Sudden Cardiac Death Risk Prediction Research Articles

Quantifying late gadolinium enhancement improved sudden cardiac death risk prediction in non-ischemic dilated cardiomyopathy

Abstract Background While previous studies have proved the late gadolinium enhancement (LGE) was associated with poor prognosis in non-ischemic dilated cardiomyopathy (DCM), the optimal method for LGE quantification and the ability of LGE extent in identifying sudden cardiac death (SCD) remain less clarified. Purpose This study sought to compare the reproducibility of LGE quantification methods and explore the asssociation between LGE extent and SCD endpoint in DCM patients. Methods In this prospective study, LGE was quantified by the 2-6 standard deviations (SD) above the remote myocardium and full-wide half-maximum (FWHM) methods. The primary endpoint was SCD and arrythmia endpoint included SCD and aborted SCD. Reproducibility of LGE quantification was measured by Bland-Altman analysis and intra-class correlation coefficients (ICC). Competing risk regression analysis, C-index and area under the curve (AUC) were performed to identify the prognostic value. Results Among the 770 patients, 336 (44%) patients had LGE. FWHM demonstrated the best reproducibility compared with other quantification methods (intraobserver variability: ICC = 0.94, mean bias: -0.43 ± 4.89%; interobserver variability: ICC = 0.90, mean bias: -2.65 ± 6.40%). After a median follow-up of 49 months, SCD occurred in 61 (8%) patients and arrythmia endpoint occurred in 87 (11%) patients. Among all quantification method, LGE extent measured by FWHM showed the highest predictive value of SCD (C index: 0.72) and arrythmia events (C index: 0.71) than other methods. In the competing risk analysis, LGE extent was independently association with SCD (Hazard ratio [HR] 1.05, 95% confidence interval [CI] 1.03 - 1.07, P < 0.001) and arrythmia endpoint (HR 1.05, 95% CI 1.03 - 1.06, P < 0.001). LGE extent >8% showed significantly higher risk of SCD and arrythmia endpoint than those with LGE extent ≤8% (P < 0.001). Incoporating LGE exent and 35% left ventricular ejection fraction (LVEF) significantly improved the SCD (C index: 0.74 vs 0.61, P < 0.001) and arrythmia endpoint (C index: 0.73 vs 0.59, P < 0.001) prediction compared with LVEF. Conclusions FWHM demonstrated best reproducibility and highest SCD prediction ability among LGE quantification methods. Adding LGE extent to LVEF significantly improved the predictive value of SCD and arrythmia endpoint.Figure 1

Implementation of a sudden cardiac death risk prediction tool in clinical practice through electronic health records (INSERT-HCM study design)

Implementation of a sudden cardiac death risk prediction tool in clinical practice through electronic health records (INSERT-HCM study design)

Ankle-Brachial Index and Risk of Sudden Cardiac Death in the Community: The ARIC Study.

Sudden cardiac death (SCD) is a significant global public health problem accounting for 15% to 20% of all deaths. A great majority of SCD is associated with coronary heart disease, which may first be detected at autopsy. The ankle-brachial index (ABI) is a simple, noninvasive measure of subclinical atherosclerosis. The purpose of this study was to examine the relationship between ABI and SCD in a middle-aged biracial general population. Participants of the ARIC (Atherosclerosis Risk in Communities) study with an ABI measurement between 1987 and 1989 were included. ABI was categorized as low (≤0.90), borderline (0.90-1.00), normal (1.00-1.40), and noncompressible (>1.40). SCD was defined as a sudden pulseless condition presumed to be caused by a ventricular tachyarrhythmia in a previously stable individual and was adjudicated by a committee of cardiac electrophysiologists, cardiologists, and internists. Cox proportional hazards models were used to evaluate the associations between baseline ABI and incident SCD. Of the 15 081 participants followed for a median of 23.5 years, 556 (3.7%) developed SCD (1.96 cases per 1000 person-years). Low and borderline ABIs were associated with an increased risk of SCD (demographically adjusted hazard ratios [HRs], 2.27 [95% CI, 1.64-3.14] and 1.52 [95% CI, 1.17-1.96], respectively) compared with normal ABI. The association between low ABI and SCD remained significant after adjustment for traditional cardiovascular risk factors (HR, 1.63 [95% CI, 1.15-2.32]). Low ABI is independently associated with an increased risk of SCD in a middle-aged biracial general population. ABI could be incorporated into future SCD risk prediction models.

