مقایسه مدل‌های فرامینگهام و گلوبوریسک در ارزیابی خطر حوادث قلبی عروقی در جمعیت ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 ﮐﺎرﺷﻨﺎس ارﺷﺪ، ﻣﺮﮐﺰ آﻣﻮزﺷﯽ، ﭘﮋوﻫﺸﯽ و درﻣﺎﻧﯽ اﺧﺘﻼﻻت ﻣﺼﺮف ﻣﻮاد و رﻓﺘﺎرﻫﺎی اﻋﺘﯿﺎدی، داﻧﺸﮕﺎه ﻋﻠﻮم ﭘﺰﺷﮑﯽ ﻣﺸﻬﺪ، ﻣﺸﻬﺪ، اﯾﺮان

2 دانشیار طب ‌چینی و مکمل، دانشکده طب ایرانی و مکمل، دانشگاه علوم پزشکی مشهد، مشهد، ایران

3 دکتری داروسازی، معاونت بهداشتی درمانی دانشگاه علوم پزشکی مشهد، مشهد، ایران

4 دکتری تخصصی اپیدمیولوژی، دانشگاه علوم پزشکی مشهد، مشهد، ایران

چکیده

زمینه و هدف: بیماری‌های قلبی- عروقی، اصلی‌ترین عامل مرگ‌ومیر در سراسر جهان هستند. ارزیابی خطر 10 ساله این بیماری گامی مهم در راستای مدیریت بیماری در آینده می‌باشد. هدف از پژوهش حاضر، مقایسه مدل‌های فرامینگهام و گلوبوریسک در ارزیابی خطر حوادث قلبی عروقی در 10 سال آینده است.
مواد و روش‌ها: مطالعه توصیفی- مقطعی با استفاده از داده‌های پرونده الکترونیک سلامت دانشگاه مشهد با متغیرهای کلسترول کل، لیپوپروتئین با چگالی بالا، فشار خون و دیابت بررسی شد. جامعه آماری افراد با سن 30 سال و بالاتر با حجم نمونه 161828 نفر بود. برای تعیین میزان ارتباط یا وابستگی بین متغیرها از آزمون‌های t-test و chi-square استفاده شد و سطح معناداری کمتر از 0/05 در نظر گرفته شد.
یافته‌ها: در این بررسی 74/1 درصد افراد زن بودند. طبق مدل گلوبوریسک 67/2 درصد از زنان و 79/9 درصد از مردان در گروه کم‌خطر قرار داشتند درصورتی‌که طبق مدل فرامینگهام این میزان در زنان و مردان به‌ترتیب 5/48 و 9/54 درصد بود. براساس مدل گلوبوریسک و فرامینگهام به‌ترتیب 66/2 و 34/1 درصد افراد 70 سال و بالاتر در گروه پرخطر بودند. همچنین هر دو مدل نشان دادند افراد مبتلا به دیابت و افراد سیگاری در معرض خطر بیشتر CVD در 10 سال آینده می‌باشند. 
نتیجه‌گیری: با توجه ‌به افزایش بیماری‌های قلبی- عروقی لازم است از توانایی‌های این مدل‌ها در ارزیابی خطر حوادث قلبی- عروقی در 10 سال آینده استفاده شود و اقداماتی در راستای سلامت عمومی جامعه و پیشگیری از حوادث قلبی عروقی صورت گیرد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Comparison of Framingham Risk Score and Globorisk Cardiovascular Risk Prediction Models in Iranian Population

نویسندگان [English]

  • behnaz beygi 1
  • hamidreza bahrami 2
  • Reza Eftekhari Gol 3
  • Ehsan Musa Farkhani 4
1 Master of science, East Educational Research division of Drug abuse and addictive behavior, Mashhad University of Medical Sciences, Mashhad, Iran
2 Associate Professor of Complementary and Chinese Medicine, Persian and Complementary Medicine Faculty, Mashhad University of Medical Sciences, Mashhad, Iran
3 Ph.D., Department of Health Network Development and Health Promotion, Khorasan Razavi Province Health Center, Mashhad, Iran
4 Ph.D of Epidemilogy, Mashhad University of Medical Sciences, Mashhad, Iran
چکیده [English]

