Abstract
There is some disparity in the morbidity and mortality rates of cardiovascular disease (CVD) according to race, ethnicity, and geographic regions. Although prediction algorithms that evaluate risk of cardiovascular events have been established using traditional risk factors, they have also demonstrated a number of differences along with race and ethnicity. Of various risk assessment modalities, coronary artery calcium (CAC) score is a sensitive marker of calcific atherosclerosis and correlates well with atherosclerotic plaque burden. Although CAC score is now utilized as a useful tool for early detection of coronary artery disease, prior studies have suggested some variability in the presence and severity of coronary calcification according to race, ethnicity, and/or geographic regions. Among Asian populations, it would appear necessary to reappraise the utility of CAC score and whether it remains superior over and above established clinical risk prediction algorithms. To this end, the Korea initiatives on coronary artery calcification (KOICA) registry has been designed to identify the effectiveness of CAC score for primary prevention of CVD in asymptomatic Korean adults. This review discusses the important role of CAC score for prognostication, while also describing the design and rationale of the KOICA registry.
Keywords: Cardiovascular disease, Coronary artery calcium score, Coronary artery disease, Risk prediction algorithm, Ethnicity
Introduction
Cardiovascular disease (CVD) is the leading cause of morbidity and mortality among industrialized nations. CVD is responsible for 31% of global mortality, accounting for approximately 17.5 million deaths from CVD in 2012 alone. Of these deaths, an estimated 7.4 million were attributable to coronary artery disease (CAD) and approximately 6.7 million due to stroke.1) Notably, prior epidemiologic evidence indicates that these morbidity and mortality rates attributable to CVD tend to vary depending on race, ethnicity, or geographic regions, even within a particular country.2),3),4),5),6) In an effort to prevent adverse cardiovascular events, accurate assessment of absolute CVD risk is paramount. Though to date, several studies have documented some discrepancies based on ethnicity or geographic region when utilizing risk prediction algorithms for the assessment of adverse cardiovascular risk.7),8),9),10),11),12),13)
Foremost, coronary artery calcium (CAC) score is considered a robust method for early detection of CAD, and the use of CAC score is primarily recommended for risk stratification in national guidelines.14) Yet, it has emerged that there exists some ethnic disparity in the features of coronary calcification.15),16),17),18) Further still, most studies thus far related to CAC score are based on populations derived from Western societies. Given that CAC score has been reported to provide additional prognostic value beyond conventional risk prediction tools, it seems prudent that the predictive value of CAC score, particularly among Asian populations, is necessary. In relation, the Korea initiatives on coronary artery calcification (KOICA) registry is a novel study designed to assess the effectiveness of CAC score for primary prevention of CVD in a large cohort of asymptomatic Korean adults. In the following review, we describe the design and rationale of the KOICA registry, and how it might lend further insight towards the clinical utility of CAC score for prognostication in non-Western populations.
Ethnic and Racial Disparity in the Presence of CVD
CVD reflects a cluster of disorders in the heart and blood vessels including CAD and stroke. Although CVD manifests clinically as various diseases, CAD and stroke account for approximately 75% of all CVD-related deaths.19) Globally, CVD accounts for the highest number of deaths related to any cause, thus contributing extensively to the economic burden associated with health costs. Notably, over three quarters of CVD deaths typically occur in low-to-middle income countries, with the rate beginning to increase further in most Asian countries.1)
In light of the epidemiologic disparities in CVD,3),4),5),6) previous studies have demonstrated differing CVD mortality rates based on race, ethnicity, and geographic region.4),20),21),22),23) While the results have been largely heterogeneous in most studies, CAD mortality in Black individuals is somewhat higher compared with other races. Moreover, Asian populations tend to present with lower mortality as compared with other racial groups (Fig. 1). In a large international study of individuals representing seven pre-defined ethnic/racial groups, Meadows et al.3) demonstrated the important ethnic-specific differences in cardiovascular risk factors as well as variations in cardiovascular mortality. Specifically, among all Asian groups that included East Asians, South Asians, and other Asians, the cardiovascular death rates were significantly lower compared with other groups (overall, 2.1% vs. 4.5%, p < 0.001). Data from World Health Organization describing age-adjusted mortality rates of CAD and stroke have also demonstrated different results between Western countries and some selected Asian countries in 2002.4)
Considering the rapid rise in Asian populations, now comprising 60% of the world's current population,24) CVD prevention in this ethnic subgroup is of significant health concern. In South Korea, stroke mortality rate has continuously declined since the 2000s, whereas CAD mortality rate has paradoxically increased since the 1980s.25) In the World Health Organization multinational monitoring of trends and determinants in cardiovascular disease project, CVD incidence among numerous countries was examined, using standardized diagnostic criteria.5),6) The age-adjusted incidence rates of acute myocardial infarction and stroke were compared among the different countries. Notably, Japan and China did not appear to have a higher incidence of stroke when compared with Western nations. However, the latter East Asian countries had a much lower incidence of acute myocardial infarction compared with those Western populations. Subsequently, East Asian countries may appear to have a lower incidence of CAD-related events as compared with Western countries.