A paradigm change in sudden cardiac death risk prediction: 'static' goes out, 'dynamic' comes in.

A paradigm change in sudden cardiac death risk prediction: 'static' goes out, 'dynamic' comes in.

Dynamic electrocardiogram changes are a novel risk marker for sudden cardiac death.

Electrocardiogram (ECG) abnormalities have been evaluated as static risk markers for sudden cardiac death (SCD), but the potential importance of dynamic ECG remodelling has not been investigated. In this study, the nature and prevalence of dynamic ECG remodelling were studied among individuals who eventually suffered SCD. The study population was drawn from two prospective community-based SCD studies in Oregon (2002, discovery cohort) and California, USA (2015, validation cohort). For this present sub-study, 231 discovery cases (2015-17) and 203 validation cases (2015-21) with ≥2 archived pre-SCD ECGs were ascertained and were matched to 234 discovery and 203 validation controls based on age, sex, and duration between the ECGs. Dynamic ECG remodelling was measured as progression of a previously validated cumulative six-variable ECG electrical risk score. Oregon SCD cases displayed greater electrical risk score increase over time vs. controls [+1.06 (95% confidence interval +0.89 to +1.24) vs. -0.05 (-0.21 to +0.11); P < .001]. These findings were successfully replicated in California [+0.87 (+0.7 to +1.04) vs. -0.11 (-0.27 to 0.05); P < .001]. In multivariable models, abnormal dynamic ECG remodelling improved SCD prediction over baseline ECG, demographics, and clinical SCD risk factors in both Oregon [area under the receiver operating characteristic curve 0.770 (95% confidence interval 0.727-0.812) increased to area under the receiver operating characteristic curve 0.869 (95% confidence interval 0.837-0.902)] and California cohorts. Dynamic ECG remodelling improved SCD risk prediction beyond clinical factors combined with the static ECG, with successful validation in a geographically distinct population. These findings introduce a novel concept of SCD dynamic risk and warrant further detailed investigation.

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 cardiac death in childhood RASopathy-associated hypertrophic cardiomyopathy: Validation of the HCM risk-kids model and predictors of events

BackgroundRASopathies account for nearly 20% of cases of childhood hypertrophic cardiomyopathy (HCM). Sudden cardiac death (SCD) occurs in patients with RASopathy-associated HCM, but the risk factors for SCD have not been systematically evaluated. AimTo validate the HCM Risk-Kids SCD risk prediction model in children with RASopathy-associated HCM and investigate potential specific SCD predictors in this population. MethodsValidation of HCM Risk-Kids was performed in a retrospective cohort of 169 patients with a RASopathy-associated HCM from 15 international paediatric cardiology centres. Multiple imputation by chained equations was used for missing values related to the HCM Risk-Kids parameters. ResultsEleven patients (6.5%) experienced a SCD or equivalent event at a median age of 12.5 months (IQR 7.7–28.64). The calculated SCD/equivalent event incidence was 0.78 (95% CI 0.43–1.41) per 100 patient years. Six patients (54.54%) with an event were in the low-risk category according to the HCM Risk-Kids model. Harrell's C index was 0.60, with a sensitivity of 9.09%, specificity of 63.92%, positive predictive value of 1.72%, and negative predictive value of 91%; with a poor distinction between the different risk groups. Unexplained syncope (HR 42.17, 95% CI 10.49–169.56, p < 0.001) and non-sustained ventricular tachycardia (HR 5.48, 95% CI 1.58–19.03, p < 0.007) were predictors of SCD on univariate analysis. ConclusionUnexplained syncope and the presence of NSVT emerge as predictors for SCD in children with RASopathy-associated HCM. The HCM Risk-Kids model may not be appropriate to use in this population, but larger multicentre collaborative studies are required to investigate this further.

