Hypertrophic Cardiomyopathy Risk-SCD Research Articles

Machine Learning in Hypertrophic Cardiomyopathy: Nonlinear Model From Clinical and CMR Features Predicting Cardiovascular Events

BackgroundThe cumulative burden of hypertrophic cardiomyopathy (HCM) is significant, with a noteworthy percentage (10%-15%) of patients with HCM per year experiencing major adverse cardiovascular events (MACEs). A current risk stratification scheme for HCM had only limited accuracy in predicting sudden cardiac death (SCD) and failed to account for a broader spectrum of adverse cardiovascular events and cardiac magnetic resonance (CMR) parameters. ObjectivesThis study sought to develop and evaluate a machine learning (ML) framework that integrates CMR imaging and clinical characteristics to predict MACEs in patients with HCM. MethodsA total of 758 patients with HCM (67% male; age 49 ± 14 years) who were admitted between 2010 and 2017 from 4 medical centers were included. The ML model was built on the internal discovery cohort (533 patients with HCM, admitted to Fuwai Hospital, Beijing, China) by using the light gradient-boosting machine and internally evaluated using cross-validation. The external test cohort consisted of 225 patients with HCM from 3 medical centers. A total of 14 CMR imaging features (strain and late gadolinium enhancement [LGE]) and 23 clinical variables were evaluated and used to inform the ML model. MACEs included a composite of arrhythmic events, SCD, heart failure, and atrial fibrillation–related stroke. ResultsMACEs occurred in 191 (25%) patients over a median follow-up period of 109.0 months (Q1-Q3: 73.0-118.8 months). Our ML model achieved areas under the curve (AUCs) of 0.830 and 0.812 (internally and externally, respectively). The model outperformed the classic HCM Risk-SCD model, with significant improvement (P < 0.001) of 22.7% in the AUC. Using the cubic spline analysis, the study showed that the extent of LGE and the impairment of global radial strain (GRS) and global circumferential strain (GCS) were nonlinearly correlated with MACEs: an elevated risk of adverse cardiovascular events was observed when these parameters reached the high enough second tertiles (11.6% for LGE, 25.8% for GRS, −17.3% for GCS). ConclusionsML-empowered risk stratification using CMR and clinical features enabled accurate MACE prediction beyond the classic HCM Risk-SCD model. In addition, the nonlinear correlation between CMR features (LGE and left ventricular pressure gradient) and MACEs uncovered in this study provides valuable insights for the clinical assessment and management of HCM.

Additional factors associated with sudden cardiac death in HCM patients

Abstract Background Hypertrophic cardiomyopathy (HCM) is a primary myocardial disease characterized by an increase in the thickness of the left ventricular (LV) wall, which is not explained only by abnormal loading conditions. Sudden cardiac death (SCD) is the most devastating complication of HCM occurring in asymptomatic or younger patients without warning. Purpose To evaluate additional SCD risk factors in Ukrainian population of HCM patients. Materials and methods We retrospectively evaluated 350 consecutive HCM patients that were followed from 2006 to 2021 in our Cardiological Department. General clinical examination, standard 12-lead ECG, transthoracic echocardiography and 24 hours Holter ECG monitoring data were estimated. The 5-year SCD risk was calculated according to the HCM Risk-SCD score. The endpoint of the study was SCD and its surrogates, including adequate performance of the implantable cardioverter-defibrillator and successful resuscitation. Results Follow-up time was 5.0 (1.5-9.5) years. During this time, 16 patients (4.6%) reached the end point, which was 0.9%/year. According to the HCM Risk-SCD score, 5 (31.3%) of this group had a high risk of SCD (≥ 6%) and 3 (18.8%) had an intermediate risk (≥ 4% and &amp;lt; 6%). In multivariable Cox regression analysis adjusted for conventional SCD risk factors it was found that independent predictors of SCD were unexplained syncope (HR 3.81, 95% CI 1.11-13.12, p=0.034), systolic blood pressure (SBP) (HR 0.97 , 95% CI 0.94- 0.99, p=0.026), "infarct-like" ST elevation on ECG (HR 6.81, 95% CI 2.09-22.16, p=0.001) and PQ interval duration (HR 1.03, 95% CI 1.01-1.05, p=0.002), Harrell's C-index 0.84, 95% CI 0.73-0.95, p &amp;lt; 0.0001). Conclusions In our study HCM Risk-SCD score predicted SCD or surrogates only in 50% of cases. In order to improve the risk stratification some additional independent predictors of SCD can be proposed. ECG parameters ("infarct-like" ST elevation and PQ interval duration) and level of SBP were found to have SCD predictive value.

