A cardiovascular risk prediction model for older people: Development and validation in a primary care population

Abstract

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.

All-Cause Mortality And Cardiovascular Events In A Spanish Nonagenarian Cohort According To Type 2 Diabetes Mellitus Status And Established Cardiovascular Disease

“…Additional research work brings diabetes at the same risk level as established cardiovascular disease but only for people with severe diabetes [29], or just for those with early-onset diabetes [30]. In the elderly, comorbidity and speci c social and medical needs may make the comparison between both conditions to predict incident cardiovascular disease or mortality even less clear, since traditional risk factors lose weight at the same time that other conditions emerge to in uence cardiovascular risk [31,32]. As a consequence, speci c cardiovascular risk prediction tools for the elderly have been put in place [33,34].…”

<p>Frailty Phenotype: Evidence of Both Physical and Mental Health Components in Community-Dwelling Early-Old Adults</p>

“…37 Age, male sex, smoking, and type 2 diabetes mellitus were independent risk factors for mortality in persons aged 70-78 years, while total cholesterol, HDL-cholesterol, and systolic blood pressure were no longer associated with increased mortality in this age strata. 38 Polypharmacy and patient apathy were identified as new relevant predictors of mortality. 38 Our data suggest that male sex is associated with higher prevalence of ever-smoking, higher blood pressure and higher prevalence of diabetes.…”

“…38 Polypharmacy and patient apathy were identified as new relevant predictors of mortality. 38 Our data suggest that male sex is associated with higher prevalence of ever-smoking, higher blood pressure and higher prevalence of diabetes. Regular alcohol consumption was also strongly associated with male sex, which consequently may result in findings of negative associations of alcohol consumption with frailty.…”

How do cardiovascular risk prediction equations developed among 30–74 year olds perform in older age groups? A validation study in 125 000 people aged 75–89 years

“…Another option is to derive models specifically for older people using a restricted age range. For example, new models published in 2019 were developed and validated from a cohort of 1800 Dutch individuals without prior CVD who were recruited from primary care practices and were aged between 70 and 78 years 28…”