BACKGROUND AND OBJECTIVES: Heart rate variability has been known to be a prognostic factor of heart disease. However, determinants of heart rate variability in general korean population without clinical heart disease have not been studied. Objectives of this study were to measure heart rate variability in general population and to investigate clinical determinants of heart rate variability. METHODS:Heart rate variability measures were obtained by LRR-03TM and MemCalcTM software (GMS, Tokyo, Japan) from public officials in a district of Seoul and their families(n=69). Predictors of heart rate variability included age, gender, heart rate, smoking status, systolic blood pressure, diastolic blood pressure, serum total cholesterol, HDL-cholesterol. Univariate analysis and analysis of variance of low frequency power(0.04-0.15 Hz), high frequency power(0.15-0.30 Hz), and total power spectrum in relation to explanatory variables were done. In order to select determinants of heart rate variability, multiple linear regression model of each heart rate variability measure was created and stepwise selection method was applied. RESULTS: Analysis of variance showed that older age, higher heart rate, body mass index > or =27, systolic blood pressure > or =140 mmHg, diastolic blood pressure > or =90 mmHg, and serum total cholesterol > or =240 mg/dl were negatively associated with one or more heart rate variability measures. Serum HDL-cholesterol > or =35 mg/dl was positively associated with low and high frequency power. Multiple linear regression analyses showed that age and heart rate were the major determinants, gender and cardiovascular risk factors such as diastolic blood pressure, HDL-cholesterol, and smoking contributed to one or more heart rate variability measures. CONCLUSIONS: Age, heart rate, gender, and cardiovascular risk factors must be considered when evaluating heart rate variability.
Blood Pressure ; Body Mass Index ; Cholesterol ; Heart Diseases ; Heart Rate* ; Heart* ; Linear Models ; Risk Factors ; Seoul ; Smoke ; Smoking

OBJECTIVES: Heart failure (HF) is a progressive syndrome that marks the end-stage of heart diseases, and it has a high mortality rate and significant cost burden. In particular, non-adherence of medication in HF patients may result in serious consequences such as hospital readmission and death. This study aims to identify predictors of medication adherence in HF patients. In this work, we applied a Support Vector Machine (SVM), a machine-learning method useful for data classification. METHODS: Data about medication adherence were collected from patients at a university hospital through self-reported questionnaire. The data included 11 variables of 76 patients with HF. Mathematical simulations were conducted in order to develop a SVM model for the identification of variables that would best predict medication adherence. To evaluate the robustness of the estimates made with the SVM models, leave-one-out cross-validation (LOOCV) was conducted on the data set. RESULTS: The two models that best classified medication adherence in the HF patients were: one with five predictors (gender, daily frequency of medication, medication knowledge, New York Heart Association [NYHA] functional class, spouse) and the other with seven predictors (age, education, monthly income, ejection fraction, Mini-Mental Status Examination-Korean [MMSE-K], medication knowledge, NYHA functional class). The highest detection accuracy was 77.63%. CONCLUSIONS: SVM modeling is a promising classification approach for predicting medication adherence in HF patients. This predictive model helps stratify the patients so that evidence-based decisions can be made and patients managed appropriately. Further, this approach should be further explored in other complex diseases using other common variables.
Heart ; Heart Diseases ; Heart Failure ; Humans ; Medication Adherence ; New York ; Patient Compliance ; Patient Readmission ; Support Vector Machine ; Surveys and Questionnaires