Predictor Of Sudden Cardiac Death Research Articles

Sudden cardiac death after early-onset myocardial infarction. A multicentre longitudinal cohort study with 20 years' follow-up.

Sudden cardiac death (SCD) is a serious consequence of a myocardial infarction (MI), but identifying patients at risk of developing SCD remains a major clinical challenge especially in the case of juvenile MI. The aim of this study was to identify predictors of SCD after early-onset MI using long-term follow-up data relating to a large nationwide patient cohort. The Italian Genetic Study on Early-onset MI enrolled 2,000 patients experiencing a first MI before the age of 45 years, who were followed up for a median of 19.9 years. Fine-Gray proportional hazard models were used to assess the associations between their clinical, demographic and index event data and the occurrence of SCD. SCD occurred in 195 patients, who were more frequently males, hypertensive and/or diabetic; had a history of previous thromboembolic events with a greater atherosclerotic burden; and had a lower left ventricular ejection fraction (LVEF) after the index event. Multivariable analysis showed that the independent predictors of SCD were diabetes, hypertension, previous thromboembolic events, higher Syntax score, and a lower LVEF. There was no clear evidence of the clustering of SCD events during follow-up. SCD was the first post-MI clinical event in 101 patients; the remaining 94 experienced SCD after a non-fatal MI or hospitalisation for coronary revascularisation. SCD frequently occurs during the 20 years after early-onset MI. The nature of the identified predictors and the absence of clustering suggests that the pathophysiological basis of SCD may be related to progressive coronary atherosclerosis.

Risk prediction of sudden cardiac death needs multiple evaluation

Risk prediction of sudden cardiac death needs multiple evaluation

Ventriculo-arterial (VA) coupling and fQRS as new selection criteria for primary prevention ICD placement

For decades, left ventricular ejection fraction (LVEF < 35%) has been a mainstay for identifying heart failure (HF) patients most likely to benefit from an implantable cardioverter defibrillator (ICD). However, LVEF is a poor predictor of sudden cardiac death (SCD) and ignores 50% of HF patients with mildly reduced and preserved LVEF. The current international guidelines for primary prophylaxis ICD therapy are inadequate. Instead of LVEF, which is not a good measure of LV contractility or hemodynamic characterization, we hypothesize ventriculo-arterial (VA) coupling combined with fragmented QRS (fQRS) will improve risk stratification and patient suitability for an ICD. Quantifying cardiac and aortic mechanics, and predicting active arrhythmogenic substrate, from varying fQRS morphologies, may help to stratify ischemic and non-ischemic patients with different functional capacities and predisposition for lethal arrhythmias. We propose HF patients with a low physiological reserve may not benefit from ICD therapy, whereas those patients with higher reserves and extensive arrhythmogenic substrate may benefit. Our hypothesis combining VA coupling with fQRS changes has the potential to widen HF patient participation (low and high LVEF) and advance personalized medicine for HF patients at high risk of SCD.

Progranulin, sICAM-1, and sVCAM-1 May Predict an Increased Risk for Ventricular Arrhythmias in Patients with Systemic Sclerosis.

In systemic sclerosis (SSc), fibrosis of the myocardium along with ongoing autoimmune inflammation can alter the electric function of the cardiac myocytes, which may increase the risk for ventricular arrhythmias and sudden cardiac death. We analyzed the electrocardiographic (ECG) variables describing ventricular repolarization such as QT interval, QT dispersion (QTd), T wave peak-to-end interval (Tpe), and arrhythmogeneity index (AIX) of 26 patients with SSc and 36 healthy controls. Furthermore, echocardiographic and laboratory parameters were examined, with a focus on inflammatory proteins like C-reactive ptotein (CRP), soluble intracellular adhesion molecule-1 (sICAM-1), soluble vascular adhesion molecule-1 (sVCAM-1), and progranulin (PGRN). The CRP, sICAM-1, and sVCAM-1 levels were positively correlated with the length of the QT interval. Although the serum PGRN levels were not increased in the SSc group compared to the controls, in SSc patients, the PGRN levels were positively correlated with the QT interval and the AIX. According to our results, we conclude that there may be a potential association between autoimmune inflammation and the risk for ventricular arrhythmias in patients with SSc. We emphasize that the measurement of laboratory parameters of inflammatory activity including CRP, PGRN, sVCAM-1, and sICAM-1 could be helpful in the prediction of sudden cardiac death in patients with SSc.