The Feasibility of Left Ventricular Strain and Strain Rate for Evaluating Hypertrophic Cardiomyopathy with Risk Factors of Sudden Cardiac Death by Feature-Tracking CMR

Sudden cardiac death (SCD) represents the most severe complication of hypertrophic cardiomyopathy (HCM). However, the relation between strain, strain rate (SR), and risk factors in SCD risk stratification remains elusive. The study aimed to assess the attenuation of strain and SR in HCM by feature tracking cardiac magnetic resonance. All strain and SRs were obtained automatically by feature tracking, with manual adjustment of endocardial and epicardial borders. Strain indicators included left ventricular global longitudinal, circumferential, global radial strain (GRS), peak diastolic-longitudinal, circumferential, and radial SR. Patients were categorized into high-risk and low-risk groups for SCD based on the 2020 American Heart Association/American College HCM risk-SCD model. The correlation between strain/SR and SCD risk factors was assessed through Spearman correlation analysis. Furthermore, a multivariate logistic regression analysis was conducted to explore the factors that influence SCD risk in HCM patients. A total of 105 HCM patients were analyzed in this study, including 38 patients in the high-risk group, and 67 patients in the low-risk group. Compared with the low-risk group, the high-risk group exhibited significantly worse strain and SR (p <0.001). Furthermore, both circumferential and GRS and SR exhibited meaningful associations with risk factors for SCD. Additionally, GRS emerged as an independent risk factor for predicting heightened SCD risk in HCM patients (p <0.001). In conclusion, left ventricular strain and SR based on feature tracking-cardiac magnetic resonance can be evaluated for SCD risk and are strongly associated with SCD risk factors.

ECG changes and their prognostic value for the development of sudden cardiac death in HCM patients

Background. Hypertrophic cardiomyopathy (HCM) is a primary myocardial disease characterized by an increase in the left ventricular (LV) wall thickness, which is not explained only by abnormal loading conditions. The development of sudden cardiac death (SCD) is one of the most dangerous complications of HCM. The aim of our study was to evaluate the prognostic value of electrocardiography (ECG) changes for the development of SCD in the Ukrainian population of patients with HCM. Materials and methods. We evaluated 350 patients with HCM who were examined in the City Clinical Hospital 8 (Kharkiv, Ukraine) from 2006 to 2021. The diagnosis of HCM was made in accordance with the current guidelines of the European Society of Cardiology. Clinical and instrumental data of patients with HCM were analyzed retrospectively, including general clinical data, 12-lead ECG, echocardiography, Holter monitoring, calculation of the 5-year risk of SCD according to the HCM Risk-SCD tool. The endpoint of the study was SCD and its surrogates, including adequate performance of the implantable cardioverter-defibrillator and the state after a successful resuscitation. Results. Follow-up was 5.0 (1.5–9.5) years. During this time, 16 patients (4.6 %) reached the endpoint, which was 0.9 %/year. According to the HCM Risk-SCD, 5 (31.3 %) patients in this group were at a high (≥ 6 %) and 3 (18.8 %) were at an intermediate risk of SCD (≥ 4 and &lt; 6 %). A multivariable Cox regression analysis has shown that independent predictors of SCD are syncope (hazard ratio (HR) 3.81, 95% confidence interval (CI) 1.11–13.12, p = 0.034), systolic blood pressure (HR 0.97, 95% CI 0.94–0.99, p = 0.026), ECG with “infarction-like” ST elevation (HR 6.81, 95% CI 2.09–22.16, p = 0.001) and PQ interval value (HR 1.03, 95% CI 1.01–1.05, p = 0.002), Harrell’s C-index (HR 0.84, 95% CI 0.73–0.95, p &lt; 0.0001). Conclusions. The proposed HCM Risk-SCD tool was not effective enough in predicting SCD in our study. In order to improve the risk stratification, ECG changes (“infarction-like” ST elevation and PQ interval prolongation) and systolic blood pressure lowering can be used, which were found to be independent predictors of SCD risk.

Prolonged mHealth-Based Arrhythmia Monitoring in Patients With Hypertrophic Cardiomyopathy (HCM-PATCH): Protocol for a Single-Center Cohort Study.

Patients with hypertrophic cardiomyopathy (HCM) are at increased risk of sudden cardiac death (SCD) due to ventricular arrhythmias and other arrhythmias. Screening for arrhythmias is mandatory to assess the individual SCD risk, but long-term electrocardiography (ECG) is rarely performed in routine clinical practice. Intensified monitoring may increase the detection rate of ventricular arrhythmias and identify more patients with an increased SCD risk who are potential candidates for the primary prophylactic implantation of an implantable cardioverter-defibrillator. To date, reliable data on the clinical benefit of prolonged arrhythmia monitoring in patients with HCM are rare. This prospective study aims to measure the prevalence of ventricular arrhythmias in patients with HCM observed by mobile health (mHealth)-based continuous rhythm monitoring over 14 days compared to standard practice (a 24- and 48-h long-term ECG). The frequency of ventricular arrhythmias in this 14-day period is compared with the frequency in the first 24 or 48 hours for the same patient (intraindividual comparison). Following the sample size calculation, 34 patients with a low or intermediate risk for SCD, assessed by the HCM Risk-SCD calculator, will need to be recruited in this single-center cohort study between June 2023 and February 2024. All patients will receive an ECG patch that records their heart activity over 14 days. In addition, cardiac magnetic resonance imaging and genetic testing data will be integrated into risk stratification. All patients will be asked to complete questionnaires about their symptoms; previous therapy; family history; and, at the end of the study, their experience with the ECG patch-based monitoring. The Hypertrophic Cardiomyopathy: Clinical Impact of a Prolonged mHealth-Based Arrhythmia Monitoring by Single-Channel ECG (HCM-PATCH) study investigates the prevalence of nonsustained ventricular tachycardia (ie, ≥3 consecutive ventricular beats at a rate of 120 beats per minute, lasting for <30 seconds) in low- to intermediate-risk patients with HCM (according to the HCM Risk-SCD calculator) with additional mHealth-based prolonged rhythm monitoring. The study was funded by third-party funding from the Department of Cardiology and Intensive Care Medicine, University Hospital Ostwestfalen-Lippe of Bielefeld University in June 2023 and approved by the institutional review board in May 2023. Data collection began in June 2023, and we plan to end the study in February 2024. Of the 34 patients, 26 have been recruited. Data analysis has not yet taken place. Publication of the results is planned for the fall of 2024. Prolonged mHealth-based rhythm monitoring could lead to differences in the prevalence of arrhythmias compared to 24- and 48-hour long-term ECGs. This may lead to improved identification of patients at high risk and trigger therapeutic interventions that may provide better protection from SCD or atrial fibrillation-related complications such as embolic stroke. Deutsches Register Klinischer Studien DRKS00032144; https://tinyurl.com/498bkrx8. DERR1-10.2196/52035.