Radiomics of Late Gadolinium Enhancement Reveals Prognostic Value of Myocardial Scar Heterogeneity in Hypertrophic Cardiomyopathy

BackgroundLate gadolinium enhancement (LGE) scar burden by cardiac magnetic resonance is a major risk factor for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM). However, there is currently limited data on the incremental prognostic value of integrating myocardial LGE radiomics (ie, shape and texture features) into SCD risk stratification models. ObjectivesThe purpose of this study was to investigate the incremental prognostic value of myocardial LGE radiomics beyond current European Society of Cardiology (ESC) and American College of Cardiology (ACC)/American Heart Association (AHA) models for SCD risk prediction in HCM. MethodsA total of 1,229 HCM patients (62% men; age 52 ± 16 years) from 3 medical centers were included. Left ventricular myocardial radiomic features were calculated from LGE images. Principal component analysis was used to reduce the radiomic features and calculate 3 principal radiomics (PrinRads). Cox and logistic regression analyses were then used to evaluate the significance of the extracted PrinRads of LGE images, alone or in combination with ESC or ACC/AHA models, to predict SCD risk. The ACC/AHA risk markers include LGE burden using a dichotomized 15% threshold of LV scar. ResultsSCD events occurred in 30 (2.4%) patients over a follow-up period of 49 ± 28 months. Risk prediction using PrinRads resulted in higher c-statistics than the ESC (0.69 vs 0.57; P = 0.02) and the ACC/AHA (0.69 vs 0.67; P = 0.75) models. Risk predictions were improved by combining the 3 PrinRads with ESC (0.73 vs 0.57; P < 0.01) or ACC/AHA (0.76 vs 0.67; P < 0.01) risk scores. The net reclassification index was improved by combining the PrinRads with ESC (0.25 [95% CI: 0.08-0.43]; P = 0.005) or ACC/AHA (0.05 [95% CI: −0.07 to 0.16]; P = 0.42) models. One PrinRad was a significant predictor of SCD risk (HR: 0.57 [95% CI: 0.39-0.84]; P = 0.01). LGE heterogeneity was a major component of PrinRads and a significant predictor of SCD risk (HR: 0.07 [95% CI: 0.01-0.75]; P = 0.03). ConclusionsMyocardial LGE radiomics are strongly associated with SCD risk in HCM and provide incremental risk stratification beyond current ESC or AHA/ACC risk models. Our proof-of-concept study warrants further validation.

Haemodynamic Gain Index Is Associated with Risk of Sudden Cardiac Death and Improves Risk Prediction: A Cohort Study

Introduction: Haemodynamic gain index (HGI) is a novel haemodynamic parameter which can be obtained from cardiopulmonary exercise testing (CPX), but its association with sudden cardiac death (SCD) is not known. We aimed to assess the association of HGI with SCD risk in a long-term prospective cohort study. Methods: HGI was calculated using heart rate and systolic blood pressure (SBP) responses measured in 1897 men aged 42–61 years during CPX from rest to peak exercise. Cardiorespiratory fitness (CRF) was measured using respiratory gas exchange analysis. Multivariable adjusted hazard ratios (HRs) (95% confidence intervals [CIs]) were estimated for SCD. Results: During a median follow-up of 28.7 years, 205 SCDs occurred. The risk of SCD decreased gradually with increasing HGI (p value for non-linearity = 0.63). A unit (bpm/mm Hg) higher HGI was associated with a decreased risk of SCD (HR: 0.84; 95% CI: 0.71–0.99), which was attenuated following adjustment for CRF. CRF was inversely associated with SCD, which remained after further adjustment for HGI (HR: 0.85; 95% CI: 0.77–0.94) per each unit higher CRF. Addition of HGI to a SCD risk prediction model containing established risk factors improved risk discrimination (C-index change = 0.0096; p = 0.017) and reclassification (net reclassification improvement [NRI] = 39.40%, p = 0.001). The corresponding values for CRF were (C-index change = 0.0178; p = 0.007) and (NRI = 43.79%, p = 0.001). Conclusion: Higher HGI during CPX is associated with a lower SCD risk, consistent with a dose-response relationship but dependent on CRF levels. Though HGI significantly improves the prediction and classification of SCD beyond common cardiovascular risk factors, CRF remains a stronger risk indicator and predictor of SCD compared to HGI.

