Percentiles de riesgo coronario: una nueva forma de adaptar las escalas de riesgo. Estudio ERVPA / Coronary risk percentiles: a new method to adapt risk functions. ERVPA study

Introducción. Existen diversas ecuaciones de cálculo de riesgo cardiovascular y adaptaciones a nuestro medio para evitar la valoración del riesgo exagerada o defectuosa. Se proponen los percentiles de riesgo como una nueva forma de adaptar las escalas de riesgo coronario. Material y método. Estudio transversal de prevalencia de factores de riesgo cardiovascular en Palencia (ERVPA: estudio de riesgo vascular en Palencia). Se han valorado las variables necesarias para calcular el riesgo coronario de 514 sujetos de 20 a 79 años de la población general, estudiados en los centros de salud de Palencia. Se ha calculado el riesgo coronario según las ecuaciones originales del estudio de Framingham y las adaptaciones de los estudios REGICOR y DORICA. Se han calculado los percentiles con las ecuaciones y el coeficiente de correlación de Spearman para cada par de ecuaciones y los coeficientes kappa de concordancia. Resultados. Las ecuaciones originales han ofrecido los riesgos más elevados. Los resultados con las ecuaciones del estudio REGICOR han sido los más bajos. Si se comparan los percentiles, las 2 ecuaciones ofrecen exactamente los mismos percentiles para cada sujeto. El coeficiente de correlación ordinal entre cualquier par de ecuaciones es 1. Calculados los percentiles, es posible extrapolar el riesgo de un sujeto joven a la edad de 60 años o a cualquier otra edad, según su percentil. Conclusiones. La adopción de los percentiles de riesgo como método de valoración del riesgo cardiovascular permite la adaptación local de cualquier ecuación de riesgo. Los percentiles permiten extrapolar el riesgo absoluto (AU)

Introduction. There are several scales or equations for calculating cardiovascular risk that can be adapted to a particular population to try to avoid over, or under-estimation of risk. We propose risk percentiles as a new method of adapting coronary risk scoring systems to our population. Material and method. A cross sectional study of the prevalence of cardiovascular risk factors in the province of Palencia in Spain (ERVPA: Cardiovascular Risk Study in Palencia) was conducted. The variables used to calculate coronary risk were evaluated in 514 subjects aged 20-79 years old from the general population in health centers in Palencia. Coronary risk was measured with the Framingham-Wilson, REGICOR and DORICA equations. Percentiles were calculated and compared with every couple of equations and Spearman's correlation coefficient and kappa agreement coefficient were calculated. Results. The highest scores were found using the Framingham equation and the lowest scores were found with the REGICOR equation. On comparing the percentiles, the concordance between equations was absolute. The ordinal correlation coefficient was 1 between any two equations. Calculation of the percentiles allows coronary risk in young subjects to be extrapolated to the age of 60 years or to any other age. Conclusions. Adopting risk percentiles as a method of coronary risk evaluation enables any risk equation to be adapted to a particular area. Risk percentiles allow us to extrapolate absolute risk for any age (AU)

[Cardiovascular risk percentiles avoid overestimation or underestimation of risk calculated by the SCORE risk function]. / La adopción de los percentiles de riesgo cardiovascular evita la sobreestimación o infraestimación del riesgo cardiovascular calculado con el SCORE.

BACKGROUND: Cardiovascular risk evaluation is of the utmost importance for treatment of patients with cardiovascular risk factors. There are various scales or equations and they can be adapted to a particular population to try to avoid over or under estimation of risk. We propose risk percentiles as a new method of adapting risk scoring systems to our population. METHODS: A cross sectional study of the prevalence of cardiovascular risk factors in the province of Palencia (ERVPA: Cardiovascular Risk Study in Palencia) was conducted. Age, gender, blood pressure, total cholesterol, tobacco consumption and diabetes were evaluated. 514 subjects aged 20 - 79 taken from the general population were studied in 9 health centers from Palencia (Spain). Fatal cardiovascular risk was measured with SCORE project equations. Percentiles were calculated and compared to both equations and a Spearman's correlation coefficient and a kappa agreement coefficient were calculated. RESULTS: The percentages of high risk range from 0% in young subjects to 92% in males over the age of 70 and 67% in females over the age of 70. The concordance with high risk country equations has a kappa coefficient of 0.741. Concordance varies greatly with age and gender. On comparing the percentiles, the concordance between both equations is almost absolute. CONCLUSIONS: Adopting risk percentiles as a method of cardiovascular risk evaluation permits us to adapt any risk equation to a particular area and avoids underestimation or overestimation of risk. Risk percentiles allow us to extrapolate absolute risk for any age.

