Should we reconsider the role of age in treatment allocation for primary prevention of cardiovascular disease?

Leening, Maarten J.G.; Cook, Nancy R.; Ridker, Paul M.

doi:10.1093/eurheartj/ehw287

Cited by 12 publications

(16 citation statements)

References 36 publications

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“…1 With increasing age, shorter life expectancy, and multimorbidity, the balance between potential harms and benefits of long-term medication may shift, requiring doctors to reconsider whether (continued) cardiovascular medication is still justified. 36 In conclusion, of the traditional risk factors, only age, sex, smoking status, and T2DM showed predictive ability in people aged 70-78 years, whereas total cholesterol, HDL-cholesterol, and SBP did not. From a set of eleven additional factors, polypharmacy and apathy symptoms were identified as new predictors for CVD in this age group.…”

Section: Limitationsmentioning

confidence: 79%

“…In middle‐aged adults weighing risks and benefits from treatment, a cutoff of 20% of CVD morbidity and mortality in the next 10 years is generally agreed on . With increasing age, shorter life expectancy, and multimorbidity, the balance between potential harms and benefits of long‐term medication may shift, requiring doctors to reconsider whether (continued) cardiovascular medication is still justified …”

Section: Discussionmentioning

confidence: 99%

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

Bussel

Richard

Busschers

et al. 2019

J of Clinical Hypertension

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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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Section: Limitationsmentioning

confidence: 79%

Section: Discussionmentioning

confidence: 99%

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

Bussel

Richard

Busschers

et al. 2019

J of Clinical Hypertension

View full text Add to dashboard Cite

show abstract

“…Beside graphical presentations, many other metrics can be used in risk communication derived from prediction models. An example is the heart age metric recommended by the European Society of Cardiology 3536. Finally, the model might be updated over time to reflect changes in the underlying population characteristics.…”

Section: Presentation Within Websites and Mobile Appsmentioning

confidence: 99%

Guide to presenting clinical prediction models for use in clinical settings

Bonnett

Snell

Collins

et al. 2019

BMJ

141

117

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Clinical prediction models estimate the risk of existing disease or future outcome for an individual, which is conditional on the values of multiple predictors such as age, sex, and biomarkers. In this article, Bonnett and colleagues provide a guide to presenting clinical prediction models so that they can be implemented in practice, if appropriate. They describe how to create four presentation formats and discuss the advantages and disadvantages of each format. A key message is the need for stakeholder engagement to determine the best presentation option in relation to the clinical context of use and the intended users

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“…In developed countries, risk factors such as hypertension, hyperglycemia, and hypercholesterolemia are accepted as having a key role in driving CVD (Leening et al, 2016). Researchers and clinicians have spent significant time and effort investigating the role of these risk factors in the development and progression of CVD, yet there have been a limited number of new medications approved for CVD-related intervention and/or treatment.…”

Section: Introductionmentioning

confidence: 99%

The Potential Therapeutic Application of Peptides and Peptidomimetics in Cardiovascular Disease

et al. 2017

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Cardiovascular disease (CVD) remains a leading cause of mortality and morbidity worldwide. Numerous therapies are currently under investigation to improve pathological cardiovascular complications, but yet, there have been very few new medications approved for intervention/treatment. Therefore, new approaches to treat CVD are urgently required. Attempts to prevent vascular complications usually involve amelioration of contributing risk factors and underlying processes such as inflammation, obesity, hyperglycaemia, or hypercholesterolemia. Historically, the development of peptides as therapeutic agents has been avoided by the Pharmaceutical industry due to their low stability, size, rate of degradation, and poor delivery. However, more recently, resurgence has taken place in developing peptides and their mimetics for therapeutic intervention. As a result, increased attention has been placed upon using peptides that mimic the function of mediators involved in pathologic processes during vascular damage. This review will provide an overview on novel targets and experimental therapeutic approaches based on peptidomimetics for modulation in CVD. We aim to specifically examine apolipoprotein A-I (apoA-I) and apoE mimetic peptides and their role in cholesterol transport during atherosclerosis, suppressors of cytokine signaling (SOCS)1-derived peptides and annexin-A1 as potent inhibitors of inflammation, incretin mimetics and their function in glucose-insulin tolerance, among others. With improvements in technology and synthesis platforms the future looks promising for the development of novel peptides and mimetics for therapeutic use. However, within the area of CVD much more work is required to identify and improve our understanding of peptide structure, interaction, and function in order to select the best targets to take forward for treatment.

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Should we reconsider the role of age in treatment allocation for primary prevention of cardiovascular disease?

Cited by 12 publications

References 36 publications

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

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

Guide to presenting clinical prediction models for use in clinical settings

The Potential Therapeutic Application of Peptides and Peptidomimetics in Cardiovascular Disease

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