Validation of the HCM Risk-SCD model in patients with hypertrophic cardiomyopathy and future perspectives.

The hypertrophic cardiomyopathy (HCM) risk- sudden cardiac death (SCD) model provides a convenient tool for determining the risk of SCD in patients with HCM even though some patients with low-risk scores still remain at risk of SCD. Hence, the aim of our study was to assess the performance of HCM Risk-SCD in a large series of consecutive patients with HCM who had been followed up in a tertiary center. The study population consisted of 389 consecutive HCM patients who had been followed up between 2004 and 2021. Demographic and clinical characteristics, estimated 5-year risk using the HCM Risk-SCD model, were compiled, and survival data were collected during follow-up. Patients were divided into 2 groups according to their long-term survival, and HCM risk-SCD scores of these two groups were compared. The long-term mortality was observed in 47 patients out of 389 patients in the during a mean follow-up of 55.5 ± 12.7 months. The mean HCM Risk-SCD score of surviving patients was significantly lower than that of non-survivors (1.8% vs. 3.0%, p < .001). The HCM Risk-SCD score was above 6% in nine (2.6%) survivors and nine (19.1%) non-survivors (p < .001). The ROC curve based on the HCM Risk-SCD score had 61% sensitivity and 61% specificity for risk threshold of for 2.0%, 38% sensitivity and 99% specificity a threshold of ≥4%, 17% sensitivity, and 99% specificity for a threshold of ≥6%. A new risk algorithm with higher sensitivity is needed, although the HCM risk-SCD model is still quite useful in identifying patients at a high risk for SCD.

Long-term follow-up in patients with hypertrophic cardiomyopathy after defibrillator implantation: analysis of appropriate and inappropriate therapy

Abstract Background Treatment with implantable cardioverter-defibrillators (ICD) is effective for prevention of sudden cardiac death (SCD) in patients with hypertrophic cardiomyopathy (HCM). Assessment of SCD risk and identification of patients most likely to benefit from primary prevention ICD is challenging. Purpose To investigate the long-term incidence of appropriate and inappropriate ICD therapy in patients with HCM. Methods This was a retrospective cohort study including all patients with HCM and ICDs implanted between 1995 to 2022 in our region (population 2.6 million). Both patients treated with primary and secondary prevention ICD were included. Data was retrieved from medical records. Patients with an ICD implanted for primary prevention were stratified into risk groups according to European Society of Cardiology HCM Risk-SCD score. Results We included 187 patients (65% male) with HCM and ICDs implanted for primary (80%) or secondary (20%) prevention. The median age at ICD implantation was 50 years (IQR: 40 to 61 years). During a mean follow-up of 9 years (IQR: 4 to 12 years), 53 patients (28%) experienced appropriate ICD therapy (antitachycardia pacing and/or shock), while 17 (9%) patients received inappropriate therapy. Patients with an ICD as secondary prevention were almost twice as likely to receive appropriate ICD therapy compared to patients with a primary prevention ICD (42% vs. 25%, p=0.04). The proportion of patients receiving appropriate shocks was 7 times higher in patients with an HCM Risk-SCD score ≥6% compared to patients with a risk score &amp;lt;4% (29% vs. 4%, p&amp;lt;0.001). There was no significant difference in the proportion of appropriate shocks between patients with a HCM Risk-SCD score ≥6% and patients with a risk score of 4%-6% (29% vs. 21%, p=0.49). (Figure 1). Conclusion One in four patients with HCM treated with an ICD experienced appropriate device intervention during 9 years of follow up. Patients with an HCM Risk-SCD score ≥6% had a 7-fold higher risk of appropriate ICD therapy compared to patients with an HCM Risk-SCD score &amp;lt;4%. There was no significant difference in the risk of appropriate shock therapy between patients with a risk score ≥6% and patients with a risk score of 4%-6%. The findings indicate the need for improved selection of patient with HCM for implantation of ICD’s.

