Mavacamten in Right Ventricular Outflow Tract Obstruction.

Right ventricular outflow tract (RVOT) obstruction is a rare complication of ventricular hypertrophy in patients with hypertrophic cardiomyopathy (HCM). This study presents an unusual case of a patient with HCM with severe RVOT obstruction that was relieved successfully through the use of mavacamten.

Long-Term Arrhythmic Follow-Up and Risk Stratification of Patients With Desmoplakin-Associated Arrhythmogenic Right Ventricular Cardiomyopathy.

Background: Patients with likely pathogenic/pathogenic desmoplakin (DSP) variants are poorly characterized. Some of them meet diagnostic criteria for arrhythmogenic right ventricular cardiomyopathy (ARVC), but it is unclear how risk stratification strategies for ARVC perform in this setting. Objectives: The purpose of this study was to characterize arrhythmic outcomes and to test the performance of the recently validated ARVC risk calculator in patients with DSP likely pathogenic/pathogenic variants fulfilling definite 2010 ARVC Task Force Criteria (DSP-TFC+). Methods: DSP-TFC+ patients were enrolled from 20 institutions across 3 continents. Ventricular arrhythmias (VA), defined as a composite of sustained ventricular tachycardia (VT), appropriate implantable cardioverter defibrillator therapies, and ventricular fibrillation/sudden cardiac death events in follow-up, were reported as the primary outcome. We tested the performance of the ARVC risk calculator for VA prediction, reporting c-statistics. Results: Among 252 DSP-TFC+ patients (age 39.6 ± 16.9 years, 35.3% male), 94 (37.3%) experienced VA over 44.5 [IQR: 19.6-78.3] months. Patients with left ventricle involvement (n = 194) were at higher VA risk (log-rank P = 0.0239). History of nonsustained VT (aHR 2.097; P = 0.004) showed the strongest association with VA occurrence during the first 5-year follow-up. Neither age (P = 0.723) nor male sex (P = 0.200) was associated with VAs at follow-up. In 204 patients without VA at diagnosis, incident VA rate was high (32.8%; 7.37%/y). The ARVC risk calculator performed poorly overall (c-statistic 0.604 [0.594-0.614]) and very poorly in patients with left ventricular disease (c-statistic 0.558 [0.556-0.560]). Conclusions: DSP-TFC+ patients are at substantial risk for VAs. The ARVC risk calculator performs poorly in DSP-TFC+ patients suggesting need for a gene-specific risk algorithm. Meanwhile, DSP-TFC+ patients with nonsustained VT should be considered as high-risk.

Implantable cardioverter defibrillator use in arrhythmogenic right ventricular cardiomyopathy in North America and Europe.

BACKGROUND AND AIMS: Implantable cardioverter-defibrillators (ICDs) are critical for preventing sudden cardiac death (SCD) in arrhythmogenic right ventricular cardiomyopathy (ARVC). This study aims to identify cross-continental differences in utilization of primary prevention ICDs and survival free from sustained ventricular arrhythmia (VA) in ARVC. METHODS: This was a retrospective analysis of ARVC patients without prior VA enrolled in clinical registries from 11 countries throughout Europe and North America. Patients were classified according to whether they received treatment in North America or Europe and were further stratified by baseline predicted VA risk into low- (<10%/5 years), intermediate- (10%-25%/5 years), and high-risk (>25%/5 years) groups. Differences in ICD implantation and survival free from sustained VA events (including appropriate ICD therapy) were assessed. RESULTS: One thousand ninety-eight patients were followed for a median of 5.1 years; 554 (50.5%) received a primary prevention ICD, and 286 (26.0%) experienced a first VA event. After adjusting for baseline risk factors, North Americans were more than three times as likely to receive ICDs {hazard ratio (HR) 3.1 [95% confidence interval (CI) 2.5, 3.8]} but had only mildly increased risk for incident sustained VA [HR 1.4 (95% CI 1.1, 1.8)]. North Americans without ICDs were at higher risk for incident sustained VA [HR 2.1 (95% CI 1.3, 3.4)] than Europeans. CONCLUSIONS: North American ARVC patients were substantially more likely than Europeans to receive primary prevention ICDs across all arrhythmic risk strata. A lower rate of ICD implantation in Europe was not associated with a higher rate of VA events in those without ICDs.

