Coronary Artery Calcium (CAC) Score for Cardiovascular Risk Stratification in a Thai Clinical Cohort: A Comparison of Absolute Scores and Age-Sex Specific Percentiles

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Method This study was a retrospective observational cohort. We included patient aged above 35 years who underwent CAC testing. The absolute and age-sex specific percentile classification were categorized as 0, 1 to 10, 11 to 100, 101 to 400, and > 400 and 0, 90th, respectively. The end point was MACE, including cardiovascular death, myocardial infarction, heart failure hospitalization, coronary artery revascularization procedures, and stroke. Multivariable Cox regression was used to estimate the hazard ratios. The discriminative performance between classification were compared using Harrell’s C-statistic. The agreement was assessed via Cohens’ Kappa. Result The study included 440 patients, with approximately 70% of Thai patients exhibiting a CAC score. CAC distributed higher in male than female and older than younger. Both CAC classification demonstrated the acceptable predictive performance. However, fair agreement was observed between classifications (Cohen’s kappa 0.51 95%CI 0.42–0.59). Within an absolute classification, the higher CAC could capture the higher hazard ratio more consistently across age-sex specific percentile level. In contrast, the association between MACE and the age-sex specific percentile classification was not consistent in all levels of the absolute CAC scale. Conclusion Both absolute and age-sex-specific percentile CAC scores showed acceptable performance in predicting MACE. However, it is likely that the classification of absolute CAC scores may be more appropriate for risk stratification in Thai clinical cohort. Figures Figure 1 Figure 2 Figure 3 Introduction The Coronary Artery Calcium (CAC) score was introduced in the late 1990s to assess coronary artery calcification with non-invasive imaging techniques [ 1 ]. The score provides a precise quantification of atherosclerotic plaque within the coronary arteries and has demonstrated a strong correlation with major adverse cardiovascular events (MACE) [ 2 ]. The presence of a CAC score is indicative of a significant risk of developing atherosclerotic cardiovascular disease (ASCVD) and coronary heart disease (CHD) [ 3 – 6 ]. In contrast, the absence of a CAC score suggests a low risk of cardiovascular events [ 7 ]. An ample body of research and guidelines supporting the potential of CAC score as a cardiovascular risk stratification tool have accumulated over the years [ 8 ]. Moreover, studies have affirmed that the CAC score not only surpasses conventional methods as a standalone predictor, but also enhances the discriminative ability of other traditional scores in predicting cardiovascular risk [ 8 , 9 ] Although the CAC score has a high prognostic ability, its interpretation can be challenging due to different ways of expressing and stratifying patients' risk [ 10 – 11 ]. CAC scores can be expressed as absolute scores, based on the exact amount of CAC score measured in Agatston units, or percentile-based rank scores, based on the percentile rank of CAC score stratified by the patient's sex, age, and/or race [ 12 – 14 ]. Owing to the distinctive rationale of score expression, disagreement between risk groups defined by different score types, even within the same population, is commonly reported [ 14 ]. However, different score types may have varied predictive performance based on population mix and prediction time frame, and one may be more beneficial in different contexts [ 15 ]. Standard guidelines recommend using both absolute and percentile-based scores for risk stratification. For example, the AHA/ACC cholesterol guideline suggests initiating statins for intermediate risk adults with a CAC score greater than 100 or the 75th percentile [ 16 – 18 ]. In Thailand, there is currently no official guidance on the use of CAC score for cardiovascular risk assessment and CAC score cut-point for statin initiation, and there is insufficient evidence to support the appropriateness of the CAC score cut-point suggested by standard western guidelines in the Thai population. Most CAC score studies have been conducted in western nations, with only a small number of Asian patients included. Additionally, there is evidence that CAC score distribution may vary across ethnic groups, and the results may not be applicable to other populations using the same cut-point system [ 19 – 20 ]. This study aims to examine the prevalence and CAC distribution, to determine the association of each CAC score classification with MACE, and to validate the discriminative performance of the CAC score for risk stratification of MACE for the first time in Thai clinical cohort. We also examined the agreement between the two CAC scoring classification and compare their performance for risk stratification. Method Study design and population We conducted a retrospective observational cohort study that included Thai patients who met the following inclusion criteria: (1) were aged above 35 years, and (2) underwent CAC testing at Maharaj Nakorn Chiang Mai hospital between 1 January 2012 and 31 March 2020. The exclusion criteria were patients with pre-existing cardiovascular diagnoses (e.g., myocardial infarction, heart failure hospitalization, and ischemic stroke or transient ischemic attack), coronary artery revascularization procedures, incomplete data for traditional cardiovascular risk calculation, or missing CAC score. This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Faculty of Medicine, Chiang Mai University (MED-2563-07435). Informed consent was waived owing to the retrospective data collection. Data collection The data regarding age, sex, smoking status, diabetes mellitus (DM), systolic blood pressure, total cholesterol, low-density cholesterol (LDL-C), high-density cholesterol (HDL-C), creatinine, and Estimated Glomerular Filtration Rate (eGFR) were collected by reviewing individual electronic medical records on the date of CAC score measurement. Calcium coronary calcium (CAC) score The CAC score was measured using a non-contrasted prospective electrocardiogram (ECG) gating scan of the heart with a 192-slice Dual Source CT scanner (Somatom Force, Siemens). The scans were prospective ECG-gating at 75% of the R-R interval, with a 3 mm slice thickness and a tube voltage of 120 kV. Certified radiologists quantified the amount of CAC using the Agatston scoring method [ 21 ]. The absolute CAC score was categorized into five groups using cut-points from previous studies [ 12 , 22 ]: 0 (absent), > 0 to 10 (minimal), 11 to 100 (moderate), 101 to 400 (high), and > 400 (extensive). As no studies have reported the distribution and percentiles of CAC across different age and sex groups in the Thai clinical cohort, we calculated the CAC percentile for each individual based on this cohort. We stratified the patient cohort into six groups (males aged 64, females aged 64) [ 23 ] and assigned patients to one of four percentile ranges based on their strata-specific CAC percentile: CAC = 0 (absent), 90th (very high) [ 13 , 14 ]. Outcome and follow-up The main outcome of the study was the occurrence of MACE, which was defined as a composite of cardiovascular death, myocardial infarction (MI), hospitalization due to heart failure, coronary artery revascularization procedures, and ischemic stroke or transient ischemic attack [ 24 ]. Cardiovascular deaths were defined as those with documented coronary artery disease and no other known causes of death. The term coronary artery revascularization refers to any procedures performed to enhance the heart's blood supply. In our institution, only two revascularization procedures were available: percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG). We followed all included patients from the time of their CAC measurements and continued until the event date, death, or loss to follow-up, whichever came first. Each patient received a phone call from a trained interviewer who inquired about their myocardial infarction event, hospitalizations for heart failure, revascularizations, and strokes. All patients' electronic medical records were reviewed and verified by investigators. Patients who did not have events and did not lose follow-up at the time the study was closed were marked as censored. Statistical analysis All analyses were performed using Stata 17.0 (StataCorp, College Station, Texas, USA). The results were considered statistically significant at a p-value < 0.05. Demographics and baseline characteristics were presented as a number (%), mean and standard deviation (SD), or median and interquartile range (IQR), as appropriate. The prevalence of each CAC score was estimated and reported with its corresponding 95% confidence interval (CI). To compare baseline characteristics across five absolute CAC categories, we used Fisher’s exact test for categorical variables and an extended Wilcoxon rank-sum test by Cuzick [ 25 ] for trending numerical variables. To identify the association with MACE, a time-to-event analysis was employed. MACE incident rates were annualized and reported per 1000 person-years. Kaplan–Meier methods were used to estimate the probabilities of MACE in both CAC cut-point systems. Log-transformed continuous CAC score and categorized CAC score (both absolute and Age-sex specific percentile) were fitted using a multivariable Cox model adjusted by confounders [ 26 , 27 ] including age, sex, diabetes mellitus, total cholesterol, HDL, LDL, smoking status, and creatinine to estimate the adjusted hazard ratio (HR) of MACE. Patients with absence CAC score (CAC score 0) were the reference group. A subgroup analysis was performed for each sex. The predictive performance of the categorized CAC scores was validated in terms of discriminative performance using Harrell’s C-statistics [ 28 ]. Harrell’s C-statistics were estimated from a univariable Cox’s proportional hazard model that includes only the variables for the categorized CAC score as we want to assess the isolated performance of the CAC. A higher Harrell’s C-statistic indicated a higher overall discriminative ability of the cut-point. To examine the agreement between the absolute CAC and the age-sex-specific percentile score, we excluded patients with absence CAC score from this part of the analysis. We hypothesized that patients with age-sex-specific percentile-based CAC scores 90th might have a similar prognosis to patients with absolute CAC scores of 1-100, 101–400, and > 400, respectively [ 14 , 15 – 17 ]. We calculated the overall agreement percentage and linear-weighted Cohen’s Kappa. Kappa values of < 0.4, 0.40–0.59, 0.60–0.74, and 0.75-1.00 would be clinically interpreted as poor, fair, good, and excellent, respectively [ 29 ]. In case the two classification types did not agree well, we performed an additional analysis to identify a more robust one for risk stratification by assessing the predictive performance and the association with MACE of absolute CAC classification in each level of age-sex specific CAC classification, and vice versa. We defined a more robust classification as the one that showed higher discriminative ability and preserved HR trend for MACE in every level of another classification. Results Prevalence of CAC and characteristics across CAC categories During the study period, a total of 722 patients received a CT coronary angiogram at our institution. Among them, 282 patients were excluded, and the final number of patients included in our analysis was 440 (Figure 1). The prevalence of each CAC category in this cohort was 31.1% (95%CI 26.8-35.7%) for CACs 0, 14.1% (95%CI 11-17.7%) for CACs >0-10, 17.5% (95%CI 14.1-21.4%) for CACs 11-100, 20.2% (95%CI 16.6-24.3%) for CACs 101-400, and 17.1% (95%CI 13.6-20.9%) for CACs >400). Table 1 compares the baseline characteristics across the CAC categories. The mean age of all patients was 62.5±10.4 years. Nearly half of the patients were men (47.3%). Age, sex, total cholesterol, LDL-C, creatinine, and eGFR showed a statistically significant trend across CAC categories. We observed an increase in the proportion of DM across higher CAC categories. Conversely, the average cholesterol and LDL-C decreased with higher CAC categories. CAC distribution Figure 2 shows age-sex-specific percentiles of CAC scores, with significantly higher percentiles observed in males and older age groups compared to females and younger age groups. A similar trend was observed in the subgroup analysis for patients