The heterogeneity in morbidity and mortality linked to CVD may be due to some ethnic dissimilarities in the prevalence of coronary artery atherosclerosis and related risk factors,26) alongside possible variability in the utilization of preventive therapies or availability of health care services.27) Despite this, some variation in CVD may result from diverse mechanisms of plaque formation, erosion, rupture, or occlusive thrombosis, all of which may lead to acute coronary events.28) Thus, any racial or ethnic differences in CVD can potentially be studied for identification of persons with subclinical atherosclerosis and correlated risk.29) Recently a remarkable decline in the risk of CAD mortality in developed Western countries may likely reflect a reduction in the prevalence of cardiovascular risk factors, along with improved medical management.30) On the other hand, as a result of industrialization and substantial economic development throughout Asia, the prevalence of obesity and type 2 diabetes mellitus (DM) has markedly risen in Asian nations, primarily due to altered diet and adopting a sedentary lifestyle.31) Despite a relatively low mean cholesterol level in most Asian countries as compared with Western countries, the increasing metabolic risk factors, such as glucose intolerance, hypercholesterolemia and insufficient control of hypertension, may well have contributed towards the trend in CAD incidence in Asian populations.
Racial and Ethnic Variation in Risk Prediction Algorithms
A significant number of patients with nonfatal myocardial infarction or sudden cardiac death often do not demonstrate a history of any prior symptoms or cardiovascular diagnosis.32) Hence, treatments focused on patients who already experienced symptoms may have some inherent limitations when attempting to improve outcomes of morbidity and mortality related to CAD. This emphasizes the importance of early screening and improved treatment strategies for occult CAD in asymptomatic individuals.32) Moreover, CAD is a multifactorial disease that is influenced by numerous risk factors as well as socioeconomic status. Therefore, it seems more clinically beneficial to estimate the risk of CAD by simultaneously considering numerous factors associated with CAD rather than focusing on single factors alone. The Framingham heart study initially developed a risk algorithm to predict the development of CAD, and included major risk factors such as age, sex, systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, smoking behavior, DM status, and electric left ventricular hypertrophy.33) Since, the Framingham risk score (FRS) has continuously been developed34) and widely adopted for the prediction of the absolute risk of future cardiovascular events worldwide.35)
A potential drawback of the FRS is its development in a predominantly Caucasian middle-class study sample; hence, generalization of this algorithm to other diverse ethnic populations may be somewhat limited. Considering most individuals tend to have multiple sub-threshold risks, determining their true personal cardiovascular risk is often more difficult. In light of this, the FRS may not be suitable or practical for identifying diverse high cardiovascular risk populations.7)
Several studies have assessed the validity and utility of the FRS. Based on results of a multiple ethnic groups investigation, for white men and women as well as for black men and women, FRS performed reasonably well for prediction of cardiovascular risk within 5 years of follow-up.8) On the other hand, FRS largely overestimated the risk among Native American women, Hispanic men, and Japanese Americans. A number of investigators also indicated that overestimation of cardiovascular risk was reported when FRS was directly applied to other populations in Asia7),8) including Korea,13) as well as across Europe, in countries such as Germany,9) Spain,10) France,11) and Italy.12) Liu et al.7) assessed the predictive performance of the FRS in a large Chinese population. In that study, FRS overestimated the 10-year absolute CAD risk in both Chinese men and women with larger predicted differences compared with actual observed rates. For instance, among men in the 10th risk decile, the predicted rate of CAD mortality was 20% compared with an observed rate of 3%. In another study consisting of French men, the overall FRS estimated that the number of CAD events was twice as high when compared with the actual observed number of CAD events.11)
The results of previous studies displaying discrepancies between observed and predicted risk of CAD have significant implications for the primary prevention of CAD, as the overestimation of absolute risk could lead to inappropriate or inadequate treatments and excessive expenditures in individuals who have actually lower CAD risk than anticipated. Against this background, prior studies have undertaken large efforts to recalibrate the original FRS, in an effort to improve predictive ability.7),8),12) In a large cohort study comprising multiple ethnic groups residing in the US, the FRS fit well in other populations following recalibration, even after considering the difference in prevalent risk factors and underlying rates of developing CAD.8) Foremost, while the original FRS model appeared to overestimate actual observed CAD events, the recalibrated FRS model was in close agreement with the new model in this study. In addition, numerous efforts have been made to establish a novel risk prediction tool that is suitable for estimating cardiovascular risk, depending on specific race or ethnicity.9),13) For example, in Korea, Jee et al.13) developed a new CAD prediction tool and demonstrated that the model provided accurate and reliable prediction of cardiovascular risk specifically among Korean individuals.
