Cardiovascular risk and status assessment

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-NSTEMI patients. Considering the obtained results with the available dataset it is possible to state that the main objective of this work was achieved.

Patient specific cardiovascular risk assessment and treatment decision support based on multiscale modelling and medical guidelines

In this work we present an enhanced medical workflow and a decision support system for atherosclerotic risk assessment and treatment, that is based both on existing medical guidelines and on patient specific multiscale data. The medical expert that uses the system is able to apply both existing medical guidelines as well as to take into account additional information for the patient by inspecting the 3D geometry of an arterial segment or the arterial tree, model the blood flow in the patient specific arterial model and predict the progression of the plaque. Moreover, the user is able to apply plaque characterization techniques in Intravascular Ultrasound images (IVUS) and Tomography Images (CT). The combination of the medical guidelines with the patient specific multiscale data provides a detailed view in the patient status for risk assessment and treatment suggestion.