with diabetes (Figure S1). Among patients without diabetes, females under 64 years old had CAC scores below 100 at the 75 th percentile, while males in all age groups had scores above 100. A similar trend was observed in patients with diabetes. Females under 64 consistently had CAC scores below the 75 th percentile, while males in all age groups had significantly higher scores. The exact CAC scores are presented in Supplementary Table S1. The association between CAC score classifications and MACE A total of 82 major adverse cardiovascular events (18.6%) occurred during follow-up (Table S2). The median duration of follow-up was 41 months (range 18-109). The probability of MACE progressively increased with higher CAC categories (Figure 3). The probability remained above 25% in those with a CAC score >100 during follow-up. Compared to CAC score 0, patients with CAC scores greater than 10 showed a significantly higher HR for MACE: CAC 11-100 (HR 5.93, 95%CI 1.63-21.55, p=0.007), CAC 101-400 (HR 10.18, 95% CI 2.91-35.49, p≤0.001), and CAC >400 (HR 18.40, 95%CI 5.38-62.87, p≤0.001) (Table 2). Patients with CAC scores <75th also had a significantly increased HR for MACE (HR 4.5, 95%CI 1.34-15.09, p=0.015) compared to patients without CAC. The results of subgroup analysis regarding the association with MACE are presented in Table 2. Predictive performance of CAC score classifications Both the absolute and age-sex specific percentile CAC score classification demonstrated similar acceptable discriminative performance based on Harrell’s C statistics, with values of 0.76 (95%CI 0.72, 0.81) and 0.75 (95%CI 0.70, 0.79), respectively. The absolute CAC classification exhibited consistent Harrell’s C statistics values for both sexes at 0.76 (95%CI 0.72, 0.81), while the age-sex specific percentile CAC classification showed a minor drop to 0.73 (95%CI 0.68, 0.77) (Table 2). Agreement between the absolute and age-sex specific percentile CAC score In patients with presence of CAC, approximately 90.7% of patients with CAC scores between 1 and 100 had scores below the 75th age-sex specific percentile stratum, while 65% of patients with scores below the 75 th percentile had CAC scores less than 100. Among patients with CAC scores between 101-400, 71.9% were categorized below the 75 th percentile, while only 23.6% of these patients had CAC scores between the 75 th and 90 th percentiles. Additionally, only 6.7% of patients with CAC scores greater than 400 were categorized below the 75 th percentile (Table 3). The linear-weighted Cohen's Kappa indicated fair agreement between the two classifications, at 0.51 (95% CI 0.42-0.59). Comparison between absolute and age-sex specific percentile CAC score Classification based on the absolute CAC score exhibited acceptable discrimination, with Harrell's C statistics surpassing 0.70 for every age-sex specific percentile category, comparable to the C-statistics value for the entire cohort. Conversely, the C-statistics for the age-sex specific percentile classification showed inadequate discriminative performance in each subgroup of the absolute CAC category (Table 4). Consistency in the association between MACE and the absolute CAC classification was observed in all levels of age-sex specific percentile classification, with higher HR observed in higher absolute CAC score categories (Table 4). However, the association between MACE and age-sex specific percentile classification was not consistent in all levels of absolute CAC classification (Table 4). Discussion In this study, we described the prevalence of Thai clinical cohort with the presence of CAC score and the distribution of CAC score based on both absolute CAC score and age-sex specific percentile score. Our findings suggest that both CAC classifications demonstrate significant associations with MACE occurrence, with higher magnitude at higher levels of classification. However, the agreement between the two classifications was only fair, rendering it inappropriate to use them interchangeably. Our analysis indicates that the absolute CAC classification is superior to the age-sex specific percentile classification in providing a more robust predictive performance for risk stratification. About 70% of patients in our cohort had CAC, and their average CAC score was higher than in previous studies conducted in Japan, Korea, and Saudi Arabia [ 22 , 30 , 31 ]. These studies reported a higher percentage of people with no CAC, ranging from 57–73% [ 22 , 30 , 31 ]. The higher baseline cardiovascular risk in our cohort could be attributed to the different ways of recruiting source populations. In Korean and Japanese studies, the study population was recruited from a health check-up program, which included people in good health and low cardiovascular risk [ 22 , 30 ]. In contrast, our study included patients aged over 35 who were sent for CAC testing due to the decision from their attending physicians. Compared to previous studies, the prevalence of CAC and the average CAC score in our cohort were similar to that of the white population in the Framingham Heart study [ 14 ]. The distribution of CAC score was higher in males, old age, and patients with diabetes, which is consistent with previous studies [ 22 , 30 , 31 ]. Our results indicate that both the absolute and age-sex specific percentile classifications of coronary artery calcium (CAC) scores demonstrated acceptable discriminative performance in stratifying the risk for MACE. The overall performance based on Harrell’s C-statistics was comparable to previous studies [ 32 , 33 ]. However, the disagreement between the two classifications of coronary artery calcium (CAC) scores becomes more apparent when examining the predictive performance of one classification across different levels of another. Our study results showed that within the absolute classification, higher hazard ratios (HRs) were consistently observed in higher absolute CAC score categories in contrast to the age-sex specific percentile classification. This result was similar to the findings from the MESA cohort [ 15 ]. It should be noted that neither classification should be preferred over the other. According to recent reviews [ 34 ], both the absolute and percentile classifications of coronary artery calcium (CAC) scores provide important information regarding MACE events. While the absolute score classification is considered the best predictor for an individual's risk in the next 5–10 years, providing short-term predictive value, the percentile score classification offers insights into long-term prognosis and is a better predictor of lifetime risk. This disagreement in our findings highlights the importance of considering different classification methods and their implications when assessing CAC scores in clinical practice. Based on our results, it appears that the absolute CAC score classification may be appropriate for risk stratification in this Thai clinical cohort. According to standard guidelines [ 16 – 18 ], statin initiation may be implied for Thai patients with an absolute CAC score ≥ 100 or ≥ 75th percentile due to the strong association with MACE. However, our findings suggest that the high MACE incidence rates in patients with an absolute CAC score between 11–100 or those with an age-sex specific percentile score below 75th may not be adequately captured by current guideline recommendations for statin initiation. Considering the retrospective nature of our data which encompassed patients who had used statins, we cannot conclude whether the current suggestion would lead to undertreatment. Therefore, large prospective studies with longer and standardized follow-up in statin-naïve patients are needed to confirm whether the current CAC score cut points are appropriate for the Thai population or whether new cut points should be identified. This study is the first to report on the prevalence and distribution of CAC scores in a clinical population in Thailand that underwent CAC testing. Our study provides a comprehensive evaluation of the predictive performance of both absolute and age-sex-specific CAC score classifications for MACE outcomes, investigates the agreement between the two classifications, and proposes a superior classification for cardiovascular risk stratification. The findings of this study have significant implications for the development of guidelines for CAC testing in the Thai patient population. However, there were some limitations to our study. First, it was a retrospective observational study, which may have been affected by biases during data collection and follow-up. However, the data was obtained from routine standardized forms with only a small proportion of missing values, and almost all variables were objective. Second, our institution had a higher rate of revascularization, accounting for 73% of all MACE, compared to other centers where it ranged from 6–42% [ 35 – 37 ]. This indicates that a high CAC score may have served as a guide for a significant number of revascularizations in our study, potentially inflating the incidence of MACE. Thus, the impact of revascularization on MACE should be carefully considered. Third, the indication for CAC testing in our cohort was not clearly defined and documented, and the sampling scheme was consecutive and non-probability. This means that not all patients visiting our institution had an equal chance of being offered CAC testing, potentially affecting the generalizability of our results. Finally, our study was conducted at a single tertiary care center in Northern Thailand, so the results may only be applicable to similar clinical contexts. Further research is recommended to investigate CAC score prevalence and distribution in a health check-up or screening population in Thailand. Conclusion About 70% of Thai patients who underwent CAC testing had a CAC score. The CAC score was increasingly distributed among male, elderly, and diabetic patients. A higher CAC score was found to be significantly associated with a higher incidence of MACE. Both absolute and age-sex-specific percentile CAC scores showed acceptable performance in predicting MACE. However, it is likely that the classification of absolute CAC scores may be more robust and appropriate for risk stratification. Declarations Acknowledgements This study was supported by faculty of medicine Chiangmai university, Thailand Ethichs approval: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Faculty of Medicine, Chiang Mai University (MED-2563-07435). Consent to participate: Informed consent was waived owing to the retrospective data collection. Consent to publish: This study does not contain any individual person’s data in any form. Funding : This research received no grant from any funding agency in the public, commercial or not-for-profit sectors. Disclosures: All Authors declare that there is no conflict of interest References A.S. Agatston, W.R. Janowitz, F.J. Hildner, N.R. Zusmer, M. Viamonte Jr.,R. Detrano, Quantification of coronary artery calcium using ultrafast computedtomography, J. Am. Coll. Cardiol. 15 (1990) 827e832. Abuzaid A, Saad M, Addoumieh A, Ha LD, Elbadawi A, Mahmoud AN, Elgendy A, Abdelaziz HK, Barakat AF, Mentias A, Adeola O, Elgendy IY, Qasim A, Budoff M. Coronary artery calcium score and risk of cardiovascular events without established coronary artery disease: a systemic review and meta-analysis. Coron Artery Dis. 2021 Jun 1;32(4):317-328. doi: 10.1097/MCA.0000000000000974. PMID: 33417339. 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Ohmoto-Sekine Y, Yanagibori R, Amakawa K, Ishihara M, Tsuji H, Ogawa K, Ishimura R, Ishiwata S, Ohno M, Yamaguchi T, Arase Y. Prevalence and distribution of coronary calcium in asymptomatic Japanese subjects in lung cancer screening computed tomography. J Cardiol. 2016 May;67(5):449-54. doi: 10.1016/j.jjcc.2015.06.010. Epub 2015 Jul 23. PMID: 26213250. Al Helali S, Abid Hanif M, Alshugair N, Al Majed A, Belfageih A, Al Qahtani H, Al Dulikan S, Hamed H, Al Mousa A. Distributions and burden of coronary calcium in asymptomatic Saudi patients referred to computed tomography. Int J Cardiol Heart Vasc. 2021 Oct 27;37:100902. doi: 10.1016/j.ijcha.2021.100902. PMID: 34761100; PMCID: PMC8566998 Arad Y, Goodman KJ, Roth M, et al. Coronary calcification, coronary disease risk factors, C-reactive protein, and atherosclerotic cardiovascular disease events: the St. Francis Heart Study. J Am Coll Cardiol. 