Predictive Value of CAC Score
To date, there have been numerous risk prediction models, serum biomarkers, and imaging modalities for the purpose of screening CVD in asymptomatic individuals. Among them, CAC score is a specific surrogate marker of calcific atherosclerosis in the coronary arteries and is frequently used to quantify coronary atherosclerotic plaque burden.36) The CAC score determined by cardiac computed tomography (CT) has been well established as a robust imaging modality for early detection of CAD, enabling direct visualization of coronary atherosclerosis, particularly in asymptomatic individuals as compared to other cardiac risk factor-based paradigms.37)
A robust relationship has been found between CAC score and atherosclerotic burden as well as future cardiovascular events in asymptomatic populations. Indeed, increasing CAC score has been consistently associated with increased cardiovascular risk.38) In addition, others have found that the CAC score provides incremental benefit over and above conventional risk factors. Although the FRS has often proven to be a useful risk algorithm, this prediction tool may misclassify a number of truly high-risk individuals who are likely to benefit from preventive treatment.39) To this end, previous studies have indicated that the CAC score provides incremental benefit in addition to the FRS and additional advantage towards risk stratification when assessing cardiovascular risk. Greenland et al.40) reported that CAC score assessment combined with FRS in asymptomatic persons provides prognostic information superior to either method alone. In the Multi-Ethnic Study of Atherosclerosis (MESA), Yeboah et al.41) compared the predictive utility of several novel risk markers for incident CVD and documented superior discrimination and reclassification based on the CAC score. Albeit, while CAC score is considered to improve risk classification as advocated by recent guidelines,14) the results from prior studies are largely derived from predominantly Caucasian populations, with findings according to other racial or ethnic groups still lacking.29)
Racial and Ethnic Differences in Coronary Artery Calcification
Few recent studies have found some differences in the prevalence and severity of CAC score according to race and ethnicity.15),16),17),18) In a sample of 1461 asymptomatic high-risk adults, Tang et al.17) demonstrated significant ethnic differences in the prevalence of coronary calcium. In that study, African Americans were found to have a significantly lower prevalence of coronary calcium as compared with Asian Americans or Caucasians. Budoff et al.16) reported that African Americans and Asians had a lower prevalence and severity of calcification compared with non-Hispanic Whites and Hispanics. These disparities persisted after adjusting for standard cardiac risk factors. In the MESA Study, there was a similar result of a substantially lower prevalence of coronary calcification among Hispanics and blacks versus whites and a slightly lower prevalence in Chinese versus whites.15) Also, in that study, even after controlling for risk factors, the prevalence of CAC score was lower in ethnic minorities. More recently, in a cross-sectional study comparing CAC score between US Caucasian men and Japanese men,42) the investigators found that Caucasian men aged between 45-74 years had a higher burden of coronary artery atherosclerosis compared with Japanese men of the same age. In addition, this trend appeared to become more prominent with advancing age, even after adjusting for traditional risk factors.
Despite recent advances offering insight into the ethnic and racial disparities in CAC scores, the precise factors that actually modify CAC score on the background of race or ethnicity are not well defined. Several possible mechanisms have been considered. Notably, ethnic minorities tend to present with a higher number of conventional risk factors, comorbidities, and a lower access to health care treatment.29) Further still, other factors including behavioral, environmental, biochemical, and genetic predispositions, may modify the CAC score according to ethnicity or race. Though, clearly, additional studies are needed to test this notion.
Reappraising the Value of CAC Score in Asian Populations
Prior studies have investigated the differences in the prevalence and severity of CAC score according to race and ethnicity. However most of the extant CAC score literature has predominantly highlighted its relationship with adverse outcomes among Caucasian populations. Several previous studies have reported CAC score incremental to conventional risk factors in Korean adults.43),44),45),46),47),48) Table 1 summarizes the results of available studies that examined the prognostic value of CAC score among study populations across Asia. Park et al.43) revealed that CAC score was an important predictor of poor cardiovascular outcomes when evaluated alongside traditional biomarkers and risk factors; however, when the extent of coronary stenosis was added to the model, CAC score did not improve prognostic power. Another study comprising 7988 Korean participants at intermediate cardiovascular risk evaluated the differences among risk stratifications along with the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III) guideline and CAC score.44) In that study, the NCEP-ATP III guidelines underestimated cardiovascular risk in approximately 10% of enrolled individuals, whereas 9.4% were correctly reclassified to the higher risk group by including CAC score, thus emphasizing that CAC score should be considered for more accurate risk classification. Nevertheless, while the effectiveness of CAC score has been universally verified in numerous populations, its utilization is often overlooked in clinical practice in Korea. Given these circumstances, further studies among Asian individuals, especially Koreans, are warranted in order to reappraise the efficacy and predictive significance of the CAC score, which has been proven to be superior to other traditional risk prediction algorithms. Hence, the KOICA registry was designed to evaluate the usefulness and prognostic importance of CAC score for predicting all-cause mortality (ACM) in a large cohort of asymptomatic Korean adults.
Table 1. The studies investigating value of CAC score in Asian population.
CAC: coronary artery calcium, FRS: Framingham risk score, CCTA: coronary computed tomography angiography, CAD: coronary artery disease, HR: hazard ratio, CI: confidence interval
KOICA Registry
Study design
The KOICA registry is a retrospective, single ethnicity multicenter observational study designed to investigate the effectiveness and prognostic value of CAC score for primary prevention of CVD in asymptomatic Korean adults. Overall, there were 3 sites involved in the KOICA registry. The study population includes self-referred individuals who underwent self-reported medical questionnaires to obtain personal clinical data and medical history with the purpose of a health check-up at a healthcare center in South Korea. A total of 48903 subjects were initially enrolled in the study between December 2002 and July 2014. The appropriate institutional review board committees at each study site approved the study protocol, and all study participants provided written informed consent.
