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Association between cardiovascular disease risk scores and subclinical atherosclerosis prevalence in non-elderly adult patients from Argentina

  • Ricardo A. Albertini
  • Dario G. Ferrer
  • Pablo A. Romagnoli
  • María E. Tinti
  • José L. Amigone
  • Raúl Capra
  • Gustavo A. Chiabrando
Original Paper

Abstract

The goal of our study was to use statistical analysis to try to associate cardiovascular disease (CVD) risk scores and the observed prevalence of subclinical atherosclerosis (SA) in a non-elderly adult local population. An observational cross-sectional study was carried out (143 male and 131 female) on non-elderly adults (20–59 years). CVD risk scores included Framingham Risk Scores for 10-year hard (FRS 10 H), 30-year lipid hard or CVD (FRS 30 L H or FRS 30 L CVD), 30 year-body mass index hard or CVD (FRS 30 BMI H or FRS 30 BMI CVD) and Pooled Cohort Risk Equations for either 10 years (PCE 10) or lifetime (PCE LT). The Carotid Ultrasound (CU) study was performed and the Coronary Artery Calcium (CAC) score were obtained to assess SA. The Receiving Operating Characteristic (ROC) curve analysis followed by Youden’s index was used to evaluate and adjust the stratification of CVD risk scores. SA was detected in 32.4% of individuals. The risk scores that showed the biggest areas under the ROC curve were FRS 30 L (H and CVD). When the cut-off values for these CVD risk scores were adjusted, the FRS 30 L H increased the negative predictive value for the low risk group from 87.7 to 97.0% and the FRS 30 L CVD increased the positive predictive values for the high risk group from 69.7 to 85.7%. The CVD risk stratification of non-elderly adults using FRS 30 L H and FRS 30 L CVD may be a useful tool for selecting candidate patients for diagnostic imaging studies that assess their SA prevalence.

Keywords

Cardiovascular disease Subclinical atherosclerosis Framingham risk scores Pooled cohort equations Argentina 

Notes

Acknowledgements

We are grateful to Dr. Paul David Hobson, native speaker, for revising the language of this manuscript. This work was funded by Secretaría de Ciencia y Tecnología de la Universidad Nacional de Córdoba (SECyT-UNC) grants 124/13 and 162/12; Fondo para la Investigación Científica y Tecnológica (FONCyT), Préstamo BID Proyecto de Investigación en Ciencia y Tecnología (PICT) grant 2012–2607; Proyecto de Investigación en Ciencia y Tecnología Orientados (PICTO)-Glaxo grant 2012-0084; and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Proyecto de Investigación Plurianual (PIP) grant 11220110100775. G.A.C. is a member of the Research Career of CONICET.

Compliance with ethical standards

Conflict of interest

R.A.A., D.G.F., P.A.R., M.E.T., J.L.A., R.C. and G.A.C. declare that they have no conflict of interest.

Supplementary material

10554_2017_1152_MOESM1_ESM.pdf (147 kb)
Supplementary material 1 (PDF 146 KB)

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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Ricardo A. Albertini
    • 1
    • 2
  • Dario G. Ferrer
    • 1
  • Pablo A. Romagnoli
    • 2
  • María E. Tinti
    • 1
  • José L. Amigone
    • 1
  • Raúl Capra
    • 1
  • Gustavo A. Chiabrando
    • 3
  1. 1.Hospital Privado Universitario de CórdobaCórdobaArgentina
  2. 2.Instituto Universitario de Ciencias Biomédicas de CórdobaCórdobaArgentina
  3. 3.Centro de Investigaciones en Bioquímica Clínica e Inmunología (CIBICI) CONICET. Departamento de Bioquímica Clínica, Facultad de Ciencias QuímicasUniversidad Nacional de CórdobaCórdobaArgentina

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