Abstract 19218: Prognosis of Apical Hypertrophic Cardiomyopathy in Patients With Implantable Cardioverter Defibrillators

Aims: This Short Communication aims to identify the difference between the prognoses of apical and other types of hypertrophic cardiomyopathy (HCM) in patients with implantable cardioverter defibrillators (ICDs). Methods: We retrospectively analysed the database of our ICD clinic. All patients were implanted with ICDs from October 2006 to August 2017. We classified patients with HCM based on left ventricular (LV) outflow tract obstruction (LVOTO) and midventricular obstruction (MVO), in addition to apical HCM and other non-obstructive HCM types. Patient backgrounds, incidences of appropriate ICD therapies, hospitalizations for heart failure, electrical storms, and total deaths were examined. Results: A total of 64 consecutive Japanese patients with HCM and ICDs were enrolled in this study. The mean follow-up period was 86 ± 24 months, the mean age was 65 ± 14 years, and 83% of the patients were male. The LV ejection fraction was 56 ± 14%, the LV max wall-thickness was 19 ± 7 mm, 9.0% had LV apical aneurysms, and the HCM Risk-SCD was 4.4 ± 2.1. We classified the patients into two groups: 14 patients with apical HCM and 50 patients with other types of HCM. During the follow-up periods, there was no significant difference in the incidence of electrical storm, hospitalization for heart failure, and death between the 2 groups (p=0.11, p=0.60, p=0.39, respectively). Appropriate therapies occurred in 6 of 14 (43%) patients with apical HCM patients and 5 of 50 (10%) patients with other types of HCM (p=0.010). Conclusions: Compared with patients with other types of HCM, patients with apical HCM have a comparative prognosis, and treatment of ventricular arrhythmias should be strongly considered.

Improving sudden cardiac death risk stratification in hypertrophic cardiomyopathy using established clinical variables and genetic information

Background and aimsThe cardiac societies of Europe and the United States have established different risk models for preventing sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM). The aim of this study is to validate current SCD risk prediction methods in a German HCM cohort and to improve them by the addition of genotype information.MethodsHCM patients without prior SCD or equivalent arrhythmic events ≥ 18 years of age were enrolled in an expert cardiomyopathy center in Germany. The primary endpoint was defined as SCD/-equivalent within 5 years of baseline evaluation. 5-year SCD-risk estimates and recommendations for ICD implantations, as defined by the ESC and AHA/ACC guidelines, were analyzed. Multivariate cox proportional hazards analyses were integrated with genetic findings as additive SCD risk.Results283 patients were included and followed for in median 5.77 years (2.92; 8.85). A disease-causing variant was found in 138 (49%) patients. 14 (5%) patients reached the SCD endpoint (5-year incidence 4.9%). Kaplan–Meier survival analysis shows significantly lower overall SCD event-free survival for patients with an identified disease-causing variant (p < 0.05). The ESC HCM Risk-SCD model showed an area-under-the-curve (AUC) of 0.74 (95% CI 0.68–0.79; p < 0.0001) with a sensitivity of 0.29 (95% CI 0.08–0.58) and specificity of 0.83 (95% CI 0.78–0.88) for a risk estimate ≥ 6%/5-years. By comparison, the AHA/ACC HCM SCD risk stratification model showed an AUC of 0.70 (95% CI 0.65–0.76; p = 0.003) with a sensitivity of 0.93 (95% CI, 0.66–0.998) and specificity of 0.28 (95% CI 0.23–0.34) at the respective cut-off. The modified SCD Risk Score with genetic information yielded an AUC of 0.76 (95% CI 0.71–0.81; p < 0.0001) with a sensitivity of 0.86 (95% CI 0.57–0.98) and specificity of 0.69 (95% CI 0.63–0.74). The number-needed-to-treat (NNT) to prevent 1 SCD event by prophylactic ICD-implantation is 13 for the ESC model, 28 for AHA/ACC and 9 for the modified Genotype-model.ConclusionThis study confirms the performance of current risk models in clinical decision making. The integration of genetic findings into current SCD risk stratification methods seem feasible and can add in decision making, especially in borderline risk-groups. A subgroup of patients with high SCD risk remains unidentified by current risk scores.Graphical abstract

Sudden Death Risk Assessment in Hypertrophic Cardiomyopathy Across the Lifespan: Reconciling the American and European Approaches.

Hypertrophic cardiomyopathy (HCM) is the most prevalent inherited cardiac disease. Since the modern description of HCM more than seven decades ago, great focus has been placed on preventing its most catastrophic complication: sudden cardiac death (SCD). Implantable cardioverter-defibrillators (ICD) have been recognized to provide effective prophylactic therapy. Over the years, two leading societies, the European Society of Cardiology (ESC) and the American Heart Association/American College of Cardiology (AHA/ACC), have proposed risk stratification models to assess SCD in adults. European guidelines rely on a risk calculator, the HCM Risk-SCD, while American guidelines propose a stand-alone risk factor approach. Recently, risk prediction models were also developed in the pediatric population. This article reviews the latest recommendations on the risk stratification of SCD in HCM and summarises current indications for ICD use.

