scholarly journals GW28-e0440 The risk prediction model of coronary heart disease for elderly hypertensive patients

2017 ◽  
Vol 70 (16) ◽  
pp. C72 ◽  
Author(s):  
Xibin Wang ◽  
Shuli Guo ◽  
Lina Han
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Hypertensive Patients ◽  
Elderly Hypertensive ◽  
Elderly Hypertensive Patients

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.

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 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.

Development and validation of a risk prediction model in patients with adult congenital heart disease

2019 ◽  
Vol 276 ◽  
pp. 87-92 ◽  
Author(s):  
Vivan J.M. Baggen ◽  
Esmee Venema ◽  
Renata Živná ◽  
Annemien E. van den Bosch ◽  
Jannet A. Eindhoven ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Prediction Model ◽  
Risk Prediction ◽  
Congenital Heart ◽  
Adult Congenital Heart Disease ◽  
Risk Prediction Model ◽  
Adult Congenital ◽  
Development And Validation

scholarly journals Development of a coronary heart disease risk prediction model for type 1 diabetes: The Pittsburgh CHD in Type 1 Diabetes Risk Model

2010 ◽  
Vol 88 (3) ◽  
pp. 314-321 ◽  
Author(s):  
Janice C. Zgibor ◽  
Kristine Ruppert ◽  
Trevor J. Orchard ◽  
Sabita S. Soedamah-Muthu ◽  
John Fuller ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Type 1 Diabetes ◽  
Heart Disease ◽  
Prediction Model ◽  
Risk Prediction ◽  
Coronary Heart Disease Risk ◽  
Risk Model ◽  
Disease Risk ◽  
Heart Disease Risk

scholarly journals Tuning and external validation of an adult congenital heart disease risk prediction model

2020 ◽  
Author(s):  
Laurie W Geenen ◽  
Alexander R Opotowsky ◽  
Cara Lachtrupp ◽  
Vivan J M Baggen ◽  
Sarah Brainard ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Prediction Model ◽  
Risk Prediction ◽  
Primary Endpoint ◽  
Congenital Heart ◽  
Adult Congenital Heart Disease ◽  
External Validation ◽  
Risk Prediction Model ◽  
Adult Congenital

Abstract Aims Adequate risk prediction can optimize the clinical management in adult congenital heart disease (ACHD). We aimed to update and subsequently validate a previously developed ACHD risk prediction model. Methods and results A prediction model was developed in a prospective cohort study including 602 moderately or severely complex ACHD patients, enrolled as outpatients at a tertiary centre in the Netherlands (2011–2013). Multivariable Cox regression was used to develop a model for predicting the 1-year risks of death, heart failure (HF), or arrhythmia (primary endpoint). The Boston ACHD Biobank study, a prospectively enrolled cohort (n = 749) of outpatients who visited a referral centre in Boston (2012–2017), was used for external validation. The primary endpoint occurred in 153 (26%) and 191 (28%) patients in the derivation and validation cohorts over median follow-up of 5.6 and 2.3 years, respectively. The final model included 5 out of 14 pre-specified predictors with the following hazard ratios; New York Heart Association class ≥II: 1.92 [95% confidence interval (CI) 1.28–2.90], cardiac medication 2.52 (95% CI 1.72–3.69), ≥1 reintervention after initial repair: 1.56 (95% CI 1.09–2.22), body mass index: 1.04 (95% CI 1.01–1.07), log2 N-terminal pro B-type natriuretic peptide (pmol/L): 1.48 (95% CI 1.32–1.65). At external validation, the model showed good discrimination (C-statistic 0.79, 95% CI 0.74–0.83) and excellent calibration (calibration-in-the-large = −0.002; calibration slope = 0.99). Conclusion These data support the validity and applicability of a parsimonious ACHD risk model based on five readily available clinical variables to accurately predict the 1-year risk of death, HF, or arrhythmia. This risk tool may help guide appropriate care for moderately or severely complex ACHD.

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