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.

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.

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.

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.

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.

Outcomes in Patients With Hypertrophic Cardiomyopathy and Left Ventricular Systolic Dysfunction.

BACKGROUND: End-stage (ES) hypertrophic cardiomyopathy (HCM) has been considered a particularly grim and unfavorable disease complication, associated with substantial morbidity and mortality, frequently requiring heart transplant. Previous reports have included small numbers of patients with relatively short follow-up, predominantly in prior treatment eras. OBJECTIVES: The purpose of this study was to re-evaluate clinical profile and prognosis for end-stage heart failure in a large HCM cohort with contemporary management strategies. METHODS: Patients at Tufts HCM Institute, from 2004 to 2017, were identified with ES and systolic dysfunction (ejection fraction [EF] <50%), followed for 5.8 ± 4.7 years (up to 18 years). RESULTS: Of the 2,447 patients, 118 (4.8%) had ES-HCM (EF 39 ± 9%; range 12% to 49%) at age 48 ± 15 years. Notably, over follow-up, 57 patients (48%) achieved clinical stability in New York Heart Association functional classes I/II with medical treatment (or cardiac resynchronization therapy), including 6 patients ≥10 years from ES diagnosis (up to 14 years). In total, 61 other patients (52%) developed refractory heart failure to disabling New York Heart Association functional classes III/IV (5.2%/year); 67% have survived, including 31 with heart transplant. Of the 118 ES patients, 21 had appropriate implantable cardioverter-defibrillator (ICD) therapy terminating potentially lethal tachyarrhythmias, with no difference in frequency of events in patients with EF 35% to 49% versus EF <35% (17% vs. 19%; p = 0.80). With all available treatment modalities, ES-related mortality was 1.9%/year, with 10-year survival of 85% (95% confidence interval: 77% to 94%). Mortality was 4-fold lower than previously reported for ES (8.0%/year), but exceeded 10-fold HCM with preserved EF (0.2%/year; p < 0.001). CONCLUSIONS: Although ES remains an important complication of HCM, contemporary treatment strategies, including ICDs and heart transplant, are associated with significantly lower mortality than previously considered. Primary prevention ICDs should be considered when EF is <50% in HCM. Rapid heart failure progression is not an inevitable consequence of ES, and some patients experience extended periods of clinical stability.

Cardiac Sarcoidosis Mimicking Hypertrophic Cardiomyopathy: The Importance of Cardiac Magnetic Resonance Imaging.

A man with a presumed diagnosis of hypertrophic cardiomyopathy presented after a ventricular fibrillation arrest. Review of prior cardiac magnetic resonance imaging revealed a pattern of late gadolinium enhancement that was atypical for hypertrophic cardiomyopathy and most consistent with cardiac sarcoidosis, with diagnosis confirmed by endomyocardial biopsy. (Level of Difficulty: Beginner.).

Prospective, Tissue-Specific Optimization of Ablation for Multiwavelet Reentry: Predicting the Required Amount, Location, and Configuration of Lesions.

BACKGROUND: Treatment of multiwavelet reentry (MWR) remains difficult. We previously developed a metric, the fibrillogenicity index, to assess the propensity of homogeneous, 2-dimensional tissues to support MWR. In this study, we demonstrate a method by which fibrillogenicity index can be generalized to heterogeneous tissues and validate an algorithm for prospective, tissue-specific optimization of ablation to reduce MWR burden. METHODS AND RESULTS: We used a computational model to simulate and measure the duration of MWR in tissues with heterogeneously distributed action potential durations and then assessed the relative efficacy of a variety of ablation strategies for reducing tissues' ability to support MWR. We then derived and tested a strategy in which multiple linear lesions partially divided a fibrillogenic tissue into functionally equivalent subsections. The composite action potential duration of heterogeneous tissue was well approximated by an inverse sum of cellular action potential durations (R(2)=0.82). Linear ablation more efficiently reduced MWR duration than branching ablation patterns and optimally reduced disease burden when positioned at a tissue's functional (rather than geometric) center. The duration of MWR after application of prospective, individually optimized ablation sets fell within 4.4% (95% confidence interval, 3-5.8) of the predicted target. CONCLUSIONS: We think that this study presents a novel approach for (1) quantifying the extent of a tissue's electric derangement, (2) prospectively determining the amount of ablation required to minimize the burden of MWR, and (3) predicting the most efficient distribution of these ablation lesions in tissue refractory to standard ablation strategies.

Prospectively quantifying the propensity for atrial fibrillation: a mechanistic formulation.

The goal of this study was to determine quantitative relationships between electrophysiologic parameters and the propensity of cardiac tissue to undergo atrial fibrillation. We used a computational model to simulate episodes of fibrillation, which we then characterized in terms of both their duration and the population dynamics of the electrical waves which drove them. Monte Carlo sampling revealed that episode durations followed an exponential decay distribution and wave population sizes followed a normal distribution. Half-lives of reentrant episodes increased exponentially with either increasing tissue area to boundary length ratio (A/BL) or decreasing action potential duration (APD), resistance (R) or capacitance (C). We found that the qualitative form of fibrillatory activity (e.g., multi-wavelet reentry (MWR) vs. rotors) was dependent on the ratio of resistance and capacitance to APD; MWR was reliably produced below a ratio of 0.18. We found that a composite of these electrophysiologic parameters, which we term the fibrillogenicity index (Fb = A/(BL*APD*R*C)), reliably predicted the duration of MWR episodes (r2 = 0.93). Given that some of the quantities comprising Fb are amenable to manipulation (via either pharmacologic treatment or catheter ablation), these findings provide a theoretical basis for the development of titrated therapies of atrial fibrillation.

Ablation of multiwavelet re-entry guided by circuit-density and distribution: maximizing the probability of circuit annihilation.

BACKGROUND: A key mechanism responsible for atrial fibrillation is multiwavelet re-entry (MWR). We have previously demonstrated improved efficiency of ablation when lesions were placed in regions of high circuit-density. In this study, we undertook a quantitative assessment of the relative effect of ablation on the probability of MWR termination and the inducibility of MWR, as a function of lesion length and circuit-density overlap. METHODS AND RESULTS: We used a computational model to simulate MWR in tissues with (and without) localized regions of decreased action potential duration and increased intercellular resistance. We measured baseline circuit-density and distribution. We then assessed the effect of various ablation lesion sets on the inducibility and duration of MWR as a function of ablation lesion length and overlap with circuit-density. Higher circuit-density reproducibly localized to regions of shorter wavelength. Ablation lines with high circuit-density overlap showed maximum decreases in duration of MWR at lengths equal to the distance from the tissue boundary to the far side of the high circuit-density region (high-overlap, -43.5% [confidence interval, -22.0% to -65.1%] versus low-overlap, -4.4% [confidence interval, 7.3% to -16.0%]). Further ablation (beyond the length required to cross the high circuit-density region) provided minimal further reductions in duration and increased inducibility. CONCLUSIONS: Ablation at sites of high circuit-density most efficiently decreased re-entrant duration while minimally increasing inducibility. Ablation lines delivered at sites of low circuit-density minimally decreased duration yet increased inducibility of MWR.