Introduction: Cardiovascular disease is the leading cause of death in the world. The 10-year risk assessment of this illness is an essential step in managing future illness. This study aimed to compare Framingham Risk Score and Globorisk cardiovascular disease prediction models in the next ten years.
Materials and Methods: A descriptive cross-sectional study was performed using Mashhad's Electronic Health Record data with total cholesterol, high-density lipoprotein, smoking, blood pressure, and diabetes. The study population was people aged 30 years and older, with a sample of 161,828 people. T-test and chi-square tests were used to determine the relationship of dependence between the variables, and P-value less than 0.05 was considered.
Results: The participants included 74.1% females. According to the Globorisk model, 67.2% of females and 79.9% of males were in the low-risk group, while in the Framingham model, this rate was 48.5% and 54.9%, respectively, in females and males. According to the Globorisk and Framingham model, 66.2% and 34.1% of people 70 years and older were high-risk. Both models also showed that people with diabetes and smokers are at higher risk for cardiovascular disease in the next ten years.
Conclusion: Considering the increased cardiovascular risk, it is necessary to use the capabilities of these models to assess the risk of disease in the next ten years and take measures to improve the community's general health and prevent cardiovascular events.

کلیدواژه‌ها [English]

  • Risk Assessments: Cardiovascular diseases:Iran
  1. Yin J, Jin X, Shan Z, Li Sh, uang H, LI P, et al. Relationship of Sleep Duration With All-Cause Mortality and Cardiovascular Events: A Systematic Review and Dose-Response Meta-Analysis of Prospective Cohort Studies. J Am Heart Assoc 2017; 6(9):  DOI: 10.1161/JAHA.117.005947
  2. Avazeh A, Jafari N, Mazloomzadeh S. Knowledge level attitude and performance of women on diet and exercise and their relation with cardiovascular diseases risk factors. Zanjan Univ Med Sci J 2010; 18: 51-60
  3. Chatripour R, Shojaeizadeh D, Tol A, Sayehmiri K. Determining Health Belief Model Constructs to Prevent Cardiovascular Diseases among Teachers of Boys high Schools in Dehloran City Sci J Ilam Univ Med Sci 2016; 25(2): 35-41
  4. Pearson-Stuttard J, Guzman-Castillo M, Penalvo J.L, Rehm C.D, Afshin A, Danaei G, et al. Modelling Future Cardiovascular Disease Mortality in the United States: National Trends and Racial and Ethnic Disparities. Circulation 2016; 133(10): 967-78
  5. Weiss EP, Albert SG, Reeds DN, Kress KS, McDaniel JL, Klein S, et al. Effects of matched weight loss from calorie restriction, exercise, or both on cardiovascular disease risk factors: a randomized intervention trial. Am J Clin Nutr 2016; 104(3): 576-86
  6. Khosravi A, Ebrahimi H. Investigation of the possibility of one-year survival and its effective factors in patients with myocardial infarction. J Shahroud Univ Med Sci 2008;3(1).
  7. Hariri N, Nasseri E, Houshiar-Rad A, Zayeri F, Bondarianzadeh D. Association between Alternative Healthy Eating Index and 10-year risk of cardiovascular diseases in male-employees in the public sector in Tehran, 1391. Iran J Nutr Sci Food Technol 2013; 8(2): 41-50
  8. Abedi SM, Bagheri S, Mohammadpour RA, Mardanshahi AR, Ghaemian A. Diagnostic value of myocardial perfusion scans for diagnosis of coronary artery disease. J Shahrekord Univ Med Sci 2016; 18(3): 109-17
  9. Levine N.G, Lange R.A, Bairey-Merz C.N, Davidson R.J, Mehta P.K, Michos E.D, et al. Meditation and Cardiovascular Risk Reduction A Scientific Statement From the American Heart Association. J Am Heart Assoc 2017; 6(10): DOI: 10.1161/JAHA.117.002218
