227A novel risk prediction model for sudden cardiac death in childhood hypertrophic cardiomyopathy (HCM Risk-Kids)

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
G Norrish ◽  
T Ding ◽  
E Field ◽  
C O'mahony ◽  
P M Elliott ◽  
...  
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
Sudden Cardiac Death ◽  
Prediction Model ◽  
Risk Prediction ◽  
Cardiac Death ◽  
Left Ventricular ◽  
Risk Prediction Model ◽  
C Statistic ◽  
Patient Will ◽  
Unexplained Syncope

Abstract Background Sudden cardiac death (SCD) is the most common mode of death in childhood hypertrophic cardiomyopathy (HCM) but there is no validated algorithm to identify those at highest risk. This study sought to develop and validate a SCD risk prediction model that provides individualized risk estimates. Methods A prognostic model was derived from an international, retrospective, multi-center longitudinal cohort study of 1024 consecutively evaluated patients aged ≤16 years. The model was developed using pre-selected predictor variables [unexplained syncope, maximal left ventricular (LV) wall thickness (MWT), left atrial diameter (LAD), LV outflow tract (LVOT) gradient and non-sustained ventricular tachycardia (NSVT)] identified from the literature and internally validated using bootstrapping. Results Over a median follow up of 5.3 years (IQR 2.6, 8.2, total patient years 5984), 89 (8.7%) patients died suddenly or had an equivalent event [annual event rate 1.49 (95% CI 1.15–1.92)]. The pediatric model was developed using pre-selected variables to predict the risk of SCD. The model's ability to predict risk at 5 years was validated; C-statistic was 0.69 (95% CI 0.66–0.72) and the calibration slope was 0.98 (95% CI 0.58–1.38). For every 10 ICDs implanted in patients with ≥6% 5-year SCD risk, potentially 1 patient will be saved from SCD at 5 years. Conclusions This new validated risk stratification model for SCD in childhood HCM provides accurate individualized estimates of risk at 5 years using readily obtained clinical risk factors. Acknowledgement/Funding British Heart Foundation

scholarly journals A novel clinical risk prediction model for sudden cardiac death in hypertrophic cardiomyopathy (HCM Risk-SCD)

2013 ◽  
Vol 35 (30) ◽  
pp. 2010-2020 ◽  
Author(s):  
C. O'Mahony ◽  
F. Jichi ◽  
M. Pavlou ◽  
L. Monserrat ◽  
A. Anastasakis ◽  
...  
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
Sudden Cardiac Death ◽  
Prediction Model ◽  
Risk Prediction ◽  
Cardiac Death ◽  
Risk Prediction Model ◽  
Clinical Risk

scholarly journals Development and Validation of a Multivariable Risk Prediction Model for Sudden Cardiac Death after Myocardial Infarction (PROFID Risk Model): Study Rationale, Design and Protocol

2021 ◽  
Author(s):  
Glen Phillp Martin ◽  
Gerhard Hindricks ◽  
Artur Akbarov ◽  
Zoher Kapacee ◽  
Le Mai Parkes ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Sudden Cardiac Death ◽  
Risk Stratification ◽  
Prediction Model ◽  
Risk Prediction ◽  
Cardiac Death ◽  
Left Ventricular ◽  
Risk Prediction Model ◽  
Left Ventricular Ejection ◽  
Analytical Approaches

Introduction Sudden cardiac death (SCD) is the leading cause of death in patients with myocardial infarction (MI) and can be prevented by the implantable cardioverter defibrillator (ICD). Currently, risk stratification for SCD and decision on ICD implantation are based solely on impaired left ventricular ejection fraction (LVEF). However, this strategy leads to over- and under-treatment of patients because LVEF alone is insufficient for accurate assessment of prognosis. Thus, there is a need for better risk stratification. This is the study protocol for developing and validating a prediction model for risk of SCD in patients with prior MI. Methods and Analysis The EU funded PROFID project will analyse 23 datasets from Europe, Israel and the US (~225,000 observations). The datasets include patients with prior MI or ischemic cardiomyopathy with reduced LVEF<50%, with and without a primary prevention ICD. Our primary outcome is SCD in patients without an ICD, or appropriate ICD therapy in patients carrying an ICD as a SCD surrogate. For analysis, we will stack 18 of the datasets into a single database (datastack), with the remaining analysed remotely for data governance reasons (remote data). We will apply 5 analytical approaches to develop the risk prediction model in the datastack and the remote datasets, all under a competing risk framework: 1) Weibull model, 2) flexible parametric survival model, 3) random forest, 4) likelihood boosting machine, and 5) neural network. These dataset-specific models will be combined into a single model (one per analysis method) using model aggregation methods, which will be externally validated using systematic leave-one-dataset-out cross-validation. Predictive performance will be pooled using random effects meta-analysis to select the model with best performance. Ethics and dissemination Local ethical approval was obtained. The final model will be disseminated through scientific publications and a web-calculator. Statistical code will be published through open-source repositories.