Study measures
Table 2 summarizes the examination components of the KOICA registry. All data were obtained using a health check database at the healthcare center of each site in South Korea. Baseline demographic parameters were measured at the time of each visit to the health care centers and collected by self-reported medical questionnaires. Each underlying disease including hypertension, CAD, hyperlipidemia, and DM was defined according to participants' self-reported medical history. Laboratory tests were obtained from a complete blood count, serum chemistry, and urinalysis that included numerous biochemical parameters. Other cardiovascular screening tests for risk assessment included arterial stiffness assessment and exercise treadmill test. Though, it bears mentioning that not all demographic parameters or tests were performed in all patients, which will likely lead to some heterogeneity in forthcoming analytic sample sizes.
Table 2. Examination components of the KOICA registry.
BMI: body mass index, SBP: systolic blood pressure, DBP: diastolic blood pressure, HR: heart rate, HTN: hypertension, CAD: coronary artery disease, DM: diabetes mellitus, CKD: chronic kidney disease, CBC: complete blood count, WBC: white blood cell, Hb: hemoglobin, BUN: blood urea nitrogen, TC: total cholesterol, TG: triglyceride, HDL: high-density lipoprotein, LDL: low-density lipoprotein, GGT: gamma-glutamyl transpeptidase, AST: aspartate aminotransferase, ALT: alanine transaminase, TSH: thyroid stimulating hormone, CAC: coronary artery calcium, Na: sodium, K: potassium, Cl: chloride, tCO2: total carbon dioxide, Ca: calcium, P: phosphorus, CRP: C-reactive protein, baPWV: brachial-ankle pulse wave velocity, CAVI: cardio-ankle vascular index, METs: metabolic equivalen, KOICA: Korea initiatives on coronary artery calcification, HbA1c: glycated hemoglobin
CAC screening procedures
CAC score was acquired using a greater than 16 slice multidetector CT scanner. Specific CT scanner types used within each center included the Philips Brilliance 256 iCT (Philips Healthcare, Cleveland, OH, USA), Philips Brilliance 40 channel MDCT (Philips Healthcare, Cleveland, OH, USA), Siemens 16-slice Sensation (Siemens, Forchheim, Germany), and GE 64-slice Lightspeed (GE Healthcare, Milwaukee, WI, USA). All three centers performed standard prospective or retrospective methods.
Study endpoint
The mean follow-up duration was 4.8 years (interquartile range: 2.7-6.7 years). The primary endpoint in the KOICA registry is ACM, which was confirmed by querying the Ministry of Security and Public Administration records up until December 2014 for 2 centers, and up until September 2014 for the remaining center, allowing for 100% ascertainment of mortality status. Over the course of the study period, a total of 415 (0.9%) events were recorded in the KOICA registry. According to the Personal Information Protection Act, only data regarding all-cause and not cause-specific mortality were available for analysis
Statistical methods and study objectives
Continuous variables are reported as mean ± standard deviation, and categorical variables are reported using counts with proportions. For continuous parameters, Student's t-test or Wilcoxon rank-sum test will be employed as appropriate for two-group comparisons, or one way ANOVA or Kruskal-Wallis test for more than two-group comparisons, as appropriate. Comparison of categorical variables will be performed using Pearson's chi-square test. The primary objective of the KOICA registry is to determine the effectiveness of CAC score for predicting adverse outcomes in asymptomatic Korean adults, with ACM as the study endpoint. Cumulative ACM event rates over time according to CAC score categories will be estimated using the Kaplan Meier survival curve and compared using the log rank test. Multivariable Cox proportional hazards regression models will be used to calculate the hazard ratios with 95% confidence intervals (95% CIs) for risk of ACM according to CAC score categories. The added prognostic value of CAC score over and above established risk factors (e.g., the FRS) will be evaluated using the likelihood ratio χ2 test, the C-statistic, as well as the continuous net reclassification improvement index. In secondary analyses, we intend to examine: 1) the usefulness of atherosclerotic burden as determined by CAC score for predicting ACM according to pre-specified subgroups; 2) the CAC score as a valid and robust cardiovascular tool among Koreans. Additional to the above mentioned statistical procedures, we will also study the diagnostic performance of CAC score by determining sensitivity, specificity, as well as negative and positive predictive values in an effort to establish the CAC score as a useful screening tool among asymptomatic Korean adults; and 3) we will analyze the disparity, if any, in the severity and outcome of CAC according to ethnic and socioeconomic background via the international cooperation institutions database. Using a binary logistic regression approach reporting odds ratios with 95% CI, we will employ a propensity matching method that will permit matching of patients from both ethnic cohorts according to available demographic characteristics, with the exception of the outcome CAC score.
Discussion
The KOICA registry will provide essential information for determining the effectiveness of CAC score for primary prevention of CVD along with its role in determining clinical CVD in a large cohort of asymptomatic Korean adults. The KOICA registry will enable investigators to identify the characteristics, severity and determinants of CAC score for the purpose of determining Asian-specific cut-off values for screening high-risk Korean adults. The analysis of the relationship between CAC score and traditional risk factors will also be performed. Based on prior findings primarily derived from Western populations, we anticipate that CAC score may provide incremental benefit over and above other risk assessment models for predicting adverse outcomes. To examine the potential disparity according to race or ethnicity, cross-sectional comparison studies will be considered between the KOICA registry and other external study databases comprising different ethnicities in order to evaluate the prevalence and distribution of CAC score and/or events. Further, the long-term protective effects of a zero CAC score can be assessed given the latter beneficial effects are yet to be determined in a Korean population. Also, the KOICA registry will have the capability to evaluate possible interactions between CAC score and other cardiac risk factors that are available in this study.