IoT-based emergency cardiac death risk rescue alert system

The use of technology in healthcare is one of the most critical application areas today. With the development of medical applications, people's quality of life has improved. However, it is impractical and unnecessary for medium-risk people to receive specialized daily hospital monitoring. Due to their health status, they will be exposed to a high risk of severe health damage or even life-threatening conditions without monitoring. Therefore, remote, real-time, low-cost, wearable, and effective monitoring is ideal for this problem. Many researchers mentioned that their studies could use electrocardiogram (ECG) detection to discover emergencies. However, how to respond to discovered emergencies in household life is still a research gap in this field.•This paper proposes a real-time monitoring of ECG signals and sending them to the cloud for Sudden Cardiac Death (SCD) prediction.•Unlike previous studies, the proposed system has an additional emergency response mechanism to alert nearby community healthcare workers when SCD is predicted to occur.

Sudden cardiac death prediction based on the complete ensemble empirical mode decomposition method and a machine learning strategy by using ECG signals

Cardiovascular diseases are a significant global health problem, often culminating in sudden cardiac death (SCD). Approximately 4 million annual deaths worldwide are attributed to SCD. Although the causes are varied, the heart’s electrical activity is always affected prior to an SCD event. This work explores the capabilities of the complete ensemble empirical mode decomposition (CEEMD) method for extracting relevant time-domain features from electrocardiogram (ECG) signals. CEEMD method is compared with EMD and EEMD methods. Additionally, a machine learning approach incorporating statistical indices, the Kruskal-Wallis test, and a support vector machine (SVM) is utilized for automatic analysis and prediction. SVM results are benchmarked against k-nearest neighbors, decision trees, and neural networks. Results demonstrate that the proposed methodology can predict the SCD event 30 min before it occurs with an accuracy of 97.28%, positively impacting on the improvement of timely interventions and, consequently, the survival rates and quality of life of people.

Arrhythmic risk stratification in non-ischemic cardiomyopathy using cardiovascular magnetic resonance

Abstract Background Myocardial fibrosis (MF) is an essential component of the arrhythmic substrate for ventricular arrhythmias (VAs). Aims To determine whether total myocardial fibrosis (TF) and gray zone fibrosis (GZF), assessed using cardiovascular magnetic resonance, are better than LVEF in predicting ventricular arrhythmias in patients with non-ischemic cardiomyopathy (NICM). Methods and results Patients with NICM in a derivation cohort (n=866) and a validation cohort (n=848) underwent quantification of TF and GZF. The primary composite endpoint was sudden cardiac death (SCD) or VAs (ventricular fibrillation or sustained ventricular tachycardia). The primary endpoint was met by 52/866 (6.0%) patients in the derivation cohort (follow-up of 7.5 years [interquartile range, IQR] 5.2-9.3). In competing risk analyses, MF on visual assessment (MFVA) predicted the primary endpoint (hazard ratio [HR] 5.83, 95% confidence intervals [CI] 3.15-10.8). Quantified MF measures permitted categorization into 3 risk groups: a TF &amp;gt;0 ≤ 10 g was associated with an intermediate risk (HR: 4.03 [95% CI 1.99-8.16]) and a TF &amp;gt; 10g with the highest risk (HR: 9.17 [95% CI 4.64-18.1]) compared to no MFVA (lowest risk) (Figure). Similar trends were observed in the validation cohort. Categorization into 3 risk groups was achievable using TF or GZF in combination or in isolation. In contrast, LVEF&amp;lt;35% was a poor predictor of the primary endpoint (validation cohort HR:1.99, 95% CI 0.99-0.41). Conclusions MFVA is a strong predictor of SCD and VAs in NICM. TF and GZF mass added incremental value to MFVA. In contrast, LVEF was not useful in arrhythmic risk stratification.

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.

Development and validation of a machine learning-based prediction model for sudden cardiac death in the general population: insights from the community cardiovascular cohort