2005;46:158–65 Becker A, Leber A, Becker C, et al. Predictive value of coronary calcifications for future cardiac events in asymptomatic individuals. Am Heart J. 2008;155:154–60. Obisesan OH, Osei AD, Uddin SMI, Dzaye O, Blaha MJ. An Update on Coronary Artery Calcium Interpretation at Chest and Cardiac CT. Radiol Cardiothorac Imaging. 2021 Feb 25;3(1):e200484. doi: 10.1148/ryct.2021200484. PMID: 33778659; PMCID: PMC7977732. Robert J H Miller, Donghee Han, Ananya Singh, Konrad Pieszko, Piotr J Slomka, Heidi Gransar, Rebekah Park, Yuka Otaki, John D Friedman, Sean Hayes, Louise Thomson, Alan Rozanski, Daniel S Berman, Relationship between ischaemia, coronary artery calcium scores, and major adverse cardiovascular events, European Heart Journal - Cardiovascular Imaging, Volume 23, Issue 11, November 2022, Pages 1423–1433, Yang, L., Xu, P.P., Schoepf, U.J. et al. Serial coronary CT angiography–derived fractional flow reserve and plaque progression can predict long-term outcomes of coronary artery disease. Eur Radiol 31, 7110–7120 (2021). 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(10):2424-31. doi: 10.1253/circj.cj-11-0087. Epub 2011 Jul 21. PMID: 21778594. Tables Table 1. Baseline characteristic in each Coronary artery calcium score category Missing n (%) (total=440) Overall (total=440) CAC score 0 (total=137) CAC score 1-10 (total=62) CAC score 11-100 (total=77) CAC score 101-40 (total=89) CAC score >400 (total=75) P-value Age (years) 0 (0%) 62.5±10.4 57.5±9.7 60.5±9.09 63.1±9.36 67.1±9.64 67.5±9.86 <0.001 Male 0 (0%) 209 (47.3%) 41(29.9%) 30(48.4%) 40(52.0%) 48(53.9%) 49(65.3%) <0.001 Current smoking 11 (2.3%) 6 (1.4%) 1(0.7%) 2(3.3%) 1(1.3%) 1(1.2%) 1(1.3%) 0.719 DM 12 (2.7%) 96 (22.4%) 24(17.9%) 10(16.4%) 15(20.6%) 21(24.7%) 26(34.7%) 0.056 SBP 19 (4.3%) 130.8±14.4 129.7±13.3 131.3±15.6 129.5±15.5 132±14.3 131.9±14.3 0.313 Total Cholesterol 0 (0%) 178.4±46.5 185.2±42.7 178.2±41.0 180.6±45.2 175.8±53.8 165.6±49.0 0.003 LDL-C 56 (12.7%) 114.7±41.5 119.7±38.4 115.4±36.7 117.2±43.0 114.2±46.0 103.5±42.7 0.006 HDL-C 61 (13.8%) 54.1±14.1 56.2±16.0 51.3±10.5 53.7±11.9 54.6±14.4 52.9±14.8 0.406 Creatinine 32 (7.3%) 0.98± 0.7 0.83±0.2 0.92±0.24 1.01±0.77 0.95±0.27 1.30±1.3 <0.001 eGFR 32 (7.3%) 80.2±19.5 87.2±18.2 80.2±16.8 77.7±18.1 77.3±15.8 73.8±24.8 <0.001 Abbreviations: DM, diabetes mellitus; SBP, systolic blood pressure; eGFR, estimated glomerular filtration rate; LDL-C, low density lipoprotein cholesterol; HDL-C, High density lipoprotein cholesterol Table 2. Incident rate and Hazard ratio for absolute and age-sex specific cut-points with subgroup analysis for sex. n MACE event Incidence rate (1000 person-years) aHR (95% CI) P - value Harrell’s C-statistic* (95% CI) CAC score (Agatston) Overall event (%) 82 (18.6) (log transform) - 1.57 (1.37-1.79) <0.001 Absolute scale 0.76 (0.72-0.81) 0 137 5 9.9 (4.1-23.9) 1.00 (Reference) 1-10 62 5 20.3 (5.9-70.3) 1.61 (0.32-8.12) 0.563 11-100 77 11 40.6 (14.1-116.8) 5.93 (1.63-21.55) 0.007 101-400 89 24 87.9 (33.6-230.5) 10.18 (2.91-35.49) 400 75 37 258.2 (101.5-656.8) 18.40 (5.38-62.87) <0.001 age sex specific percentile 0.75 (0.70-0.79) 0 137 5 9.9 (4.1-23.9) 1.00 (Reference) < P75 195 34 49.1 (19.2-125.5) 4.5 (1.34-15.09) 0.015 P75-90 67 20 115.7 (43.4-308.3) 10.30 (2.97-35.70) 90 41 23 339.0 (128.7-890.4) 18.75 (5.43-64.75) <0.001 Subgroup analysis in men Absolute 0.76 (0.72-0.81) 0 41 3 20.8 (6.7-64.6) 1.00 (Reference) 1-10 30 3 26.0 (5.2-128.8) 2.05 (0.18-23.83) 0.566 11-100 40 8 52. (13.9-198.6) 8.46 (1.03-69.04) 0.046 101-400 48 12 80.1 (22.7-283.7) 10.34 (1.26-84.84) 0.030 >400 49 26 278.3 (84.2-919.4) 18.40 (2.37-143.06) 0.005 age sex specific percentile 0.73 (0.68-0.77) 0 41 3 20.8 (6.7-64.6) 1.00 (Reference) 90 20 12 360.7 (101.7-1278.2) 19.20 (2.28-161.60) 0.007 Subgroup analysis in women Absolute 0.76 (0.72-0.81) 0 96 2 5.6 (1.3-22.2) 1.00 (Reference) 1-10 32 2 15.4(2.2-109.5) 1.18 (0.10-13.54) 0.894 11-100 37 3 25.2(4.2-151.0) 3.39 (0.56-2-.61) 0.186 101-400 41 12 97.7 (21.9-436.7) 11.65 (2.41-56.38) 0.002 >400 26 11 220.6 (48.9-995.1) 21.80 (4.59-103.56) 0.000 age sex specific percentile in women 0.73 (0.68-0.77) 0 96 2 5.6 (1.3-22.2) 1.00 (Reference) 90 21 11 318.4 (70.5-1436.2) 23.91 (5.09-112.33) <0.001 P values were estimated from Cox regression. *, Harrell’s C-statistic was estimated from from a univariable Cox’s proportional hazard model Abbreviations: CI, confidential interval; aHR, adjusted hazard ratio; Table 3 . Degree of disagreement between absolute and age-sex specific cut-point represented by the inconsistency of CAC proportion and weighted Cohens’ Kappa P 90 Total CAC 1-100 126 (64.6%) (90.7%) 13 (19.4%) (9.4%) 0 (0%) 0(0%) 139 (100%) CAC 101-400 64 (32.8%) (71.9%) 21 (31.3%) (23.6%) 4 (9.8%) (4.4%) 89 (100%) CAC > 400 5 (0.03%) (6.7%) 33 (49.3%) (44%) 37 (90.2%) (49.3%) 75 (100%) Total 195 (100%) 67 (100%) 41 (100%) 303 Weight of disagreement Agreement Cohens’Kappa (95% CI) Linear weighted 79.5% 0.51 (0.42-0.59) Abbreviations: CI, confidential interval Table 4 . Trending of adjusted Hazard ratio by each absolute scale in subgroup analysis across age-sex specific and vice versa n (%) HR (95%CI) P - value Harrell’s C-statistic Across age-sex specific percentile CAC 400 5 (0.03) 6.19 (1.07-35.88) 0.042 CAC 75 th – 90 th 0.74 (0.69-0.79) 1-100 13 (19.4) 1.00 (Reference) 101-400 21 (31.3) 5.12 (0.50-52.45) 0.169 >400 33 (49.3) 5.63 (0.18-175.43) 0.325 CAC >90 th NA 1-100 0 (0) NA NA 101-400 4 (9.8) NA NA >400 37 (90.2) NA NA Across Absolute scale CAC 1-100 0.44 (0.39-0.49) Age -sex specific 90 th 0 (0) NA NA CAC 101-400 0.47 (0.40-0.54) Age -sex specific 90 th 4 (4.5) 0.35 (0.02-6.59) 0.486 CAC > 400 0.53 (0.46-0.60) Age -sex specific 90 th 37 (49.3) 1.40 (0.31-6.21) 0.662 Abbreviations: CI, confidential interval; HR, Hazard ratio; NA, not applicable Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2994349","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":204864270,"identity":"f26f850d-c163-4a57-aff9-7a4189b8373c","order_by":0,"name":"Supitcha Kitjanukit","email":"","orcid":"","institution":"Department of Internal Medicine, Faculty of medicine, Chiang Mai University, Thailand","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Supitcha","middleName":"","lastName":"Kitjanukit","suffix":""},{"id":204864271,"identity":"12835361-369f-46f7-a8b1-ae2a7687ebb7","order_by":1,"name":"Pakpoom wongyikul","email":"","orcid":"","institution":"Center for Clinical Epidemiology and Clinical Statistics, Faculty of Medicine, Chiang Mai University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Pakpoom","middleName":"","lastName":"wongyikul","suffix":""},{"id":204864272,"identity":"6449e0f6-e07e-4cd9-abe5-11d91afe239e","order_by":2,"name":"Srun Kuanprasert","email":"","orcid":"","institution":"Division of Cardiology, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Srun","middleName":"","lastName":"Kuanprasert","suffix":""},{"id":204864273,"identity":"f9c30bf8-896a-4fd7-a6e9-3a0af34fb48f","order_by":3,"name":"Pannipa Suwannasom","email":"","orcid":"","institution":"Division of Cardiology, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Pannipa","middleName":"","lastName":"Suwannasom","suffix":""},{"id":204864274,"identity":"f0461f8a-8b16-4132-b77d-8641cda32206","order_by":4,"name":"Arintaya Phrommintikul","email":"","orcid":"","institution":"Division of Cardiology, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Arintaya","middleName":"","lastName":"Phrommintikul","suffix":""},{"id":204864275,"identity":"7d9e6fd9-f60b-447c-91ea-f509f2782674","order_by":5,"name":"Phichayut Phinyo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYDACCcYGZgiLh+EAkJQDMQ88IEYLD1SLMVhLAl4tDAxwLSCQ2AAi8Wkxl25u/FxQczhxv3TvwcOFbTbp88MOPwTaYien24Bdi+Wcg83SM44dTuyROZdweGZbWu7G22kGQC3JxmYHsGsxuJHYxszDBtQikWNwmLftcO7G2QkgLQcSt+HV8g+hJd1wdvoHwlqAKuFaEuSlc/DbYjkjsVmaty/duOfOGYPDPOfSDDdI5xQcSDDA7RdzifSHn3m+Wcu2z+4x/sxTZiMvPzt984cPFXZyOL0PoZrBEcTAyAYUOYAkjkdLHUQLwx8GBvkG3KpHwSgYBaNgZAIALLZmwWs5nOkAAAAASUVORK5CYII=","orcid":"","institution":"Center for Clinical Epidemiology and Clinical Statistics, Faculty of Medicine, Chiang Mai University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Phichayut","middleName":"","lastName":"Phinyo","suffix":""}],"badges":[],"createdAt":"2023-05-29 07:59:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2994349/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2994349/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1016/j.heliyon.2023.e23901","type":"published","date":"2024-01-01T04:58:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":37798789,"identity":"48800309-2817-408c-87e7-fe4b677f0e08","added_by":"auto","created_at":"2023-05-31 22:09:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45004,"visible":true,"origin":"","legend":"\u003cp\u003ePatient flow diagram\u003c/p\u003e\n\u003cp\u003eCaption: Abbreviation: CAC; coronary artery calcium, HF; heart failure, ACS; acute coronary syndrome\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2994349/v1/8311e1092a5685c9edb95063.png"},{"id":37798788,"identity":"1f9b9716-61c1-4169-ad49-b1e7c751bc87","added_by":"auto","created_at":"2023-05-31 22:09:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":143185,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of CAC score stratified by sex and age groups\u003c/p\u003e\n\u003cp\u003eCaption: Patients distribution in each subgroup were presented in percentile using box plot. Red solid line is CAC score at 100.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbbreviation: CAC; coronary artery calcium\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2994349/v1/6b3208ddf71f8fc1a1f947c9.jpg"},{"id":37799399,"identity":"0e737076-8969-4cdf-a7a4-08b6d7a4582d","added_by":"auto","created_at":"2023-05-31 22:17:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":616365,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan Meier estimated for MACE probability for each CAC category\u003c/p\u003e\n\u003cp\u003eCaption: Figure 3A,\u003cstrong\u003e \u003c/strong\u003ethe estimated MACE probability in Absolute cut-point; Figure 3B, the estimated MACE probability in Age-sex specific percentile\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e: CAC; coronary artery calcium\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2994349/v1/925c4fe77b4c84fc0ee1e6de.jpg"},{"id":52829027,"identity":"a535d7a8-ebfc-4542-a318-56986c44151a","added_by":"auto","created_at":"2024-03-17 04:58:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":706668,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2994349/v1/59dc77b2-6ce0-4267-8577-7bff53c3e1bd.pdf"},{"id":37798790,"identity":"d5531748-c1c9-400b-abb6-41684db1cd04","added_by":"auto","created_at":"2023-05-31 22:09:28","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":158826,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable12MAY23.docx","url":"https://assets-eu.researchsquare.com/files/rs-2994349/v1/3e0aac98bf90ad018ebff49c.docx"},{"id":37798792,"identity":"3e34069d-d915-4405-9276-e0e4646d3d61","added_by":"auto","created_at":"2023-05-31 22:09:28","extension":"jpg","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":141716,"visible":true,"origin":"","legend":"","description":"","filename":"fig1sagesexdm.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2994349/v1/3e4b1f78503ae274ced3d53d.