Periprocedural Management and Multidisciplinary Care Pathways for Patients With Cardiac Implantable Electronic Devices: A Scientific Statement From the American Heart Association.

The rapid technological advancements in cardiac implantable electronic devices such as pacemakers, implantable cardioverter defibrillators, and loop recorders, coupled with a rise in the number of patients with these devices, necessitate an updated clinical framework for periprocedural management. The introduction of leadless pacemakers, subcutaneous and extravascular defibrillators, and novel device communication protocols underscores the imperative for clinical updates. This scientific statement provides an inclusive framework for the periprocedural management of patients with these devices, encompassing the planning phase, procedure, and subsequent care coordinated with the primary device managing clinic. Expert contributions from anesthesiologists, cardiac electrophysiologists, and cardiac nurses are consolidated to appraise current evidence, offer patient and health system management strategies, and highlight key areas for future research. The statement, pertinent to a wide range of health care professionals, underscores the importance of quality care pathways for patient safety, optimal device function, and minimization of hemodynamic disturbances or arrhythmias during procedures. Our primary objective is to deliver quality care to the expanding patient cohort with cardiac implanted electronic devices, offering direction in the era of evolving technologies and laying a foundation for sustained education and practice enhancement.

Competing risks of monomorphic vs. non-monomorphic ventricular arrhythmias in primary prevention implantable cardioverter-defibrillator recipients: Global Electrical Heterogeneity and Clinical Outcomes (GEHCO) study.

AIMS: Ablation of monomorphic ventricular tachycardia (MMVT) has been shown to reduce shock frequency and improve survival. We aimed to compare cause-specific risk factors for MMVT and polymorphic ventricular tachycardia (PVT)/ventricular fibrillation (VF) and to develop predictive models. METHODS AND RESULTS: The multicentre retrospective cohort study included 2668 patients (age 63.1 ± 13.0 years; 23% female; 78% white; 43% non-ischaemic cardiomyopathy; left ventricular ejection fraction 28.2 ± 11.1%). Cox models were adjusted for demographic characteristics, heart failure severity and treatment, device programming, and electrocardiogram metrics. Global electrical heterogeneity was measured by spatial QRS-T angle (QRSTa), spatial ventricular gradient elevation (SVGel), azimuth, magnitude (SVGmag), and sum absolute QRST integral (SAIQRST). We compared the out-of-sample performance of the lasso and elastic net for Cox proportional hazards and the Fine-Gray competing risk model. During a median follow-up of 4 years, 359 patients experienced their first sustained MMVT with appropriate implantable cardioverter-defibrillator (ICD) therapy, and 129 patients had their first PVT/VF with appropriate ICD shock. The risk of MMVT was associated with wider QRSTa [hazard ratio (HR) 1.16; 95% confidence interval (CI) 1.01-1.34], larger SVGel (HR 1.17; 95% CI 1.05-1.30), and smaller SVGmag (HR 0.74; 95% CI 0.63-0.86) and SAIQRST (HR 0.84; 95% CI 0.71-0.99). The best-performing 3-year competing risk Fine-Gray model for MMVT [time-dependent area under the receiver operating characteristic curve (ROC(t)AUC) 0.728; 95% CI 0.668-0.788] identified high-risk (> 50%) patients with 75% sensitivity and 65% specificity, and PVT/VF prediction model had ROC(t)AUC 0.915 (95% CI 0.868-0.962), both satisfactory calibration. CONCLUSION: We developed and validated models to predict the competing risks of MMVT or PVT/VF that could inform procedural planning and future randomized controlled trials of prophylactic ventricular tachycardia ablation. CLINICAL TRIAL REGISTRATION: URL:www.clinicaltrials.gov Unique identifier:NCT03210883.

Spatially Conserved Spiral Wave Activity During Human Atrial Fibrillation.