Abstract Background/Introduction Accurately predicting and managing the risk of sudden cardiac death (SCD) in the general population is a formidable challenge. Purpose This study aimed to develop and validate a machine learning (ML)-based prediction model for SCD using data from the Community Cardiovascular Cohort (CCCC) in Taiwan. Methods A community-based cohort study was used for model construction . Four ML algorithms, including extreme gradient boosting (XGBoost), random forests (RF), logistic regression, and deep neural network (DNN), underwent meticulous evaluation. Performance assessment encompassed receiver operating characteristic (ROC) curves, area under the curves (AUC), and accuracy metrics. Significant risk factors were identified through feature importance analysis. Results We employed a random partitioning strategy, allocating 1744 participants (55%) to the training dataset, 952 participants (30%) to the test dataset, and 476 participants (15%) to the validation dataset. In the data training stage, the XGBoost algorithm demonstrated outstanding prediction performance for SCD (AUC: 1.000); In the validation stage, the XGBoost algorithm demonstrated good prediction performance for SCD prediction (AUC: 0.791, 95%CI: 0.617-0.964; Accuracy: 0.971). Using XGBoost-generated predicted probabilities (P(XGB)), we identified individuals with P(XGB) ≥ 0.5 (Group 1) who displayed significantly higher SCD risk (HR 32.0; 95% CI: 9.29-109.9) compared to those with P(XGB) &amp;lt; 0.5 (Group 0). Conclusions Our study demonstrates the effectiveness of ML algorithms, particularly XGBoost, in predicting event of SCD in the general population. The identification of these pivotal risk factors holds profound implications, empowering the identification of high-risk individuals and facilitating the implementation of precisely tailored preventive strategies.Figure 1:KM sruvival curveFigure 2:Feature importance analysis

Age-stratified profiles and outcomes of patients with heart failure with preserved ejection fraction.

This study aimed to elucidate age-stratified clinical profiles and outcomes in patients with heart failure (HF) with preserved left ventricular ejection fraction (LVEF) (HFpEF). The Chronic Heart Failure Registry and Analysis in the Tohoku District-2 (CHART-2) Study included 2824 consecutive HFpEF patients with LVEF≥50% (mean age 69.0±12.3years; 67.7% male) with a median follow-up of 9.8years. We stratified them into five age groups: ≤54 (N=349, 12.4%), 55-64 (N=529, 18.7%), 65-74 (N=891, 31.6%), 75-84 (N=853, 30.2%), and ≥85years (N=202, 7.2%), and we categorized these age groups into younger (≤64years) and older (≥65years) groups. We compared the clinical profiles and outcomes of HFpEF patients across age groups. Younger HFpEF groups exhibited a male predominance, elevated body mass index (BMI), and poorly controlled diabetes (haemoglobin A1c>7.0%). Older HFpEF groups were more likely to be female with multiple comorbidities, including coronary artery disease, hypertension, renal impairment, and atrial fibrillation. The positive association between elevated BMI and HFpEF was more pronounced with lower classes of age from ≥85 to ≤54years, especially in males. With higher classes of age from ≤54 to ≥85years, mortality rates increased, and HF death became proportionally more prevalent (Ptrend<0.001), whereas sudden cardiac death (SCD) exhibited the opposite trend (Ptrend=0.002). Poorly controlled diabetes emerged as the only predictor of SCD in the younger groups (adjusted hazard ratio 4.26; 95% confidence interval 1.45-12.5; P=0.008). Multiple comorbidities were significantly associated with an increased risk of HF-related mortality in the older groups. Younger HFpEF patients (≤64years) exhibit a male predominance, elevated BMI, and poorly controlled diabetes, highlighting the importance of glycaemic control in reducing SCD risk. Older HFpEF patients (≥65years) are more likely to be female, with multiple comorbidities linked to an increased risk of HF-related mortality. These findings underscore the need for physicians to recognize age-related, distinct HFpEF phenotypes for personalized patient management.

The Prognostic Value of Left Ventricular Entropy From T1 Mapping in Patients With Hypertrophic Cardiomyopathy

BackgroundThe prognostic value of left ventricular (LV) entropy in hypertrophic cardiomyopathy (HCM) is unclear. ObjectivesThis study aimed to assess the prognostic value of LV entropy from T1 mapping in HCM. MethodsA total of 748 participants with HCM, who underwent cardiovascular magnetic resonance (CMR), were consecutively enrolled. LV entropy was quantified by native T1 mapping. A competing risk analysis and a Cox proportional hazards regression analysis were performed to identify potential associations of LV entropy with sudden cardiac death (SCD) and cardiovascular death (CVD), respectively. ResultsA total of 40 patients with HCM experienced SCD, and 65 experienced CVD during a median follow-up of 43 months. Participants with increased LV entropy (≥4.06) were more likely to experience SCD and CVD (all P < 0.05) in the entire study cohort or the subgroup with low late gadolinium enhancement (LGE) extent (<15%). After adjustment for the European Society of Cardiology predictors and the presence of high LGE extent (≥15%), LV mean entropy was an independent predictor for SCD (HR: 1.03; all P < 0.05) by the multivariable competing risk analysis and CVD (HR: 1.06; 95% CI: 1.03-1.09; P < 0.001) by multivariable Cox regression analysis. ConclusionsLV mean entropy derived from native T1 mapping, reflecting myocardial tissue heterogeneity, was an independent predictor of SCD and CVD in participants with HCM. (Cardiac Magnetic Resonance Imaging Clinical Application Registration Study; ChiCTR1900024094)

TBX5 genetic variants and SCD-CAD susceptibility: insights from Chinese Han cohorts.

