Predictive ability of the pooled cohort risk assessment for the incidence and progression of coronary artery calcification

Research output: Contribution to journalArticlepeer-review

Abstract

Objective The aim of the present study was to evaluate the predictive ability of pooled cohort risk assessment (PRA) models for the incidence and progression of coronary artery calcification (CAC) over time comparing the Framingham risk score (FRS) in a healthy Korean population. Methods The study cohort included 4194 individuals, aged 40 or older, without a history of cardiovascular disease or statin therapy, who underwent repeated cardiac computed tomography for CAC scoring from 2011 to 2014. Logistic regression models and Cox proportional hazards models were used. Results Of the 4194 individuals, 1244 had CAC progression and during over 5184 person-years of follow-up, 204 participants developed incident CAC. Compared with patients whose FRS was less than 10%, the odds ratios (ORs) [95% confidence intervals (CIs)] for CAC progression in patients whose FRS ranged from 10 to 20% and whose FRS was at least 20% were 2.54 (2.17-2.98) and 3.73 (2.69-5.15), respectively. Similarly, compared with the reference group, whose PRA was less than 5%, the ORs (95% CIs) for CAC progression in patients whose PRAs ranged from 5 to less than 7.5%, 7.5 to less than 10%, and at least 10% were 1.85 (95% CI 1.52-2.25), 2.63 (95% CI 2.01-3.46), and 3.58 (95% CI 2.73-4.70), respectively. Similarly, both PRA and FRS were associated positively with the development of CAC in a dose-response manner. In discriminating incident CAC and progression using receiver operating characteristic analysis, PRA was not significantly different from FRS. Conclusion In a large sample of asymptomatic Korean adults without cardiovascular disease, both the PRA and FRS similarly predict the onset and progression of subclinical atherosclerosis over time.

Original languageEnglish
Pages (from-to)504-510
Number of pages7
JournalCoronary Artery Disease
Volume27
Issue number6
DOIs
StatePublished - 1 Sep 2016

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • cardiovascular disease
  • cardiovascular prediction models
  • coronary artery calcium
  • pooled cohort equation

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