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Cardiovascular risk prediction tools made relevant for GPs and patients
  1. Tamar I de Vries,
  2. Frank L J Visseren
  1. Department of Vascular Medicine, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
  1. Correspondence to Dr Frank L J Visseren, Department of Vascular Medicine, University Medical Centre Utrecht, 3508 GA Utrecht, The Netherlands; F.L.J.Visseren{at}umcutrecht.nl

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Learning objectives

  • To understand the rationale for using cardiovascular risk prediction tools to make effective and appropriate risk factor treatment decisions in clinical practice.

  • To understand which online cardiovascular risk prediction tools are available, and to be able to choose which to use for a given patient in clinical practice.

  • To be able to interpret and communicate different prediction outcomes of cardiovascular prediction tools.

Introduction

Cardiovascular disease (CVD) remains the most important cause of morbidity and mortality worldwide.1 For prevention of CVD, cardiovascular risk management is advocated in international guidelines.2 3 Many cohort studies and randomised controlled clinical trials (RCTs) have demonstrated the benefits of risk factor management, including smoking cessation, lipid lowering, blood pressure lowering, antithrombotic therapy, glucose lowering and more recently, anti-inflammatory therapies, on CVD risk.4–9 Besides these interventions, healthy lifestyle behaviour should always be promoted at individual and population level. With this growing plethora of choices in cardiovascular prevention, it can be difficult for both healthcare professional and patient to make the most appropriate treatment decisions for each individual person.

Identifying those patients who will benefit most from risk factor treatment is pivotal in the global CVD prevention effort. Risk stratification is a cornerstone in international CVD prevention guidelines, aiming to identify those at highest risk of future CVD in order to most effectively apply preventive strategies. Risk assessment using risk prediction tools can thus play a highly important part in global CVD prevention efforts in choosing the right treatment and the right treatment goals, for the right patient. This narrative review aims to guide clinicians in using risk stratification tools as decision support tool in CVD prevention.

Why should CVD risk prediction be used in clinical practice?

Prevention is better than cure, also in the context of CVD. Recommendations in international guidelines are rooted in this simple concept: the higher the absolute risk, the higher the …

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Footnotes

  • Contributors Both authors have contributed to the manuscript according to the ICMJE Recommendations on authorship.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Patient consent for publication Not required.

  • Provenance and peer review Commissioned; externally peer reviewed.

  • Data availability statement There are no data in this work

  • Author note References which include a * are considered to be key references.

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