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J Am Coll Cardiol, 2007; 49:1860-1870, doi:10.1016/j.jacc.2006.10.079
(Published online 19 April 2007). © 2007 by the American College of Cardiology Foundation |
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* Harbor-UCLA Los Angeles Biomedical Research Institute, Torrance, California
Cedars-Sinai Medical Center, Los Angeles, California
EBT Research Foundation, Nashville, Tennessee
Division of Cardiology and Department of Radiology, Emory University, Atlanta, Georgia.
Manuscript received March 6, 2006; revised manuscript received September 18, 2006, accepted October 16, 2006.
* Reprint requests and correspondence: Dr. Matthew J. Budoff, Harbor-UCLA Research and Education Institute, 1124 West Carson Street, RB2, Torrance, California 90502. (Email: mbudoff{at}labiomed.org).
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Abstract |
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Background: Several smaller studies have documented the efficacy of CAC testing for assessment of cardiovascular risk. Larger studies with longer follow-up will lend strength to the hypothesis that CAC testing will improve outcomes, cost-effectiveness, and safety of primary prevention efforts.
Methods: We used an observational outcome study of a cohort of 25,253 consecutive, asymptomatic individuals referred by their primary physician for CAC scanning to assess cardiovascular risk. Multivariable Cox proportional hazards models were developed to predict all-cause mortality. Risk-adjusted models incorporated traditional risk factors for coronary disease and CAC scores.
Results: The frequency of CAC scores was 44%, 14%, 20%, 13%, 6%, and 4% for scores of 0, 1 to 10, 11 to 100, 101 to 400, 401 to 1,000, and >1,000, respectively. During a mean follow-up of 6.8 ± 3 years, the death rate was 2% (510 deaths). The CAC was an independent predictor of mortality in a multivariable model controlling for age, gender, ethnicity, and cardiac risk factors (model chi-square = 2,017, p < 0.0001). The addition of CAC to traditional risk factors increased the concordance index significantly (0.61 for risk factors vs. 0.81 for the CAC score, p < 0.0001). Risk-adjusted relative risk ratios for CAC were 2.2-, 4.5-, 6.4-, 9.2-, 10.4-, and 12.5-fold for scores of 11 to 100, 101 to 299, 300 to 399, 400 to 699, 700 to 999, and >1,000, respectively (p < 0.0001), when compared with a score of 0. Ten-year survival (after adjustment for risk factors, including age) was 99.4% for a CAC score of 0 and worsened to 87.8% for a score of >1,000 (p < 0.0001).
Conclusions: This large observational data series shows that CAC provides independent incremental information in addition to traditional risk factors in the prediction of all-cause mortality.
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Methods |
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EBT methods. All study subjects underwent EBT with an Imatron C-150XL Ultrafast computed tomography scanner (GE-Imatron, South San Francisco, California). The study was approved by the Institutional Review Board of Harbor-UCLA Medical Center. Thirty to 40 contiguous tomographic slices were obtained at 3-mm intervals beginning 1 cm below the carina and progressing caudally to include the entire coronary tree. Exposure time was 100 ms/tomographic slice, and total irradiation dose was 0.6 mSv/scan.
An attenuation threshold of 130 HU and a minimum of 3 contiguous pixels were used for identification of a calcific lesion. Each focus exceeding the minimum criteria was scored with the algorithm developed by Agatston et al. (3), calculated by multiplying the lesion area by a density factor derived from the maximal HU within this area. The density factor was assigned in the following manner: 1 for lesions with peak attenuation of 130 to 199 HU, 2 for lesions with peak attenuation of 200 to 299 HU, 3 for lesions with peak attenuation of 300 to 399 HU, and 4 for lesions with peak attenuation >400 HU. The total CAC score was determined by summing individual lesion scores from each of 4 anatomic sites (left main, left anterior descending, circumflex, and right coronary arteries) (3).
Follow-up data collection.
Epidemiologic methods for follow-up included ascertainment of death by individuals who were blinded to historical and CAC score results (4,5). The occurrence of all-cause death was verified with the National Death Index (6). Individuals who underwent cardiovascular screening were followed for a mean of 6.8 years (SEM = 0.019) and median of 5.8 years (25th to 75th percentile = 4.7 to 8.9 years). Follow-up was completed in 100% of patients. In this sample, 5,218 patients had follow-up 
10 years and 1,404 patients had follow-up 12 years.
Data validation in a prior 10,377 patient series. We compared our survival analysis with a similar referral population from patients enrolled in a prior registry (7,8) to examine near-term (3- to 5-year) versus long-term (7- to 10-year) survival. We pooled both datasets for validation of our mortality model in the current 25,253 patient series and the previously reported data in 10,377 patients.
Statistical methods. We presented continuous measures as mean ± SD and frequency data as proportions. Categorical variables comparing CAC patient subsets with historical variables were compared with a chi-square likelihood ratio test. For comparing CAC subsets by age and other continuous measures, we employed analysis of variance techniques. A p value < 0.05 was considered statistically significant.
