Journal of Korean Biological Nursing Science

Korean society of Biological Nursing Science (한국기초간호학회)
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Decision-Tree Analysis to Predict Blood Pressure Control Status Among Hypertension Patients Taking Antihypertensive Medications

약물복용 중인 고혈압 환자의 혈압관리양상 예측을 위한 의사결정나무분석

  • Kim, Hee Sun (College of Nursing.Research Institute of Nursing Science, Chonbuk National University) ;
  • Jeong, Seok Hee (College of Nursing.Research Institute of Nursing Science, Chonbuk National University) ;
  • Park, Sook Kyoung (College of Nursing.Research Institute of Nursing Science, Chonbuk National University)
  • 김희선 (전북대학교 간호대학.간호과학연구소) ;
  • 정석희 (전북대학교 간호대학.간호과학연구소) ;
  • 박숙경 (전북대학교 간호대학.간호과학연구소)
  • Received : 2019.01.05
  • Accepted : 2019.02.11
  • Published : 2019.02.28
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Abstract

Purpose: This study was performed to analyze the levels of blood pressure and to identify good or poor blood pressure control (BPC) groups among hypertension patients. The study was based on the Korea National Health and Nutrition Examination Survey (KNHANES VI and VII) conducted from 2013 to 2016. Methods: The sociodemographic and clinical data of 4,151 Korean hypertension patients aged 20-79 years and who were taking antihypertensive medications was extracted from the KNHANES VI and VII database. Descriptive statistics for complex samples and a decision-tree analysis were performed using the SPSS WIN 24.0 program. Results: The mean age was $62.46{\pm}0.21years$. The mean systolic blood pressure (SBP) was $128.07{\pm}0.28mmHg$, and the diastolic blood pressure (DBP) was $76.99{\pm}0.21mmHg$. 71.9% of participants showed normal blood pressure (SBP < 140mmHg and DBP < 90mmHg). From the decisiontrees analysis, the characteristics of participants related to good BPC group were presented with 9 different pathways same as those from the poor BPC group. Good or poor BPC groups were classified according to the patients' characteristics such as age, living status, occupation, education, hypertension diagnosis period, numbers of comorbidity, perceived health status, total cholesterol, high density lipoprotein-cholesterol, alcohol drinking per month, and depressive mood. Total cholesterol level (< 201mg/dL or ${\geq}201mg/dL$ cutoff point) was the most significant predictor of the participants' BPC group. Conclusion: This decision-tree model with the 18 different pathways can form a basis for the screening of hypertension patients with good or poor BPC in either clinical or community settings.