Catheter Ablation for Ventricular Tachycardia in Patients With Desmoplakin Cardiomyopathy.

BACKGROUND: Desmoplakin (DSP) pathogenic/likely pathogenic (P/LP) variants are associated with malignant phenotypes of arrhythmogenic cardiomyopathy (DSP-ACM). Reports of outcomes after ventricular tachycardia (VT) ablation in DSP-ACM are scarce. OBJECTIVES: In this study, the authors sought to report on long-term outcomes of VT ablation in DSP-ACM. METHODS: Patients with P/LP DSP variants at 9 institutions undergoing VT ablation were included. Demographic, clinical, and instrumental data as well as all ventricular arrhythmia (VA) events were collected. Sustained VAs after the index procedure were the primary outcome. A per-patient before and after ablation comparison of rates of VA episodes per year was performed as well. RESULTS: Twenty-four DSP-ACM patients (39.3 ± 12.1 years of age, 62.5% male, median 6,116 [Q1-Q3: 3,362-7,760] premature ventricular complexes [PVCs] per 24 hours, median 4 [Q1-Q3: 2-11] previous VA episodes per patient at ablation) were included. Index procedure was most commonly endocardial/epicardial (19/24) The endocardium of the right ventricle (RV), the left ventricle (LV), or both ventricles were mapped in 8 (33.3%), 9 (37.5%), and 7 (29.2%) cases, respectively. Low voltage potentials were found in 10 of 15 patients in the RV and 11 of 16 in the LV. Endocardial ablation was performed in 18 patients (75.0%). Epicardial mapping in 19 patients (79.2%) identified low voltage potentials in 17, and 16 received epicardial ablation. Over the following 2.9 years (Q1-Q3: 1.8-5.5 years), 13 patients (54.2%) experienced VA recurrences. A significant reduction in per-patient event/year before and after ablation was observed (1.4 [Q1-Q3: 0.5-2.4] to 0.1 [Q1-Q3: 0.0-0.4]; P = 0.009). Two patients needed heart transplantation, and 4 died (3 of heart failure and 1 noncardiac death). CONCLUSIONS: VT ablation in DSP-ACM is effective in reducing the VA burden of the disease, but recurrences are common. Most VT circuits are epicardial, with both LV and RV low voltage abnormalities. Heart failure complicates clinical course and is an important cause of mortality.

Identification of high-risk imaging features in hypertrophic cardiomyopathy using electrocardiography: A deep-learning approach.

BACKGROUND: Patients with hypertrophic cardiomyopathy (HCM) are at risk of sudden death, and individuals with ≥1 major risk markers are considered for primary prevention implantable cardioverter-defibrillators. Guidelines recommend cardiac magnetic resonance (CMR) imaging to identify high-risk imaging features. However, CMR imaging is resource intensive and is not widely accessible worldwide. OBJECTIVE: The purpose of this study was to develop electrocardiogram (ECG) deep-learning (DL) models for the identification of patients with HCM and high-risk imaging features. METHODS: Patients with HCM evaluated at Tufts Medical Center (N = 1930; Boston, MA) were used to develop ECG-DL models for the prediction of high-risk imaging features: systolic dysfunction, massive hypertrophy (≥30 mm), apical aneurysm, and extensive late gadolinium enhancement. ECG-DL models were externally validated in a cohort of patients with HCM from the Amrita Hospital HCM Center (N = 233; Kochi, India). RESULTS: ECG-DL models reliably identified high-risk features (systolic dysfunction, massive hypertrophy, apical aneurysm, and extensive late gadolinium enhancement) during holdout testing (c-statistic 0.72, 0.83, 0.93, and 0.76) and external validation (c-statistic 0.71, 0.76, 0.91, and 0.68). A hypothetical screening strategy using echocardiography combined with ECG-DL-guided selective CMR use demonstrated a sensitivity of 97% for identifying patients with high-risk features while reducing the number of recommended CMRs by 61%. The negative predictive value with this screening strategy for the absence of high-risk features in patients without ECG-DL recommendation for CMR was 99.5%. CONCLUSION: In HCM, novel ECG-DL models reliably identified patients with high-risk imaging features while offering the potential to reduce CMR testing requirements in underresourced areas.

