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

Author:

Kim Hee SunORCID,Jeong Seok HeeORCID

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.

Funder

Jeonbuk National University

Publisher

Korean Society of Biological Nursing Science

Subject

General Medicine,General Earth and Planetary Sciences,General Environmental Science

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Factors related to undiagnosed diabetes in Korean adults: a secondary data analysis;Journal of Korean Biological Nursing Science;2023-11-30

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