Prediction of Sudden Cardiac Death: Looking Beyond Ejection Fraction

Sudden cardiac death (SCD) is a major public health burden accounting for 15-20% of global mortality. Contemporary guidelines for SCD prevention are centered around the presence of low left ventricular ejection fraction, although the majority of SCD accrues in those not meeting contemporary criteria for SCD prevention. The goal of this review is to elaborate on the contemporary landscape of SCD prediction tools and further highlight gaps and opportunities in SCD prediction and prevention. There have been considerable advancements in both non-invasive and invasive measures for SCD risk prediction including clinical morbidities, electrocardiographic measures, cardiac imaging (nuclear, magnetic resonance, computed tomography), serum biomarkers, genetics, and invasively assessed electrophysiological characteristics. Novel methodological approaches including application of machine learning, incorporation of competing risk, and use of computational modeling have underscored a future of personalized risk prediction. SCD remains a vital public health challenge. Emerging methods highlight opportunities to improve SCD prediction in the majority of those at risk who do not meet contemporary criteria for SCD prevention therapies. Future efforts will need to focus on easily deployed, multi-parametric risk models that enrich for SCD risk and not for competing mortality.

Abstract 11615: The Prevalence and Association of Exercise Test Abnormalities With Sudden Cardiac Death and Transplant-Free Survival in Childhood Hypertrophic Cardiomyopathy

Introduction: Hypertrophic cardiomyopathy (HCM) can be associated with an abnormal exercise response. In adults with HCM, an abnormal exercise stress test (EST) is predictive of heart failure outcomes. Our goal was to determine if an abnormal exercise response is associated with adverse outcomes in pediatric HCM patients. Methods: : In an international cohort study with 20 centers (PRIMaCY), children &lt;18 years with primary HCM were included. Abnormal EST was defined as an abnormal heart rate or blood pressure response, arrhythmias and/or ischemic ECG changes on exercise. Sudden cardiac death (SCD) events were defined as a composite of SCD and aborted SCD including appropriate implantable cardioverter defibrillator discharges. Using Kaplan-Meier survival analyses, we analyzed the association of abnormal EST with SCD and transplant-free survival. Results: Of 724 eligible patients, 604 underwent at least one EST on follow-up. There were no differences in clinical characteristics between those with or without an EST. The median age at EST was 13.8yrs (IQR 11.0-15.7yrs), 76% were male, 55% were on beta-blockers. 299 (49.5%) had an abnormal EST. Patients with an abnormal EST had more severe septal hypertrophy, higher left atrial diameter z-scores, higher resting LV outflow gradient at first evaluation, and a higher proportion were genotype-positive (p&lt;0.05) compared to those with a normal EST. An abnormal EST was associated with lower 5-year transplant-free survival (99% vs. 95%, p=0.0082) and lower 5-year freedom from SCD events (96% vs. 92%, p=0.025) (Figure 1). Conclusions: Exercise abnormalities are common in childhood HCM. An abnormal EST in children with HCM was associated with lower transplant-free survival and more SCD events. Further analysis will determine if the inclusion of EST results can increase the predictive accuracy of current pediatric SCD risk prediction models.

Machine learning in sudden cardiac death risk prediction: a systematic review.

Most patients who receive implantable cardioverter defibrillators (ICDs) for primary prevention do not receive therapy during the lifespan of the ICD, whilst up to 50% of sudden cardiac death (SCD) occur in individuals who are considered low risk by conventional criteria. Machine learning offers a novel approach to risk stratification for ICD assignment. Systematic search was performed in MEDLINE, Embase, Emcare, CINAHL, Cochrane Library, OpenGrey, MedrXiv, arXiv, Scopus, and Web of Science. Studies modelling SCD risk prediction within days to years using machine learning were eligible for inclusion. Transparency and quality of reporting (TRIPOD) and risk of bias (PROBAST) were assessed. A total of 4356 studies were screened with 11 meeting the inclusion criteria with heterogeneous populations, methods, and outcome measures preventing meta-analysis. The study size ranged from 122 to 124 097 participants. Input data sources included demographic, clinical, electrocardiogram, electrophysiological, imaging, and genetic data ranging from 4 to 72 variables per model. The most common outcome metric reported was the area under the receiver operator characteristic (n = 7) ranging between 0.71 and 0.96. In six studies comparing machine learning models and regression, machine learning improved performance in five. No studies adhered to a reporting standard. Five of the papers were at high risk of bias. Machine learning for SCD prediction has been under-applied and incorrectly implemented but is ripe for future investigation. It may have some incremental utility in predicting SCD over traditional models. The development of reporting standards for machine learning is required to improve the quality of evidence reporting in the field.