Deep learning-derived 12-lead electrocardiogram-based genotype prediction for hypertrophic cardiomyopathy: a pilot study

Objective: Given the psychosocial and ethical burden, patients with hypertrophic cardiomyopathy (HCMs) could benefit from the establishment of genetic probability prior to the test. This study aimed to develop a simple tool to provide genotype prediction for HCMs. Methods: A convolutional neural network (CNN) was built with the 12-lead electrocardiogram (ECG) of 124 HCMs who underwent genetic testing (GT), externally tested by predicting the genotype on another HCMs cohort (n = 54), and compared with the conventional methods (the Mayo and Toronto score). Using a third cohort of HCMs (n = 76), the role of the network in risk stratification was explored by calculating the sudden cardiac death (SCD) risk scorers (HCM risk-SCD) across the predicted genotypes. Score-CAM was employed to provide a visual explanation of the network. Results: Overall, 80 of 178 HCMs (45%) were genotype-positive. Using the 12-lead ECG as input, the network showed an area under the curve (AUC) of 0.89 (95% CI, 0.83–0.96) on the test set, outperforming the Mayo score (0.69 [95% CI, 0.65–0.78], p < 0.001) and the Toronto score (0.69 [95% CI, 0.64–0.75], p < 0.001). The network classified the third cohort into two groups (predicted genotype-negative vs. predicted genotype-positive). Compared with the former, patients predicted genotype-positive had a significantly higher HCM risk-SCD (0.04 ± 0.03 vs. 0.03 ± 0.02, p <0.01). Visualization indicated that the prediction was heavily influenced by the limb lead. Conclusions: The network demonstrated a promising ability in genotype prediction and risk assessment in HCM.

Cardiac index: A superior parameter of cardiac function than left ventricular ejection fraction in risk stratification of hypertrophic cardiomyopathy

An appropriate indicator of cardiac function in the risk stratification of hypertrophic cardiomyopathy (HCM) patients is urgently needed. Cardiac index that reflects cardiac pumping function may be suitable. To investigate the clinical significance of reduced cardiac index in HCM patients. A total of 927 HCM patients were enrolled. The primary endpoint was cardiovascular death; the secondary endpoints were sudden cardiac death (SCD) and all-cause death. Combination models were constructed by adding reduced cardiac index and reduced left ventricular ejection fraction (LVEF) to the HCM risk-SCD model. Predictive accuracy was determined by C-statistics. Reduced cardiac index was defined as cardiac index of ≤2.42 L/min/m2. During a median follow-up period of 4.3 years, 51 patients reached the endpoint. The reduced cardiac index independently increased the risk of cardiovascular death (adjusted HR [aHR] 2.976, P=0.007), SCD (aHR 6.385, P=0.001), and all-cause death (aHR 2.428, P=0.010). By adding reduced cardiac index to the HCM risk-SCD model, the model C-statistic increased from 0.691 to 0.762, with an integrated discrimination improvement of 0.021 (P=0.018) and a net reclassification improvement of 0.560 (P=0.007); the addition of reduced LVEF failed to improve the original model. Better predictive accuracy for all endpoints was also indicated in reduced cardiac index than in reduced LVEF. Reduced cardiac index is an independent predictor of poor prognoses in HCM patients. Combining reduced cardiac index rather than reduced LVEF improved the HCM risk-SCD stratification strategy. The reduced cardiac index showed better predictive accuracy than reduced LVEF for all endpoints.

Prevalence, characteristics, and natural history of apical phenotype in a large cohort of patients with hypertrophic cardiomyopathy

BackgroundApical hypertrophic cardiomyopathy (ApHCM) is a variant of hypertrophic cardiomyopathy (HCM) with distinct imaging and clinical characteristics. Data on the prognosis of this HCM subgroup appear conflicting. Our study aims to clarify the natural history of ApHCM and identify predictors of outcomes. Materials and methodsA total of 856 patients with HCM were retrospectively examined. ApHCM was defined as asymmetric left ventricular hypertrophy confined predominantly at the apex, either isolated (pure ApHCM type) or with co-existent hypertrophy of the interventricular septum (mixed ApHCM). Echocardiographic, clinical, and survival data were compared between individuals with ApHCM and non-ApHCM. ResultsA total of 143 (16.7%) patients were diagnosed with ApHCM. Compared with non-ApHCM, subjects with apical HCM were diagnosed at an older age and had better echocardiographic indices and more comorbidities at baseline. Apical aneurysms were more prevalent among the ApHCM phenotype (6.3% vs. 1.7%, p = 0.003). During a mean follow-up of 6 ± 3 years, ApHCM was characterized by lower all-cause, cardiovascular, heart failure-related mortality, and ventricular arrhythmic events compared with non-ApHCM. Multivariate analysis identified atrial fibrillation and HCM risk-sudden cardiac death (SCD) as independent predictors of the composite outcome of overall mortality and hospitalizations for cardiovascular reasons (hazard ratio [HR] 4.3, 95% confidence interval [CI] 1.9-9.5 for atrial fibrillation and HR 1.2, 95% CI 1.02-1.3 for HCM risk-SCD) in ApHCM. ConclusionsApHCM exhibited a lower rate of all-cause mortality and arrhythmic events in the middle-aged population of patients with HCM. Atrial fibrillation and HCM risk-sudden cardiac death were independent predictors of a composite of overall mortality and cardiovascular hospitalizations among those with ApHCM.

