Articles

Relationship Between Duke Treadmill Score and Severity of CAD in Suspected Patients

Abstract

Nowadays, cardiovascular disease, including coronary artery disease, is the leading cause of death around the world. Duke Treadmill Score (DTS) is used as a prognostic score for patients suspected of coronary artery disease. Investigating the Relationship between DTS and syntax score (SxScore) as an indicator of complexity and severity of coronary artery disease in patients with intermediate and high Duke Score. This cross-sectional study was conducted at the exercise test unit of Heshmat Hospital in Rasht from September 2017 to December 2018. Among 1033 patients that passed exercise cardiac stress testing (EST), 118 patients who had positive exercise testing for CAD were enrolled. Coronary angiography was performed, and SxScore, a marker of CAD complexity, was determined. The relationship between DTS and SxScore was then evaluated. The data were analyzed by SPSS version 21. The risk of positive EST raised age more than 61 years (OR=1.072; 95%; CI=1.046-1.099), Hypertension (OR=3.235; 95%; CI=2.097-4.992), Hyperlipidemia (OR=2.109; 95%; CI=1.371-3.242) and Diabetes Mellitus (OR=2.15; CI=1.22-3.14). The presence of the following factors reduced positive EST: female (OR=0.377;95%; CI=0.133-1.068), retired (OR=0.128;95%; CI=0.045-0.361). The results of the present study showed that there was no significant difference between the degree of coronary artery involvement based on syntax with Duke scores (P=0.328). Although both DTS and Syntax scores are useful in evaluating coronary artery disease, there isn't a significant relationship between these scores, and they don't coincide. In other words, we cannot use DTS to predict the Syntax score.