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Coronary Artery Calcium (CAC) Score for Cardiovascular Risk Stratification in a Thai Clinical Cohort: A Comparison of Absolute Scores and Age-Sex Specific Percentiles","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Coronary Artery Calcium (CAC) score was introduced in the late 1990s to assess coronary artery calcification with non-invasive imaging techniques [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The score provides a precise quantification of atherosclerotic plaque within the coronary arteries and has demonstrated a strong correlation with major adverse cardiovascular events (MACE) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The presence of a CAC score is indicative of a significant risk of developing atherosclerotic cardiovascular disease (ASCVD) and coronary heart disease (CHD) [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In contrast, the absence of a CAC score suggests a low risk of cardiovascular events [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. An ample body of research and guidelines supporting the potential of CAC score as a cardiovascular risk stratification tool have accumulated over the years [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Moreover, studies have affirmed that the CAC score not only surpasses conventional methods as a standalone predictor, but also enhances the discriminative ability of other traditional scores in predicting cardiovascular risk [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAlthough the CAC score has a high prognostic ability, its interpretation can be challenging due to different ways of expressing and stratifying patients' risk [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. CAC scores can be expressed as absolute scores, based on the exact amount of CAC score measured in Agatston units, or percentile-based rank scores, based on the percentile rank of CAC score stratified by the patient's sex, age, and/or race [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Owing to the distinctive rationale of score expression, disagreement between risk groups defined by different score types, even within the same population, is commonly reported [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, different score types may have varied predictive performance based on population mix and prediction time frame, and one may be more beneficial in different contexts [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Standard guidelines recommend using both absolute and percentile-based scores for risk stratification. For example, the AHA/ACC cholesterol guideline suggests initiating statins for intermediate risk adults with a CAC score greater than 100 or the 75th percentile [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e In Thailand, there is currently no official guidance on the use of CAC score for cardiovascular risk assessment and CAC score cut-point for statin initiation, and there is insufficient evidence to support the appropriateness of the CAC score cut-point suggested by standard western guidelines in the Thai population. Most CAC score studies have been conducted in western nations, with only a small number of Asian patients included. Additionally, there is evidence that CAC score distribution may vary across ethnic groups, and the results may not be applicable to other populations using the same cut-point system [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This study aims to examine the prevalence and CAC distribution, to determine the association of each CAC score classification with MACE, and to validate the discriminative performance of the CAC score for risk stratification of MACE for the first time in Thai clinical cohort. We also examined the agreement between the two CAC scoring classification and compare their performance for risk stratification.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective observational cohort study that included Thai patients who met the following inclusion criteria: (1) were aged above 35 years, and (2) underwent CAC testing at Maharaj Nakorn Chiang Mai hospital between 1 January 2012 and 31 March 2020. The exclusion criteria were patients with pre-existing cardiovascular diagnoses (e.g., myocardial infarction, heart failure hospitalization, and ischemic stroke or transient ischemic attack), coronary artery revascularization procedures, incomplete data for traditional cardiovascular risk calculation, or missing CAC score. This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Faculty of Medicine, Chiang Mai University (MED-2563-07435). Informed consent was waived owing to the retrospective data collection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe data regarding age, sex, smoking status, diabetes mellitus (DM), systolic blood pressure, total cholesterol, low-density cholesterol (LDL-C), high-density cholesterol (HDL-C), creatinine, and Estimated Glomerular Filtration Rate (eGFR) were collected by reviewing individual electronic medical records on the date of CAC score measurement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCalcium coronary calcium (CAC) score\u003c/h2\u003e \u003cp\u003eThe CAC score was measured using a non-contrasted prospective electrocardiogram (ECG) gating scan of the heart with a 192-slice Dual Source CT scanner (Somatom Force, Siemens). The scans were prospective ECG-gating at 75% of the R-R interval, with a 3 mm slice thickness and a tube voltage of 120 kV. Certified radiologists quantified the amount of CAC using the Agatston scoring method [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe absolute CAC score was categorized into five groups using cut-points from previous studies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]: 0 (absent), \u0026gt; 0 to 10 (minimal), 11 to 100 (moderate), 101 to 400 (high), and \u0026gt;\u0026thinsp;400 (extensive). As no studies have reported the distribution and percentiles of CAC across different age and sex groups in the Thai clinical cohort, we calculated the CAC percentile for each individual based on this cohort. We stratified the patient cohort into six groups (males aged\u0026thinsp;\u0026lt;\u0026thinsp;55, males aged 55\u0026ndash;65, males aged\u0026thinsp;\u0026gt;\u0026thinsp;64, females aged\u0026thinsp;\u0026lt;\u0026thinsp;55, females aged 55\u0026ndash;64, and females aged\u0026thinsp;\u0026gt;\u0026thinsp;64) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and assigned patients to one of four percentile ranges based on their strata-specific CAC percentile: CAC\u0026thinsp;=\u0026thinsp;0 (absent), \u0026lt; 75th (intermediate), 75th \u0026ndash; 90th (high), and \u0026gt;\u0026thinsp;90th (very high) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutcome and follow-up\u003c/h2\u003e \u003cp\u003eThe main outcome of the study was the occurrence of MACE, which was defined as a composite of cardiovascular death, myocardial infarction (MI), hospitalization due to heart failure, coronary artery revascularization procedures, and ischemic stroke or transient ischemic attack [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Cardiovascular deaths were defined as those with documented coronary artery disease and no other known causes of death. The term coronary artery revascularization refers to any procedures performed to enhance the heart's blood supply. In our institution, only two revascularization procedures were available: percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG).\u003c/p\u003e \u003cp\u003eWe followed all included patients from the time of their CAC measurements and continued until the event date, death, or loss to follow-up, whichever came first. Each patient received a phone call from a trained interviewer who inquired about their myocardial infarction event, hospitalizations for heart failure, revascularizations, and strokes. All patients' electronic medical records were reviewed and verified by investigators. Patients who did not have events and did not lose follow-up at the time the study was closed were marked as censored.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were performed using Stata 17.0 (StataCorp, College Station, Texas, USA). The results were considered statistically significant at a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Demographics and baseline characteristics were presented as a number (%), mean and standard deviation (SD), or median and interquartile range (IQR), as appropriate. The prevalence of each CAC score was estimated and reported with its corresponding 95% confidence interval (CI). To compare baseline characteristics across five absolute CAC categories, we used Fisher\u0026rsquo;s exact test for categorical variables and an extended Wilcoxon rank-sum test by Cuzick [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] for trending numerical variables.\u003c/p\u003e \u003cp\u003eTo identify the association with MACE, a time-to-event analysis was employed. MACE incident rates were annualized and reported per 1000 person-years. Kaplan\u0026ndash;Meier methods were used to estimate the probabilities of MACE in both CAC cut-point systems. Log-transformed continuous CAC score and categorized CAC score (both absolute and Age-sex specific percentile) were fitted using a multivariable Cox model adjusted by confounders [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] including age, sex, diabetes mellitus, total cholesterol, HDL, LDL, smoking status, and creatinine to estimate the adjusted hazard ratio (HR) of MACE. Patients with absence CAC score (CAC score 0) were the reference group. A subgroup analysis was performed for each sex.\u003c/p\u003e \u003cp\u003eThe predictive performance of the categorized CAC scores was validated in terms of discriminative performance using Harrell\u0026rsquo;s C-statistics [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Harrell\u0026rsquo;s C-statistics were estimated from a univariable Cox\u0026rsquo;s proportional hazard model that includes only the variables for the categorized CAC score as we want to assess the isolated performance of the CAC. A higher Harrell\u0026rsquo;s C-statistic indicated a higher overall discriminative ability of the cut-point.\u003c/p\u003e \u003cp\u003eTo examine the agreement between the absolute CAC and the age-sex-specific percentile score, we excluded patients with absence CAC score from this part of the analysis. We hypothesized that patients with age-sex-specific percentile-based CAC scores\u0026thinsp;\u0026lt;\u0026thinsp;75th, 75th \u0026ndash; 90th, and \u0026gt;\u0026thinsp;90th might have a similar prognosis to patients with absolute CAC scores of 1-100, 101\u0026ndash;400, and \u0026gt;\u0026thinsp;400, respectively [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. We calculated the overall agreement percentage and linear-weighted Cohen\u0026rsquo;s Kappa. Kappa values of \u0026lt;\u0026thinsp;0.4, 0.40\u0026ndash;0.59, 0.60\u0026ndash;0.74, and 0.75-1.00 would be clinically interpreted as poor, fair, good, and excellent, respectively [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn case the two classification types did not agree well, we performed an additional analysis to identify a more robust one for risk stratification by assessing the predictive performance and the association with MACE of absolute CAC classification in each level of age-sex specific CAC classification, and vice versa. We defined a more robust classification as the one that showed higher discriminative ability and preserved HR trend for MACE in every level of another classification.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePrevalence of CAC and characteristics across CAC categories\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the study period, a total of 722 patients received a CT coronary angiogram at our institution. Among them, 282 patients were excluded, and the final number of patients included in our analysis was 440 (Figure 1). The prevalence of each CAC category in this cohort was 31.1% (95%CI 26.8-35.7%) for CACs 0, 14.1% (95%CI 11-17.7%) for CACs \u0026gt;0-10, 17.5% (95%CI 14.1-21.4%) for CACs 11-100, 20.2% (95%CI 16.6-24.3%) for CACs 101-400, and 17.1% (95%CI 13.6-20.9%) for CACs \u0026gt;400). \u003c/p\u003e\n\u003cp\u003eTable 1 compares the baseline characteristics across the CAC categories. The mean age of all patients was 62.5\u0026plusmn;10.4 years. Nearly half of the patients were men (47.3%). Age, sex, total cholesterol, LDL-C, creatinine, and eGFR showed a statistically significant trend across CAC categories. We observed an increase in the proportion of DM across higher CAC categories. Conversely, the average cholesterol and LDL-C decreased with higher CAC categories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCAC distribution \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2 shows age-sex-specific percentiles of CAC scores, with significantly higher percentiles observed in males and older age groups compared to females and younger age groups. A similar trend was observed in the subgroup analysis for patients with diabetes (Figure S1). Among patients without diabetes, females under 64 years old had CAC scores below 100 at the 75\u003csup\u003eth\u003c/sup\u003e percentile, while males in all age groups had scores above 100. A similar trend was observed in patients with diabetes. Females under 64 consistently had CAC scores below the 75\u003csup\u003eth\u003c/sup\u003e percentile, while males in all age groups had significantly higher scores. The exact CAC scores are presented in Supplementary Table S1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe association between CAC score classifications and MACE \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 82 major adverse cardiovascular events (18.6%) occurred during follow-up (Table S2). The median duration of follow-up was 41 months (range 18-109). The probability of MACE progressively increased with higher CAC