TITLE:
Heart Rate Variability Applied to Short-Term Cardiovascular Event Risk Assessment
AUTHORS:
Simao Paredes, Teresa Rocha, Paulo de Carvalho, Jorge Henriques, Ramona Cabiddu, João Morais
KEYWORDS:
CVD Risk Assessment; Knowledge Management; Management of Cardiovascular Diseases; Decision-Support Systems
JOURNAL NAME:
Engineering,
Vol.5 No.10B,
October
31,
2013
ABSTRACT:
Cardiovascular disease (CVD) risk assessment is an
important instrument to enhance the clinical decision in the daily practice as
well as to improve the preventive health care promoting the transfer from the
hospital to patient’s home. Due to its importance, clinical guidelines recommend
the use of risk scores to predict the risk of a cardiovascular disease event. Therefore,
there are several well known risk assessment tools, unfortunately they present
some limitations.This work addresses this problem with two different methodologies:1)
combination of risk assessment tools based on fusion of Bayesian classifiers
complemented with genetic algorithm optimization;2) personalization of risk assessment
through the creation of groups of patients that maximize the performance of
each risk assessment tool. This last approach is
implemented based on subtractive clustering applied to a reduced-dimension
space.Both methodologies were developed to short-term CVD risk prediction for
patients with Acute Coronary Syndromes without ST segment eleva-tion
(ACS-NSTEMI). Two different real patients’ datasets were considered to validate
the developed strategies:1) Santa Cruz Hospital, Portugal, N=460
patients;2)LeiriaPombal Hospital
Centre, Portugal, N=99 patients.This work improved the
performance in relation to current risk assessment tools reaching maximum
values of sensitivity, specificity and geometric mean of, respectively, 80.0%, 82.9%,
81.5%. Besides this enhancement, the proposed methodologies allow the
incorporation of new risk factors, deal with missing risk factors and avoid the
selection of a single tool to be applied in the daily clinical practice. In
spite of these achievements, the CVD risk assessment (patient stratification)
should be improved. The incorporation of new risk factors recognized as
clinically significant, namely parameters derived from heart
rate variability (HRV), is introduced in this work. HRV is a strong and
independent predictor of mortality in patients following acute myocardial
infarction. The impact of HRV parameters in the characterization of coronary
artery disease (CAD) patients will be conducted during hospitalization of these
patients in the Leiria-Pombal Hospital Centre (LPHC).