Arrhythmic risk stratification in arrhythmogenic right ventricular cardiomyopathy.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a heritable cardiomyopathy characterized by a predominantly arrhythmic presentation. It represents the leading cause of sudden cardiac death (SCD) among athletes and poses a significant morbidity threat in the general population. As a causative treatment for ARVC is still not available, the placement of an implantable cardioverter defibrillator represents the current cornerstone for SCD prevention in this setting. Thanks to international ARVC-dedicated efforts, significant steps have been achieved in recent years towards an individualized, patient-centred risk stratification approach. A novel risk calculator algorithm estimating the 5-year risk of arrhythmias of patients with ARVC has been introduced in clinical practice and subsequently validated. The purpose of this article is to summarize the body of evidence that has allowed the development of this tool and to discuss the best way to implement its use in the care of an individual patient.

Predicting ventricular tachycardia circuits in patients with arrhythmogenic right ventricular cardiomyopathy using genotype-specific heart digital twins.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic cardiac disease that leads to ventricular tachycardia (VT), a life-threatening heart rhythm disorder. Treating ARVC remains challenging due to the complex underlying arrhythmogenic mechanisms, which involve structural and electrophysiological (EP) remodeling. Here, we developed a novel genotype-specific heart digital twin (Geno-DT) approach to investigate the role of pathophysiological remodeling in sustaining VT reentrant circuits and to predict the VT circuits in ARVC patients of different genotypes. This approach integrates the patient's disease-induced structural remodeling reconstructed from contrast-enhanced magnetic-resonance imaging and genotype-specific cellular EP properties. In our retrospective study of 16 ARVC patients with two genotypes: plakophilin-2 (PKP2, n = 8) and gene-elusive (GE, n = 8), we found that Geno-DT accurately and non-invasively predicted the VT circuit locations for both genotypes (with 100%, 94%, 96% sensitivity, specificity, and accuracy for GE patient group, and 86%, 90%, 89% sensitivity, specificity, and accuracy for PKP2 patient group), when compared to VT circuit locations identified during clinical EP studies. Moreover, our results revealed that the underlying VT mechanisms differ among ARVC genotypes. We determined that in GE patients, fibrotic remodeling is the primary contributor to VT circuits, while in PKP2 patients, slowed conduction velocity and altered restitution properties of cardiac tissue, in addition to the structural substrate, are directly responsible for the formation of VT circuits. Our novel Geno-DT approach has the potential to augment therapeutic precision in the clinical setting and lead to more personalized treatment strategies in ARVC.

Predicting Ventricular Tachycardia Circuits in Patients with Arrhythmogenic Right Ventricular Cardiomyopathy using Genotype-specific Heart Digital Twins.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic cardiac disease that leads to ventricular tachycardia (VT), a life-threatening heart rhythm disorder. Treating ARVC remains challenging due to the complex underlying arrhythmogenic mechanisms, which involve structural and electrophysiological (EP) remodeling. Here, we developed a novel genotype-specific heart digital twin (Geno-DT) approach to investigate the role of pathophysiological remodeling in sustaining VT reentrant circuits and to predict the VT circuits in ARVC patients of different genotypes. This approach integrates the patient's disease-induced structural remodeling reconstructed from contrast-enhanced magnetic-resonance imaging and genotype-specific cellular EP properties. In our retrospective study of 16 ARVC patients with two genotypes: plakophilin-2 (PKP2, n = 8) and gene-elusive (GE, n = 8), we found that Geno-DT accurately and non-invasively predicted the VT circuit locations for both genotypes (with 100%, 94%, 96% sensitivity, specificity, and accuracy for GE patient group, and 86%, 90%, 89% sensitivity, specificity, and accuracy for PKP2 patient group), when compared to VT circuit locations identified during clinical EP studies. Moreover, our results revealed that the underlying VT mechanisms differ among ARVC genotypes. We determined that in GE patients, fibrotic remodeling is the primary contributor to VT circuits, while in PKP2 patients, slowed conduction velocity and altered restitution properties of cardiac tissue, in addition to the structural substrate, are directly responsible for the formation of VT circuits. Our novel Geno-DT approach has the potential to augment therapeutic precision in the clinical setting and lead to more personalized treatment strategies in ARVC.