The Value of Myocardial Fibrosis Parameters Derived from Cardiac Magnetic Resonance Imaging in Risk Stratification for Patients with Hypertrophic Cardiomyopathy

The aim of the study was to determine whether myocardial fibrosis parameters of cardiac magnetic resonance imaging (MRI) has added value in the risk stratification of hypertrophic cardiomyopathy (HCM) patients. In this retrospective study, 108 patients with HCM (mean age ± standard deviation, 55.5 ± 13.4 years) were included from January 2019 to April 2022, and were followed up for 2 years to record sudden cardiac death (SCD) adverse events. All HCM patients underwent cardiac MRI and were divided into a training cohort (n=81; mean age, 56.1 ± 13.0 years) and a validation cohort (n=27; mean age, 57.8 ± 13.9 years). According to the presence of SCD risk factors defined by the 2020 AHA/ACC guidelines, HCM patients were classified into low-risk and high-risk groups. Cardiac MRI features, including late gadolinium enhancement (LGE), T1 mapping, and extracellular volume fraction (ECV), were assessed and compared between the two groups. Logistic regression analysis was used to select the optimal predictors of SCD from cardiac MRI features and HCM Risk-SCD score to construct prediction models. Receiver operating curve (ROC) analysis was used to assess the predictive performance of the constructed prediction model. Cox regression analysis was also used to determine the optimal predictors of SCD adverse events. Multivariate logistic analysis showed that the global ECV was the single myocardial fibrosis parameter predictive of the risk of SCD (p < 0.001). The areas under the ROC curves (AUC) of global ECV were higher than those of LGE, global native T1, global postcontrast T1, and HCM Risk-SCD (AUC=0.85 vs. 0.74, 0.77, 0.63, 0.78). An integrative risk stratification model combining global ECV (odds ratio, 1.36 [95% CI: 1.16-1.60]; p < 0.001) and HCM Risk-SCD score (odds ratio, 1.63 [95% CI: 1.08-2.47]; p< 0.001) achieved an AUC of 0.89 (95% CI: 0.81-0.96) in the training cohort, which was significantly higher than that of HCM Risk-SCD score alone (p=0.03). The AUC of the integrative model was 0.93 (95% CI: 0.84-1.00) in the validation cohort. Multivariate Cox regression analysis also showed that the global ECV was an independent predictor of SCD adverse events (hazard ratio, 1.27 [95% CI: 1.10-1.47]). The ECV derived from cardiac MRI is comparable to the HCM Risk-SCD scale in predicting the SCD risk stratification in patients with HCM.

Prognosis of patients with hypertrophic cardiomyopathy and low-normal left ventricular ejection fraction

ObjectiveTo investigate whether low-normal left ventricular ejection fraction (LVEF) is associated with adverse outcomes in hypertrophic cardiomyopathy (HCM) and evaluate the incremental value of predictive power of LVEF in the...

104 EFFECTIVENESS OF 2014 ESC HCM RISK-SCD SCORE IN PREDICTION OF APPROPRIATE ICD SHOCK

Abstract Background Since 2014, European Guidelines have recommended using a novel clinical risk prediction model, called the HCM Risk-SCD, to identify which patients with hypertrophic cardiomyopathy (HCM) who are at higher risk for sudden cardiac death (SCD) and would benefit most from having a prophylactic cardioverter defibrillator (ICD) implantable. In recent years, evidence has called into question the effectiveness of this model in patient selection. Material and Methods Data from consecutive patients with HCM and ICD who were followed at our Cardiology Department from January 2000 to January 2022 were retrospectively collected. Results Among 702 HCM patients, 52 (7%) received an ICD (female, 31%; mean age at implantation, 49 ± 20; S-ICD, 13%; single-chamber ICD, 50%). Of them, 7 (13%) patients were implanted for secondary prevention while 45 (87%) for primary prevention. Out of the 45 primary prevention patients, 7 (16%) experienced 8 appropriate shocks (AS) while 10 (22%) had 15 inappropriate shocks (IS), during a mean follow up of 8 ± 5 years. Overall, mean HCM Risk-SCD score was 6.8 ± 4.7. Patients with AS showed a non-significant higher risk score when compared to non-AS subjects (7.1 ± 5.4 vs 4.8 ± 5.1, p=0.27). By stratifying study population according to pre-established and clinically adopted HCM Risk-SCD score cut-off (such as &amp;lt; 4 vs. &amp;gt; or = 4), we failed to highlight any significant difference in term of AS between groups. Indeed, out of 10 (22%) patients with HCM Risk-SCD score &amp;lt; 4, 3 (7%) experienced AS, while among 35 (78%) patients with HCM Risk-SCD score &amp;gt; or = 4, 4 (9%) had AS (30% vs. 11%, p=0.17). Conclusions The HCM Risk-SCD score does not seem to be able to predict the occurrence of life-threatening ventricular arrhythmias and AS in HCM patients. Our data highlight the need to elaborate a new score in order to improve SCD risk stratification and better select patients for ICD implantation.