The prevention and prediction of sudden cardiac death (SCD) present persistent challenges, prompting exploration into common genetic variations for potential insights. T-box 5 (TBX5), a critical cardiac transcription factor, plays a pivotal role in cardiovascular development and function. This study systematically examined variants within the 500-bp region downstream of the TBX5 gene, focusing on their potential impact on susceptibility to SCD associated with coronary artery disease (SCD-CAD) in four different Chinese Han populations. In a comprehensive case-control analysis, we explored the association between rs11278315 and SCD-CAD susceptibility using a cohort of 553 controls and 201 SCD-CAD cases. Dual luciferase reporter assays and genotype-phenotype correlation studies using human cardiac tissue samples as well as integrated in silicon analysis were applied to explore the underlining mechanism. Binary logistic regression results underscored a significantly reduced risk of SCD-CAD in individuals harboring the deletion allele (odds ratio = 0.70, 95% CI [0.55-0.88], p = 0.0019). Consistent with the lower transcriptional activity of the deletion allele observed in dual luciferase reporter assays, genotype-phenotype correlation studies on human cardiac tissue samples affirmed lower expression levels associated with the deletion allele at both mRNA and protein levels. Furthermore, our investigation revealed intriguing insights into the role of rs11278315 in TBX5 alternative splicing, which may contribute to alterations in its ultimate functional effects, as suggested by sQTL analysis. Gene ontology analysis and functional annotation further underscored the potential involvement of TBX5 in alternative splicing and cardiac-related transcriptional regulation. In summary, our current dataset points to a plausible correlation between rs11278315 and susceptibility to SCD-CAD, emphasizing the potential of rs11278315 as a genetic risk marker for aiding in molecular diagnosis and risk stratification of SCD-CAD.

Electrical Instability of the Myocardium in Children and Adolescents

Currently, there is no doubt that the problem of electrical instability of the myocardium in pediatric cardiology is relevant. The determination of various indicators of electrical instability of the myocardium, which are predictors of life-threatening rhythm disorders and sudden cardiac death, presents an underdeveloped task not only for functional diagnostics specialists, but also for pediatricians, neonatologists, pediatric cardiologists and doctors of other specialties. Non-invasive research methods such as electrocardiography (ECG), Holter ECG monitoring (HM ECG), available in almost all pediatric treatment and prevention institutions, are quite informative in terms of detecting electrophysiological heterogeneity of the myocardium as a predictor of sudden cardiac death, which is especially important in children at risk, as well as young athletes. Thus, the determination of indicators of electrical instability of the myocardium is of great interest, it is a promising direction in modern clinical practice, allowing to predict the risk of developing fatal arrhythmias in children and adolescents.

Natural history and outcomes in paediatric RASopathy-associated hypertrophic cardiomyopathy.

This study aimed to describe the natural history and predictors of all-cause mortality and sudden cardiac death (SCD)/equivalent events in children with a RASopathy syndrome and hypertrophic cardiomyopathy (HCM). This is a retrospective cohort study from 14 paediatric cardiology centres in the United Kingdom and Ireland. We included children <18years with HCM and a clinical and/or genetic diagnosis of a RASopathy syndrome [Noonan syndrome (NS), NS with multiple lentigines (NSML), Costello syndrome (CS), cardiofaciocutaneous syndrome (CFCS), and NS with loose anagen hair (NS-LAH)]. One hundred forty-nine patients were recruited [111 (74.5%) NS, 12 (8.05%) NSML, 6 (4.03%) CS, 6 (4.03%) CFCS, 11 (7.4%) Noonan-like syndrome, and 3 (2%) NS-LAH]. NSML patients had higher left ventricular outflow tract (LVOT) gradient values [60 (36-80) mmHg, P=0.004]. Over a median follow-up of 197.5 [inter-quartile range (IQR) 93.58-370] months, 23 patients (15.43%) died at a median age of 24.1 (IQR 5.6-175.9) months. Survival was 96.45% [95% confidence interval (CI) 91.69-98.51], 90.42% (95% CI 84.04-94.33), and 84.12% (95% CI 75.42-89.94) at 1, 5, and 10years, respectively, but this varied by RASopathy syndrome. RASopathy syndrome, symptoms at baseline, congestive cardiac failure (CCF), non-sustained ventricular tachycardia (NSVT), and maximal left ventricular wall thickness were identified as predictors of all-cause mortality on univariate analysis, and CCF, NSVT, and LVOT gradient were predictors for SCD or equivalent event. These findings highlight a distinct category of patients with Noonan-like syndrome with a milder HCM phenotype but significantly worse survival and identify potential predictors of adverse outcome in patients with RASopathy-related HCM.