BACKGROUND: Atrial fibrillation is the most common cardiac arrhythmia in the world and increases the risk for stroke and morbidity. During atrial fibrillation, the electric activation fronts are no longer coherently propagating through the tissue and, instead, show rotational activity, consistent with spiral wave activation, focal activity, collision, or partial versions of these spatial patterns. An unexplained phenomenon is that although simulations of cardiac models abundantly demonstrate spiral waves, clinical recordings often show only intermittent spiral wave activity. METHODS: In silico data were generated using simulations in which spiral waves were continuously created and annihilated and in simulations in which a spiral wave was intermittently trapped at a heterogeneity. Clinically, spatio-temporal activation maps were constructed using 60 s recordings from a 64 electrode catheter within the atrium of N=34 patients (n=24 persistent atrial fibrillation). The location of clockwise and counterclockwise rotating spiral waves was quantified and all intervals during which these spiral waves were present were determined. For each interval, the angle of rotation as a function of time was computed and used to determine whether the spiral wave returned in step or changed phase at the start of each interval. RESULTS: In both simulations, spiral waves did not come back in phase and were out of step." In contrast, spiral waves returned in step in the majority (68%; P=0.05) of patients. Thus, the intermittently observed rotational activity in these patients is due to a temporally and spatially conserved spiral wave and not due to ones that are newly created at the onset of each interval. CONCLUSIONS: Intermittency of spiral wave activity represents conserved spiral wave activity of long, but interrupted duration or transient spiral activity, in the majority of patients. This finding could have important ramifications for identifying clinically important forms of atrial fibrillation and in guiding treatment.

Comparative arrhythmia patterns among patients on tyrosine kinase inhibitors.

BACKGROUND: Tyrosine kinase inhibitors (TKIs) are widely used in the treatment of hematologic malignancies. Limited studies have shown an association between treatment-limiting arrhythmias and TKI, particularly ibrutinib, a Bruton's tyrosine kinase (BTK) inhibitor. We sought to comprehensively assess the arrhythmia burden in patients receiving ibrutinib vs non-BTK TKI vs non-TKI therapies. METHODS: We performed a retrospective analysis of consecutive patients who received long-term cardiac event monitors while on ibrutinib, non-BTK TKIs, or non-TKI therapy for a hematologic malignancy between 2014 and 2022. RESULTS: One hundred ninety-three patients with hematologic malignancies were included (ibrutinib = 72, non-BTK TKI = 46, non-TKI therapy = 75). The average duration of TKI therapy was 32 months in the ibrutinib group vs 64 months in the non-BTK TKI group (p = 0.003). The ibrutinib group had a higher prevalence of atrial fibrillation (n = 32 [44%]) compared to the non-BTK TKI (n = 7 [15%], p = 0.001) and non-TKI (n = 15 [20%], p = 0.002) groups. Similarly, the prevalence of non-sustained ventricular tachycardia was higher in the ibrutinib group (n = 31, 43%) than the non-BTK TKI (n = 8 [17%], p = 0.004) and non-TKI groups (n = 20 [27%], p = 0.04). TKI therapy was held in 25% (n = 18) of patients on ibrutinib vs 4% (n = 2) on non-BTK TKIs (p = 0.005) secondary to arrhythmias. CONCLUSIONS: In this large retrospective analysis of patients with hematologic malignancies, patients receiving ibrutinib had a higher prevalence of atrial and ventricular arrhythmias compared to those receiving other TKI, with a higher rate of treatment interruption due to arrhythmias.

Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure.

BACKGROUND: Deep learning models may combat widening racial disparities in heart failure outcomes through early identification of individuals at high risk. However, demographic biases in the performance of these models have not been well-studied. METHODS: This retrospective analysis used 12-lead ECGs taken between 2008 and 2018 from 326 518 patient encounters referred for standard clinical indications to Stanford Hospital. The primary model was a convolutional neural network model trained to predict incident heart failure within 5 years. Biases were evaluated on the testing set (160 312 ECGs) using the area under the receiver operating characteristic curve, stratified across the protected attributes of race, ethnicity, age, and sex. RESULTS: There were 59 817 cases of incident heart failure observed within 5 years of ECG collection. The performance of the primary model declined with age. There were no significant differences observed between racial groups overall. However, the primary model performed significantly worse in Black patients aged 0 to 40 years compared with all other racial groups in this age group, with differences most pronounced among young Black women. Disparities in model performance did not improve with the integration of race, ethnicity, sex, and age into model architecture, by training separate models for each racial group, or by providing the model with a data set of equal racial representation. Using probability thresholds individualized for race, age, and sex offered substantial improvements in F1 scores. CONCLUSIONS: The biases found in this study warrant caution against perpetuating disparities through the development of machine learning tools for the prognosis and management of heart failure. Customizing the application of these models by using probability thresholds individualized by race, ethnicity, age, and sex may offer an avenue to mitigate existing algorithmic disparities.