categories (Figure 3). The probability remained above 25% in those with a CAC score \u0026gt;100 during follow-up. Compared to CAC score 0, patients with CAC scores greater than 10 showed a significantly higher HR for MACE: CAC 11-100 (HR 5.93, 95%CI 1.63-21.55, p=0.007), CAC 101-400 (HR 10.18, 95% CI 2.91-35.49, p\u0026le;0.001), and CAC \u0026gt;400 (HR 18.40, 95%CI 5.38-62.87, p\u0026le;0.001) (Table 2). Patients with CAC scores \u0026lt;75th also had a significantly increased HR for MACE (HR 4.5, 95%CI 1.34-15.09, p=0.015) compared to patients without CAC. The results of subgroup analysis regarding the association with MACE are presented in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive performance of CAC score classifications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth the absolute and age-sex specific percentile CAC score classification demonstrated similar acceptable discriminative performance based on Harrell\u0026rsquo;s C statistics, with values of 0.76 (95%CI 0.72, 0.81) and 0.75 (95%CI 0.70, 0.79), respectively. The absolute CAC classification exhibited consistent Harrell\u0026rsquo;s C statistics values for both sexes at 0.76 (95%CI 0.72, 0.81), while the age-sex specific percentile CAC classification showed a minor drop to 0.73 (95%CI 0.68, 0.77) (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAgreement between the absolute and age-sex specific percentile CAC score\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn patients with presence of CAC, approximately 90.7% of patients with CAC scores between 1 and 100 had scores below the 75th age-sex specific percentile stratum, while 65% of patients with scores below the 75\u003csup\u003eth\u003c/sup\u003e percentile had CAC scores less than 100. Among patients with CAC scores between 101-400, 71.9% were categorized below the 75\u003csup\u003eth\u003c/sup\u003e percentile, while only 23.6% of these patients had CAC scores between the 75\u003csup\u003eth\u003c/sup\u003e and 90\u003csup\u003eth\u003c/sup\u003e percentiles. Additionally, only 6.7% of patients with CAC scores greater than 400 were categorized below the 75\u003csup\u003eth\u003c/sup\u003e percentile (Table 3). The linear-weighted Cohen\u0026apos;s Kappa indicated fair agreement between the two classifications, at 0.51 (95% CI 0.42-0.59).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison between absolute and age-sex specific percentile CAC score\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClassification based on the absolute CAC score exhibited acceptable discrimination, with Harrell\u0026apos;s C statistics surpassing 0.70 for every age-sex specific percentile category, comparable to the C-statistics value for the entire cohort. Conversely, the C-statistics for the age-sex specific percentile classification showed inadequate discriminative performance in each subgroup of the absolute CAC category (Table 4). \u003c/p\u003e\n\u003cp\u003eConsistency in the association between MACE and the absolute CAC classification was observed in all levels of age-sex specific percentile classification, with higher HR observed in higher absolute CAC score categories (Table 4). However, the association between MACE and age-sex specific percentile classification was not consistent in all levels of absolute CAC classification (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e \u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we described the prevalence of Thai clinical cohort with the presence of CAC score and the distribution of CAC score based on both absolute CAC score and age-sex specific percentile score. Our findings suggest that both CAC classifications demonstrate significant associations with MACE occurrence, with higher magnitude at higher levels of classification. However, the agreement between the two classifications was only fair, rendering it inappropriate to use them interchangeably. Our analysis indicates that the absolute CAC classification is superior to the age-sex specific percentile classification in providing a more robust predictive performance for risk stratification.\u003c/p\u003e \u003cp\u003eAbout 70% of patients in our cohort had CAC, and their average CAC score was higher than in previous studies conducted in Japan, Korea, and Saudi Arabia [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These studies reported a higher percentage of people with no CAC, ranging from 57\u0026ndash;73% [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The higher baseline cardiovascular risk in our cohort could be attributed to the different ways of recruiting source populations. In Korean and Japanese studies, the study population was recruited from a health check-up program, which included people in good health and low cardiovascular risk [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In contrast, our study included patients aged over 35 who were sent for CAC testing due to the decision from their attending physicians. Compared to previous studies, the prevalence of CAC and the average CAC score in our cohort were similar to that of the white population in the Framingham Heart study [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The distribution of CAC score was higher in males, old age, and patients with diabetes, which is consistent with previous studies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur results indicate that both the absolute and age-sex specific percentile classifications of coronary artery calcium (CAC) scores demonstrated acceptable discriminative performance in stratifying the risk for MACE. The overall performance based on Harrell\u0026rsquo;s C-statistics was comparable to previous studies [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, the disagreement between the two classifications of coronary artery calcium (CAC) scores becomes more apparent when examining the predictive performance of one classification across different levels of another. Our study results showed that within the absolute classification, higher hazard ratios (HRs) were consistently observed in higher absolute CAC score categories in contrast to the age-sex specific percentile classification. This result was similar to the findings from the MESA cohort [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It should be noted that neither classification should be preferred over the other. According to recent reviews [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], both the absolute and percentile classifications of coronary artery calcium (CAC) scores provide important information regarding MACE events. While the absolute score classification is considered the best predictor for an individual's risk in the next 5\u0026ndash;10 years, providing short-term predictive value, the percentile score classification offers insights into long-term prognosis and is a better predictor of lifetime risk.\u003c/p\u003e \u003cp\u003eThis disagreement in our findings highlights the importance of considering different classification methods and their implications when assessing CAC scores in clinical practice. Based on our results, it appears that the absolute CAC score classification may be appropriate for risk stratification in this Thai clinical cohort. According to standard guidelines [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], statin initiation may be implied for Thai patients with an absolute CAC score\u0026thinsp;\u0026ge;\u0026thinsp;100 or \u0026ge;\u0026thinsp;75th percentile due to the strong association with MACE. However, our findings suggest that the high MACE incidence rates in patients with an absolute CAC score between 11\u0026ndash;100 or those with an age-sex specific percentile score below 75th may not be adequately captured by current guideline recommendations for statin initiation. Considering the retrospective nature of our data which encompassed patients who had used statins, we cannot conclude whether the current suggestion would lead to undertreatment. Therefore, large prospective studies with longer and standardized follow-up in statin-na\u0026iuml;ve patients are needed to confirm whether the current CAC score cut points are appropriate for the Thai population or whether new cut points should be identified.\u003c/p\u003e \u003cp\u003eThis study is the first to report on the prevalence and distribution of CAC scores in a clinical population in Thailand that underwent CAC testing. Our study provides a comprehensive evaluation of the predictive performance of both absolute and age-sex-specific CAC score classifications for MACE outcomes, investigates the agreement between the two classifications, and proposes a superior classification for cardiovascular risk stratification. The findings of this study have significant implications for the development of guidelines for CAC testing in the Thai patient population. However, there were some limitations to our study. First, it was a retrospective observational study, which may have been affected by biases during data collection and follow-up. However, the data was obtained from routine standardized forms with only a small proportion of missing values, and almost all variables were objective. Second, our institution had a higher rate of revascularization, accounting for 73% of all MACE, compared to other centers where it ranged from 6\u0026ndash;42% [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This indicates that a high CAC score may have served as a guide for a significant number of revascularizations in our study, potentially inflating the incidence of MACE. Thus, the impact of revascularization on MACE should be carefully considered. Third, the indication for CAC testing in our cohort was not clearly defined and documented, and the sampling scheme was consecutive and non-probability. This means that not all patients visiting our institution had an equal chance of being offered CAC testing, potentially affecting the generalizability of our results. Finally, our study was conducted at a single tertiary care center in Northern Thailand, so the results may only be applicable to similar clinical contexts. Further research is recommended to investigate CAC score prevalence and distribution in a health check-up or screening population in Thailand.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAbout 70% of Thai patients who underwent CAC testing had a CAC score. The CAC score was increasingly distributed among male, elderly, and diabetic patients. A higher CAC score was found to be significantly associated with a higher incidence of MACE. Both absolute and age-sex-specific percentile CAC scores showed acceptable performance in predicting MACE. However, it is likely that the classification of absolute CAC scores may be more robust and appropriate for risk stratification.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by faculty of medicine Chiangmai university, Thailand\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthichs approval: \u0026nbsp;\u003c/strong\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Faculty of Medicine, Chiang Mai University (MED-2563-07435).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e Informed consent was waived owing to the retrospective data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u003c/strong\u003e This study does not contain any individual person\u0026rsquo;s data in any form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This research received no grant from any funding agency in the public, commercial or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures:\u0026nbsp;\u003c/strong\u003eAll Authors declare that there is no conflict of interest\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n\u003cli\u003eA.S. Agatston, W.R. Janowitz, F.J. Hildner, N.R. Zusmer, M. Viamonte Jr.,R. Detrano, Quantification of coronary artery calcium using ultrafast computedtomography, J. Am. Coll. Cardiol. 15 (1990) 827e832.\u003c/li\u003e\n\u003cli\u003eAbuzaid A, Saad M, Addoumieh A, Ha LD, Elbadawi A, Mahmoud AN, Elgendy A, Abdelaziz HK, Barakat AF, Mentias A, Adeola O, Elgendy IY, Qasim A, Budoff M. Coronary artery calcium score and risk of cardiovascular events without established coronary artery disease: a systemic review and meta-analysis. Coron Artery Dis. 2021 Jun 1;32(4):317-328. doi: 10.1097/MCA.0000000000000974. PMID: 33417339.\u003c/li\u003e\n\u003cli\u003eKavousi M, Elias-Smale S, Rutten JH, Leening MJ, Vliegenthart R, Verwoert GC, Krestin GP, Oudkerk M, de Maat MP, Leebeek FW, Mattace-Raso FU, Lindemans J, Hofman A, Steyerberg EW, van der Lugt A, van den Meiracker AH, Witteman JC. Evaluation of newer risk markers for coronary heart disease risk classification: a cohort study. Ann Intern Med. 2012 Mar 20;156(6):438-44. doi: 10.7326/0003-4819-156-6-201203200-00006. PMID: 22431676.