Abstract P2118: Modeling Arrhythmia Risk In Human Stem Cell Derived Cardiomyocytes

In older (45+ years) populations, sudden cardiac death (SCD) is primarily caused by ischemic heart disease while in younger populations, SCD is often due to genetic causes. Despite recent advances in risk stratification based on clinical evaluation and genetic testing, our ability to predict SCD risk in the younger population remains inadequate. Implantable cardioverter defibrillators (ICDs) have been an essential intervention to reduce the number of out-of-hospital cardiac arrests in patients determined to be at risk of SCD. Nevertheless, one third of patients with an ICD receive inappropriate shocks and are exposed to infection without clear benefit. In addition, some children and young adults receive an ICD that will never deliver a shock even after decades of follow-up. No clear guidelines have been established to detect SCD-predisposing conditions due to cost, sensitivity, and specificity of testing modalities. Human iPSC-derived cardiomyocytes (hiPSC-CMs) are a widely recognized and reliable in vitro platform to study clinical phenotypes but have never been used in the context of SCD risk prediction. We hypothesize that we can establish an hiPSC-CMs cellular model to predict SCD risk. Sixty individuals were recruited and classified in one of five categories, ranging from healthy controls with no known cardiac disease (category 1) to individuals that show the highest SCD risk (category 5). These 5 categories are defined by 3 main parameters: 1) absence or presence of ICD, 2) whether the ICD was placed as primary or secondary prevention and 3) the number of shocks received after ICD implantation. hiPSCs were reprogrammed using blood samples and then differentiated into cardiomyocytes. Molecular and electrophysiology measurements were performed for the different risk categories using high-throughput systems. hiPSC-CMs were assayed by single-cell calcium imaging, microelectrode array and automated action potential and current recordings. In vitro measurements revealed that specific parameters such as field potential duration correlate with the SCD risk and outcome. In conclusion, this study will provide a cellular-based tool to determine patient-specific SCD risk in order to advance medical care for at-risk patients and their family members.

Coronary Artery Calcium for Risk Stratification of Sudden Cardiac Death: The Coronary Artery Calcium Consortium

BackgroundCoronary artery calcium (CAC) is a marker of plaque burden. Whether CAC improves risk stratification for incident sudden cardiac death (SCD) beyond atherosclerotic cardiovascular disease (ASCVD) risk factors is unknown. ObjectivesSCD is a common initial manifestation of coronary heart disease (CHD); however, SCD risk prediction remains elusive. MethodsThe authors studied 66,636 primary prevention patients from the CAC Consortium. Multivariable competing risks regression and C-statistics were used to assess the association between CAC and SCD, adjusting for demographics and traditional risk factors. ResultsThe mean age was 54.4 years, 33% were women, 11% were of non-White ethnicity, and 55% had CAC >0. A total of 211 SCD events (0.3%) were observed during a median follow-up of 10.6 years, 91% occurring among those with baseline CAC >0. Compared with CAC = 0, there was a stepwise higher risk (P trend < 0.001) in SCD for CAC 100 to 399 (subdistribution hazard ratio [SHR]: 2.8; 95% CI: 1.6-5.0), CAC 400 to 999 (SHR: 4.0; 95% CI: 2.2-7.3), and CAC >1,000 (SHR: 4.9; 95% CI: 2.6-9.9). CAC provided incremental improvements in the C-statistic for the prediction of SCD among individuals with a 10-year risk <7.5% (ΔC-statistic = +0.046; P = 0.02) and 7.5% to 20% (ΔC-statistic = +0.069; P = 0.003), which were larger when compared with persons with a 10-year risk >20% (ΔC-statistic = +0.01; P = 0.54). ConclusionsHigher CAC burden strongly associates with incident SCD beyond traditional risk factors, particularly among primary prevention patients with low-intermediate risk. SCD risk stratification can be useful in the early stages of CHD through the measurement of CAC, identifying patients most likely to benefit from further downstream testing.