Advances in cardiac pacing with leadless pacemakers and conduction system pacing.

PURPOSE OF REVIEW: The field of cardiac pacing has undergone significant evolution with the introduction and adoption of conduction system pacing (CSP) and leadless pacemakers (LLPMs). These innovations provide benefits over conventional pacing methods including avoiding lead related complications and achieving more physiological cardiac activation. This review critically assesses the latest advancements in CSP and LLPMs, including their benefits, challenges, and potential for future growth. RECENT FINDINGS: CSP, especially of the left bundle branch area, enhances ventricular depolarization and cardiac mechanics. Recent studies show CSP to be favorable over traditional pacing in various patient populations, with an increase in its global adoption. Nevertheless, challenges related to lead placement and long-term maintenance persist. Meanwhile, LLPMs have emerged in response to complications from conventional pacemaker leads. Two main types, Aveir and Micra, have demonstrated improved outcomes and adoption over time. The incorporation of new technologies allows LLPMs to cater to broader patient groups, and their integration with CSP techniques offers exciting potential. SUMMARY: The advancements in CSP and LLPMs present a transformative shift in cardiac pacing, with evidence pointing towards enhanced clinical outcomes and reduced complications. Future innovations and research are likely to further elevate the clinical impact of these technologies, ensuring improved patient care for those with conduction system disorders.

Segmenting computed tomograms for cardiac ablation using machine learning leveraged by domain knowledge encoding.

Background: Segmentation of computed tomography (CT) is important for many clinical procedures including personalized cardiac ablation for the management of cardiac arrhythmias. While segmentation can be automated by machine learning (ML), it is limited by the need for large, labeled training data that may be difficult to obtain. We set out to combine ML of cardiac CT with domain knowledge, which reduces the need for large training datasets by encoding cardiac geometry, which we then tested in independent datasets and in a prospective study of atrial fibrillation (AF) ablation. Methods: We mathematically represented atrial anatomy with simple geometric shapes and derived a model to parse cardiac structures in a small set of N = 6 digital hearts. The model, termed "virtual dissection," was used to train ML to segment cardiac CT in N = 20 patients, then tested in independent datasets and in a prospective study. Results: In independent test cohorts (N = 160) from 2 Institutions with different CT scanners, atrial structures were accurately segmented with Dice scores of 96.7% in internal (IQR: 95.3%-97.7%) and 93.5% in external (IQR: 91.9%-94.7%) test data, with good agreement with experts (r = 0.99; p < 0.0001). In a prospective study of 42 patients at ablation, this approach reduced segmentation time by 85% (2.3 ± 0.8 vs. 15.0 ± 6.9 min, p < 0.0001), yet provided similar Dice scores to experts (93.9% (IQR: 93.0%-94.6%) vs. 94.4% (IQR: 92.8%-95.7%), p = NS). Conclusions: Encoding cardiac geometry using mathematical models greatly accelerated training of ML to segment CT, reducing the need for large training sets while retaining accuracy in independent test data. Combining ML with domain knowledge may have broad applications.

Optimizing patient selection for primary prevention implantable cardioverter-defibrillator implantation: utilizing multimodal machine learning to assess risk of implantable cardioverter-defibrillator non-benefit.