\u003c/li\u003e\n\u003cli\u003eTota-Maharaj R, Blaha MJ, Blankstein R, Silverman MG, Eng J, Shaw LJ, Blumenthal RS, Budoff MJ, Nasir K. Association of coronary artery calcium and coronary heart disease events in young and elderly participants in the multi-ethnic study of atherosclerosis: a secondary analysis of a prospective, population-based cohort. Mayo Clin Proc. 2014 Oct;89(10):1350-9. doi: 10.1016/j.mayocp.2014.05.017. Epub 2014 Sep 15. PMID: 25236430; PMCID: PMC4424047.\u003c/li\u003e\n\u003cli\u003eBlaha MJ, Budoff MJ, DeFilippis AP, Blankstein R, Rivera JJ, Agatston A, O\u0026apos;Leary DH, Lima J, Blumenthal RS, Nasir K. Associations between C-reactive protein, coronary artery calcium, and cardiovascular events: implications for the JUPITER population from MESA, a population-based cohort study. Lancet. 2011 Aug 20;378(9792):684-92. doi: 10.1016/S0140-6736(11)60784-8. PMID: 21856482; PMCID: PMC3173039.\u003c/li\u003e\n\u003cli\u003eMichael G. Silverman, Michael J. Blaha, Harlan M. Krumholz, Matthew J. Budoff, Ron Blankstein, Christopher T. Sibley, Arthur Agatston, Roger S. Blumenthal, Khurram Nasir, Impact of coronary artery calcium on coronary heart disease events in individuals at the extremes of traditional risk factor burden: the Multi-Ethnic Study of Atherosclerosis, European Heart Journal, Volume 35, Issue 33, 1 September 2014, Pages 2232\u0026ndash;2241\u003c/li\u003e\n\u003cli\u003eBlaha M, Budoff MJ, Shaw LJ, Khosa F, Rumberger JA, Berman D, Callister T, Raggi P, Blumenthal RS, Nasir K. Absence of coronary artery calcification and all-cause mortality. JACC Cardiovasc Imaging. 2009 Jun;2(6):692-700. doi: 10.1016/j.jcmg.2009.03.009. PMID: 19520338.\u003c/li\u003e\n\u003cli\u003eJoseph Yeboah, et al., Comparison of Novel Risk Markers for Improvement in Cardiovascular Risk Assessment in Intermediate Risk Individuals, The Multi Ethnic Study of Atherosclerosis, JAMA (2012), https://doi.org/10.1001/ jama.2012.9624.\u003c/li\u003e\n\u003cli\u003eBell KJL, White S, Hassan O, et al. Evaluation of the Incremental Value of a Coronary Artery Calcium Score Beyond Traditional Cardiovascular Risk Assessment: A Systematic Review and Meta-analysis. JAMA Intern Med. 2022;182(6):634\u0026ndash;642. doi:10.1001/jamainternmed.2022.1262\u003c/li\u003e\n\u003cli\u003eWong ND, Budoff MJ, Pio J, Detrano RC. Coronary calcium and cardiovascular event risk: evaluation by age- and sex-specific quartiles. Am Heart J. 2002 Mar;143(3):456-9. doi: 10.1067/mhj.2002.120409. PMID: 11868051.\u003c/li\u003e\n\u003cli\u003eSaydam CD. Subclinical cardiovascular disease and utility of coronary artery calcium score. Int J Cardiol Heart Vasc. 2021 Nov 17;37:100909. doi: 10.1016/j.ijcha.2021.100909. PMID: 34825047; PMCID: PMC8604741.\u003c/li\u003e\n\u003cli\u003eRumberger JA, Brundage BH, Rader DJ et al (1999) Electron beam computed tomographic coronary calcium scanning: a review and guidelines for use in asymptomatic persons. Mayo Clin Proc 74(3):243\u0026ndash;252\u003c/li\u003e\n\u003cli\u003eNeves PO, Andrade J, Mon\u0026ccedil;\u0026atilde;o H. Coronary artery calcium score: current status. Radiol Bras [Internet]. 2017May;50(Radiol Bras, 2017 50(3)). Available from: https://doi.org/10.1590/0100-3984.2015.0235\u003c/li\u003e\n\u003cli\u003eHoffmann, et al., Defining Normal Distribution of Coronary Artery Calcium in Women and Men from the Framingham Heart Study, Am J Cardiol. (2008), https://doi.org/10.1016/j.amjcard.2008.06.038.\u003c/li\u003e\n\u003cli\u003eBudoff MJ, Nasir K, McClelland RL, Detrano R, Wong N, Blumenthal RS, Kondos G, Kronmal RA. Coronary calcium predicts events better with absolute calcium scores than age-sex-race/ethnicity percentiles: MESA (Multi-Ethnic Study of Atherosclerosis). J Am Coll Cardiol. 2009 Jan 27;53(4):345-52. doi: 10.1016/j.jacc.2008.07.072. Erratum in: J Am Coll Cardiol. 2009 Apr 21;53(16):1474. PMID: 19161884; PMCID: PMC2652569.\u003c/li\u003e\n\u003cli\u003ePiepoli MF, Abreu A, Albus C, et al. Update on cardiovascular prevention in clinical practice: A position paper of the European Association of Preventive Cardiology of the European Society of Cardiology. Eur J Prev Cardiol 2020;27:181-205. doi:10.1177/2047487319893035\u003c/li\u003e\n\u003cli\u003eGrundy SM, Stone NJ, Bailey AL, et al. 2018 AHA/ACC/AACVPR/ AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation 2019;139:e1082-143\u003c/li\u003e\n\u003cli\u003eArnett DK, Blumenthal RS, Albert MA, et al. 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation 2019;140:e596-646. doi:10.1161/CIR.0000000000000678\u003c/li\u003e\n\u003cli\u003ePletcher MJ, Sibley CT, Pignone M, Vittinghoff E, Greenland P. Interpretation of the coronary artery calcium score in combination with conventional cardiovascular risk factors: the Multi-Ethnic Study of Atherosclerosis (MESA). Circulation. 2013 Sep 3;128(10):1076-84. doi: 10.1161/CIRCULATIONAHA.113.002598. Epub 2013 Jul 24. PMID: 23884352; PMCID: PMC3840900.\u003c/li\u003e\n\u003cli\u003eAkira Sekikawa, Hirotsugu Ueshima, Takashi Kadowaki, Aiman El-Saed, Tomonori Okamura, Tomoko Takamiya, Atsunori Kashiwagi, Daniel Edmundowicz, Kiyoshi Murata, Kim Sutton-Tyrrell, Hiroshi Maegawa, Rhobert W. Evans, Yoshikuni Kita, Lewis H. Kuller, for the ERA JUMP Study Group, Less Subclinical Atherosclerosis in Japanese Men in Japan than in White Men in the United States in the Post\u0026ndash;World War II Birth Cohort, American Journal of Epidemiology, Volume 165, Issue 6, 15 March 2007, Pages 617\u0026ndash;624\u003c/li\u003e\n\u003cli\u003eMcCollough CH, Ulzheimer S, Halliburton SS, Shanneik K, White RD, Kalender WA: Coronary artery calcium: a multi-institutional, multimanufacturer international standard for quantification at cardiac CT. Radiology. 2007, 243:527-38. \u003c/li\u003e\n\u003cli\u003eJang, S.Y., Kim, S.M., Sung, J. et al. Coronary artery calcium scores and cardiovascular risk factors in 31,545 asymptomatic Korean adults. Int J Cardiovasc Imaging 32 (Suppl 1), 139\u0026ndash;145 (2016). https://doi.org/10.1007/s10554-016-0892-2\u003c/li\u003e\n\u003cli\u003eRobyn L. McClelland, et al., Distribution of Coronary Artery Calcium by Race, Gender, and Age: Results from the Multi-Ethnic Study of Atherosclerosis (MESA), Circulation (2006), https://doi.org/10.1161/CIRCULATIONAHA.105.580696\u003c/li\u003e\n\u003cli\u003eBosco, E., Hsueh, L., McConeghy, K.W. et al. Major adverse cardiovascular event definitions used in observational analysis of administrative databases: a systematic review. BMC Med Res Methodol 21, 241 (2021). https://doi.org/10.1186/s12874-021-01440-5\u003c/li\u003e\n\u003cli\u003eCuzick, J. 1985. A Wilcoxon-type test for trend. Statistics in Medicine 4: 87\u0026ndash;90. https://doi.org/10.1002/sim.4780040112\u003c/li\u003e\n\u003cli\u003eD\u0026apos;Agostino RB Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008 Feb 12;117(6):743-53. doi: 10.1161/CIRCULATIONAHA.107.699579. Epub 2008 Jan 22. PMID: 18212285.\u003c/li\u003e\n\u003cli\u003eKronmal RA, McClelland RL, Detrano R, Shea S, Lima JA, Cushman M, Bild DE, Burke GL. Risk factors for the progression of coronary artery calcification in asymptomatic subjects: results from the Multi-Ethnic Study of Atherosclerosis (MESA). Circulation. 2007 May 29;115(21):2722-30. doi: 10.1161/CIRCULATIONAHA.106.674143. Epub 2007 May 14. PMID: 17502571.\u003c/li\u003e\n\u003cli\u003eHarrell FE Jr, Califf RM, Pryor DB, Lee KL, Rosati RA. Evaluating the yield of medical tests. JAMA. 1982 May 14;247(18):2543-6. PMID: 7069920.\u003c/li\u003e\n\u003cli\u003evan der Wulp I, van Stel HF. Adjusting weighted kappa for severity of mistriage decreases reported reliability of emergency department triage systems: a comparative study. J Clin Epidemiol. 2009 Nov;62(11):1196-201. doi: 10.1016/j.jclinepi.2009.01.007. Epub 2009 Apr 23. PMID: 19398298.\u003c/li\u003e\n\u003cli\u003eOhmoto-Sekine Y, Yanagibori R, Amakawa K, Ishihara M, Tsuji H, Ogawa K, Ishimura R, Ishiwata S, Ohno M, Yamaguchi T, Arase Y. Prevalence and distribution of coronary calcium in asymptomatic Japanese subjects in lung cancer screening computed tomography. J Cardiol. 2016 May;67(5):449-54. doi: 10.1016/j.jjcc.2015.06.010. Epub 2015 Jul 23. PMID: 26213250.\u003c/li\u003e\n\u003cli\u003eAl Helali S, Abid Hanif M, Alshugair N, Al Majed A, Belfageih A, Al Qahtani H, Al Dulikan S, Hamed H, Al Mousa A. Distributions and burden of coronary calcium in asymptomatic Saudi patients referred to computed tomography. Int J Cardiol Heart Vasc. 2021 Oct 27;37:100902. doi: 10.1016/j.ijcha.2021.100902. PMID: 34761100; PMCID: PMC8566998\u003c/li\u003e\n\u003cli\u003eArad Y, Goodman KJ, Roth M, et al. Coronary calcification, coronary disease risk factors, C-reactive protein, and atherosclerotic cardiovascular disease events: the St. Francis Heart Study. J Am Coll Cardiol. 2005;46:158\u0026ndash;65\u003c/li\u003e\n\u003cli\u003eBecker A, Leber A, Becker C, et al. Predictive value of coronary calcifications for future cardiac events in asymptomatic individuals. Am Heart J. 2008;155:154\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eObisesan OH, Osei AD, Uddin SMI, Dzaye O, Blaha MJ. An Update on Coronary Artery Calcium Interpretation at Chest and Cardiac CT. Radiol Cardiothorac Imaging. 2021 Feb 25;3(1):e200484. doi: 10.1148/ryct.2021200484. PMID: 33778659; PMCID: PMC7977732.\u003c/li\u003e\n\u003cli\u003eRobert J H Miller, Donghee Han, Ananya Singh, Konrad Pieszko, Piotr J Slomka, Heidi Gransar, Rebekah Park, Yuka Otaki, John D Friedman, Sean Hayes, Louise Thomson, Alan Rozanski, Daniel S Berman, Relationship between ischaemia, coronary artery calcium scores, and major adverse cardiovascular events, European Heart Journal - Cardiovascular Imaging, Volume 23, Issue 11, November 2022, Pages 1423\u0026ndash;1433,\u003c/li\u003e\n\u003cli\u003eYang, L., Xu, P.P., Schoepf, U.J. et al. Serial coronary CT angiography\u0026ndash;derived fractional flow reserve and plaque progression can predict long-term outcomes of coronary artery disease. Eur Radiol 31, 7110\u0026ndash;7120 (2021). \u003c/li\u003e\n\u003cli\u003eYamamoto 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(10):2424-31. doi: 10.1253/circj.cj-11-0087. Epub 2011 Jul 21. PMID: 21778594.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u0026nbsp;Table 1. Baseline characteristic in each Coronary artery calcium score category\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"678\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003cp\u003e(total=440)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003cp\u003e(total=440)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.323529411764707%\"\u003e\n \u003cp\u003eCAC score\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(total=137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003eCAC score\u003c/p\u003e\n \u003cp\u003e1-10\u003c/p\u003e\n \u003cp\u003e(total=62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003eCAC score\u003c/p\u003e\n \u003cp\u003e11-100\u003c/p\u003e\n \u003cp\u003e(total=77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003eCAC score\u003c/p\u003e\n \u003cp\u003e101-40\u003c/p\u003e\n \u003cp\u003e(total=89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.205882352941176%\"\u003e\n \u003cp\u003eCAC score\u003c/p\u003e\n \u003cp\u003e\u0026gt;400\u003c/p\u003e\n \u003cp\u003e(total=75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.117647058823529%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e62.5\u0026plusmn;10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.323529411764707%\"\u003e\n \u003cp\u003e57.5\u0026plusmn;9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e60.5\u0026plusmn;9.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003e63.1\u0026plusmn;9.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e67.1\u0026plusmn;9.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.205882352941176%\"\u003e\n \u003cp\u003e67.5\u0026plusmn;9.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.117647058823529%\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e209 (47.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.323529411764707%\"\u003e\n \u003cp\u003e41(29.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e30(48.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003e40(52.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e48(53.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.205882352941176%\"\u003e\n \u003cp\u003e49(65.