Assessment of Lifetime Risk for Cardiovascular Disease: Time to Move Forward.
Evangelia G SigalaDemosthenes B PanagiotakosPublished in: Current cardiology reviews (2024)
Over the past decades, there has been a notable increase in the risk of Cardiovascular Disease (CVD), even among younger individuals. Policymakers and the health community have revised CVD prevention programs to include younger people in order to take these new circumstances into account. A variety of CVD risk assessment tools have been developed in the past years with the aim of identifying potential CVD candidates at the population level; however, they can hardly discriminate against younger individuals at high risk of CVD.Therefore, in addition to the traditional 10-year CVD risk assessment, lifetime CVD risk assessment has recently been recommended by the American Heart Association/American College of Cardiology and the European Society of Cardiology prevention guidelines, particularly for young individuals. Methodologically, the benefits of these lifetime prediction models are the incorporation of left truncation observed in survival curves and the risk of competing events which are not considered equivalent in the common survival analysis. Thus, lifetime risk data are easily understandable and can be utilized as a risk communication tool for Public Health surveillance. However, given the peculiarities behind these estimates, structural harmonization should be conducted in order to create a sex-, race-specific tool that is sensitive to accurately identifying individuals who are at high risk of CVD. In this review manuscript, we present the most commonly used lifetime CVD risk tools, elucidate several methodological and critical points, their limitations, and the rationale behind their integration into everyday clinical practice.
Keyphrases
- public health
- risk assessment
- cardiovascular disease
- clinical practice
- human health
- healthcare
- mental health
- heart failure
- heavy metals
- type diabetes
- machine learning
- coronary artery disease
- middle aged
- big data
- cardiovascular risk factors
- deep learning
- free survival
- health information
- clinical evaluation
- health promotion