Keywords

References

  1. Korean Academy of Medical Sciences. Evidence-based recommendations for hypertension in primary care. Final report. Seoul: Korean Academy of Medical Sciences (South Korea); 2018 Jan. Report No.: 11-1352159-000556-14.
  2. Statistic Korea. 2017 the cause of death statistics. [internet]. Seoul: Statistic Korea; 2017 [cited 2018 Dec 30]. Available from: https://meta.narastat.kr/metasvc/index.do?confmNo=101054&inputYear=2017
  3. Korea Centers for Disease Control & Prevention. Korea health statistics 2016: Korea national health and nutrition examination survey (KNHANES VII-1). [internet]. Seoul: Korea Centers for Disease Control & Prevention; 2017 [cited 2018 Dec 30]. Available from: https://knhanes.cdc.go.kr/knhanes/sub04/sub04_03.do
  4. The Korean Society of Hypertension. Korean hypertension fact sheet 2018. [internet]. Seoul: The Korean Society of Hypertension; 2018 [cited 2018 Dec 30]. Available from: https://www.mdon.co.kr/news/download.html?no=16318&atno=47467
  5. Ettehad D, Emdin CA, Kiran A, Anderson SG, Callender T, Emberson J, et al. Blood pressure lowering for prevention of cardiovascular disease and death: A systematic review and meta-analysis. Lancet. 2016;387:957-967. http://dx.doi.org/10.1016/S0140-6736(15)01225-8
  6. Bae SG, Jeon HJ, Yang HS, Kim BK, Park KS. Medication adherence and its predictors in community elderly patients with hypertension. Korean Journal of Health Promotion. 2015;15(3):121-128. http://dx.doi.org/10.15384/kjhp.2015.15.3.121
  7. Min ES, Hur MH. Predictors of compliance in hypertensive patients. Journal of Korean Academic Fundamental Nursing. 2012;19(4):474-482. http://dx.doi.org/10.7739/jkafn.2012.19.4.474
  8. Teshome DF, Demssie AF, Zeleke BM. Determinants of blood pressure control amongst hypertensive patients in Northwest Ethiopia. Public Library of Science One. 2018;13(5):e0196535. https://doi.org/10.1371/journal.pone.0196535
  9. Kang CD, Tsang PP, Li WT, Wang HH, Liu, KQ, Griffiths SM, et al. Determinants of medication adherence and blood pressure control among hypertensive patients in Hong Kong: A cross-sectional study. International Journal of Cardiology. 2015;182:250-257. https://doi.org/10.1016/j.ijcard.2014.12.064
  10. Lee ES. Effects of abdominal obesity and risk drinking on the hypertension risk in Korean adults. Journal ofKorean Academic Community Health Nursing. 2018;29(3):349-358. https://doi.org/10.12799/jkachn.2018.29.3.349
  11. Zavala-Loayza JA, Benziger CP, Cardenas MK, Carrillo-Larco1 RM, Bernabe-Ortiz A, Gilman RH, et al. Characteristics associated with antihypertensive treatment and blood pressure control: A 15-month population-based follow up study in Peru. Global Heart. 2016;11(1):109-119. https://doi:10.1016/j.gheart.2015.12.002
  12. Cho E, Lee CY, Kim I, Lee T, Kim GS, Lee H, et al. Factors influencing medication adherence in patients with hypertension: Based on the 2008 Korean National Health and Nutrition Examination Survey. Journal of Korean Academy of Community Health Nursing. 2013;24(4):419-426. https://doi.org/10.12799/jkachn.2013.24.4.419
  13. Katzmann J, Mahfoud F, Bohm M, Schulz M, Laufs U. Association of medication adherence and depression with the control of low-density lipoprotein cholesterol and blood pressure in patients at high cardiovascular risk. Patient Preference and Adherence. 2018;13:9-19. http://dx.doi.org/10.2147/PPA.S182765
  14. Ghanbari J, Mohammadpooras A, Jahangiry L, Farhangi MA, Amirzadeh J, Ponnet K. Subgroups of lifestyle patterns among hypertension patients: A latent-class analysis. Medical Research Methodology. 2018;18:127-135. https://doi.org/10.1186/s12874-018-0607-6
  15. Lee KJ, Lee HJ, Oh KJ. Using fuzzy-neural network to predict hedge fund survival. Journal of Korean Data & Information Science Society. 2015;26(6):1189-1198. https://doi.org/10.7465/jkdi.2015.26.6.1189
  16. Huh MI. SPSS statistics classification analysis. Seoul: Data Solution: 2012. p.107-175.
  17. Lee HY. New definition for hypertension. Journal of Korean Medicine Association. 2018;61(8):485-492. https://doi.org/10.5124/jkma.2018.61.8.485
  18. Egan BM, Li J, Qanungo S, Wolfman TE. Blood pressure and cholesterol control in hypertensive hypercholesterolemic patients: NHANES 1988-2001. Circulation. 2013;128(1):29-41. http://dx.doi:10.1161/CIRCULATIONAHA.112.000500.