AIMS: Left ventricular ejection fraction (LVEF) is suboptimal as a sole marker for predicting sudden cardiac death (SCD). Machine learning (ML) provides new opportunities for personalized predictions using complex, multimodal data. This study aimed to determine if risk stratification for implantable cardioverter-defibrillator (ICD) implantation can be improved by ML models that combine clinical variables with 12-lead electrocardiograms (ECG) time-series features. METHODS AND RESULTS: A multicentre study of 1010 patients (64.9 ± 10.8 years, 26.8% female) with ischaemic, dilated, or non-ischaemic cardiomyopathy, and LVEF ≤ 35% implanted with an ICD between 2007 and 2021 for primary prevention of SCD in two academic hospitals was performed. For each patient, a raw 12-lead, 10-s ECG was obtained within 90 days before ICD implantation, and clinical details were collected. Supervised ML models were trained and validated on a development cohort (n = 550) from Hospital A to predict ICD non-arrhythmic mortality at three-year follow-up (i.e. mortality without prior appropriate ICD-therapy). Model performance was evaluated on an external patient cohort from Hospital B (n = 460). At three-year follow-up, 16.0% of patients had died, with 72.8% meeting criteria for non-arrhythmic mortality. Extreme gradient boosting models identified patients with non-arrhythmic mortality with an area under the receiver operating characteristic curve (AUROC) of 0.90 [95% confidence intervals (CI) 0.80-1.00] during internal validation. In the external cohort, the AUROC was 0.79 (95% CI 0.75-0.84). CONCLUSIONS: ML models combining ECG time-series features and clinical variables were able to predict non-arrhythmic mortality within three years after device implantation in a primary prevention population, with robust performance in an independent cohort.

Quantifying a spectrum of clinical response in atrial tachyarrhythmias using spatiotemporal synchronization of electrograms.

AIMS: There is a clinical spectrum for atrial tachyarrhythmias wherein most patients with atrial tachycardia (AT) and some with atrial fibrillation (AF) respond to ablation, while others do not. It is undefined if this clinical spectrum has pathophysiological signatures. This study aims to test the hypothesis that the size of spatial regions showing repetitive synchronized electrogram (EGM) shapes over time reveals a spectrum from AT, to AF patients who respond acutely to ablation, to AF patients without acute response. METHODS AND RESULTS: We studied n = 160 patients (35% women, 65.0 ± 10.4 years) of whom (i) n = 75 had AF terminated by ablation propensity matched to (ii) n = 75 without AF termination and (iii) n = 10 with AT. All patients had mapping by 64-pole baskets to identify areas of repetitive activity (REACT) to correlate unipolar EGMs in shape over time. Synchronized regions (REACT) were largest in AT, smaller in AF termination, and smallest in non-termination cohorts (0.63 ± 0.15, 0.37 ± 0.22, and 0.22 ± 0.18, P < 0.001). Area under the curve for predicting AF termination in hold-out cohorts was 0.72 ± 0.03. Simulations showed that lower REACT represented greater variability in clinical EGM timing and shape. Unsupervised machine learning of REACT and extensive (50) clinical variables yielded four clusters of increasing risk for AF termination (P < 0.01, χ2), which were more predictive than clinical profiles alone (P < 0.001). CONCLUSION: The area of synchronized EGMs within the atrium reveals a spectrum of clinical response in atrial tachyarrhythmias. These fundamental EGM properties, which do not reflect any predetermined mechanism or mapping technology, predict outcome and offer a platform to compare mapping tools and mechanisms between AF patient groups.

Atrial fibrillation ablation outcome prediction with a machine learning fusion framework incorporating cardiac computed tomography.

BACKGROUND: Structural changes in the left atrium (LA) modestly predict outcomes in patients undergoing catheter ablation for atrial fibrillation (AF). Machine learning (ML) is a promising approach to personalize AF management strategies and improve predictive risk models after catheter ablation by integrating atrial geometry from cardiac computed tomography (CT) scans and patient-specific clinical data. We hypothesized that ML approaches based on a patient's specific data can identify responders to AF ablation. METHODS: Consecutive patients undergoing AF ablation, who had preprocedural CT scans, demographics, and 1-year follow-up data, were included in the study for a retrospective analysis. The inputs of models were CT-derived morphological features from left atrial segmentation (including the shape, volume of the LA, LA appendage, and pulmonary vein ostia) along with deep features learned directly from raw CT images, and clinical data. These were merged intelligently in a framework to learn their individual importance and produce the optimal classification. RESULTS: Three hundred twenty-one patients (64.2 ± 10.6 years, 69% male, 40% paroxysmal AF) were analyzed. Post 10-fold nested cross-validation, the model trained to intelligently merge and learn appropriate weights for clinical, morphological, and imaging data (AUC 0.821) outperformed those trained solely on clinical data (AUC 0.626), morphological (AUC 0.659), or imaging data (AUC 0.764). CONCLUSION: Our ML approach provides an end-to-end automated technique to predict AF ablation outcomes using deep learning from CT images, derived structural properties of LA, augmented by incorporation of clinical data in a merged ML framework. This can help develop personalized strategies for patient selection in invasive management of AF.

