Cardiovascular disease risk assessment innovative approaches developed in HeartCycle project

Abstract

Two innovative CVD event risk assessment strategies were developed in the scope of HeartCycle project: i) combination of individual risk assessment tools; ii) personalization of risk assessment based on grouping of patients. These approaches aimed to defeat some of the major limitations of the tools currently applied in the daily clinical practice, namely to: i) improve the risk prediction performance when comparing it to the one achieved by the individual current risk assessment tools; ii) consider the available knowledge provided by other risk assessment tools; iii) cope with missing risk factors; iv) incorporate additional clinical knowledge. Two different real patients' datasets were applied to validate the developed strategies: i) Santa Cruz Hospital, Portugal, N=460 ACS-NSTEMI1 patients; ii) Leiria Pombal Hospital Centre, Portugal, N=99 ACS-NSTEMI. Based on the gathered results, we propose a new strategy in order to improve patients' stratification.

Strategies for personalized risk assessment for CVD management

“…A personalization strategy, based on groups of patients, was also derived to address the problem of lack of performance. That methodology was supported on the evidence that a specific risk assessment tool may have a good performance within a given group of patients and might perform poorly within other groups [9]. The current work further explores this personalization concept, comparing the results obtained through two different strategies: i) clustering patients approach; ii) similarity measures approach.…”

“…However, despite their clinical relevance, these tools present some important limitations [9]. Our recent research has addressed this problem through the development of different methodologies [9].…”

“…However, despite their clinical relevance, these tools present some important limitations [9]. Our recent research has addressed this problem through the development of different methodologies [9]. Firstly, a global framework (Bayesian model) was created directly from the fusion of the individual models parameters exploring the particular features of Bayesian inference mechanism.…”