scholarly journals Development of a Novel Risk Prediction Model for Sudden Cardiac Death in Childhood Hypertrophic Cardiomyopathy (HCM Risk-Kids)

2019 ◽  
Vol 4 (9) ◽  
pp. 918 ◽  
Author(s):  
Gabrielle Norrish ◽  
Tao Ding ◽  
Ella Field ◽  
Lidia Ziólkowska ◽  
Iacopo Olivotto ◽  
...  
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
Sudden Cardiac Death ◽  
Prediction Model ◽  
Risk Prediction ◽  
Cardiac Death ◽  
Risk Prediction Model

Supplementary role of left ventricular global longitudinal strain for predicting sudden cardiac death in hypertrophic cardiomyopathy

2021 ◽  
Author(s):  
Hyun-Jung Lee ◽  
Hyung-Kwan Kim ◽  
Sang Chol Lee ◽  
Jihoon Kim ◽  
Jun-Bean Park ◽  
...  
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
Sudden Cardiac Death ◽  
Risk Score ◽  
Primary Endpoint ◽  
Cardiac Death ◽  
Longitudinal Strain ◽  
Global Longitudinal Strain ◽  
Left Ventricular ◽  
C Statistic

Abstract Aims We investigated the prognostic role of left ventricular global longitudinal strain (LV-GLS) and its incremental value to established risk models for predicting sudden cardiac death (SCD) in patients with hypertrophic cardiomyopathy (HCM). Methods and results LV-GLS was measured with vendor-independent software at a core laboratory in a cohort of 835 patients with HCM (aged 56.3 ± 12.2 years) followed-up for a median of 6.4 years. The primary endpoint was SCD events, including appropriate defibrillator therapy, within 5 years after the initial evaluation. The secondary endpoint was a composite of SCD events, heart failure admission, heart transplantation, and all-cause mortality. Twenty (2.4%) and 85 (10.2%) patients experienced the primary and secondary endpoints, respectively. Lower absolute LV-GLS quartiles, especially those worse than the median (−15.0%), were associated with progressively higher SCD event rates (P = 0.004). LV-GLS was associated with an increased risk for the primary endpoint, independent of the LV ejection fraction, apical aneurysm, and 2014 European Society of Cardiology (ESC) risk score [adjusted hazard ratio (aHR) 1.14, 95% confidence interval (CI) 1.02–1.28] or 2011 American College of Cardiology/American Heart Association (ACC/AHA) risk factors (aHR 1.18, 95% CI 1.05–1.32). LV-GLS was also associated with a higher risk for the composite secondary endpoint (aHR 1.06, 95% CI 1.01–1.12). The addition of LV-GLS enhanced the performance of the ESC risk score (C-statistic 0.756 vs. 0.842, P = 0.007) and the 2011 ACC/AHA risk factor strategy (C-statistic 0.743 vs. 0.814, P = 0.007) for predicting SCD. Conclusion LV-GLS is an important prognosticator in patients with HCM and provides additional information to established risk stratification strategies for predicting SCD.

scholarly journals Development and Validation of a Risk Prediction Model for Ventricular Arrhythmia in Elderly Patients with Coronary Heart Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ying Dong ◽  
Yajun Shi ◽  
Jinli Wang ◽  
Qing Dan ◽  
Ling Gao ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Sudden Cardiac Death ◽  
Elderly Patients ◽  
Prediction Model ◽  
Risk Prediction ◽  
Cardiac Death ◽  
Risk Prediction Model ◽  
Model Group ◽  
Arrhythmic Death

