A cardiovascular risk prediction model for older people: Development and validation in a primary care population
SourceThe Journal of Clinical Hypertension, 21, 8, (2019), pp. 1145-1152
Article / Letter to editor
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The Journal of Clinical Hypertension
SubjectRadboudumc 3: Disorders of movement DCMN: Donders Center for Medical Neuroscience
Cardiovascular risk prediction is mainly based on traditional risk factors that have been validated in middle-aged populations. However, associations between these risk factors and cardiovascular disease (CVD) attenuate with increasing age. Therefore, for older people the authors developed and internally validated risk prediction models for fatal and non-fatal CVD, (re)evaluated the predictive value of traditional and new factors, and assessed the impact of competing risks of non-cardiovascular death. Post hoc analyses of 1811 persons aged 70-78 year and free from CVD at baseline from the preDIVA study (Prevention of Dementia by Intensive Vascular care, 2006-2015), a primary care-based trial that included persons free from dementia and conditions likely to hinder successful long-term follow-up, were performed. In 2017-2018, Cox-regression analyses were performed for a model including seven traditional risk factors only, and a model to assess incremental predictive ability of the traditional and eleven new factors. Analyses were repeated accounting for competing risk of death, using Fine-Gray models. During an average of 6.2 years of follow-up, 277 CVD events occurred. Age, sex, smoking, and type 2 diabetes mellitus were traditional predictors for CVD, whereas total cholesterol, HDL-cholesterol, and systolic blood pressure (SBP) were not. Of the eleven new factors, polypharmacy and apathy symptoms were predictors. Discrimination was moderate (concordance statistic 0.65). Accounting for competing risks resulted in slightly smaller predicted absolute risks. In conclusion, we found, SBP, HDL, and total cholesterol no longer predict CVD in older adults, whereas polypharmacy and apathy symptoms are two new relevant predictors. Building on the selected risk factors in this study may improve CVD prediction in older adults and facilitate targeting preventive interventions to those at high risk.
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