Radiomics Analysis Derived From LGE-MRI Predict Sudden Cardiac Death in Participants With Hypertrophic Cardiomyopathy

Objectives: To identify significant radiomics features derived from late gadolinium enhancement (LGE) images in participants with hypertrophic cardiomyopathy (HCM) and assess their prognostic value in predicting sudden cardiac death (SCD) endpoint.Method: The 157 radiomic features of 379 sequential participants with HCM who underwent cardiovascular magnetic resonance imaging (MRI) were extracted. CoxNet (Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net) and Random Forest models were applied to optimize feature selection for the SCD risk prediction and cross-validation was performed.Results: During a median follow-up of 29 months (interquartile range, 20–42 months), 27 participants with HCM experienced SCD events. Cox analysis revealed that two selected features, local binary patterns (LBP) (19) (hazard ratio (HR), 1.028, 95% CI: 1.032–1.134; P = 0.001) and Moment (1) (HR, 1.212, 95%CI: 1.032–1.423; P = 0.02) provided significant prognostic value to predict the SCD endpoints after adjustment for the clinical risk predictors and late gadolinium enhancement. Furthermore, the univariately significant risk predictor was improved by the addition of the selected radiomics features, LBP (19) and Moment (1), to predict SCD events (P < 0.05).Conclusion: The radiomics features of LBP (19) and Moment (1) extracted from LGE images, reflecting scar heterogeneity, have independent prognostic value in identifying high SCD risk patients with HCM.

Abstract 10353: Independent Validation of the Ecg Risk Score for Sudden Death Risk Stratification in Pediatric Hypertrophic Cardiomyopathy

Background: Hypertrophic cardiomyopathy (HCM) is the leading cause of sudden cardiac death (SCD) in children and young adults. An ECG risk score tested in a Swedish cohort of 155 children with HCM had 97% sensitivity, 80% specificity, 45% positive predictive value (PPV), and 99% negative predictive value (NPV) for SCD risk prediction. Our goal was to validate the ECG risk score in an independent HCM cohort. Methods: Phenotype-positive pediatric HCM patients seen at a single center between 1987-2019 with at least 5 years follow-up from diagnosis, or a SCD event within 5 years of diagnosis, were retrospectively identified. First-evaluation ECGs were analyzed by a single operator blinded to patient outcomes. ECG risk score was calculated using QRS electrical axis, amplitude, duration, ST/T-wave abnormalities and QTc duration. The primary outcome was a composite of SCD, aborted sudden cardiac arrest, and appropriate shock from a prophylactic ICD. Results: Of 144 eligible patients with a median follow-up 14.6 yrs, 22 experienced SCD events within five years from diagnosis at a median age 12.1 yrs. The ECG risk score in patients experiencing a SCD event was 2-fold higher i.e. 8 (IQR 7-10) compared to the remaining cohort i.e. 4 (IQR 2-8) (p&lt;0.001). An ECG risk score &gt;5 had a sensitivity of 95% (CI 77%-100%), specificity 56% (46%-65%), PPV 28% (24%-33%), NPV 99% (91%-100%), and a receiver operating characteristic area under the curve of 0.76 for its ability to discriminate between patients with and without SCD events ( Figure 1 ). Compared to the discovery cohort, the score had strong sensitivity and NPV, but lower specificity and PPV. Conclusions: The ECG risk score for SCD risk stratification performed well in an independent Canadian validation cohort but overestimated SCD risk. Futher analysis will determine if inclusion of the ECG risk score can enhance the performance of currently available SCD risk prediction models for pediatric HCM.

Abstract 9715: Feasibility of Using Machine Learning to Establish a Risk Prediction Model for Sudden Cardiac Death in Patients with Hypertrophic Cardiomyopathy

Introduction: Risk stratification for sudden cardiac death (SCD) is a great challenge in managing hypertrophic cardiomyopathy (HCM). Hypothesis: This study attempted for the first time to build a machine learning (ML) model for SCD risk prediction. Methods: The present study consisted of 1631 consecutive adult HCM patients without a history of SCD events. Extreme gradient boosting (XGBoost) was applied for ML model construction. We compared the predictive performance of the ML model with that of conventional models by calculating C-statistics and interpreted the ML model output using the SHapley Additive exPlanation (SHAP) value. Results: Fifty patients reached SCD endpoints during a median follow-up of 4.6 years. The C-statistic of the ML model was 0.745 [95% confidence interval (CI), 0.723-0.766], which was significantly higher than that of the model recommended in the 2014 European Society of Cardiology guidelines (C-statistics =0.609; 95% CI 0.585-0.623) and the model recommended in the 2020 American College of Cardiology/American Heart Association guidelines (C-statistics =0.628; 95% CI 0.604-0.652). The ML model incorporated 12 features as predictors, including 5 conventional SCD risk markers (NSVT, quantification of LGE, family history of SCD, LVEF 50%, unexplained syncope), WBC, creatine, uric acid, TBIL and cholesterol, NT-proBNP and family history of HCM. Using SHAP values, we quantified the contribution of each feature within the ML model to the final prediction and visualized the relationship between features and SCD risk. Conclusion: Our study constructed an ML model for the prediction of SCD risk in patients with HCM and provided a clinically intuitive interpretation. The ML model showed better discrimination performance than conventional models, demonstrating the feasibility of ML in SCD risk prediction for patients with HCM.

