Percentiles de riesgo coronariouna nueva forma de adaptar las escalas de riesgo. Estudio ERVPA

  1. José I. Cuende 1
  2. Alfredo Acebal 2
  3. Alejandro Suárez Fernández 3
  4. Gladys Hurtarte Cabrera 4
  5. Miguel A. Martínez Flores 5
  6. Fernando Sánchez García 6
  7. Pilar García Medina 7
  8. Isaac Villalba 7
  1. 1 Servicio de Medicina Interna. Complejo Hospitalario de Palencia. Palencia
  2. 2 Sección de Nefrología. Hospital Universitario del Río Hortega. Valladolid
  3. 3 Centro de Salud de Frómista. Sanidad de Castilla y León. Frómista. Palencia
  4. 4 Centro de Salud de Villarramiel. Sanidad de Castilla y León. Villarramiel. Palencia
  5. 5 Centro de Salud de Torquemada. Sanidad de Castilla y León. Torquemada. Palencia
  6. 6 Centro de Salud de Paredes de Nava. Sanidad de Castilla y León. Paredes de Nava. Palencia
  7. 7 Centro de Salud de Herrera de Pisuerga. Sanidad de Castilla y León. Herrera de Pisuerga. Palencia
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Journal:
Clínica e investigación en arteriosclerosis

ISSN: 0214-9168,  1578-1879

Year of publication: 2006

Volume: 18

Issue: 6

Pages: 218-225

Type: Article

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More publications in: Clínica e investigación en arteriosclerosis

Abstract

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.

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