAI-DDD). RESULTS: 701 patients underwent TAVR at the University Hospital of Basel. One hundred seventy-seven patients presented with new-onset or pre-existing LBBB the day following TAVR and underwent EP testing. An HV interval > 55 ms was found in 58 patients (33%) and an HV interval ≥ 70 ms in 21 patients (12%). 51 patients (mean age 84 ± 6.2 years, 45% women) agreed to receive a PM, out of which 20 (39%) patients had an HV Interval over 70 ms. Atrial fibrillation was present in 53% of the patients. A dual chamber PM was implanted in 39 (77%), and a single chamber PC in 12 (23%) patients, respectively. Median follow-up was 21 months. The median VP burden overall was 3%. The median VP burden was not significantly different between patients with an HV ≥ 70 ms (6.5 [0.8-52]) and those with an HV between 55 and 69 ms (2 [0-17], p = .23). 31% of patients demonstrated a VP burden < 1%, 27% 1%-5% and 41% > 5%. The median HV intervals in patients with VP burdens < 1%, 1%-5% and >5% were 66 (IQR 62-70) ms, 66 (IQR 63-74) ms and 68 (IQR 60-72) ms, respectively, p = .52. When only assessing patients with an HV interval 55-69 ms, 36% demonstrated a VP burden of <1%, 29% of 1%-5% and 35% of >5%. In patients with an HV Interval ≥ 70 ms, 25% demonstrated a VP burden < 1%, 25% of 1%-5% and 50% of >5% %, p = .64 (Figure). CONCLUSION: In patients with LBBB after TAVR and IHCD defined by an HV interval > 55 ms, VP burden is relevant in a non-negligible amount of patients during follow-up. Further studies are warranted to define the optimal cut-off value for the HV interval or to develop risk models incorporating HV measurements and other risk factors to trigger PM implantation in patients with LBBB after TAVR.

Clinical validation of an artificial intelligence algorithm offering cross-platform detection of atrial fibrillation using smart device electrocardiograms.

BACKGROUND: Several smart devices are able to detect atrial fibrillation automatically by recording a single-lead electrocardiogram, and have created a work overload at the hospital level as a result of the need for over-reads by physicians. AIM: To compare the atrial fibrillation detection performances of the manufacturers' algorithms of five smart devices and a novel deep neural network-based algorithm. METHODS: We compared the rate of inconclusive tracings and the diagnostic accuracy for the detection of atrial fibrillation between the manufacturers' algorithms and the deep neural network-based algorithm on five smart devices, using a physician-interpreted 12-lead electrocardiogram as the reference standard. RESULTS: Of the 117 patients (27% female, median age 65 years, atrial fibrillation present at time of recording in 30%) included in the final analysis (resulting in 585 analyzed single-lead electrocardiogram tracings), the deep neural network-based algorithm exhibited a higher conclusive rate relative to the manufacturer algorithm for all five models: 98% vs. 84% for Apple; 99% vs. 81% for Fitbit; 96% vs. 77% for AliveCor; 99% vs. 85% for Samsung; and 97% vs. 74% for Withings (P<0.01, for each model). When applying our deep neural network-based algorithm, sensitivity and specificity to correctly identify atrial fibrillation were not significantly different for all assessed smart devices. CONCLUSION: In this clinical validation, the deep neural network-based algorithm significantly reduced the number of tracings labeled inconclusive, while demonstrating similarly high diagnostic accuracy for the detection of atrial fibrillation, thereby providing a possible solution to the data surge created by these smart devices.

Echocardiographic pattern of left ventricular function recovery in tachycardia-induced cardiomyopathy patients.

AIMS: Tachycardia-induced cardiomyopathy (TCM) represents a partially reversible type of cardiomyopathy (CM) that is often underdiagnosed and cardiac chamber remodelling in TCM remains incompletely understood. We aim to explore differences in the dimensions of the left ventricle and functional recovery in patients with TCM compared with patients with other forms of CM. METHODS AND RESULTS: We identified patients with reduced ejection fraction (≤50%) and/or atrial fibrillation or flutter with a left ventricular ejection fraction that improved from baseline (≥15% in left ventricular ejection fraction at follow-up or normalization of cardiac function with at least 10% improvement). Patients were then divided into two groups: (A) TCM patients and (B) patients with other forms of CM (controls). Two hundred thirty-eight patients were included (31% female, 70 years median age), 127 patients had TCM, and 111 had other forms of CM. Patients with TCM did not significantly improve indexed left ventricular volume (LVEDVI) after treatment (60 [45, 84] mL/m2 versus 56 [45, 70] mL/m2 , P = ns) compared with controls (67 [54, 81] mL/m2 versus 52 [42, 69] mL/m2 , P < 0.001). Patients with TCM patients had significantly worse fractional shortening at baseline than controls (15.5 [12, 23] vs. 20 [13, 30], P = 0.01) and higher indexed left atrial volume (LAVI) at baseline than controls (48 [37, 58] vs. 41 [33, 51], P = 0.01) that remained dilated at follow-up (follow-up LAVI 41 [33, 52] mL/m2 ). Good predictors of TCM were: normal LVEDVI (LVEDVI < 58 mL/m2 (M) and < 52 mL/m2 (F)) (odds ratio [OR] 5.2; 95% confidence interval [CI] 2.2-13.3, P < 0.001), fractional shortening < 30% (OR 3.5; 95% CI 1.4-9.2, P = 0.009), LAVI >40 mL/m2 (OR 3.4; 95% CI 1.6-7.3, P = 0.001) and normal wall thickness left ventricle (OR 3.2; 95% CI 1.4-7.8, P = 0.008). 54% of patients with TCM demonstrated diastolic dysfunction at follow-up, without differences from controls (54% vs. 43%, P = ns). 21% of patients with TCM showed persistent heart failure symptoms at follow-up compared with 4.5% of controls, P = 0.004. CONCLUSIONS: TCM patients have a specific pattern of functional recovery with persistent remodelling of the left atria and left ventricle. Several echocardiographic parameters might help identify TCM before treatment.