Hypertrophic cardiomyopathy in children.

Hypertrophic cardiomyopathy (HCM) characterized by asymmetric ventricular septal hypertrophy, is the commonest cause of sudden cardiac death (SCD) in the young. The underlying etiology of HCM in the childhood and adolescent patients is diverse. Moreover, the prognosis of pediatric HCM depends on the age of presentation and etiology. Despite the complexity of children with obstructive HCM, surgical treatment results in a favorable outcome for carefully selected patients in experienced tertiary referral center in contemporary era. Implantable cardioverter-defibrillator (ICD) remains the most effective and reliable treatment to prevent SCD. New pediatric SCD risk prediction model, which has good discrimination and calibration and can distinguish patients who are most benefit from an ICD implantation, is expected to be further refined in the future.

Cardiorespiratory optimal point during exercise testing and sudden cardiac death: A prospective cohort study

BackgroundCardiorespiratory optimal point (COP) during exercise, a potentially useful submaximal cardiopulmonary exercise testing (CPET) variable, may be an independent risk factor for cardiovascular disease outcomes. However, the relationship of COP with risk of sudden cardiac death (SCD) has not been previously investigated. We sought to evaluate the association between COP during exercise and SCD risk and determine whether COP improves SCD risk prediction. MethodsCOP, the minimum value of the ventilatory equivalent for oxygen (VE/VO2) in a given minute of a CPET, was ascertained in 2190 men who underwent clinical exercise testing. Hazard ratios (HRs) (95% confidence intervals [CIs]) and measures of risk discrimination for SCD were calculated. ResultsA total of 240 SCDs death occurred during a median follow-up of 28.8 years. COP was linearly associated with SCD in a dose-response manner. In a multivariable model comprising several established and emerging CVD risk factors, the HR (95% CI) for SCD was 2.51 (1.36–4.62) per standard deviation increase in COP. This was minimally attenuated to 2.36 (1.27–4.37) on further adjustment for high sensitivity C-reactive protein. The association did not vary importantly in several relevant clinical subgroups. Addition of COP to a SCD risk score was associated with a C-index change of 0.0086 (0.0005 to 0.0167; p = .038) and difference in −2 log likelihood (p = .017). ConclusionsCOP during exercise is strongly, inversely and independently associated with SCD in a graded fashion. COP during exercise may potentially be used for the prediction of the long-term risk for SCD beyond established CVD risk factors.

Left ventricular hypertrophy and sudden cardiac death

Sudden cardiac death (SCD) is among the leading causes of death worldwide, and it remains a public health problem, as it involves young subjects. Current guideline-directed risk stratification for primary prevention is largely based on left ventricular (LV) ejection fraction (LVEF), and preventive strategies such as implantation of a cardiac defibrillator (ICD) are justified only for documented low LVEF (i.e., ≤ 35%). Unfortunately, only a small percentage of primary prevention ICDs, implanted on the basis of a low LVEF, will deliver life-saving therapies on an annual basis. On the other hand, the vast majority of patients that experience SCD have LVEF > 35%, which is clamoring for better understanding of the underlying mechanisms. It is mandatory that additional variables be considered, both independently and in combination with the EF, to improve SCD risk prediction. LV hypertrophy (LVH) is a strong independent risk factor for SCD regardless of the etiology and the severity of symptoms. Concentric and eccentric LV hypertrophy, and even earlier concentric remodeling without hypertrophy, are all associated with increased risk of SCD. In this paper, we summarize the physiology and physiopathology of LVH, review the epidemiological evidence supporting the association between LVH and SCD, briefly discuss the mechanisms linking LVH with SCD, and emphasize the need to evaluate LV geometry as a potential risk stratification tool regardless of the LVEF.