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

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 χ2 = 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.