Clinical Validation of 5 Direct-to-Consumer Wearable Smart Devices to Detect Atrial Fibrillation: BASEL Wearable Study.

BACKGROUND: Multiple smart devices capable to detect atrial fibrillation (AF) are presently available. Sensitivity and specificity for the detection of AF may differ between available smart devices, and this has not yet been adequately investigated. OBJECTIVES: The aim was to assess the accuracy of 5 smart devices in identifying AF compared with a physician-interpreted 12-lead electrocardiogram as the reference standard in a real-world cohort of patients. METHODS: We consecutively enrolled patients presenting to a cardiology service at a tertiary referral center in a prospective, diagnostic study. RESULTS: We prospectively analyzed 201 patients (31% women, median age 66.7 years). AF was present in 62 (31%) patients. Sensitivity and specificity for the detection of AF were comparable between devices: 85% and 75% for the Apple Watch 6, 85% and 75% for the Samsung Galaxy Watch 3, 58% and 75% for the Withings Scanwatch, 66% and 79% for the Fitbit Sense, and 79% and 69% for the AliveCor KardiaMobile, respectively. The rate of inconclusive tracings (the algorithm was unable to determine the heart rhythm) was 18%, 17%, 24%, 21%, and 26% for the Apple Watch 6, Samsung Galaxy Watch 3, Withings Scan Watch, Fitbit Sense, and AliveCor KardiaMobile (P < 0.01 for pairwise comparison), respectively. By manual review of inconclusive tracings, the rhythm could be determined in 955 (99%) of 969 single-lead electrocardiograms. Regarding patient acceptance, the Apple Watch was ranked first (39% of participants). CONCLUSIONS: In this clinical validation of 5 direct-to-consumer smart devices, we found differences in the amount of inconclusive tracings diminishing sensitivity and specificity of the smart devices. In a clinical setting, manual review of tracings is required in about one-fourth of cases.

Risk factors for the development of premature ventricular complex-induced cardiomyopathy: a systematic review and meta-analysis.

BACKGROUND: Premature ventricular complexes (PVCs) are a potentially reversible cause of heart failure. However, the characteristics of patients most likely to develop impaired left ventricular function are unclear. Hence, the objective of this study is to systematically assess risk factors for the development of PVC-induced cardiomyopathy. METHODS: We performed a structured database search of the scientific literature for studies investigating risk factors for the development of PVC-induced cardiomyopathy (PVC-CM). We investigated the reporting of PVC-CM risk factors (RF) and assessed the comparative association of the different RF using random-effect meta-analysis. RESULTS: A total of 26 studies (9 prospective and 17 retrospective studies) involving 16,764,641 patients were analyzed (mean age 55 years, 58% women, mean PVC burden 17%). Eleven RF were suitable for quantitative analysis (≥ 3 occurrences in multivariable model assessing a binary change in left ventricular (LV) function). Among these, age (OR 1.02 per increase in the year of age, 95% CI [1.01, 1.02]), the presence of symptoms (OR 0.18, 95% CI [0.05, 0.64]), non-sustained ventricular tachycardias (VT) (OR 3.01, 95% CI [1.39, 6.50]), LV origin (OR 2.20, 95% CI [1.14, 4.23]), epicardial origin (OR 4.72, 95% CI [1.81, 12.34]), the presence of interpolation (OR 4.93, 95% CI [1.66, 14.69]), PVC duration (OR 1.05 per ms increase in QRS-PVC duration [1.004; 1.096]), and PVC burden (OR 1.06, 95% CI [1.04, 1.08]) were all significantly associated with PVC-CM. CONCLUSIONS: In this meta-analysis, the most consistent risk factors for PVC-CM were age, non-sustained VT, LV, epicardial origin, interpolation, and PVC burden, whereas the presence of symptoms significantly reduced the risk. These findings help tailor stringent follow-up of patients presenting with frequent PVCs and normal LV function.