J. Morais

Improvement of CVD risk assessment tools' performance through innovative patients' grouping strategies

There are available in the clinical community several practical risk tools to assess the risk of occurrence of a cardiovascular event. Although valuable, these tools typically present some lack of performance (low sensitivity/low specificity) when applied to a general (average) patient. This flaw is addressed in this work through an innovative personalization strategy that is supported on the evidence that current risk assessment tools perform differently among different populations/groups of patients. The proposed methodology is based on two main hypotheses: i) patients are grouped through a proper dimension reduction technique complemented with an unsupervised learning algorithm, ii) for each group the most suitable risk assessment tool can be selected improving the risk prediction performance. As a result, risk personalization is simply achieved by the identification of the group that patients belong to. The validation of the strategy is carried out through the combination of three current risk assessment tools (GRACE, TIMI, PURSUIT) developed to predict the risk of an event in coronary artery disease patients. The combination of these tools is validated with a real patient testing dataset: Santa Cruz Hospital, Portugal, N=460 ACS-NSTEMI1 patients. Considering the obtained results with the available dataset it is possible to state that the main objective of this work was achieved.

Long term cardiovascular risk models’ combination - a new approach

This work addresses two major drawbacks of the current cardiovascular risk score systems: reduced number of risk factors considered by each individual tool and the inability of these tools to deal with incomplete information. To achieve this goal a two phase strategy was followed. In the first phase, a common representation procedure was considered, based on a Naïve-Bayes classifier methodology. Conditional probabilities parameters were initially evaluated through a frequency estimation method and after that optimized using a Genetic Algorithm approach. In a second phase, a combination scheme was proposed exploiting the particular features of Bayes probabilistic reasoning. This strategy was applied to describe and combine SCORE, ASSIGN and Framingham models. Validation results were obtained based on individual models, assuming their statistical correctness. The achieved results are very promising, showing the potential of the strategy to accomplish the desired goals.

Cardiovascular risk and status assessment

This work focuses on the development of models to support the assessment of a patients global cardiovascular condition. Three types of models, based on different types of information, have been developed: long term cardiovascular risk models, that evaluate the risk of occurring of cardiovascular event within a long period of time (years); short term cardiovascular risk models, to assess the risk of death within a short period of time (months); cardiovascular status assessment models, to estimate the current cardiovascular condition of a patient. Three major drawbacks of current cardiovascular tools are addressed: reduced number of risk factors considered by each individual tool, inappropriateness of these tools to incorporate empirical clinical expertise and incapacity of these tools to deal with incomplete information. Methodologies and preliminary results, obtained under FP7 HeartCycle project, as well as future directions of research are also presented in this paper.

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%).