Abstract 12506: Assessment of the Left Ventricular Trabeculation Phenotype Contributes to Risk Stratification in Hypertrophic Cardiomyopathy Patients

Aims: To investigate clinical values associated with the “left ventricular (LV) trabeculation phenotype” that did not meet the diagnostic criteria of LV noncompaction (LVNC) in hypertrophic cardiomyopathy (HCM). Hypothesis: The presence of left ventricular trabeculation phenotype was associated with a poor prognosis in HCM patients. Genetic factors contributed to this particular phenotype. Methods: A total of 940 HCM patients were recruited. The primary endpoint was cardiovascular death; the secondary endpoints were sudden cardiac death (SCD) and all-cause death. The LV trabeculation phenotype was assessed by cardiac magnetic resonance. Baseline and survival analyses regarding the LV trabeculation phenotype were conducted in general HCM patients and obstructive subset. A combination model was constructed by adding the LV trabeculation phenotype to the HCM risk-SCD model. The whole exome sequencing was used for genetic analysis. Results: The LV trabeculation phenotype was recognized in 34.5% HCM patients and was mostly observed at the apex and free wall. Patients with the LV trabeculation phenotype were younger, had a higher prevalence of family history of SCD and more frequent presence of late gadolinium enhancement. Cox proportional hazards regression analysis showed that the LV trabeculation phenotype increased the risk of cardiovascular death (P=0.031), SCD (P=0.008), and all-cause death (P=0.047) in obstructive HCM patients. Furthermore, the LV trabeculation phenotype-combined model improved risk stratification for SCD. The genetic analysis showed that DTNA variants were more frequently identified in patients with the LV trabeculation phenotype. Conclusions: The LV trabeculation phenotype contributes to risk stratification in obstructive HCM, thus this parameter should be assessed in obstructive HCM patients even if the degree of trabeculation does not fulfil the LVNC criteria. DTNA mutation underlies the cause of this phenotype.

Ventricular activation time as a marker for complex ventricular arrhythmias, sudden cardiac death and ICD implantation in hypertrophic cardiomyopathy

Abstract Background Identification of patients with hypertrophic cardiomyopathy (HCM) who are at high risk of sudden cardiac death (SCD) is essential, as life-threatening ventricular arrhythmias (VAs) can be effectively prevented with an implantable cardioverter defibrillator (ICD). Risk stratification using HCM Risk-SCD score is recommended in the 2014 ESC guidelines for the management of patients with HCM. However, the role of electrocardiogram (ECG) in risk stratification is less well established. Purpose We investigated the association between the ventricular activation time (VAT), an ECG marker of the duration of ventricular depolarization, with the HCM Risk-SCD score, the presence of complex VAs, the implantation of an ICD and ICD-therapy for VAs. Methods Patients with a history of HCM, documented by echocardiography, were studied. The 12-lead ECG and echocardiography were performed on the same day. On ECG, we evaluated the VAT in milliseconds, between the onset of the QRS complex to the peak of R wave on V5 or V6 precordial ECG leads. A careful personal and family history for unexplained syncope and SCD was obtained, respectively. The presence of non-sustained ventricular tachycardia (NSVT) and ICD therapy were assessed either with 48h ECG recording or ICD interrogation. HCM Risk-SCD score was calculated by the corresponding ESC online calculator. Statistical analysis was performed using the SPSS software. Results Thirty-three patients with HCM were included (mean age 51±12 years, 78.8% male). According to the HCM Risk-SCD score, HCM patients were divided into three groups: low-risk (&amp;lt;4, 45.5%), intermediate-risk (4 to &amp;lt;6, 24.2%) and high-risk (&amp;gt;6, 30.3%) for SCD. Approximately half of the HCM patients had episodes of NSVT (48.5%) and an ICD (48.5%). Ventricular activation time was significantly prolonged in high-risk group compared either with intermediate-risk (74±24.5 vs 48.7±16.4, p=0.006) or low-risk (74±24.5 vs 44.3±12.6, p&amp;lt;0.001). HCM patients with NSVT had a significantly prolonged VAT compared with those without NSVT (62.19±25.2 vs 47.06±15.3, p=0.022). Moreover, HCM patients with an ICD had a higher VAT than those without an ICD (62.1±25.2 vs 47±15.3, p=0.003). VAT was also significantly prolonged in the group with appropriate ICD therapy for VAs compared with that without ICD therapy (82.8±22.1 vs 52.2±16.4, p=0.007). Finally, there was a significant positive correlation between VAT and NSVT (r=0.352, p=0.044), history of syncope (r=0.604, p&amp;lt;0.001), HCM risk-SCD score (r=0.493, p=0.004), ICD implantation (r=0.508, p=0.003) and ICD therapy (r=0.648, p=0.007) for VAs while there was not a correlation with the family history of SCD. Conclusion Prolongation of VAT is associated with NSVT, HCM risk score for SCD, ICD implantation and ICD therapy for VAs in patients with HCM and it seems to be a novel, easy to measure, ECG marker for risk stratification. Funding Acknowledgement Type of funding sources: None.

