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.

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 Prediction of coronary heart disease in rural Chinese adults: a cross sectional study

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12259
Author(s):  
Qian Wang ◽  
Wenxing Li ◽  
Yongbin Wang ◽  
Huijun Li ◽  
Desheng Zhai ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Prediction Model ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Model ◽  
Support Vector ◽  
High Morbidity ◽  
Rural Residents ◽  
Chd Risk

Background Coronary heart disease (CHD) is a common cardiovascular disease with high morbidity and mortality in China. The CHD risk prediction model has a great value in early prevention and diagnosis. Methods In this study, CHD risk prediction models among rural residents in Xinxiang County were constructed using Random Forest (RF), Support Vector Machine (SVM), and the least absolute shrinkage and selection operator (LASSO) regression algorithms with identified 16 influencing factors. Results Results demonstrated that the CHD model using the RF classifier performed best both on the training set and test set, with the highest area under the curve (AUC = 1 and 0.9711), accuracy (one and 0.9389), sensitivity (one and 0.8725), specificity (one and 0.9771), precision (one and 0.9563), F1-score (one and 0.9125), and Matthews correlation coefficient (MCC = one and 0.8678), followed by the SVM (AUC = 0.9860 and 0.9589) and the LASSO classifier (AUC = 0.9733 and 0.9587). Besides, the RF model also had an increase in the net reclassification index (NRI) and integrated discrimination improvement (IDI) values, and achieved a greater net benefit in the decision curve analysis (DCA) compared with the SVM and LASSO models. Conclusion The CHD risk prediction model constructed by the RF algorithm in this study is conducive to the early diagnosis of CHD in rural residents of Xinxiang County, Henan Province.

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 Online Adaptive Coronary Heart Disease Risk Prediction Model

2017 ◽  
Vol 125 ◽  
pp. 02071 ◽  
Author(s):  
Jostinah Lam ◽  
Eko Supriyanto ◽  
Faris Yahya ◽  
Muhammad Haikal Satria ◽  
Suhaini Kadiman ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Prediction Model ◽  
Risk Prediction ◽  
Coronary Heart Disease Risk ◽  
Disease Risk ◽  
Risk Prediction Model ◽  
Heart Disease Risk

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
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