Though we anticipate the KOICA registry will provide important and additional information to the extant CAC literature, our registry has some inherent limitations that should be emphasized. The present study is retrospective and observational in nature and only recorded a single measure of the CAC score, which will make it a challenge to interpret a long-term causal relationship. Although the KOICA registry incorporates a substantial number of predictors and covariates, as is the case with most observational studies, we cannot discount the possibility of residual confounding due to unmeasured parameters. Our study participants were self-referred at routine health check-up programs, which may have inferred selection bias (i.e., healthy volunteers, or higher socio-economic status). Thus rendering our study sample not fully representative of the overall Korean population. Our study sample is predominantly male; hence caution should be taken when extrapolating our findings to women. Further still, despite the large sample size, the incidence of ACM is relatively small, as this registry was derived from a health check-up database, which limits the event rate that occurred during the follow-up period. In addition, the current study includes only self-reported information regarding certain participants' demographics (i.e., medication use), which, in some cases, may potentially lead to a diminished risk prediction. Last, there is no information regarding cause-specific mortality in this registry. Though, prior reports have revealed that the assessment in the accuracy of death certificates has been mostly problematic, typically with an overestimation of cardiac death.49) To this end, the use of ACM as the primary endpoint provides an unbiased endpoint in the absence of cause-specific mortality.50)
Conclusion
Revaluating the prognostic utility of CAC score, which has been proven to be superior to other traditional risk prediction algorithms, is urgently needed, especially among non-Western populations. In light of this, the KOICA registry will provide some information regarding the effectiveness and prognostic value of CAC score for primary prevention of CAD, and may also help identify optimal criteria with regards to screening for cardiovascular risk among asymptomatic Asian (i.e., Korean) adults. Foremost, the anticipated findings from the KOICA registry may prove useful for eliminating unnecessary medical costs by providing appropriate individualized treatment strategies. In time, it is expected that the KOICA registry will lend substantially towards understanding the important role CAC score might have for prognostication in asymptomatic Korean adults and how these finding may reflect differently when compared with other racial or ethnic groups.
Acknowledgements
This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI13C0715).
References
- 1.Who.int [Internet]. Cardiovascular diseases (CVDs) Geneva: World Health Organization; [updated 2015 Jan]. [cited 2015 Sep 1]. Available from: http://www.who.int/mediacentre/factsheets/fs317/en/ [Google Scholar]
- 2.Zhang XH, Lu ZL, Liu L. Coronary heart disease in China. Heart. 2008;94:1126–1131. doi: 10.1136/hrt.2007.132423. [DOI] [PubMed] [Google Scholar]
- 3.Meadows TA, Bhatt DL, Cannon CP, Gersh BJ, Röther J, Goto S, Liau CS, Wilson PW, Salette G, Smith SC, Steg PG REACH Registry Investigators. Ethnic differences in cardiovascular risks and mortality in atherothrombotic disease: insights from the Reduction of Atherothrombosis for Continued Health (REACH) registry. Mayo Clin Proc. 2011;86:960–967. doi: 10.4065/mcp.2011.0010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Ueshima H, Sekikawa A, Miura K, Turin TC, Takashima N, Kita Y, Watanabe M, Kadota A, Okuda N, Kadowaki T, Nakamura Y, Okamura T. Cardiovascular disease and risk factors in Asia: a selected review. Circulation. 2008;118:2702–2709. doi: 10.1161/CIRCULATIONAHA.108.790048. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Tunstall-Pedoe H, Kuulasmaa K, Amouyel P, Arveiler D, Rajakangas AM, Pajak A. Myocardial infarction and coronary deaths in the World Health Organization MONICA Project. Registration procedures, event rates, and case-fatality rates in 38 populations from 21 countries in four continents. Circulation. 1994;90:583–612. doi: 10.1161/01.cir.90.1.583. [DOI] [PubMed] [Google Scholar]