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review.

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.

Clinical Applications, Methodology, and Scientific Reporting of Electrocardiogram Deep-Learning Models: A Systematic Review.

BACKGROUND: The electrocardiogram (ECG) is one of the most common diagnostic tools available to assess cardio-vascular health. The advent of advanced computational techniques such as deep learning has dramatically expanded the breadth of clinical problems that can be addressed using ECG data, leading to increasing popularity of ECG deep-learning models aimed at predicting clinical endpoints. OBJECTIVES: The purpose of this study was to define the current landscape of clinically relevant ECG deep-learning models and examine practices in the scientific reporting of these studies. METHODS: We performed a systematic review of PubMed and EMBASE databases to identify clinically relevant ECG deep-learning models published through July 1, 2022. RESULTS: We identified 44 manuscripts including 53 unique, clinically relevant ECG deep-learning models. The rate of publication of ECG deep-learning models is increasing rapidly. The most common clinical applications of ECG deep learning were identification of cardiomyopathy (14/53 [26%]), followed by arrhythmia detection (9/53 [17%]). Methodologic reporting varied; while 33/44 (75%) publications included model architecture diagrams, complete information required to reproduce these models was provided in only 10/44 (23%). Saliency analysis was performed in 20/44 (46%) of publications. Only 18/53 (34%) models were tested within external validation cohorts. Model code or resources allowing for model implementation by external groups were available for only 5/44 (11%) publications. CONCLUSIONS: While ECG deep-learning models are increasingly clinically relevant, their reporting is highly variable, and few publications provide sufficient detail for methodologic reproduction or model validation by external groups. The field of ECG deep learning would benefit from adherence to a set of standardized scientific reporting guidelines.

Programmed Ventricular Stimulation as an Additional Primary Prevention Risk Stratification Tool in Arrhythmogenic Right Ventricular Cardiomyopathy: A Multinational Study.

BACKGROUND: A novel risk calculator based on clinical characteristics and noninvasive tests that predicts the onset of clinical sustained ventricular arrhythmias (VA) in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC) has been proposed and validated by recent studies. It remains unknown whether programmed ventricular stimulation (PVS) provides additional prognostic value. METHODS: All patients with a definite ARVC diagnosis, no history of sustained VAs at diagnosis, and PVS performed at baseline were extracted from 6 international ARVC registries. The calculator-predicted risk for sustained VA (sustained or implantable cardioverter defibrillator treated ventricular tachycardia [VT] or fibrillation, [aborted] sudden cardiac arrest) was assessed in all patients. Independent and combined performance of the risk calculator and PVS on sustained VA were assessed during a 5-year follow-up period. RESULTS: Two hundred eighty-eight patients (41.0±14.5 years, 55.9% male, right ventricular ejection fraction 42.5±11.1%) were enrolled. At PVS, 137 (47.6%) patients had inducible ventricular tachycardia. During a median of 5.31 [2.89-10.17] years of follow-up, 83 (60.6%) patients with a positive PVS and 37 (24.5%) with a negative PVS experienced sustained VA (P<0.001). Inducible ventricular tachycardia predicted clinical sustained VA during the 5-year follow-up and remained an independent predictor after accounting for the calculator-predicted risk (HR, 2.52 [1.58-4.02]; P<0.001). Compared with ARVC risk calculator predictions in isolation (C-statistic 0.72), addition of PVS inducibility showed improved prediction of VA events (C-statistic 0.75; log-likelihood ratio for nested models, P<0.001). PVS inducibility had a 76% [67-84] sensitivity and 68% [61-74] specificity, corresponding to log-likelihood ratios of 2.3 and 0.36 for inducible (likelihood ratio+) and noninducible (likelihood ratio-) patients, respectively. In patients with a ARVC risk calculator-predicted risk of clinical VA events <25% during 5 years (ie, low/intermediate subgroup), PVS had a 92.6% negative predictive value. CONCLUSIONS: PVS significantly improved risk stratification above and beyond the calculator-predicted risk of VA in a primary prevention cohort of patients with ARVC, mainly for patients considered to be at low and intermediate risk by the clinical risk calculator.

Longitudinal Prediction of Ventricular Arrhythmic Risk in Patients With Arrhythmogenic Right Ventricular Cardiomyopathy.

