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

Abstract

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.

Heart Rate Variability Applied to Short-Term Cardiovascular Event Risk Assessment

“…More detailed results obtained with the validation of these two methodologies, can be found on [16,17].…”

“…This allows that each cluster be assigned to the tool that presents the best performance. The final classification of a particular patient that belongs to a given cluster corresponds to the classification obtained with the individual tool that has the best performance with patients from that cluster [17].…”

“…of risk factors and it assures the uniformization of each patient's data (same scale). A non-linear mapping is implemented directly supported on the outputs of the selected set of risk assessment tools [17]. Thus, all instances The second phase is the identification of risk assessment tools, where the performance of the several individual tools is assessed within each cluster.…”

“…Figure 1 presents the developed strategies. These methodologies were further detailed in previous publications of this research team [16,17].…”