- 6.Thorvaldsen P, Asplund K, Kuulasmaa K, Rajakangas AM, Schroll M. Stroke incidence, case fatality, and mortality in the WHO MONICA project. World Health Organization Monitoring Trends and Determinants in Cardiovascular Disease. Stroke. 1995;26:361–367. doi: 10.1161/01.str.26.3.361. [DOI] [PubMed] [Google Scholar]
- 7.Liu J, Hong Y, D'Agostino RB, Sr, Wu Z, Wang W, Sun J, Wilson PW, Kannel WB, Zhao D. Predictive value for the Chinese population of the Framingham CHD risk assessment tool compared with the Chinese Multi-Provincial Cohort Study. JAMA. 2004;291:2591–2599. doi: 10.1001/jama.291.21.2591. [DOI] [PubMed] [Google Scholar]
- 8.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;286:180–187. doi: 10.1001/jama.286.2.180. [DOI] [PubMed] [Google Scholar]
- 9.Hense HW, Schulte H, Löwel H, Assmann G, Keil U. Framingham risk function overestimates risk of coronary heart disease in men and women from Germany--results from the MONICA Augsburg and the PROCAM cohorts. Eur Heart J. 2003;24:937–945. doi: 10.1016/s0195-668x(03)00081-2. [DOI] [PubMed] [Google Scholar]
- 10.Marrugat J, Solanas P, D'Agostino R, Sullivan L, Ordovas J, Cordón F, Ramos R, Sala J, Masià R, Rohlfs I, Elosua R, Kannel WB. [Coronary risk estimation in Spain using a calibrated Framingham function] Rev Esp Cardiol. 2003;56:253–261. doi: 10.1016/s0300-8932(03)76861-4. [DOI] [PubMed] [Google Scholar]
- 11.Vergnaud AC, Bertrais S, Galan P, Hercberg S, Czernichow S. Tenyear risk prediction in French men using the Framingham coronary score: results from the national SU.VI.MAX cohort. Prev Med. 2008;47:61–65. doi: 10.1016/j.ypmed.2008.02.023. [DOI] [PubMed] [Google Scholar]
- 12.Ferrario M, Chiodini P, Chambless LE, Cesana G, Vanuzzo D, Panico S, Sega R, Pilotto L, Palmieri L, Giampaoli S CUORE Project Research Group. Prediction of coronary events in a low incidence population. Assessing accuracy of the CUORE Cohort Study prediction equation. Int J Epidemiol. 2005;34:413–421. doi: 10.1093/ije/dyh405. [DOI] [PubMed] [Google Scholar]
- 13.Jee SH, Jang Y, Oh DJ, Oh BH, Lee SH, Park SW, Seung KB, Mok Y, Jung KJ, Kimm H, Yun YD, Baek SJ, Lee DC, Choi SH, Kim MJ, Sung J, Cho B, Kim ES, Yu BY, Lee TY, Kim JS, Lee YJ, Oh JK, Kim SH, Park JK, Koh SB, Park SB, Lee SY, Yoo CI, Kim MC, Kim HK, Park JS, Kim HC, Lee GJ, Woodward M. A coronary heart disease prediction model: the Korean Heart Study. BMJ Open. 2014;4:e005025. doi: 10.1136/bmjopen-2014-005025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Goff DC, Jr, Lloyd-Jones DM, Bennett G, Coady S, D'Agostino RB, Gibbons R, Greenland P, Lackland DT, Levy D, O'Donnell CJ, Robinson JG, Schwartz JS, Shero ST, Smith SC, Jr, Sorlie P, Stone NJ, Wilson PW, Jordan HS, Nevo L, Wnek J, Anderson JL, Halperin JL, Albert NM, Bozkurt B, Brindis RG, Curtis LH, DeMets D, Hochman JS, Kovacs RJ, Ohman EM, Pressler SJ, Sellke FW, Shen WK, Smith SC, Jr, Tomaselli GF American College of Cardiology/American Heart Association Task Force on Practice Guidelines. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129(25 Suppl 2):S49–S73. doi: 10.1161/01.cir.0000437741.48606.98. [DOI] [PubMed] [Google Scholar]
- 15.Bild DE, Detrano R, Peterson D, Guerci A, Liu K, Shahar E, Ouyang P, Jackson S, Saad MF. Ethnic differences in coronary calcification: the Multi-Ethnic Study of Atherosclerosis (MESA) Circulation. 2005;111:1313–1320. doi: 10.1161/01.CIR.0000157730.94423.4B. [DOI] [PubMed] [Google Scholar]
- 16.Budoff MJ, Nasir K, Mao S, Tseng PH, Chau A, Liu ST, Flores F, Blumenthal RS. Ethnic differences of the presence and severity of coronary atherosclerosis. Atherosclerosis. 2006;187:343–350. doi: 10.1016/j.atherosclerosis.2005.09.013. [DOI] [PubMed] [Google Scholar]
- 17.Tang W, Detrano RC, Brezden OS, Georgiou D, French WJ, Wong ND, Doherty TM, Brundage BH. Racial differences in coronary calcium prevalence among high-risk adults. Am J Cardiol. 1995;75:1088–1091. doi: 10.1016/s0002-9149(99)80735-8. [DOI] [PubMed] [Google Scholar]
- 18.Hatwalkar A, Agrawal N, Reiss DS, Budoff MJ. Comparison of prevalence and severity of coronary calcium determined by electron beam tomography among various ethnic groups. Am J Cardiol. 2003;91:1225–1227. doi: 10.1016/s0002-9149(03)00268-6. [DOI] [PubMed] [Google Scholar]
- 19.World Health Organization. Global atlas on cardiovascular disease prevention and control [Internet] Geneva: World Health Organization; 2011. [cited 2015 Sep 1]. Available from: http://www.who.int/cardiovascular_diseases/publications/atlas_cvd/en/ [Google Scholar]