BACKGROUND: The arrhythmogenic right ventricular cardiomyopathy (ARVC) risk calculator stratifies risk for incident sustained ventricular arrhythmias (VA) at the time of ARVC diagnosis. However, included risk factors change over time, and how well the ARVC risk calculator performs at follow-up is unknown. METHODS: This was a retrospective analysis of patients with definite ARVC and without prior sustained VA. Risk factors for VA including age, nonsustained ventricular tachycardia, premature ventricular complex burden, T-wave inversions on electrocardiogram, cardiac syncope, right ventricular function, therapeutic medication use, and exercise intensity were assessed at the time of 2010 Task Force Criteria based ARVC diagnosis and upon repeat evaluations. Changes in these risk factors were analyzed over 5-year follow-up. The 5-year risk of VA was predicted longitudinally using (1) the baseline ARVC risk calculator prediction, (2) the ARVC risk prediction calculated using updated risk factors, and (3) time-varying Cox regression. Discrimination and calibration were assessed in comparison to observed VA event rates. RESULTS: Four hundred eight patients with ARVC experiencing 132 primary VA events were included. Matched comparison of risk factors at baseline versus at 5 years of follow-up revealed decreased burdens of premature ventricular complexes (-1200/day) and nonsustained ventricular tachycardia (-14%). Presence of significant right ventricular dysfunction and number of T-wave inversions on electrocardiogram were unchanged. Observed risk for VA decreased by 13% by 5 years follow-up. The baseline ARVC risk calculator's ability to predict 5-year VA risk worsened during follow-up (C statistics, 0.83 at diagnosis versus 0.68 at 5 years). Both the updated ARVC risk calculator (C statistics of 0.77) and time-varying Cox regression model (C statistics, 0.77) had strong discrimination. The updated ARVC risk calculator overestimated 5-year VA risk by an average of +6%. CONCLUSIONS: Risk factors for VA in ARVC are dynamic, and overall risk for incident sustained VA decreases during follow-up. Up-to-date risk factor assessment improves VA risk stratification.

Ventricular Septal Myectomy Decreases Long-Term Risk for Atrial Fibrillation in Patients With Hypertrophic Cardiomyopathy.

Atrial fibrillation (AF) is the most common sustained arrhythmia in hypertrophic cardiomyopathy (HCM) and is an important cause of morbidity and embolic stroke. The impact of outflow obstruction and the influence of surgical septal myectomy on the development of new-onset AF has not been well described. Consecutive patients with HCM without previous AF were followed for 5.0 ± 3.6 years for new-onset AF, including 717 with obstruction who did not undergo surgical myectomy (outflow gradients ≥30 mm Hg at rest or after provocation), 555 with nonobstructive HCM (outflow gradients <30 mm Hg), and 503 who underwent surgical myectomy. Patients with obstructive HCM who did not undergo myectomy had a 1.5-fold increased risk for new-onset AF compared with nonobstructive HCM (26% vs 16% at 10 years, hazard ratio = 0.69, p = 0.02). Patients who underwent myectomy had more advanced heart failure (95% vs 18% New York Heart Association class III, p <0.001) and had larger left atrium dimension (42 ± 7 vs 41 ± 7 mm; p <0.01) as compared with patients with obstructive HCM who did not undergo myectomy. However, after myectomy, the risk of new-onset AF was significantly lower than nonoperated obstructive (17% vs 26% at 10 years, p = 0.04) and no different from the risk of AF in patients with nonobstructive HC (hazard ratio 0.95, p = 0.81). In conclusion, patients with HCM with outflow obstruction are at a higher risk for AF compared with patients with nonobstructive HCM. However, after surgical myectomy, the risk for new-onset AF is substantially reduced. In addition to the known benefits of myectomy to permanently relieve outflow tract obstruction and mitral regurgitation, reverse heart failure symptoms, and increase longevity, myectomy is now shown to decrease susceptibility to AF in HCM.

Association of Premature Ventricular Contraction Burden on Serial Holter Monitoring With Arrhythmic Risk in Patients With Arrhythmogenic Right Ventricular Cardiomyopathy.

