Heart Rate Variability Applied to Short-Term Cardiovascular Event Risk Assessment
- 1 Computer Science and Systems Engineering Department, Polytechnic Institute of Coimbra (IPC/ISEC), Coimbra, Portugal
- 2 Computer Science and Systems Engineering Department, Polytechnic Institute of Coimbra (IPC/ISEC), Coimbra, Portugal
- 3 Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
- 4 Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
- 5 Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
- 6 CardiologyDepartment, Leiria-Pombal Hospital Centre, Leiria, Portugal
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).
- WHO, World Health Organization “Cardiovascular Diseases,” Fact Sheet No. 317, 2009. http://www.who.int/mediacentre
- EHN, European Heart Network, “Healthy Hearts for All,” Annual Report 2009, 2010. http://www.ehnheart.org/publications/annual-reports.html
- H. Reiter, et al., “HeartCycle: Compliance and Effectiveness in HF and CAD Closed-Loop Management,” Proceedings of the 31st Conference of the IEEE EMBS, 2009.
- N. Boye, et al., “PREVE White Paper—ICT Research Directions in Disease Prevention,” 2010.
- I. Graham, et al., “Guidelines on Preventing Cardiovascular Disease in Clinical Practice: Executive Summary,” European Heart Journal, Vol. 28, 2007, pp. 2375-2414.
- J. Perk, et al., “European Guidelines on Cardiovascular Disease Prevention in Clinical Practice,” European Heart Journal, Vol. 33, 2012, pp. 1635-1701.
- NVDPA, “Guidelines for the Assessment of Absolute Cardiovascular Disease Risk,” National Heart Foundation of Australia, 2009.
- E. Antman, et al., “The TIMI Risk Score for Unstable Angina/Non-St Elevation MI—A Method for Prognostication and Herapeutic Decision Making,” Journal of American Medical Association—JAMA, Vol. 284, No. 7, 2000, pp. 835-842.
- E. Boersma, et al., “Predictors of Outcome in Patients with Acute Coronary Syndromes without Persistent ST-Segment Elevation; Results from an International Trial of 9461 Patients,” Circulation, American Heart Association—AHA, Vol. 101, 2000, pp. 2557-2657.
- E. Tang, C. Wong and P. Herbinson, “Global Registry of Acute Coronary Events (GRACE) Hospital Discharge Risk Scores Accurately Predicts Long Term Mortality Post-Acute Coronary Syndrome,” American Heart Journal, Vol. 153, No. 1, 2007, pp. 30-35.
- E. Auer and R. Kohavi, “An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting and Variants,” Machine Learning, Vol. 36, 1998, pp. 1-38.
- Tsymbal, et al., “Ensemble Feature Selection with the Simple Bayesian Classification,” Information Fusion, Vol. 4, No. 2, 2003, pp. 87-100.
- G. Samsa, G. Hu and M. Root, “Combining Information from Multiple Data Sources to Create Multivariable Risk Models: Illustration and Preliminary Assessment of a New Method,” Journal of Biomedical Biotechnology, Vol. 2, 2005, pp. 113-123.
- Twardy, “Knowledge Engineering Cardiovascular Bayesian Networks from the Literature,” Technical Report 2005/170, Monash University, 2005.