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Updated: Jul 22, 2026

Evaluation of Coronary Flow Reserve After Myocardial Ischemia Reperfusion in Rats
Published on: June 28, 2019
[Comparison of coronary risk estimates derived using the Framingham and REGICOR equations]
Joan Cristóbal1, Fernando Lago, Javier de la Fuente
1Centro de Salud de Sárdoma, Vigo, Pontevedra, Spain. sbd@vagusgruppe.com
Introduction And Objectives:
To compare two equations for evaluating coronary risk, the Framingham-Wilson equation and the Framingham equation adjusted for the Spanish population (REGICOR), in a group of dyslipidemic patients in our healthcare area. In addition, the therapeutic implications of using the 2 methods were also evaluated.
Patients And Method:
The study included 815 dyslipidemic patients, aged 35-74 years, from our healthcare area. Coronary risk was determined using the 2 equations and subjects were categorized as either low-risk (0%-9%), moderate-risk (10%-19%), or high-risk (> or =20%). To compare the application of the 2 equations, we evaluated differences in derived scores, coronary risk category, and the number of patients regarded as potentially treatable with hypolipidemic drugs.
Results:
The best correlation observed between the 2 methods was for quantitative scores (r=0.983; P<.001). The correlation was poorer when coronary risk categories were compared (r=0.489; P<.001). Overall, the concordance was poor (kappa=0.06), and was only acceptable for low-risk patients (kappa=0.53). The coronary risk estimates derived from the Wilson table were 2.4 times higher than those obtained using REGICOR. The main differences were for moderate and high-risk patients. In addition, the number of patients regarded as potentially treatable with hypolipidemic drugs was five times higher when the Wilson equation was used.
Conclusions:
The overestimate of coronary risk obtained using the Framingham-Wilson equation leads to a greater number of patients being regarded as candidates for hypolipidemic treatment. Our data show the importance of using tables adjusted for the Spanish population.
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