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Evaluating Cardiovascular Risk Factors Among Healthcare Professionals in Iran
Maryam Moshkani Farahani1, Seyyed Mohammad Saeed Ghiasi1, Maziar Karamali2
1Atherosclerosis Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Abstract:
BACKGROUND Cardiovascular disease is a leading cause of morbidity and mortality worldwide, necessitating accurate risk prediction models for effective prevention strategies. This study compares the predictive capabilities of 3 widely used 10-year cardiovascular risk assessment tools - Atherosclerotic Cardiovascular Disease (ASCVD) risk score, Framingham Risk Score (FRS), and Systematic Coronary Risk Evaluation 2 (SCORE2) - in healthcare professionals in Iran. MATERIAL AND METHODS This cross-sectional study analyzed data from 222 healthcare professionals at Jamaran Hospital in Tehran, assessing cardiovascular risk profiles using the ASCVD, FRS, and SCORE2 tools. Risk factors included age, sex, dyslipidemia, hypertension, diabetes, obesity, and smoking status. Risk scores were compared to evaluate concordance and classification accuracy across different risk levels. RESULTS The prevalence of cardiovascular risk factors was notable: hypertension (30.1%), dyslipidemia (65.7%), diabetes (7.2%), and obesity (24.3%). SCORE2 showed varied distributions of fatal and nonfatal cardiovascular events. ASCVD categorized most participants as low risk (85.6%), with smaller percentages in borderline (6.8%), intermediate (7.2%), and high-risk (0.5%) groups. FRS classified 97.7% as low risk and 2.3% as intermediate risk. Moderate agreement was observed between SCORE2 and ASCVD in moderate-risk (P<0.001, kappa=0.43) and high-risk (P=0.007, kappa=0.45) regions, with fair agreement between FRS and ASCVD (P<0.001, kappa=0.435). CONCLUSIONS This study highlights the significant prevalence of cardiovascular risk factors among healthcare professionals in Iran. It emphasizes the need for recalibrating cardiovascular risk models to better reflect Iran's unique population characteristics, ultimately improving risk prediction accuracy and preventive strategies.
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