Machine Learning-Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes.

BACKGROUND: Machine learning is a promising approach to personalize atrial fibrillation management strategies for patients after catheter ablation. Prior atrial fibrillation ablation outcome prediction studies applied classical machine learning methods to hand-crafted clinical scores, and none have leveraged intracardiac electrograms or 12-lead surface electrocardiograms for outcome prediction. We hypothesized that (1) machine learning models trained on electrograms or electrocardiogram (ECG) signals can perform better at predicting patient outcomes after atrial fibrillation ablation than existing clinical scores and (2) multimodal fusion of electrogram, ECG, and clinical features can further improve the prediction of patient outcomes. METHODS: Consecutive patients who underwent catheter ablation between 2015 and 2017 with panoramic left atrial electrogram before ablation and clinical follow-up for at least 1 year following ablation were included. Convolutional neural network and a novel multimodal fusion framework were developed for predicting 1-year atrial fibrillation recurrence after catheter ablation from electrogram, ECG signals, and clinical features. The models were trained and validated using 10-fold cross-validation on patient-level splits. RESULTS: One hundred fifty-six patients (64.5±10.5 years, 74% male, 42% paroxysmal) were analyzed. Using electrogram signals alone, the convolutional neural network achieved an area under the receiver operating characteristics curve (AUROC) of 0.731, outperforming the existing APPLE scores (AUROC=0.644) and CHA2DS2-VASc scores (AUROC=0.650). Similarly using 12-lead ECG alone, the convolutional neural network achieved an AUROC of 0.767. Combining electrogram, ECG, and clinical features, the fusion model achieved an AUROC of 0.859, outperforming single and dual modality models. CONCLUSIONS: Deep neural networks trained on electrogram or ECG signals improved the prediction of catheter ablation outcome compared with existing clinical scores, and fusion of electrogram, ECG, and clinical features further improved the prediction. This suggests the promise of using machine learning to help treatment planning for patients after catheter ablation.

Mapping Atrial Fibrillation After Surgical Therapy to Guide Endocardial Ablation.

BACKGROUND: Surgical ablation for atrial fibrillation (AF) can be effective, yet has mixed results. It is unclear which endocardial lesions delivered as part of hybrid therapy' will best augment surgical lesion sets in individual patients. We addressed this question by systematically mapping AF endocardially after surgical ablation and relating findings to early recurrence, then performing tailored endocardial ablation as part of hybrid therapy. METHODS: We studied 81 consecutive patients undergoing epicardial surgical ablation (stage 1 hybrid), of whom 64 proceeded to endocardial catheter mapping and ablation (stage 2). Stage 2 comprised high-density mapping of pulmonary vein (PV) or posterior wall (PW) reconnections, low-voltage zones (LVZs), and potential localized AF drivers. We related findings to postsurgical recurrence of AF. RESULTS: Mapping at stage 2 revealed PW isolation reconnection in 59.4%, PV isolation reconnection in 28.1%, and LVZ in 42.2% of patients. Postsurgical recurrence of AF occurred in 36 patients (56.3%), particularly those with long-standing persistent AF (P=0.017), but had no relationship to reconnection of PVs (P=0.53) or PW isolation (P=0.75) when compared with those without postsurgical recurrence of AF. LVZs were more common in patients with postsurgical recurrence of AF (P=0.002), long-standing persistent AF (P=0.002), advanced age (P=0.03), and elevated CHA2DS2-VASc (P=0.046). AF mapping revealed 4.4±2.7 localized focal/rotational sites near and also remote from PV or PW reconnection. After ablation at patient-specific targets, arrhythmia freedom at 1 year was 81.0% including and 73.0% excluding previously ineffective antiarrhythmic medications. CONCLUSIONS: After surgical ablation, AF may recur by several modes particularly related to localized mechanisms near low voltage zones, recovery of posterior wall or pulmonary vein isolation, or other sustaining mechanisms. LVZs are more common in patients at high clinical risk for recurrence. Patient-specific targeting of these mechanisms yields excellent long-term outcomes from hybrid ablation.

