Personalization algorithms applied to cardiovascular disease risk assessment.

Cardiovascular disease (CVD) is the major cause of death in the world. Clinical guidelines recommend the use of risk assessment tools (scores) to identify the CVD risk of each patient as the correct stratification of patients may significantly contribute to the optimization of the health care strategies. This work further explores 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. Two main personalization methods based on the proper creation of groups of patients are presented: i) clustering patients approach; ii) similarity measures approach. These two methodologies were validated in a Portuguese population (460 Acute Coronary Syndrome with non-ST segment elevation (ACS-NSTEMI) patients). The similarity measures approach had the best performance, achieving maximum 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 applied in clinical practice (78.5%, 53.2%, 64.4%).

Fusion of risk assessment models with application to coronary artery disease patients.

Several risk score models are available in literature to predict death/myocardial infarction event for coronary artery disease (CAD) patients, within a short period of time. However, the choice of the most adequate model is not straightforward since there might not be a consensus about the best model to use in clinical practice Moreover, individually, these models present some weaknesses, such as the inability to deal with missing information. This work addresses these problems, proposing a Bayesian classifier strategy enabling the simultaneous use of several models (models' fusion). Thus, a higher number of risk factors can be used in the common model, while it can deal with missing information. The validation of the strategy is carried out through the combination of three current risk score models (GRACE, TIMI, PURSUIT). Results were obtained based on a dataset that comprises 460 consecutive patients admitted to the Cardiology Department of Santa Cruz Hospital, Lisbon, from 1999 to 2001. A comparison with the voting scheme, which considers exclusively the outputs of models to combine (models output combination) is also carried out. The proposed Bayesian approach had very satisfactory results, confirming the potential of its application to the clinical practice.