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Development and Validation of a Risk Prediction Model for Ventricular Arrhythmia in Elderly Patients with Coronary
Ying Dong1, Yajun Shi1, Jinli Wang1
1Department of Cardiology, First Medical Center of Chinese PLA General Hospital, Beijing, China.
Insights
A new model predicts ventricular arrhythmias (VA) in elderly patients with coronary heart disease (CHD) using echocardiology and ECG indexes. Echocardiology and ECG factors like LVEF, LAV, QTcd, and Tp-e/QT improve VA risk prediction.
Area of Science:
- Cardiology
- Geriatrics
- Medical Prediction Models
Background:
- Sudden cardiac death (SCD) is a major cause of mortality in elderly individuals with coronary heart disease (CHD).
- Ventricular arrhythmias (VA) are a primary driver of SCD in this population.
- Existing risk prediction models often lack comprehensive integration of noninvasive markers.
Purpose of the Study:
- To develop and validate a multiparameter risk prediction model for VA in elderly patients with CHD.
- To identify independent predictors of VA occurrence using clinical, ECG, biomarker, and echocardiology data.
- To assess the combined predictive efficiency of clinical and ECG indexes for VA.
Main Methods:
- A retrospective study involving 1983 elderly patients (≥60 years) with CHD for model development.
- Collection of clinical characteristics, ECG indexes (QTcd, Tp-e/QT, HRV), biomarkers, and echocardiology data.
- Validation of the model in a separate group of 406 elderly patients with CHD.
Main Results:
- Seven independent predictors for VA were identified: LVEF, LAV, HLP, QTcd, sex, Tp-e/QT, and age.
- Increased HLP, Tp-e/QT, QTcd, age, and LAV were risk factors; female sex and increased LVEF were protective.
- The model demonstrated good predictive performance (AUC: 0.721-0.73) and calibration (Hosmer-Lemeshow P=0.095).
Conclusions:
- LVEF, LAV, QTcd, Tp-e/QT, gender, age, and HLP are significant independent predictors of VA risk in elderly CHD patients.
- Echocardiology indexes (LVEF, LAV) and ECG indexes (QTcd, Tp-e/QT) significantly influence predictive accuracy.
- The validated model offers a valuable clinical reference for predicting VA in this high-risk population.
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.
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