Background. Sudden cardiac death is a leading cause of death from coronary heart disease (CHD). The risk of sudden cardiac death (SCD) increases with age, and sudden arrhythmic death remains a major cause of mortality in elderly individuals, especially ventricular arrhythmias (VA). We developed a risk prediction model by combining ECG and other clinical noninvasive indexes including biomarkers and echocardiology for VA in elderly patients with CHD. Method. In the retrospective study, a total of 2231 consecutive elderly patients (≥60 years old) with CHD hospitalized were investigated, and finally 1983 patients were enrolled as the model group. The occurrence of VA within 12 months was mainly collected. Study parameters included clinical characteristics (age, gender, height, weight, BMI, and past medical history), ECG indexes (QTcd, Tp-e/QT, and HRV indexes), biomarker indexes (NT-proBNP, Myo, cTnT, CK-MB, CRP, K+, and Ca2+), and echocardiology indexes. In the respective study, 406 elderly patients (≥60 years old) with CHD were included as the verification group to verify the model in terms of differentiation and calibration. Results. In the multiparameter model, seven independent predictors were selected: LVEF, LAV, HLP, QTcd, sex, Tp-e/QT, and age. Increased HLP, Tp-e/QT, QTcd, age, and LAV were risk factors (RR > 1), while female and increased LVEF were protective factors (RR < 1). This model can well predict the occurrence of VA in elderly patients with CHD (for model group, AUC: 0.721, 95% CI: 0.669∼0.772; for verification group, AUC: 0.73, 95% CI: 0.648∼0.818; Hosmer–Lemeshow χ 2  = 13.541, P = 0.095 ). After adjusting the predictors, it was found that the combination of clinical indexes and ECG indexes could predict VA more efficiently than using clinical indexes alone. Conclusions. LVEF, LAV, QTcd, Tp-e/QT, gender, age, and HLP were independent predictors of VA risk in elderly patients with CHD. Among these factors, the echocardiology indexes LVEF and LAV had the greatest influence on the predictive efficiency of the model, followed by ECG indexes, QTcd and Tp-e/QT. After verification, the model had a good degree of differentiation and calibration, which can provide a certain reference for clinical prediction of the VA occurrence in elderly patients with CHD.

N-terminal pro-brain natriuretic peptide and sudden cardiac death in hypertrophic cardiomyopathy

Heart ◽  
2020 ◽  
pp. heartjnl-2020-317701
Author(s):  
Guixin Wu ◽  
Jie Liu ◽  
Shuiyun Wang ◽  
Shiqin Yu ◽  
Ce Zhang ◽  
...  
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
Sudden Cardiac Death ◽  
Brain Natriuretic Peptide ◽  
Natriuretic Peptide ◽  
Cardiac Death ◽  
Cardiac Fibrosis ◽  
Discrimination Performance ◽  
Net Reclassification Improvement ◽  
C Statistic ◽  
Increased Risk

ObjectiveElevated levels of N-terminal pro-brain natriuretic peptide (NT-proBNP) are associated with heart failure-related death in hypertrophic cardiomyopathy (HCM), but the relationship between NT-proBNP level and sudden cardiac death (SCD) in HCM remains undefined.MethodsThe study prospectively enrolled 977 unrelated patients with HCM with available NT-proBNP results who were prospectively enrolled and followed for 3.0±2.1 years. The Harrell’s C-statistic under the receiver operating characteristic curve was calculated to evaluate discrimination performance. A combination model was constructed by adding NT-proBNP tertiles to the HCM Risk-SCD model. The correlation between log NT-proBNP level and cardiac fibrosis as measured by late gadolinium enhancement (LGE) or Masson’s staining was analysed.ResultsDuring follow-up, 29 patients had SCD. Increased log NT-proBNP levels were associated with an increased risk of SCD events (adjusted HR 22.27, 95% CI 10.93 to 65.63, p<0.001). The C-statistic of NT-proBNP in predicting SCD events was 0.80 (p<0.001). The combined model significantly improved the predictive efficiency of the HCM Risk-SCD model from 0.72 to 0.81 (p<0.05), with a relative integrated discrimination improvement of 0.002 (p<0.001) and net reclassification improvement of 0.67 (p<0.001). Furthermore, log NT-proBNP levels were significantly correlated with cardiac fibrosis as detected either by LGE (r=0.257, p<0.001) or by Masson’s trichrome staining in the myocardium (r=0.198, p<0.05).ConclusionNT-proBNP is an independent predictor of SCD in patients with HCM and may help with risk stratification of this disease.

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