1. Battram T, Hoskins L, Hughes DA, Kettunen J, Ring SM, Smith GD, et al. Coronary artery disease, genetic risk and the metabolome in young individuals. Wellcome Open Res 2019;3:114.
2. Tuso P, Stoll SR, Li WW. A plant-based diet, atherogenesis, and coronary artery disease prevention. Perm J 2015 Winter;19:62-7.
3. Zoet GA, Benschop L, Boersma E, Budde RP, Fauser BC, Van Der Graaf Y, et al. Prevalence of subclinical coronary artery disease assessed by coronary computed tomography angiography in 45-to 55-year-old women with a history of preeclampsia. Circulation 2018;137:877-9.
4. Fakhrzadeh H, Bandarian F, Adibi H, Samavat T, Malekafzali H, Hodjatzadeh E, et al. Coronary heart disease and associated risk factors in Qazvin: a population-based study. East Mediterr Health J 2000;14:33-41.
5. Cheng MY, Zhang RX, Wang MJ, Chang MY. Relationship between cognitive emotion regulation strategies and coronary heart disease: an empirical examination of heart rate variability and coronary stenosis. Psychol Health 2022;37:230-45.
6. Arbab-Zadeh A, Fuster V. The myth of the "vulnerable plaque" transitioning from a focus on individual lesions to atherosclerotic disease burden for coronary artery disease risk assessment. J Am Coll Cardiol 2015;65:846-55.
7. Buklioska-Ilievska D, Minov J, Kochovska-Kamchevska N, Gigovska I, Doneva A, Baloski M. Carotid Artery Disease and Lower Extremities Artery Disease in Patients with Chronic Obstructive Pulmonary Disease. Open Access Maced J Med Sci 2019;7:2102-7.
8. Khera AV, Kathiresan S. Genetics of coronary artery disease: discovery, biology and clinical translation. Nat Rev Genet 2017;18:331-44.
9. Klarin D, Zhu QM, Emdin CA, Chaffin M, Horner S, McMillan BJ, et al. Genetic analysis in UK Biobank links insulin resistance and transendothelial migration pathways to coronary artery disease. Nat Genet 2017;49:1392-7.
10. Kong PK, Kanasan SD, Yazid MS, Khamdan HA, Ismail O. Duke Treadmill Score Predicts Coronary Artery Disease Status Among Asian Women Patients. Int J Cardiol 2017;249:S33-4.
11. Malahfji M, Mahmarian JJ. Imaging to stratify coronary artery disease risk in asymptomatic patients with diabetes. Methodist Debakey Cardiovasc J 2018;14:266-72.
12. McGuinn LA, Ward-Caviness CK, Neas LM, Schneider A, Diaz-Sanchez D, Cascio WE, et al. Association between satellite-based estimates of long-term PM2. 5 exposure and coronary artery disease. Environ Res 2016;145:9-17.
13. Nikpay M, Goel A, Won HH, Hall LM, Willenborg C, Kanoni S, et al. A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet 2015;47:1121-30.
14. Ali SN, Shehzad A, Qureshi BA, Hashmi KA. Pattern of angiographic findings in patients with stable angina having high risk duke score on exercise testing. Pak Heart J 2016;49.
15. Asad M, Khadim R, Iqbal T, Shafique HM, Baloch MW, Rehmat S, et al. Role of Duke Treadmill score in assessing the severity of coronary artery disease in patients presenting with angina. Pak Armed Forces Med J 2018;68:147-52.
16. Uçar H, Gür M, Börekçi A, Yıldırım A, Baykan AO, Kalkan GY, et al. Relationship between extent and complexity of coronary artery disease and different left ventricular geometric patterns in patients with coronary artery disease and hypertension. Anatol J Cardiol 2015;15:789-94.
17. Uysal OK, Duran M, Yilmaz Y, Sahin DY, Cayli M. Predictors of Coronary Lesions Complexity in Patients With Stable Coronary Artery Disease. Angiology 2013;64:312.
18. Yanagisawa S, Miki K, Yasuda N, Hirai T, Suzuki N, Tanaka T. The prognostic value of treadmill exercise testing in very elderly patients: heart rate recovery as a predictor of mortality in octogenarians. Europace 2011;13:114-20.
19. Günaydın ZY, Bektaş O, Gürel YE, Karagöz A, Kaya A, Kırış T, et al. The value of the Duke treadmill score in predicting the presence and severity of coronary artery disease. Kardiol Pol 2016;74:127-34.
20. Acar Z, Korkmaz L, Agac MT, Erkan H, Dursun I, Kalaycioglu E, et al. Relationship between Duke Treadmill Score and coronary artery lesion complexity. Clin Invest Med 2012;35:E365-9.
21. Kumamaru KK, Arai T, Morita H, Sekine T, Takamura K, Takase S, et al. Overestimation of pre-test probability of coronary artery disease by Duke clinical score in patients undergoing coronary CT angiography in a Japanese population. J Cardiovasc Comput Tomogr 2014;8:198-204.
22. Kania M, Maniewski R, Zaczek R, Kobylecka M, Zbieć A, Królicki L, et al. High-resolution body surface potential mapping in exercise assessment of ischemic heart disease. Ann Biomed Eng 2019;47:1300-13.
23. Popovic D, Guazzi M, Jakovljevic DG, Lasica R, Banovic M, Ostojic M, et al. Quantification of coronary artery disease using different modalities of cardiopulmonary exercise testing. Int J Cardiol 2019;285:11-3.
24. Löffler AI, Perez MV, Nketiah EO, Bourque JM, Keeley EC. Usefulness of achieving≥ 10 METs with a negative stress electrocardiogram to screen for high-risk obstructive coronary artery disease in patients referred for coronary angiography after exercise stress testing. Am J Cardiol 2018;121:289-93.
25. Sharif S, Alway SE. The diagnostic value of exercise stress testing for cardiovascular disease is more than just st segment changes: A review. J Integr Cardiol 2016;2:341-55.
26. Mitu O, Roca M, Leon MM, Mitu F. Predictive value of a positive exercise stress testing and correlations with cardiovascular risk factors. Rev Med Chir Soc Med Nat Iasi 2014;118:57-62.
Files
IssueVol 60 No 11 (2022) QRcode
SectionArticles
DOI https://doi.org/10.18502/acta.v60i11.11657
Keywords
Coronary artery disease Exercise test Ischemia Duke treadmill score
Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
1.
Mirbolouk F, Salari A, Pourbahador R, Gholipour M, Pourtahmasb A. Relationship Between Duke Treadmill Score and Severity of CAD in Suspected Patients. Acta Med Iran. 2023;60(11):714-719.