  10. hods R, Gorji N, Moeini R, Ghorbani F. Semiology and management of heart failure according to Traditional Persian Medicine views. Complement Med J 2017; 1(22): 1791-804
  11. Van’t Hof J.A, Duval S, Walts A, Kopecky S.L, Luepker R.V, Hirsch A.T. Contemporary Primary Prevention Aspirin Use by Cardiovascular Disease Risk: Impact of US Preventive Services Task Force Recommendations, 2007—2015: A Serial, Cross-sectional Study. J Am Heart Assoc 2017; 6(10): e006328. DOI: 10.1161/JAHA.117.006328
  12. Bansal M, Kasliwal R.R, Trehan N. Comparative accuracy of different risk scores in assessing cardiovascular risk in Indians: A study in patients with first myocardial infarction. Indian Heart J 2014; 66: 580-6
  13. Moran AE. New country-specific CVD risk charts: recalibrate and refine. Lancet Diabetes Endocrinol 2017; 5(3): 155-57
  14. Hajifathalian K, Ueda P, Lu Y, Woodward M, Ahmadvand A, Aguilar-Salinas C.A, et al. A novel risk score to predict cardiovascular disease risk in national populations (Globorisk): a pooled analysis of prospective cohorts and health examination surveys. Lancet Diabetes Endocrinol 2015; 3(5): 339-55
  15. Anderson K.M., Wilson P.W., Odell P.M., Kannel W.B. An updated coronary risk profile. A statement for health professionals. Circulation. 1991; 83(1): 356-62
  16. Vergnaud A.C, Bertrais S, Galan P,Hercberg S, Czernichow S. Ten-year risk prediction in French men using the Framingham coronary score: Results from the national SU.VI.MAX cohort. Prev Med. 2008; 47: 61-5
  17. RH Dalton A, Bottle A, Soljak M, Okoro C, Majeed A, Millett C. The comparison of cardiovascular risk scores using two methods of substituting missing risk factor data inpatient medical records. Inform Prim Care. 2011; 19: 225-32
  18. Garg N, Muduli S.K, Kapoor A, Tewari S, Kumar S, Khanna R, et al. Comparison of different cardiovascular risk score calculators for cardiovascular risk prediction and guideline-recommended statin uses. Indian Heart J 2017; 69(2017): 458-63
  19. Bozorgmanesh M, Hadaegh F, Azizi F. Predictive accuracy of the ‘Framingham’s general CVD algorithm’ in a Middle Eastern population: Tehran Lipid and Glucose Study. Int J Clin Pract. 2011; 65(3): 264-73
  20. Artigao-Rodenas L.M, Carbayo-Herencia J.A, Divisón-Garrote J.A, GilGuillén V.F, Massó-Orozco J, Simarro-Rueda M, et al. Framingham Risk Score for Prediction of Cardiovascular Diseases: A Population-Based Study from Southern Europe. PLoS ONE 2013; 8(9): e73529
  21. Carroll SJ, Paquet C, Howard NJ, Adams RJ, Taylor AW, Daniel M. Validation of continuous clinical indices of cardiometabolic risk in a cohort of Australian adults. BMC Cardiovasc Disord 2014; 14:27-35
  22. Marma AK, Lloyd-Jones DM. Systematic Examination of the Updated Framingham Heart Study General Cardiovascular Risk Profile. Circulation 2009; 120: 384-9
  23. Yousefzadeh GhR, Shokoohi M, Najafipour H, Shadkamfarokhi M. Applying the Framingham risk score for prediction of metabolic syndrome: The Kerman Coronary Artery Disease Risk Study, Iran. ARYA Atheroscler 2014; 11(3): 179-85
  24. Selvarajah Sh, Kaur G, Haniff J, Chee Cheong K, Hiong TG, van der Graaf Y, et al. Comparison of the Framingham Risk Score, SCORE and WHO/ISH cardiovascular risk prediction models in an Asian population. Int J Cardiol. 2014; 176: 211–8
  25. Keto J, Ventola H, Jokelainen J, Linden K, Keinänen-Kiukaanniemi S, Timonen M, et al. Cardiovascular disease risk factors in relation to smoking behavior and history: a population-based cohort study. Open Heart 2016; 3(2): e000358. DOI: 10.1136/openhrt-2015-000358.