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.117647058823529%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003eCurrent smoking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e11 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e6 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.323529411764707%\"\u003e\n \u003cp\u003e1(0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e2(3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003e1(1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e1(1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.205882352941176%\"\u003e\n \u003cp\u003e1(1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.117647058823529%\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003eDM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e12 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e96 (22.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.323529411764707%\"\u003e\n \u003cp\u003e24(17.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e10(16.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003e15(20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e21(24.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.205882352941176%\"\u003e\n \u003cp\u003e26(34.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.117647058823529%\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e19 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e130.8\u0026plusmn;14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.323529411764707%\"\u003e\n \u003cp\u003e129.7\u0026plusmn;13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e131.3\u0026plusmn;15.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003e129.5\u0026plusmn;15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e132\u0026plusmn;14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.205882352941176%\"\u003e\n \u003cp\u003e131.9\u0026plusmn;14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.117647058823529%\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003eTotal Cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e178.4\u0026plusmn;46.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.323529411764707%\"\u003e\n \u003cp\u003e185.2\u0026plusmn;42.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e178.2\u0026plusmn;41.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003e180.6\u0026plusmn;45.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e175.8\u0026plusmn;53.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.205882352941176%\"\u003e\n \u003cp\u003e165.6\u0026plusmn;49.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.117647058823529%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e56 (12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e114.7\u0026plusmn;41.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.323529411764707%\"\u003e\n \u003cp\u003e119.7\u0026plusmn;38.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e115.4\u0026plusmn;36.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003e117.2\u0026plusmn;43.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e114.2\u0026plusmn;46.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.205882352941176%\"\u003e\n \u003cp\u003e103.5\u0026plusmn;42.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.117647058823529%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e61 (13.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e54.1\u0026plusmn;14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.323529411764707%\"\u003e\n \u003cp\u003e56.2\u0026plusmn;16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e51.3\u0026plusmn;10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003e53.7\u0026plusmn;11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e54.6\u0026plusmn;14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.205882352941176%\"\u003e\n \u003cp\u003e52.9\u0026plusmn;14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.117647058823529%\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e32 (7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e0.98\u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.323529411764707%\"\u003e\n \u003cp\u003e0.83\u0026plusmn;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e0.92\u0026plusmn;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003e1.01\u0026plusmn;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e0.95\u0026plusmn;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.205882352941176%\"\u003e\n \u003cp\u003e1.30\u0026plusmn;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.117647058823529%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e32 (7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e80.2\u0026plusmn;19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.323529411764707%\"\u003e\n \u003cp\u003e87.2\u0026plusmn;18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.470588235294118%\"\u003e\n \u003cp\u003e80.2\u0026plusmn;16.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.176470588235293%\"\u003e\n \u003cp\u003e77.7\u0026plusmn;18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\"\u003e\n \u003cp\u003e77.3\u0026plusmn;15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.205882352941176%\"\u003e\n \u003cp\u003e73.8\u0026plusmn;24.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.117647058823529%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eDM, diabetes mellitus; SBP, systolic blood pressure; eGFR, estimated glomerular filtration rate; LDL-C, low density lipoprotein cholesterol; HDL-C, High density lipoprotein cholesterol \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Incident rate and Hazard ratio for absolute and age-sex specific cut-points with subgroup analysis for sex.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\"\u003e\n \u003cp\u003eMACE event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003eIncidence rate\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1000 person-years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\"\u003e\n \u003cp\u003eaHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003eP\u003cem\u003e-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003eHarrell\u0026rsquo;s\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eC-statistic*\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAC score (Agatston)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall event (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\" valign=\"top\"\u003e\n \u003cp\u003e82 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(log transform)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e1.57 (1.37-1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbsolute scale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e0.76 (0.72-0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\" valign=\"top\"\u003e\n \u003cp\u003e9.9\u0026nbsp;(4.1-23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\" valign=\"top\"\u003e\n \u003cp\u003e20.3 (5.9-70.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e1.61 (0.32-8.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\" valign=\"top\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e11-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\" valign=\"top\"\u003e\n \u003cp\u003e40.6 (14.1-116.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e5.93 (1.63-21.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e101-400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\" valign=\"top\"\u003e\n \u003cp\u003e87.9 (33.6-230.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e10.18 (2.91-35.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\" valign=\"top\"\u003e\n \u003cp\u003e258.2 (101.5-656.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e18.40 (5.38-62.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.083333333333332%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eage sex specific percentile \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.452380952380953%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e0.75\u0026nbsp;(0.70-0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e9.9 (4.1-23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt; P75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e49.1 (19.2-125.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e4.5 (1.34-15.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\"\u003e\n \u003cp\u003eP75-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e115.7 (43.4-308.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e10.30 (2.97-35.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\"\u003e\n \u003cp\u003eP\u0026gt; 90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e339.0 (128.7-890.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e18.75 (5.43-64.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.083333333333332%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroup analysis in men\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.452380952380953%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.648286140089418%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbsolute\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.326378539493293%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e0.76 (0.72-0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.648286140089418%\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.326378539493293%\" colspan=\"2\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e20.8 (6.7-64.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.648286140089418%\"\u003e\n \u003cp\u003e1-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.326378539493293%\" colspan=\"2\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e26.0 (5.2-128.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e2.05 (0.18-23.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.648286140089418%\"\u003e\n \u003cp\u003e11-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.326378539493293%\" colspan=\"2\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e52. (13.9-198.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e8.46 (1.03-69.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.648286140089418%\"\u003e\n \u003cp\u003e101-400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.326378539493293%\" colspan=\"2\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e80.1 (22.7-283.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e10.34 (1.26-84.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.648286140089418%\"\u003e\n \u003cp\u003e\u0026gt;400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.326378539493293%\" colspan=\"2\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e278.3 (84.2-919.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e18.40 (2.37-143.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.083333333333332%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eage sex specific percentile\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.452380952380953%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e0.73 (0.68-0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e20.8 (6.7-64.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;P75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e59.3 (17.7-197.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e6.97 (0.91-53.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\"\u003e\n \u003cp\u003eP75-P90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e178.9 (51.0-627.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e14.25 (1.76-115.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\"\u003e\n \u003cp\u003eP\u0026gt;90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e360.7 (101.7-1278.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e19.20 (2.28-161.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.083333333333332%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroup analysis in women\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.452380952380953%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbsolute\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e0.76 (0.72-0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e5.6 (1.3-22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\"\u003e\n \u003cp\u003e1-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e15.4(2.2-109.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e1.18 (0.10-13.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\"\u003e\n \u003cp\u003e11-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e25.2(4.2-151.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e3.39 (0.56-2-.