Deceleration and acceleration capacities of heart rate in patients with hypertrophic cardiomyopathy: results of a five-year prospective study

Aim. To study the features of deceleration capacity (DC) and acceleration capacity (AC) in patients with hypertrophic cardiomyopathy (HCM) and assess the correlation of these indicators with known complications and risk factors for sudden cardiac death (SCD).Material and methods. A total of 50 patients with HCM were examined. Comparable by sex and age, the control group included 50 individuals without cardiovascular diseases. All patients underwent 24-hour electrocardiographic monitoring with the determination of DC and AC. The follow-up period lasted 5 years. Lethal outcomes were recorded in 6%, including SCD — 4%.Results. Patients with HCM were characterized by significantly lower mean DC — 5,5 [3,7; 7,4] vs 7,8 [7,1; 8,5] ms (p=0,0001) and significantly higher AC– -7,4 [-8,9; -5,8] vs -9,3 [-10,0; -8,6] ms (p=0,001) compared with the control group. In 36% of patients with HCM, pathological values of DC ≤4,5 ms were detected, while in all individuals in the control group, DC corresponded to a favorable prognosis (p=0,001). In patients with and without nonsustained ventricular tachycardia (NSVT), a decrease in DC was detected in 56% and 25,5% (p=0,043), respectively, while with and without heart failure (HF) — in 78% and 27% (p=0,016), respectively. In patients with HF (p=0,003) and paroxysmal atrial fibrillation (p=0,023), mean DC values were significantly lower than in patients without these complications. HF was an independent predictor of DC reduction. DC decrease was not associated with an increase in SCD risk estimated using the HCM Risk-SCD calculator.Conclusion. Patients with HCM compared with healthy individuals are characterized by lower DC and higher AC values. Among patients with HCM, a decrease in DC is typical for patients with a more severe disease course (HF, AF, NSVT). However, the rationale for using this indicator in SCD risk stratification needs to be clarified.

Machine learning-based phenotyping and risk assessment of hypertrophic cardiomyopathy - linking ECGs, morphology and genotypes

Abstract Funding Acknowledgements Type of funding sources: None. Background Integrating clinical data to distinguish hypertrophic cardiomyopathy (HCM) phenotypes is relevant in clinical practice. Machine learning (ML) can help - deep learning (DL) networks can automate detection and segmentation of 12-lead electrocardiograms (ECGs), whereas unsupervised learning can group patients to compare ECG, imaging and genetic characteristics. The aim is to automate ECG morphology analysis from all 12 ECG leads and multiple beats, and relate this to HCM genotypes and imaging phenotypes. Methods The single-center cohort included phenotype- and genotype-positive (G+) HCM patients (n = 104) and their phenotype-negative relatives (n = 50, 42% G+). All patients had a digital 12-lead ECG, echocardiography, and a magnetic resonance (CMR) study performed. The workflow is shown in Fig 1. A U-Net DL network was used for ECG delineation (P, QRS, T onsets/offsets) for all cardiac cycles. Three heartbeats were selected for each patient based on their morphology, with the aim of capturing beat-to-beat variability. An unsupervised representation learning algorithm was used to fuse ECG data and assess inter-patient similarities. Patients were clustered based on similarities of ECG biomarkers, and compared with regards to genotypes, family history of sudden cardiac death (SCD), history of ventricular arrhythmias/syncope/aborted SCD, implanted defibrillators (ICD), left ventricular (LV) obstruction, maximal wall thickness, late gadolinium enhancement (LGE), and HCM risk-SCD score. Results ML based on ECG biomarkers provided a good separation of HCM patients and relatives (Fig 1A), also showing G- and patients with variants of uncertain significance grouping together (Fig 1B). Clustering resulted in 6 ECG phenogroups (C1-6). C1 and 2 were related to less comorbidities, cardiac remodeling, and HCM risk score, capturing the majority of G- patients. C3 and 4 were related to LV obstruction – where C4 consisted of symptomatic patients with high ICD implantation and event rates, high LGE, and impaired systolic function. C5 captured patients with high comorbidities, extremely remodeled hearts, but no obstruction, whereas C6 patients with positive family history and high arrhythmic events (Fig 1C, Table 1). The average ECG morphology is shown side-by-side for C1 and C5 in Fig 1D – negative T waves, increased R/S wave amplitudes, left axis deviation (LAD) and ST elevation can be recognized as macro-biomarkers in C5 (yellow arrows). Conclusion ML can automate the analysis of complex clinical data, simultaneously taking into account the morphology of all ECG components in all 12-leads, throughout multiple beats, compare it with clinical and imaging data, and identify clinically sensible phenogroups as validated by structural and functional findings, as well as with genotypes and clinical information. Automated and comprehensive cardiac data analysis has diagnostic and research potential to help screen populations and phenotype disease etiologies. Abstract Figure 1: analysis pipeline Abstract Table 1: clinical variables