  19. Jung SM, Yoo BL, Kim YJ, Oh BN, Kang HC. Associations between trends in aging, physical activity, central obesity and body mass index with diabetes prevalence in Korea: A series of cross-sectional studies from the 1st to 5th Korean National Health and Nutrition Examination Survey (1998-2012). Korean Journal of Family Practice. 2017;7(2):195-201. https://doi.org/10.21215/kjfp.2017.7.2.195
  20. Rubio-Guerra AF, Rodriguez-Lopez L, Vargas-Ayala G, Huerta-Ramirez S, Serna DC, Lozano-Nuevo JJ. Depression increases the risk for uncontrolled hypertension. Experimental Clinical Cardiology. 2013;18(1):10-12.
  21. Kim WS. The effect of obesity, blood pressure and lifestyle on lipid indices and blood pressure in men of age 40s. Journal of The Korean Society of Physical Medicine. 2013; 8(2):239-243. http://dx.doi.org/10.13066/kspm.2013.8.2.239
  22. Shin MG, Yoon KH, Song MY. Comparison of health behaviors and nutritional status related to dyslipidemia in Korean middle-aged adults-from the Korean National Health and Nutrition Examination Surveys, 2007-2010. Korean Journal of Food Nutrition. 2016;29(5):724-734. http://dx.doi.org/10.9799/ksfan.2016.29.5.724
  23. The Korean Society of Lipid and Atherosclerosis. Atherosclerosis, dyslipidemia_ Diagnostic criteria. [internet]. Seoul: Korea lipid; 1996 Jan 1 [cited 2018. April 2]. Available from: http://www.lipid.or.kr/artery/diagnose1_2.php
  24. Choi JH, Han ST, Kang HC, Kim ES, Kim MK, Lee SK. Prediction and utilization of data mining using answer tree 3.0. Seoul: SPSS Academy; 2002. p.17-31.
  25. Qin G, Luo L, Lv L, Xiao Y, Tu J, Tao L, et al. Decision tree analysis of traditional risk factors of carotid atherosclerosis and a cutpoint-based prevention strategy. Public Library of Science One. 2014; 9(11):e111769. https://doi.org/10.1371/journal.pone.0111769
  26. Talukdar A, Ghosal MK, Sanyal D, Talukdar PS, Guha P, Guha SK, et al. Determinants of quality of life in HIV-infected patients receiving highly active antiretroviral treatment at a medical college ART center in Kolkata, India. Journal of the International Association of Providers of AIDS Care. 2013;12(4):284-290. https://doi.org/10.1177/1545109712445924
  27. Kang SH, Choi SH. Group classification on management behavior of diabetic mellitus. Journal of the Korea Academia-Industrial Cooperation Society. 2011;12(2):765-774. https://doi.org/10.5762/kais.2011.12.2.765
  28. Kim HK, Jeong SH. Identification of subgroups with lower level of stroke knowledge using decision-tree analysis. Journal of Korean Academy of Nursing. 2014;44(1):97-107. http://dx.doi.org/10.4040/jkan.2014.44.1.97
  29. Kim HS, Jeong SH, Park SK. Identification of risky subgroups with sleep problems among adult cancer survivors using decision-tree analyses: Based on the Korean national health and nutrition examination survey from 2013 to 2016. Journal of Korean Biological Nursing Science. 2018;20(2):103-113. https://doi.org/10.7586/jkbns.2018.20.2.103
  30. Andrew JD, Derek H, Patrick FX, Vincent C, Patrica A, Timothy P. Predicting recovery in patients suffering from traumatic brain injury by using admission variables and physiological data: A comparison between decision tree analysis and logistic regression. Journal of Neurosurgery. 2002;97:326-336. http://dx.doi: 10.3171/jns.2002.97.2.0326ꠔ
  31. Kim YC, Choi JH, Lee YJ, Kim BS, Jang MA, Kang JY. Socioeconomic factors contributing on controlling hypertension. Korean Journal of Family Practice. 2015;8(3):1042-1050.
  32. Oh MU, Choi MN, Kim GS, Sunwoo S. Association between hypertension management and blood pressure screening among adults in 30s and 40s. Korean Journal of Health Promotion. 2013;13(2):61-68.
  33. Jeong HM, Lee MH, Kim HY. Factors influencing self-care in elders with hypertension living at home. Journal of Korean Academic Fundamental Nursing. 2017;24(1):72-83. http://dx.doi.org/10.7739/jkafn.2017.24.1.72
  34. Oh JH, Lee JE. Factors influencing blood-pressure control among the elderly living alone: A comparison of the young-old and the old-old. Korean Journal of Health EducationPromotion. 2018;35(1):69-80 https://doi.org/10.14367/kjhep.2018.35.1.69