Time to death from all causes was estimated with a Cox proportional hazards model. Unadjusted survival and risk-adjusted survival rates controlling for age, gender, ethnicity, and other cardiac risk factors, detailed in Table 1, were calculated. For all variables in a model, univariable and risk-adjusted relative risk ratios (RRs) with 95% confidence intervals (CIs) were calculated.
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We then evaluated multivariable or risk-adjusted Cox models with the CAC score in several coding schemes: 1) model 1: total CAC score using categories of 0, 1 to 10, 11 to 100, 101 to 400, 401 to 699, 700 to 999, and 1,000; and 2) model 2: dividing the arterial segment analysis of the CAC score into 0 to 3 vessels with CAC score 100. In particular, for each of these models, we calculated unadjusted analyses as well as age-adjusted and other risk-factor-adjusted (Table 1) survival models. From the multivariable models, we evaluated the added value of the CAC score by calculating a C-index and 95% CIs. As well, we calculated the Delta chi-square, a measure of the risk predictive content of a given variable within a multivariable model.
To understand to what extent longer-term outcomes from the current data series relates to near-term outcome, we performed a comparative analysis of the variability in long-term survival as compared with previous reports on near term (i.e., 5-year) in the report by Shaw et al. (8). This modeling was accomplished by pooling the datasets to include the total CAC score as well as other cardiac risk factors. A stratified Cox proportional hazards model was used to plot survival for the Shaw series and the current data set. This was done to examine how longer-term outcome would further define risk in asymptomatic individuals. It was our contention that the observation of 10 years might provide a closer approximation to longer-term outcome data available from other asymptomatic cohort studies, such as the Framingham study (7).
We also performed a post hoc sample size calculation for the comparison of survival rates across CAC scores. The calculations demonstrated that the available sample size of 25,253 patients was sufficiently large with a beta 0.80 and alpha = 0.01 to detect mortality differences between patients with CAC scores of 0, 1 to 10, 11 to 100, 101 to 400, 401 to 1,000, and >1,000, respectively (SamplePower, version 2.0, SPSS Inc., Chicago, Illinois).
Finally, we evaluated the prevalence of CAC in this clinical registry as compared with other population estimates, including data from MESA (Multi-Ethnic Study of Atherosclerosis) and CARDIA (Coronary Artery Risk Development In Young Adults Study) as well as the Nashville and Torrance clinical registries with meta-analytic techniques on the prevalence of detectable coronary calcium. The frequency of calcium in each of the cohorts was compared with Comprehensive Meta-Analysis software (Biostat, Englewood, New Jersey) with a random effects model.
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In subsets with more extensive CAC scores, patients were older and had more frequent cardiac risk factors. Nearly one-half of the patients with CAC scores 1,000 were male (p < 0.0001), hypertensive (p < 0.0001), hyperlipidemic (p < 0.0001), or had a family history of premature CAD (p = 0.052).
CAC scores. More than one-half of patients had detectable CAC (i.e., CAC >0). Detectable CAC was most common in the left anterior descending and left circumflex coronary arteries. By comparison, 11.9% and 35.0% of patients had detectable CAC in the left main and right coronary arteries (Table 2).
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Univariable clinical risk factor and CAC predictors of death. Significant clinical predictors of death included age, diabetes, smoking, male gender, hypertension, and family history of premature CAD (all p < 0.0001). The relative RRs for these cardiac risk factors ranged from 1.1 (95% CI 1.09 to 1.11)/decade of increasing age to 3.86 (95% CI 3.03 to 4.92) for diabetes (both p < 0.0001). The RR for family history of premature CAD was 0.63 (95% CI 0.51 to 0.78) in large part because those referred for evaluation were younger (p < 0.0001) (Table 3).
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1,000 (p < 0.0001). After adjustment for risk factors, including age, in patients with CAC scores of 0, 1 to 10, 11 to 100, 101 to 399, 400 to 699, 700 to 999, and 1,000, 10-year survival was 99.4%, 99.3%, 98.1%, 96.6%, 94.7%, 93.9%, 92.4%, and 87.8%, respectively (p < 0.0001) (Fig. 1A).
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1,000 showed RR of 2.56 (95% CI 1.54 to 4.27), 6.73 (95% CI 4.58 to 9.87), 12.83 (95% CI 8.71 to 18.91), 23.17 (95% CI 14.31 to 37.52), 27.58 (95% CI 18.19 to 41.80), 36.43 (95% CI 23.21 to 57.19), and 62.58 (95% CI 43.04 to 91.00), respectively (p < 0.0001). The CAC remained highly significant (p < 0.0001) in a multivariable model controlling for age, gender, ethnicity, diabetes, hyperlipidemia, hypertension, as well as other cardiac risk factors listed in Table 1. However, in the risk-adjusted model, the statistical difference between a CAC score of 0 vs. 1 to 10 was no longer significant (p = 0.29).
