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Predicting Short-Term Risk of Cardiovascular Events in the Elderly Population: A Retrospective Study in Shanghai,
Wenqing Zhu1, Shuoyuan Tan2, Zhitong Zhou1
1Tongji University School of Medicine, Tongji University, Shanghai, People's Republic of China.
Insights
This study developed a short-term cardiovascular disease (CVD) risk model for older adults in Shanghai, identifying key predictors like hypertension and diabetes for better prevention strategies.
Area of Science:
- Gerontology
- Cardiology
- Epidemiology
Background:
- Cardiovascular diseases (CVD) are a major global health concern, particularly in China.
- Accurate CVD risk prediction and timely preventive interventions are crucial for the elderly population.
Purpose of the Study:
- To develop and validate a short-term CVD risk prediction model for individuals aged 60 and above in Shanghai, China.
- To identify significant predictors of CVD events in this demographic.
Main Methods:
- Utilized stratified random sampling to recruit elderly individuals.
- Analyzed retrospective data from 2016-2022 using Lasso-Cox and multivariable Cox regression models.
- Assessed model performance with calibration plots and receiver operating characteristic curves, visualizing risk scoring via nomogram.
Main Results:
- Included 9,636 individuals aged ≥60 years.
- Identified male gender, older age, higher BMI, higher systolic blood pressure, higher fasting plasma glucose, hypertension, diabetes, and lipid-lowering medication use as risk factors.
- Achieved C-indices of 0.642 (training) and 0.623 (validation), with good calibration.
Conclusions:
- The developed short-term CVD predictive model demonstrates good accuracy and moderate discriminative ability for the elderly.
- Further research is needed to refine predictors like gender, lipid profiles, blood pressure, hypertension, and medication use for enhanced CVD prevention.
Introduction:
Cardiovascular diseases (CVD) represents a leading cause of morbidity and mortality worldwide, including China. Accurate prediction of CVD risk and implementation of preventive measures are critical. This study aimed to develop a short-term risk prediction model for CVD events among individuals aged ≥60 years in Shanghai, China.
Methods:
Stratified random sampling recruited elderly individuals. Retrospective data (2016-2022) were analyzed using Lasso-Cox regression, followed by a multivariable Cox regression model. The risk scoring was visualized through a nomogram, and the model performance was assessed using calibration plots and receiver operating characteristic curves.
Results:
A total of 9,636 individuals aged ≥60 years were included. The Lasso-Cox regression analysis showed male gender (HR=1.482), older age (HR=1.035), higher body mass index (HR=1.015), lower high-density lipoprotein cholesterol (HR=0.992), higher systolic blood pressure (HR=1.009), lower diastolic blood pressure (HR=0.982), higher fasting plasma glucose (HR=1.068), hypertension (HR=1.904), diabetes (HR=1.128), and lipid-lowering medication (HR=1.384) were related to higher CVD risk. The C-index in the training and validation data was 0.642 and 0.623, respectively. Calibration plots indicated good agreement between predicted and actual probabilities.
Conclusion:
This short-term predictive model for CVD events among the elderly population exhibits good accuracy but moderate discriminative ability. More studies are warranted to investigate predictors (gender, high-density lipoprotein cholesterol, systolic blood pressure, diastolic blood pressure, hypertension, and lipid-lowering medication) of CVD incidence for the development of preventive measures.
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