La adopción de los percentiles de riesgo cardiovascular evita la sobreestimación o infraestimación del riesgo cardiovascular calculado con el score / Cardiovascular risk percentiles avoid overestimation or underestimation of risk calculated by the SCORE risk function

Fundamento: La evaluación del riesgo cardiovascular es demáxima importancia para un correcto tratamiento de pacientes conriesgo. Hay varias ecuaciones de riesgo que pueden ser adaptadas auna población para evitar la sobre o infraestimación del riesgo. Seproponen a los percentiles de riesgo como un nuevo método paraadaptar los sistemas de valoración del riesgo a nuestra población.Métodos: Se ha realizado un estudio transversal de factores deriesgo cardiovascular en Palencia (ERVPA: Estudio de Riesgo Vascularen PAlencia). Se han registrado las variables edad, sexo, tabaquismo,tensión arterial, diabetes y colesterolemia. Se han estudiado514 sujetos entre 20 y 79 años procedentes de 9 centros de salud delárea sanitaria. Se ha calculado el riesgo cardiovascular fatal segúnlas ecuaciones del proyecto SCORE. Se han calculado y comparadolos percentiles obtenidos con cada ecuación, mediante el cálculo delos coeficientes de Spearman y kappa.Resultados: Los porcentajes de sujetos con riesgo alto varíanentre el 0% en jóvenes hasta el 92% en los varones y el 67% en lasmujeres mayores de 70 años. El coeficiente kappa entre las ecuacionespara países de bajo y alto riesgo es de 0.741. Dicha concordanciavaría de forma importante según el sexo y la edad. La concordanciaentre los percentiles es casi total. Conclusiones: Los percentiles de riesgo es un método de evaluacióndel riesgo que permite adaptar cualquier ecuación a unapoblación particular evitando la sobre o infraestimación del riesgo ypermiten extrapolar el riesgo a cualquier edad

Background: Cardiovascular risk evaluation is of the utmostimportance for treatment of patients with cardiovascular risk factors.There are various scales or equations and they can be adaptedto a particular population to try to avoid over or under estimation ofrisk. We propose risk percentiles as a new method of adapting riskscoring systems to our population.Methods: A cross sectional study of the prevalence of cardiovascularrisk factors in the province of Palencia (ERVPA: CardiovascularRisk Study in Palencia) was conducted. Age, gender, bloodpressure, total cholesterol, tobacco consumption and diabetes wereevaluated. 514 subjects aged 20 - 79 taken from the general populationwere studied in 9 health centers from Palencia (Spain). Fatalcardiovascular risk was measured with SCORE project equations.Percentiles were calculated and compared to both equations and aSpearman´s correlation coefficient and a kappa agreement coefficientwere calculated.Results: The percentages of high risk range from 0% in youngsubjects to 92% in males over the age of 70 and 67% in females overthe age of 70. The concordance with high risk country equations hasa kappa coefficient of 0.741. Concordance varies greatly with ageand gender. On comparing the percentiles, the concordance betweenboth equations is almost absolute. Conclusions: Adopting risk percentiles as a method of cardiovascularrisk evaluation permits us to adapt any risk equation to aparticular area and avoids underestimation or overestimation of risk.Risk percentiles allow us to extrapolate absolute risk for any age

[Risk percentiles: a new adapted method for evaluating vascular risk. ERVPA study]. / Percentiles de riesgo: un nuevo método adaptado para la valoración del riesgo vascular. Estudio ERVPA.

BACKGROUND AND OBJECTIVE: Various cardiovascular scoring systems estimating the risk in the Spanish population are currently used. The aim of our study was to create graphs and charts of populational percentiles to assess the total burden of risk of a population and to place each subject individually in the context of their own population setting by using cardiovascular risk charts. SUBJECTS AND METHOD: A cross-sectional study of 514 individuals of both sexes from a general population setting was carried out. Cardiovascular risk was calculated according to the SCORE project equations and charts and graphs of cardiovascular risk were formulated. RESULTS: In primary prevention, 17.11% of men and 22.36% of women aged 20-79 showed a high cardiovascular risk. The percentages vary from 0% in persons of both sexes aged 20-39 to 91.2% of men and 97.9% of women in their 70s. Risk medians range in men from 0.11% (age 20-39) to 8.86% (age 70-79) and in women from 0.02% (age 20-39) to 15.20% (age 70-79). Graphs of cardiovascular risk percentiles were formulated for both sexes; graphs can be used to situate each subject individually in the context of their own population setting and to project the risk to any age. CONCLUSIONS: Nearly a fifth of the population has a high cardiovascular risk. More than 90% of men and women in their 70s have a high cardiovascular risk.