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.

Kiosk 4R-FA-02 - A Simplified Scoring System for the Prediction of Sudden Cardiac Death in Pediatric Patients with Hypertrophic Cardiomyopathy

Kiosk 4R-FA-02 - A Simplified Scoring System for the Prediction of Sudden Cardiac Death in Pediatric Patients with Hypertrophic Cardiomyopathy

ЭЛЕКТРОКАРДИОГРАФИЧЕСКИЕ СИНДРОМЫ

Introduction. Sudden cardiac death (SCD) is a global health concern, often associated with specific clinical and electrocardiographic syndromes. While electrocardiography (ECG) is a crucial diagnostic tool in cardiology, it does not always provide a direct diagnosis or prediction of SCD. However, there are indirect criteria and syndromes that aid in diagnosing complex clinical cases in cardiology. Aim. This study aims to review various electrocardiographic syndromes known to predict sudden cardiac death, drawing from both domestic and foreign literature. Results. Wellens syndrome (WS) is an ECG pattern indicating a high risk of anterior wall myocardial infarction caused by anterior interventricular artery occlusion or critical stenosis. The de Winter pattern is an ECG sign associated with proximal LAD occlusion and the absence of ST segment elevation. Aslanger pattern is linked to coronary artery occlusion in patients with multivessel coronary artery disease, reflecting a combination of inferior MI and diffuse ischemia. Chaterrier's phenomenon (cardiac memory phenomenon) manifests as abnormal T wave inversion during normal sinus rhythm after myocardial electrical stimulation cessation.The Sgarbossa criteria aid in diagnosing acute myocardial infarction in patients with previous left bundle branch block. ECG signs of arrhythmogenic dysplasia of the right ventricle include negative T-waves in leads V1-V3 and an "epsilon" wave in lead V1, reflecting delayed depolarization of the right ventricle. Conclusion. Early recognition of these ECG patterns can aid in secondary prevention of AMI, fatal rhythm disturbances, and SCD, while also preventing overdiagnosis of myocardial ischemia in patients with implanted devices.

Electrocardiogram (ECG) risk score model in heart failure with reduced ejection fraction (HFrEF): Predictor of sudden cardiac death

Electrocardiogram (ECG) risk score model in heart failure with reduced ejection fraction (HFrEF): Predictor of sudden cardiac death

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.

Sudden cardiac death in childhood RASopathy-associated hypertrophic cardiomyopathy: validation of HCM Risk-Kids model and predictors of events

Abstract RASopathies represent an important proportion of hypertrophic cardiomyopathy (HCM) cases presenting in childhood. Despite this, there are no published risk factors for sudden cardiac death (SCD) in this cohort of patients. Our primary aim was to validate the HCM Risk-Kids model in children with RASopathy-associated HCM and to investigate additional predictors of SCD in a cohort of 169 patients from 15 international paediatric cardiology centres (UK, Ireland and Germany). Eleven (6.5%) patients experienced a SCD equivalent event [5 (45.5%) an aborted cardiac arrest; 3 (27.3%) SCD; 2 (18.2%) sustained ventricular tachycardia (VT); and 1 (9.1%) appropriate implantable cardioverter-defibrillator (ICD) shock] 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 a SCD equivalent event were in the low-risk category according to the HCM Risk-Kids model. Harrell’s C index was 0.5959 (95% CI 0.26-1.22) with a sensitivity of 9.09%, specificity of 63.92%, positive predictive value of 1.72%, and negative predictive value of 90.99%; there was poor distinction between the different risk groups. Unexplained syncope (HR 102.63 (95% CI 14.94 – 704.99), p&amp;lt;0.001) and non-sustained VT [NSVT (HR 5.67 (95% CI 1.30 – 24.70), p=0.021)] were independent predictors of SCD. The HCM Risk-Kids model should not be used for patients with RASopathy-associated HCM but NSVT and unexplained syncope appear to be predictors of SCD in this population. Larger multicentre collaborative studies are required to further investigate predictors of SCD in this group of patients.