Atrial fibrillation signatures on intracardiac electrograms identified by deep learning.

BACKGROUND: Automatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet suboptimally groups AF, flutter or tachycardia (AT) together as 'high rate events'. This may delay or misdirect therapy. OBJECTIVE: We hypothesized that deep learning (DL) can accurately classify AF from AT by revealing electrogram (EGM) signatures. METHODS: We studied 86 patients in whom the diagnosis of AF or AT was established at electrophysiological study (25 female, 65 ± 11 years). Custom DL architectures were trained to identify AF using N = 29,340 unipolar and N = 23,760 bipolar EGM segments. We compared DL to traditional classifiers based on rate or regularity. We explained DL using computer models to assess the impact of controlled variations in shape, rate and timing on AF/AT classification in 246,067 EGMs reconstructed from clinical data. RESULTS: DL identified AF with AUC of 0.97 ± 0.04 (unipolar) and 0.92 ± 0.09 (bipolar). Rule-based classifiers misclassified ∼10-12% of cases. DL classification was explained by regularity in EGM shape (13%) or timing (26%), and rate (60%; p < 0.001), and also by a set of unipolar EGM shapes that classified as AF independent of rate or regularity. Overall, the optimal AF 'fingerprint' comprised these specific EGM shapes, >15% timing variation, <0.48 correlation in beat-to-beat EGM shapes and CL < 190 ms (p < 0.001). CONCLUSIONS: Deep learning of intracardiac EGMs can identify AF or AT via signatures of rate, regularity in timing or shape, and specific EGM shapes. Future work should examine if these signatures differ between different clinical subpopulations with AF.

Immediate and Delayed Response of Simulated Human Atrial Myocytes to Clinically-Relevant Hypokalemia.

Although plasma electrolyte levels are quickly and precisely regulated in the mammalian cardiovascular system, even small transient changes in K+, Na+, Ca2+, and/or Mg2+ can significantly alter physiological responses in the heart, blood vessels, and intrinsic (intracardiac) autonomic nervous system. We have used mathematical models of the human atrial action potential (AP) to explore the electrophysiological mechanisms that underlie changes in resting potential (Vr) and the AP following decreases in plasma K+, [K+]o, that were selected to mimic clinical hypokalemia. Such changes may be associated with arrhythmias and are commonly encountered in patients (i) in therapy for hypertension and heart failure; (ii) undergoing renal dialysis; (iii) with any disease with acid-base imbalance; or (iv) post-operatively. Our study emphasizes clinically-relevant hypokalemic conditions, corresponding to [K+]o reductions of approximately 1.5 mM from the normal value of 4 to 4.5 mM. We show how the resulting electrophysiological responses in human atrial myocytes progress within two distinct time frames: (i) Immediately after [K+]o is reduced, the K+-sensing mechanism of the background inward rectifier current (IK1) responds. Specifically, its highly non-linear current-voltage relationship changes significantly as judged by the voltage dependence of its region of outward current. This rapidly alters, and sometimes even depolarizes, Vr and can also markedly prolong the final repolarization phase of the AP, thus modulating excitability and refractoriness. (ii) A second much slower electrophysiological response (developing 5-10 minutes after [K+]o is reduced) results from alterations in the intracellular electrolyte balance. A progressive shift in intracellular [Na+]i causes a change in the outward electrogenic current generated by the Na+/K+ pump, thereby modifying Vr and AP repolarization and changing the human atrial electrophysiological substrate. In this study, these two effects were investigated quantitatively, using seven published models of the human atrial AP. This highlighted the important role of IK1 rectification when analyzing both the mechanisms by which [K+]o regulates Vr and how the AP waveform may contribute to "trigger" mechanisms within the proarrhythmic substrate. Our simulations complement and extend previous studies aimed at understanding key factors by which decreases in [K+]o can produce effects that are known to promote atrial arrhythmias in human hearts.