IMPORTANCE: A high burden of premature ventricular contractions (PVCs) at disease diagnosis has been associated with an overall higher risk of ventricular arrhythmias in arrhythmogenic right ventricular cardiomyopathy (ARVC). Data regarding dynamic modification of PVC burden at follow-up with Holter monitoring and its impact on arrhythmic risk in ARVC are scarce. OBJECTIVE: To describe changes in the PVC burden and to assess whether serial Holter monitoring is dynamically associated with sustained ventricular arrhythmias during follow-up in patients with ARVC. DESIGN, SETTINGS, AND PARTICIPANTS: In this cohort study, patients with a definite ARVC diagnosis, available Holter monitoring results at disease diagnosis, and at least 2 additional results of Holter monitoring during follow-up were enrolled from 6 ARVC registries in North America and Europe. Data were collected from June 1 to September 15, 2021. MAIN OUTCOMES AND MEASURES: The association between prespecified variables retrieved at each Holter monitoring follow-up (ie, overall PVC burden; presence of sudden PVC spikes, defined as absolute increase in PVC burden ≥5000 per 24 hours or a relative ≥75% increase, with an absolute increase of ≥1000 PVCs; presence of nonsustained ventricular tachycardia [NSVT]; and use of ß-blockers and class III antiarrhythmic drugs) and sustained ventricular arrhythmias occurring within 12 months after that Holter examination was assessed using a mixed logistical model. RESULTS: In 169 enrolled patients with ARVC (mean [SD] age, 36.3 [15.0] years; 95 men [56.2%]), a total of 723 Holter examinations (median, 4 [IQR, 4-5] per patient) were performed during a median follow-up of 54 (IQR, 42-63) months and detected 75 PVC spikes and 67 sustained ventricular arrhythmias. The PVC burden decreased significantly from the first to the second Holter examination (mean, 2906 [95% CI, 1581-4231] PVCs per 24 hours; P < .001). A model including 24-hour PVC burden (odds ratio [OR] 1.50 [95% CI, 1.10-2.03]; P = .01), PVC spikes (OR, 6.20 [95 CI, 2.74-13.99]; P < .001), and NSVT (OR, 2.29 [95% CI, 1.10-4.51]; P = .03) at each follow-up Holter examination was associated with sustained ventricular arrhythmia occurrence in the following 12 months. CONCLUSIONS AND RELEVANCE: These findings suggest that in patients with ARVC, changes in parameters derived from each Holter examination performed during follow-up are associated with the risk of sustained ventricular arrhythmias within 12 months of disease diagnosis.

Impact of comorbidities on atrial fibrillation and sudden cardiac death in hypertrophic cardiomyopathy.

BACKGROUND: The impact of comorbid disease states on the development of atrial and ventricular arrhythmias in patients with hypertrophic cardiomyopathy (HCM) remains unresolved. OBJECTIVE: Evaluate the association of comorbidities linked to arrhythmias in other cardiovascular diseases (e.g., obesity, systemic hypertension, diabetes, obstructive sleep apnea, renal disorders, tobacco, and alcohol use) to atrial fibrillation (AF) and sudden cardiac death (SCD) events in a large cohort of HCM patients. METHODS: A total  of 2269 patients, 54 ± 15 years of age, 1392 males, were evaluated at the Tufts HCM Institute between 2004 and 2018 and followed for an average of 4 ± 3 years for new-onset clinical AF and SCD events (appropriate defibrillation for ventricular tachyarrhythmias, resuscitated cardiac arrest, or SCD). RESULTS: One or more comorbidity was present in 75% of HCM patients, including 50% with ≥2 comorbidities, most commonly obesity (body mass index [BMI] ≥ 30 kg/m2 ) in 43%. New-onset atrial fibrillation developed in 11% of our cohort (2.6%/year). On univariate analysis, obesity was associated with a 1.7-fold increased risk for AF (p = .03) with 12% of obese patients developing AF (3.3%/year) as compared to 7% of patients with BMI < 25 kg/m2 (1.6%/year; p = .006). On multivariate analysis, age and LA transverse dimension emerged as the only variables predictive of AF. Comorbidities, including obesity, were not independently associated with AF development (p > .10 for each). SCD events occurred in 3.3% of patients (0.8%/year) and neither obesity nor other comorbidities were associated with increased risk for SCD (p > .10 for each). CONCLUSIONS: In adult HCM patients comorbidities do not appear to impact AF or SCD risk. Therefore, for most patients with HCM, adverse disease related events of AF and SCD appear to be primarily driven by underlying left ventricular and atrial myopathy as opposed to comorbidities.

