Korean Journal of Clinical Laboratory Science (대한임상검사과학회지)

Korean Society for Clinical Laboratory Science (대한임상검사과학회)
권호 표지
DOI QR코드

DOI QR Code

The Assessment of Framingham Risk Score and 10 Year CHD Risk according to Application of LDL Cholesterol or Total Cholesterol

LDL Cholesterol 또는 Total Cholesterol의 적용에 따른 Framingham Risk Score와 10년 내 심혈관질환 발생 위험도 평가

  • Kwon, Se Young (Department of Biomedical Laboratory Science, Daegu Health College) ;
  • Na, Young Ak (Department of Biomedical Laboratory Science, Daegu Health College)
  • 권세영 (대구보건대학교 임상병리과) ;
  • 나영악 (대구보건대학교 임상병리과)
  • Received : 2016.02.12
  • Accepted : 2016.03.21
  • Published : 2016.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Studies on assessment tools for predicting cardiovascular disease risk (CDR), along with the studies to prevent CDR have been consistently reported. The validity of the Framingham risk score (FRS), a commonly known tool, has been verified through the precedent studies. In this study, we examined the differences of FRS according to the application of categories of LDL cholesterol (LDL-C) or Total cholesterol (TC), and attempted to evaluate the agreement of 10 yr CHD risk judgment based on the above-mentioned application. Excluding those diagnosed as cardiovascular diseases, data on subjects (755 men and 775 women) from the 2011 Korean National Health and Nutrition Examination Survey were used. We found differences of FRS and 10 yr CHD risk depending on the application of categories of LDL cholesterol (LDL-C) or Total cholesterol (TC). FRS of TC points were higher than those of LDL-C in both men and women. In classification of low risk (<10%), intermediate risk (10~19%), and high risk (${\geq}20%$), there were disagreements for 106 men and 26 women. Women showed almost perfect agreement from Coefficient of Cohen's Kappa (0.718 in men, and 0.884 in women). In assessment of 10 yr CHD risk, R-squared value from regression including TC was higher than that of LDC-C in both men and women (0.972 vs 0.885). From this result, we can draw a conclusion that correlation coefficients of FRS and CHD risk including TC were higher than those of LDC-C, and women showed a greater degree of agreement than men.

증가하고 있는 심혈관질환을 예방하기 위한 연구와 함께 심혈관질환 위험도를 예측할 수 있는 평가도구에 대한 연구도 꾸준히 진행되고 있다. 가장 널리 알려져 있는 Framingham risk score (FRS)는 여러 선행 연구에서 그 타당성이 검증되었다. 본 연구에서는 연구 대상자들의 LDL 콜레스테롤과 총 콜레스테롤의 적용에 따른 FRS의 점수 차이를 살펴보고, 두 변수의 선택 적용에 따른 10년 내 심혈관질환 발생 위험도의 판정에 대한 일치도를 평가해 보고자 하였다. 2011 국민건강영양조사 데이터 중 심혈관질환 진단을 받은 자를 제외한 1,530명(남성 755명, 여성 775명)의 자료를 이용하였다. LDL 콜레스테롤 또는 총 콜레스테롤 중에 어떤 항목을 적용하느냐에 따라 FRS와 심혈관질환의 10년 예측위험도는 차이가 있었다. 남녀 모두 FRS는 LDL 콜레스테롤 적용 점수 보다 총콜레스테롤 적용 점수가 더 높았다. 위험도 10% 미만의 저위험군, 10~19%의 중등도 위험군, 20% 이상의 고위험군 분류에서 남성 106명, 여성 26명의 판정이 일치하지 않았다. 코헨의 카파 계수는 남성의 경우 0.718, 여성의 경우 0.884로 나타나 여성의 경우 더 높은 일치성을 보였다. 심혈관질환의 10년 예측위험도와의 관련성에서도 LDL 콜레스테롤을 포함한 회귀식 보다 총 콜레스테롤을 포함한 회귀식에서 남녀 모두 설명력이 더 높아 총 콜레스테롤을 반영한 FRS 산출과 10년 예측 위험도의 평가가 더 상관성이 더 높고, 더불어 남성 보다는 여성에서 더 일치하는 결과가 나타남을 알 수 있었다.

