Journal of Korean Biological Nursing Science

Korean society of Biological Nursing Science (한국기초간호학회)
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Identification of subgroups with poor lipid control among patients with dyslipidemia using decision tree analysis: the Korean National Health and Nutrition Examination Survey from 2019 to 2021

의사결정나무 분석을 이용한 이상지질혈증 유병자의 지질관리 취약군 예측: 2019-2021년도 국민건강영양조사 자료

  • Hee Sun Kim (College of Nursing, Research Institute of Nursing Science, Jeonbuk National University) ;
  • Seok Hee Jeong (College of Nursing, Research Institute of Nursing Science, Jeonbuk National University)
  • 김희선 (전북대학교 간호대학, 간호과학연구소) ;
  • 정석희 (전북대학교 간호대학, 간호과학연구소)
  • Received : 2023.04.20
  • Accepted : 2023.05.17
  • Published : 2023.05.31
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Abstract

Purpose: The aim of this study was to assess lipid levels and to identify groups with poor lipid control group among patients with dyslipidemia. Methods: Data from 1,399 Korean patients with dyslipidemia older than 20 years were extracted from the Korea National Health and Nutrition Examination Survey. Complex sample analysis and decision-tree analysis were conducted with using SPSS for Windows version 27.0. Results: The mean levels of total cholesterol (TC), triglyceride (TG), low density lipoprotein-cholesterol (LDL-C), and high density lipoprotein cholesterol were 211.38±1.15 mg/dL, 306.61±1.15 mg/dL, 118.48±1.08 mg/dL, and 42.39±1.15 mg/dL, respectively. About 61% of participants showed abnormal lipid control. Poor glycemic control groups (TC ≥ 200 mg/dL or TG ≥ 150 mg/dL or LDL-C ≥ 130 mg/dL) were identified through seven different pathways via decision-tree analysis. Poor lipid control groups were categorized based on patients' characteristics such as gender, age, education, dyslipidemia medication adherence, perception of dyslipidemia, diagnosis of myocardial infarction or angina, diabetes mellitus, perceived health status, relative hand grip strength, hemoglobin A1c, aerobic exercise per week, and walking days per week. Dyslipidemia medication adherence was the most significant predictor of poor lipid control. Conclusion: The findings demonstrated characteristics that are predictive of poor lipid control and can be used to detect poor lipid control in patients with dyslipidemia.

Keywords

Acknowledgement

This research was supported by the "Research Base Construction Fund Support Program" funded by Jeonbuk National University in 2021.

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