Development and Validation of a Clinical Predictive Model for Identifying Hypertrophic Cardiomyopathy Patients at Risk for Atrial Fibrillation: The HCM-AF Score.

[Figure: see text].

Competitive Drivers of Atrial Fibrillation: The Interplay Between Focal Drivers and Multiwavelet Reentry.

BACKGROUND: There is debate whether human atrial fibrillation is driven by focal drivers or multiwavelet reentry. We propose that the changing activation sequences surrounding a focal driver can at times self-sustain in the absence of that driver. Further, the relationship between focal drivers and surrounding chaotic activation is bidirectional; focal drivers can generate chaotic activation, which may affect the dynamics of focal drivers. METHODS AND RESULTS: In a propagation model, we generated tissues that support structural micro-reentry and moving functional reentrant circuits. We qualitatively assessed (1) the tissue's ability to support self-sustaining fibrillation after elimination of the focal driver, (2) the impact that structural-reentrant substrate has on the duration of fibrillation, the impact that micro-reentrant (3) frequency, (4) excitable gap, and (5) exposure to surrounding fibrillation have on micro-reentry in the setting of chaotic activation, and finally the likelihood fibrillation will end in structural reentry based on (6) the distance between and (7) the relative lengths of an ablated tissue's inner and outer boundaries. We found (1) focal drivers produced chaotic activation when waves encountered heterogeneous refractoriness; chaotic activation could then repeatedly initiate and terminate micro-reentry. Perpetuation of fibrillation following elimination of micro-reentry was predicted by tissue properties. (2) Duration of fibrillation was increased by the presence of a structural micro-reentrant substrate only when surrounding tissue had a low propensity to support self-sustaining chaotic activation. Likelihood of micro-reentry around the structural reentrant substrate increased as (3) the frequency of structural reentry increased relative to the frequency of fibrillation in the surrounding tissue, (4) the excitable gap of micro-reentry increased, and (5) the exposure of the structural circuit to the surrounding tissue decreased. Likelihood of organized tachycardia following termination of fibrillation increased with (6) decreasing distance and (7) disparity of size between focal obstacle and external boundary. CONCLUSION: Focal drivers such as structural micro-reentry and the chaotic activation they produce are continuously interacting with one another. In order to accurately describe cardiac tissue's propensity to support fibrillation, the relative characteristics of both stationary and moving drivers must be taken into account.

Clinical Predictive Models of Sudden Cardiac Arrest: A Survey of the Current Science and Analysis of Model Performances.

Background More than 500 000 sudden cardiac arrests (SCAs) occur annually in the United States. Clinical predictive models (CPMs) may be helpful tools to differentiate between patients who are likely to survive or have good neurologic recovery and those who are not. However, which CPMs are most reliable for discriminating between outcomes in SCA is not known. Methods and Results We performed a systematic review of the literature using the Tufts PACE (Predictive Analytics and Comparative Effectiveness) CPM Registry through February 1, 2020, and identified 81 unique CPMs of SCA and 62 subsequent external validation studies. Initial cardiac rhythm, age, and duration of cardiopulmonary resuscitation were the 3 most commonly used predictive variables. Only 33 of the 81 novel SCA CPMs (41%) were validated at least once. Of 81 novel SCA CPMs, 56 (69%) and 61 of 62 validation studies (98%) reported discrimination, with median c-statistics of 0.84 and 0.81, respectively. Calibration was reported in only 29 of 62 validation studies (41.9%). For those novel models that both reported discrimination and were validated (26 models), the median percentage change in discrimination was -1.6%. We identified 3 CPMs that had undergone at least 3 external validation studies: the out-of-hospital cardiac arrest score (9 validations; median c-statistic, 0.79), the cardiac arrest hospital prognosis score (6 validations; median c-statistic, 0.83), and the good outcome following attempted resuscitation score (6 validations; median c-statistic, 0.76). Conclusions Although only a small number of SCA CPMs have been rigorously validated, the ones that have been demonstrate good discrimination.