- 20.Arizonahealthmatters.org [Internet] Age-adjusted death rate due to coronary heart disease. Arizona: Arizona Health Matters; [updated 2015 Jan]. [cited 2015 Sep 1]. Available from: http://www.arizonahealthmatters.org/modules.php?op=modload±name=NS-Indicator±file=indicator±iid=17274946. [Google Scholar]
- 21.Lewey J, Choudhry NK. The current state of ethnic and racial disparities in cardiovascular care: lessons from the past and opportunities for the future. Curr Cardiol Rep. 2014;16:530. doi: 10.1007/s11886-014-0530-3. [DOI] [PubMed] [Google Scholar]
- 22.Cooper R, Cutler J, Desvigne-Nickens P, Fortmann SP, Friedman L, Havlik R, Hogelin G, Marler J, McGovern P, Morosco G, Mosca L, Pearson T, Stamler J, Stryer D, Thom T. Trends and disparities in coronary heart disease, stroke, and other cardiovascular diseases in the United States: findings of the national conference on cardiovascular disease prevention. Circulation. 2000;102:3137–3147. doi: 10.1161/01.cir.102.25.3137. [DOI] [PubMed] [Google Scholar]
- 23.Nhlbi.nih.gov [Internet] 4. Disease Statistics. Maryland: National Heart, Lung, and Blood Institute; [updated 2012]. [cited 2015 Sep 1]. Available from: http://www.worldpopulationstatistics.com/asia-population-2013/ [Google Scholar]
- 24.Worldpopulationstatistics.com [Internet] World Population Statistics. Asia Population 2013; [updated 2013 May 20]. [cited 2015 Sep 1]. Available from: http://www.worldpopulationstatistics.com/asia-population-2013/ [Google Scholar]
- 25.Korea National Statistical Office. Cause of death statistics in 1983-2013. Daejeon: Korea National Statistical Office; 2014. [Google Scholar]
- 26.Hutchinson RG, Watson RL, Davis CE, Barnes R, Brown S, Romm F, Spencer JM, Tyroler HA, Wu K. Racial differences in risk factors for atherosclerosis. The ARIC Study. Atherosclerosis Risk in Communities. Angiology. 1997;48:279–290. doi: 10.1177/000331979704800401. [DOI] [PubMed] [Google Scholar]
- 27.Ibrahim SA, Whittle J, Bean-Mayberry B, Kelley ME, Good C, Conigliaro J. Racial/ethnic variations in physician recommendations for cardiac revascularization. Am J Public Health. 2003;93:1689–1693. doi: 10.2105/ajph.93.10.1689. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Fuster V, Moreno PR, Fayad ZA, Corti R, Badimon JJ. Atherothrombosis and high-risk plaque: part I: evolving concepts. J Am Coll Cardiol. 2005;46:937–954. doi: 10.1016/j.jacc.2005.03.074. [DOI] [PubMed] [Google Scholar]
- 29.Nasir K, Shaw LJ, Liu ST, Weinstein SR, Mosler TR, Flores PR, Flores FR, Raggi P, Berman DS, Blumenthal RS, Budoff MJ. Ethnic differences in the prognostic value of coronary artery calcification for all-cause mortality. J Am Coll Cardiol. 2007;50:953–960. doi: 10.1016/j.jacc.2007.03.066. [DOI] [PubMed] [Google Scholar]
- 30.Wijeysundera HC, Machado M, Farahati F, Wang X, Witteman W, van der Velde G, Tu JV, Lee DS, Goodman SG, Petrella R, O'Flaherty M, Krahn M, Capewell S. Association of temporal trends in risk factors and treatment uptake with coronary heart disease mortality, 1994-2005. JAMA. 2010;303:1841–1847. doi: 10.1001/jama.2010.580. [DOI] [PubMed] [Google Scholar]
- 31.Hong Y. Burden of cardiovascular disease in Asia: big challenges and ample opportunities for action and making a difference. Clin Chem. 2009;55:1450–1452. doi: 10.1373/clinchem.2009.125369. [DOI] [PubMed] [Google Scholar]
- 32.Choi EK, Choi SI, Rivera JJ, Nasir K, Chang SA, Chun EJ, Kim HK, Choi DJ, Blumenthal RS, Chang HJ. Coronary computed tomography angiography as a screening tool for the detection of occult coronary artery disease in asymptomatic individuals. J Am Coll Cardiol. 2008;52:357–365. doi: 10.1016/j.jacc.2008.02.086. [DOI] [PubMed] [Google Scholar]
- 33.Kannel WB, McGee D, Gordon T. A general cardiovascular risk profile: the Framingham Study. Am J Cardiol. 1976;38:46–51. doi: 10.1016/0002-9149(76)90061-8. [DOI] [PubMed] [Google Scholar]
- 34.Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837–1847. doi: 10.1161/01.cir.97.18.1837. [DOI] [PubMed] [Google Scholar]
- 35.National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) 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) final report. Circulation. 2002;106:3143–3421. [PubMed] [Google Scholar]