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\"\u003e\n \u003cp\u003e101-400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e97.7 (21.9-436.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e11.65 (2.41-56.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026gt;400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e220.6 (48.9-995.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e21.80 (4.59-103.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.083333333333332%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eage sex specific percentile in women\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.452380952380953%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e0.73 (0.68-0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e5.6 (1.3-22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;P75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e34.8 (7.6-158.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e3.52 (0.71-17.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\"\u003e\n \u003cp\u003eP75-P90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e70.2 (14.6-337.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e7.63 (1.56-37.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.946348733233979%\" colspan=\"2\"\u003e\n \u003cp\u003eP\u0026gt;90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.028315946348734%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.922503725782414%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.47988077496274%\"\u003e\n \u003cp\u003e318.4 (70.5-1436.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.98956780923994%\" valign=\"top\"\u003e\n \u003cp\u003e23.91 (5.09-112.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.538002980625931%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.09538002980626%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eP values were estimated from Cox regression. *, Harrell\u0026rsquo;s C-statistic was estimated from from a univariable Cox\u0026rsquo;s proportional hazard model \u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eCI, confidential interval; aHR, adjusted hazard ratio; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. Degree of disagreement between absolute and age-sex specific cut-point represented by the inconsistency of CAC proportion and weighted Cohens\u0026rsquo; Kappa \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eP \u0026lt;75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eP75-90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eP \u0026gt;90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAC 1-100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e126 (64.6%)\u003c/p\u003e\n \u003cp\u003e(90.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e13 (19.4%)\u003c/p\u003e\n \u003cp\u003e(9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e139\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAC 101-400\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e64 (32.8%)\u003c/p\u003e\n \u003cp\u003e(71.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e21 (31.3%)\u003c/p\u003e\n \u003cp\u003e(23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e4 (9.8%)\u003c/p\u003e\n \u003cp\u003e(4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003cp\u003e(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAC \u0026gt; 400\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e5 (0.03%)\u003c/p\u003e\n \u003cp\u003e(6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e33 (49.3%)\u003c/p\u003e\n \u003cp\u003e(44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e37 (90.2%)\u003c/p\u003e\n \u003cp\u003e(49.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003cp\u003e(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e195 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e67 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e41 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight of disagreement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003eAgreement\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003eCohens\u0026rsquo;Kappa\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLinear weighted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e79.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0.51 (0.42-0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eCI, confidential interval\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e4\u003c/strong\u003e. Trending of\u0026nbsp;adjusted Hazard ratio by each absolute scale in subgroup analysis across age-sex specific and vice versa\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"594\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\"\u003e\n \u003cp\u003en (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\"\u003e\n \u003cp\u003eP\u003cem\u003e-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003eHarrell\u0026rsquo;s\u003c/p\u003e\n \u003cp\u003eC-statistic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.38383838383838%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcross age-sex specific percentile\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAC \u0026lt; 75\u003csup\u003eth\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e0.76 (0.72-0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e1-100\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e126 (64.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e101-400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e64 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e2.29 (0.92-5.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e\u0026gt;400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\"\u003e\n \u003cp\u003e5 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\"\u003e\n \u003cp\u003e6.19 (1.07-35.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAC 75\u003csup\u003eth\u003c/sup\u003e \u0026ndash; 90\u003csup\u003eth\u003c/sup\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e0.74 (0.69-0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e1-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e13 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e101-400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e21 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e5.12 (0.50-52.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e\u0026gt;400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e33 (49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e5.63 (0.18-175.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAC \u0026gt;90\u003csup\u003eth\u003c/sup\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e1-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e101-400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e4 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e\u0026gt;400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e37 (90.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcross Absolute scale\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAC 1-100\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e0.44 (0.39-0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003eAge -sex specific \u0026lt; 75\u003csup\u003eth\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e126 (90.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003eAge -sex specific 75\u003csup\u003eth\u003c/sup\u003e -90\u003csup\u003eth\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e13 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e1.82 (0.16-20.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003eAge -sex specific \u0026gt; 90\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAC 101-400\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e0.47 (0.40-0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003eAge -sex specific \u0026lt; 75\u003csup\u003eth\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e64 (71.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003eAge -sex specific 75\u003csup\u003eth\u003c/sup\u003e -90\u003csup\u003eth\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e21 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e0.39 (0.64-2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003eAge -sex specific \u0026gt; 90\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e4 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e0.35 (0.02-6.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAC \u0026gt; 400\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e0.53 (0.46-0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003eAge -sex specific \u0026lt; 75\u003csup\u003eth\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e5 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003eAge -sex specific 75\u003csup\u003eth\u003c/sup\u003e -90\u003csup\u003eth\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e33 (44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e0.50 (0.12-2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003eAge -sex specific \u0026gt; 90\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e37 (49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"top\"\u003e\n \u003cp\u003e1.40 (0.31-6.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\" valign=\"top\"\u003e\n \u003cp\u003e0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eCI, confidential interval; HR, Hazard ratio; NA, not applicable\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2994349/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2994349/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study aims to examine the prevalence and CAC distribution and to evaluate the association of each CAC classifications with major adverse cardiovascular event (MACE).\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThis study was a retrospective observational cohort. We included patient aged above 35 years who underwent CAC testing. The absolute and age-sex specific percentile classification were categorized as 0, 1 to 10, 11 to 100, 101 to 400, and \u0026gt;\u0026thinsp;400 and 0, \u0026lt; 75th, 75th \u0026ndash; 90th, and \u0026gt;\u0026thinsp;90th, respectively. The end point was MACE, including cardiovascular death, myocardial infarction, heart failure hospitalization, coronary artery revascularization procedures, and stroke. Multivariable Cox regression was used to estimate the hazard ratios. The discriminative performance between classification were compared using Harrell\u0026rsquo;s C-statistic. The agreement was assessed via Cohens\u0026rsquo; Kappa.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eThe study included 440 patients, with approximately 70% of Thai patients exhibiting a CAC score. CAC distributed higher in male than female and older than younger. Both CAC classification demonstrated the acceptable predictive performance. However, fair agreement was observed between classifications (Cohen\u0026rsquo;s kappa 0.51 95%CI 0.42\u0026ndash;0.59). Within an absolute classification, the higher CAC could capture the higher hazard ratio more consistently across age-sex specific percentile level. In contrast, the association between MACE and the age-sex specific percentile classification was not consistent in all levels of the absolute CAC scale.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eBoth absolute and age-sex-specific percentile CAC scores showed acceptable performance in predicting MACE. However, it is likely that the classification of absolute CAC scores may be more appropriate for risk stratification in Thai clinical cohort.\u003c/p\u003e","manuscriptTitle":"Coronary Artery Calcium (CAC) Score for Cardiovascular Risk Stratification in a Thai Clinical Cohort: A Comparison of Absolute Scores and Age-Sex Specific Percentiles","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-05-31 22:09:23","doi":"10.21203/rs.3.rs-2994349/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"926cb158-032c-4187-92b7-3a81c12fec57","owner":[],"postedDate":"May 31st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-03-17T04:58:48+00:00","versionOfRecord":{"articleIdentity":"rs-2994349","link":"https://doi.org/10.1016/j.heliyon.2023.e23901","journal":{"identity":"heliyon","isVorOnly":true,"title":"Heliyon"},"publishedOn":"2024-01-01 04:58:47","publishedOnDateReadable":"January 1st, 2024"},"versionCreatedAt":"2023-05-31 22:09:23","video":"","vorDoi":"10.1016/j.heliyon.2023.e23901","vorDoiUrl":"https://doi.org/10.1016/j.heliyon.2023.e23901","workflowStages":[]},"version":"v1","identity":"rs-2994349","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2994349","identity":"rs-2994349","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}