Strategies for personalized risk assessment for CVD management

Cardiovascular disease (CVD) is one of the major causes of death in the world. The correct stratification of patients may significantly contribute to the optimization of the required health care strategies. As result, clinical guidelines recommend the use of risk assessment tools (scores) to identify the CVD risk of each patient in clinical practice. This work proposes a set of strategies for the personalization of CVD risk assessment, supported on the evidence that a specific CVD risk assessment tool may have good performance within a given group of patients and might perform poorly within other groups. In particular, two main personalization methods based on the proper creation of groups of patients are proposed: i) clustering patients approach; ii) similarity measures approach. These two methodologies were validated in a Portuguese population (460 ACS-NSTEMI patients). The similarity measures approach had the best performance, achieving values of sensitivity, specificity and geometric mean of, respectively, 77.7%, 63.2%, 69.7%. These values represent an enhancement in relation to the best performance obtained with current CVD risk assessment tools, respectively 78.5%, 53.2%, 64.4%.