- 36.Sangiorgi G, Rumberger JA, Severson A, Edwards WD, Gregoire J, Fitzpatrick LA, Schwartz RS. Arterial calcification and not lumen stenosis is highly correlated with atherosclerotic plaque burden in humans: a histologic study of 723 coronary artery segments using nondecalcifying methodology. J Am Coll Cardiol. 1998;31:126–133. doi: 10.1016/s0735-1097(97)00443-9. [DOI] [PubMed] [Google Scholar]
- 37.Hecht HS. Coronary artery calcium: utilization for primary prevention of CHD. Curr Cardiol Rep. 2011;13:465–474. doi: 10.1007/s11886-011-0217-y. [DOI] [PubMed] [Google Scholar]
- 38.Shaw LJ, Raggi P, Schisterman E, Berman DS, Callister TQ. Prognostic value of cardiac risk factors and coronary artery calcium screening for allcause mortality. Radiology. 2003;228:826–833. doi: 10.1148/radiol.2283021006. [DOI] [PubMed] [Google Scholar]
- 39.Blaha MJ, Silverman MG, Budoff MJ. Is there a role for coronary artery calcium scoring for management of asymptomatic patients at risk for coronary artery disease?: Clinical risk scores are not sufficient to define primary prevention treatment strategies among asymptomatic patients. Circ Cardiovasc Imaging. 2014;7:398–408. discussion 408. doi: 10.1161/CIRCIMAGING.113.000341. [DOI] [PubMed] [Google Scholar]
- 40.Greenland P, LaBree L, Azen SP, Doherty TM, Detrano RC. Coronary artery calcium score combined with Framingham score for risk prediction in asymptomatic individuals. JAMA. 2004;291:210–215. doi: 10.1001/jama.291.2.210. [DOI] [PubMed] [Google Scholar]
- 41.Yeboah J, McClelland RL, Polonsky TS, Burke GL, Sibley CT, O'Leary D, Carr JJ, Goff DC, Greenland P, Herrington DM. Comparison of novel risk markers for improvement in cardiovascular risk assessment in intermediate-risk individuals. JAMA. 2012;308:788–795. doi: 10.1001/jama.2012.9624. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Fujiyoshi A, Miura K, Ohkubo T, Kadowaki T, Kadowaki S, Zaid M, Hisamatsu T, Sekikawa A, Budoff MJ, Liu K, Ueshima H SESSA Research Group; MESA Research Group. Cross-sectional comparison of coronary artery calcium scores between Caucasian men in the United States and Japanese men in Japan: the multi-ethnic study of atherosclerosis and the Shiga epidemiological study of subclinical atherosclerosis. Am J Epidemiol. 2014;180:590–598. doi: 10.1093/aje/kwu169. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Park HE, Chun EJ, Choi SI, Lee SP, Yoon CH, Kim HK, Youn TJ, Kim YJ, Choi DJ, Sohn DW, Cho GY. Clinical and imaging parameters to predict cardiovascular outcome in asymptomatic subjects. Int J Cardiovasc Imaging. 2013;29:1595–1602. doi: 10.1007/s10554-013-0235-5. [DOI] [PubMed] [Google Scholar]
- 44.Kim BJ, Kim BS, Kang JH. Conventional versus image-based cardiovascular risk assessment in Korean adults. Coron Artery Dis. 2014;25:118–124. doi: 10.1097/MCA.0000000000000071. [DOI] [PubMed] [Google Scholar]
- 45.Sung J, Lim SJ, Choe Y, Choi YH, Lee MK, Lee SH, Hong KP, Park JE. Comparison of the coronary calcium score with the estimated coronary risk. Coron Artery Dis. 2008;19:475–479. doi: 10.1097/MCA.0b013e3283078f9f. [DOI] [PubMed] [Google Scholar]
- 46.Itani Y, Sone S, Nakayama T, Suzuki T, Watanabe S, Ito K, Takashima S, Fushimi H, Sanada H. Coronary artery calcification detected by a mobile helical computed tomography unit and future cardiovascular death: 4-year follow-up of 6120 asymptomatic Japanese. Heart Vessels. 2004;19:161–163. doi: 10.1007/s00380-003-0759-z. [DOI] [PubMed] [Google Scholar]
- 47.Fujimoto S, Kondo T, Kumamaru KK, Shinozaki T, Takamura K, Kawaguchi Y, Matsumori R, Hiki M, Miyauchi K, Daida H, Rybicki FJ. Prognostic Value of Coronary Computed Tomography (CT) Angiography and Coronary Artery Calcium Score Performed Before Revascularization. J Am Heart Assoc. 2015;4:e002264. doi: 10.1161/JAHA.115.002264. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Yamamoto H, Ohashi N, Ishibashi K, Utsunomiya H, Kunita E, Oka T, Horiguchi J, Kihara Y. Coronary calcium score as a predictor for coronary artery disease and cardiac events in Japanese high-risk patients. Circ J. 2011;75:2424–2431. doi: 10.1253/circj.cj-11-0087. [DOI] [PubMed] [Google Scholar]
- 49.Lloyd-Jones DM, Martin DO, Larson MG, Levy D. Accuracy of death certificates for coding coronary heart disease as the cause of death. Ann Intern Med. 1998;129:1020–1026. doi: 10.7326/0003-4819-129-12-199812150-00005. [DOI] [PubMed] [Google Scholar]
- 50.Lauer MS, Blackstone EH, Young JB, Topol EJ. Cause of death in clinical research: time for a reassessment? J Am Coll Cardiol. 1999;34:618–620. doi: 10.1016/s0735-1097(99)00250-8. [DOI] [PubMed] [Google Scholar]