Keywords

References

  1. World Health Organization. 2012 Deaths: Top 10 causes [Internet]. Geneva: World Health Organization; 2014. [cited 2016 January 10] Available: http://www.who.int/mediacentre/factsheets/fs310/en/index2.html
  2. Korean Statistical Information Service. 2014 Cause of death web statistics system [Internet]. Seoul: Korean Statistical Information Service; 2015 [cited 2016 January 10]. Available from: http://kosis.kr/statisticsList/statisticsList_01List.jsp.
  3. Emberson JR, Whincup PH, Morris RW, Walker M. Re-assessing the contribution of serum total cholesterol, blood pressure and cigarette smoking to the aetiology of coronary heart disease: impact of regression dilution bias. Eur Heart J. 2003;24(19):1719-1726. https://doi.org/10.1016/S0195-668X(03)00471-8
  4. Shin KA, Kim HY, Kim NJ. Association between exercise capacity and cardiovascular risk factors among obesity types in adult man. Korean J Clin Lab Sci. 2013;45(3):96-101.
  5. Jin BH, Han MH. Comparison of clinical laboratory data and prevalence according to arterial stiffness in stroke patients. Korean J Clin Lab Sci. 2014;46(4):143-149. https://doi.org/10.15324/kjcls.2014.46.4.143
  6. Pearson TA, Blair SN, Daniels SR, Eckel RH, Fair JM, Fortmann SP, et al. AHA guidelines for primary prevention of cardiovascular disease and stroke: 2002 update. Circulation. 2002;106(3):388-391. https://doi.org/10.1161/01.CIR.0000020190.45892.75
  7. Executive summary of the third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA. 2001;285:2486-2497. https://doi.org/10.1001/jama.285.19.2486
  8. Emberson JR, Whincup PH, Morris RW, Walker M, Ebrahim S. Evaluating the impact of population and high-risk strategies for the primary prevention of cardiovascular disease. Eur Heart J. 2004;25(6):484-491. https://doi.org/10.1016/j.ehj.2003.11.012
  9. Wilson PWF, D' Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart diseases using risk factor categories. Circulation. 1998;97:1837-1847. https://doi.org/10.1161/01.CIR.97.18.1837
  10. Murray CJ, Lauer JA, Hutubessy RC, Niessen L, Tomijima N, Rodgers A, et al. Effectiveness and costs of intervention to lower systolic blood pressure and cholesterol: A global and regional analysis on reduction of cardiovascular-disease risk. Lancet. 2003;361(9359):717-725. https://doi.org/10.1016/S0140-6736(03)12655-4
  11. Ford ES, Giles WH, Mokdad AH. The distribution of 10-Year risk for coronary heart disease among US adults: findings from the National Health and Nutrition Examination Survey III. J Am Coll Cardiol. 2004;43:1791-1796. https://doi.org/10.1016/j.jacc.2003.11.061
  12. Assmann G, Cullen P, Schulte H. Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the prospective cardiovascular Munster (PROCAM) study. Circulation. 2002;105(3):310-315. https://doi.org/10.1161/hc0302.102575
  13. Mancia G, Fagard R, Narkiewicz K, Redon J, Zanchetti A, Christiaens T, et al. 2013 ESH/ESC Guidelines for the management of arterial hypertension: The task force for the management of arterial hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). J Hypertens. 2013;31:1281-1355. https://doi.org/10.1097/01.hjh.0000431740.32696.cc
  14. Korea Occupational Safety and Health Agency. Risk assessment for the prevention of cardio-cerebrovascular disease at workplace (KOSHA Code H-1-2013) [Internet]. Seoul: Korea Occupational Safety and Health Agency; 2013 [cited 2016 January 15]. Available from: http://www.kosha.or.kr/info/getKoshaCodeGuideList.do.
  15. Versteylen MO, Joosen IA, Shaw LJ, Narula J, Hofstra L. Comparison of Framingham, PROCAM, SCORE, and Diamond Forrester to predict coronary atherosclerosis and cardiovascular events. J Nucl Cardiol. 2011;18(5):904-911. https://doi.org/10.1007/s12350-011-9425-5
  16. D'Agostino RB Sr, Grundy S, Sullivan LM, Wilson P; CHD Risk Prediction Group. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA. 2001;286(2):180-187. https://doi.org/10.1001/jama.286.2.180
  17. Brindle PM, McConnachie A, Upton MN, Hart CL, Davey Smith G, Watt GCM. The predictive accuracy of the Framingham risk score in different socio-economic groups: a large prospective study. Br J Gen Pract. 2005;55(520):838-845.
  18. Brindle PM, Beswick AD, Fahey T, Ebrahim SB. The accuracy and impact of risk assessment in the primary prevention of cardiovascular disease: A systematic review. Heart. 2006;92(12):1752-1759. https://doi.org/10.1136/hrt.2006.087932
  19. Kim MB, Park WJ, Jang KH, Lee DK, Chae HJ, Moon JD. Comparison of cardiovascular disease risk assessment tools by using coronary CT angiography. Korean J Occup Environ Med. 2010;22(2):102-113. https://doi.org/10.35371/kjoem.2010.22.2.102
  20. Um IY, Choi WJ, Lee D, Oh JS, Yi MK, Yoon JW, et al. Risk assessment for cardiovascular diseases in male workers: comparing KOSHA guidelines and the framingham risk score system. Korean J Occup Environ Med, 2012;24(4):365-374. https://doi.org/10.35371/kjoem.2012.24.4.365
  21. Viera AJ, Garrett JM. Understanding interobserver agreement: The kappa statistic. Fam Med. 2005;37(5):360-363.
  22. Grundy SM, Pasternak R, Greenland P, Smith S, Fuster V. Assessment of cardiovascular risk by use of multiple-risk-factor assessment equations. Circulation. 1999;100:1481-1492. https://doi.org/10.1161/01.CIR.100.13.1481
  23. Kim HJ, Min HG, Kim YL, Lee SK. Correlation between serum homocysteine level and framingham risk score. Korean J Fam Pract. 2014;4(3):227-231.
  24. Park KO, Seo JY. Gender differences in factors influencing the framingham risk score-coronary heart disease by BMI. J Korean Acad Community Health Nurs. 2014;25(4):248-258. https://doi.org/10.12799/jkachn.2014.25.4.248
  25. Kim YK, Kim DM, The relation of pulse wave velocity with framingham risk score and SCORE risk score. Korean Circ J. 2005;35(1):22-29. https://doi.org/10.4070/kcj.2005.35.1.22

Cited by

  1. Targeting the Gut Microbiome to Ameliorate Cardiovascular Diseases vol.23, pp.3, 2016, https://doi.org/10.15616/bsl.2017.23.3.166
  2. Relationship of Oral Health-related Behaviors to High-sensitivity CRP: Data from the Sixth (2015) Korea National Health and Nutrition Examination Survey vol.43, pp.1, 2018, https://doi.org/10.21032/jhis.2018.43.1.64
  3. Impact of COVID-19 and Associated Preventive Measures on Cardiometabolic Risk Factors in South Korea vol.30, pp.3, 2016, https://doi.org/10.7570/jomes21046