When grouped by the number of vascular territories with CAC scores of 100, a measure of atherosclerotic disease extent, survival varied significantly for patients with no vessel involvement as compared with 1 to 3 vessel CAC disease. For patients with CAC <100, unadjusted survival at 12 years was 98.8% as compared with survival rates of 90.4% (RR 3.81, 95% CI 2.63 to 5.53), 83.8% (RR 6.38, 95% CI 3.59 to 11.34), 78.0% (RR 8.41, 95% CI 2.70 to 6.19), and 74.8% (RR 10.26, 95% CI 6.46 to 6.28), respectively, for patients with 1, 2, and 3 vessel CAC and left main disease (p < 0.0001). After adjustment for risk factors including age, the survival at 12 years was 98.2% for patients with a CAC score <100 as compared with survival rates of 97.1%, 95.7%, and 94.6%, respectively, for patients with 1, 2, and 3 vessel CAC and left main disease (p < 0.0001) (Fig. 1B).
We further evaluated the long-term survival in patients with single vessel CAC scores ranging from 11 to 100 (Fig. 1C) Twelve-year unadjusted survival ranged from 99.4% for no vessel involvement to 84.7% for patients with 3 vessels with scores of 11 to 100 (p < 0.0001). Risk-adjusted survival for patients with CAC scores from 11 to 100 is plotted in Figure 1C (p < 0.0001). The ensuing risk-adjusted RR was elevated 1.66-fold (p = 0.031) and 1.84-fold (p = 0.003) for patients with 3 vessel and 3 vessel plus left main CAC scores from 11 to 100 as compared with CAC score <11 in any of the major epicardial coronary arteries.
Comparative analysis of 5- and 12-year survival from 2 databases. Previous reports have limited follow-up from 3 to 5 years of observation (8). To compare near- and long-term outcome by CAC scores, we examined unadjusted and risk-adjusted survival in cohorts from Nashville, Tennessee (n = 10,377) and Los Angeles, California (n = 25,253) (Fig. 2). There were no differences in prevalence of CAC in each cohort (p = 0.88) (Table 4). Cumulative survival at 5 years ranged from 99.4% to 81.8% for patients with CAC scores of 0 to 10 to >1,000 (p < 0.0001). In a stratified Cox model with follow-up up to 5 years after imaging, survival was similar between the 2 cohorts for patients with a CAC score 0 to 10, 11 to 100, 101 to 400, 401 to 1,000, and >1,000 (all p > 0.90). By 12 years of follow-up, cumulative survival rates were 99.4%, 97.8%, 94.5%, 93.0%, and 76.9% for patients with CAC scores of 0 to 10, 11 to 100, 101 to 400, 401 to 1,000, and >1,000, respectively (p < 0.0001). Risk of mortality for each score category increased with increasing score. In comparing survival rates between the Nashville, Tennessee and Torrance, California databases, mortality increased from 0.7% (p = 0.0073) to 4.9% (p < 0.0001) for more extended follow-up from the Torrance registry; even in risk-adjusted models that controlled for age, gender, hypertension, hyperlipidemia, and diabetes (Fig. 2).
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0.80) to evaluate the prognostic significance of CAC. The results are concordant with other similar studies, demonstrating that increasing plaque burden is associated with increasing risk. Earlier reports on this modality were based on highly selected cohorts with the numbers of end points often being quite low. This study, similar to the study by Shaw et al. (8), used all-cause mortality as a more definitive end point. All-cause mortality is not subject to misclassification of the cause of death on physician’s reports (9). Furthermore, in the U.S., atherosclerosis accounts for a sizeable proportion of deaths, and more so in a population at-risk for the disease (10). Thus, our analysis that was conducted, with established epidemiologic methods in a sufficiently large population, showed that the extent of CAC is highly correlated with mortality risk. The present study, a large cohort with long follow-up, provides supportive evidence that there is a linear relationship between the extent of CAC and all-cause mortality. The calcium score also added incremental value to variables contained within the Framingham risk model. When CAC scores were added to risk factors in patients in the study by Shaw et al. (8), the estimation of risk increased vis-à-vis a significant improvement in the C-index (p < 0.001). In our analyses with an array of univariable and multivariable methodologies, C-indexes consistently improved after the addition of CAC measures to risk factor models (p < 0.0001). The C-index and odds ratio similarly increased in other large studies of CAC. A study by Raggi et al. (11) demonstrated odds ratios of 21.5 for future hard cardiovascular events for patients in the highest quartile of calcium scores. That correlates well with this study, in which a score of 300 to 399 was associated with an RR of 23, and score of 400 to 699 with an RR of 27, as compared with patients without CAC. In the St. Francis Heart Study (12), in which over 4,900 patients were followed for 4.3 years, a score >400 was associated with a 30-fold increased risk for CAD death or myocardial infarction. From this report by Arad et al., the CAC score predicted CAD events more accurately than the Framingham risk index. The area under the ROC curve was 0.79 ± 0.03 for the calcium score versus 0.68 ± 0.03 for the Framingham index (p < 0.0006). In the recently reported Prospective Army Coronary Calcium Project, in which younger patients were evaluated with EBT and followed prospectively, CAC was associated with a 12-fold increased risk for hard CHD events (p = 0.004) even after controlling for the Framingham risk score (13).
In the current study, patients with CAC scores 1,000 had a very high risk of all-cause mortality. This parallels the incidence of hard cardiac events demonstrated by Wayhs et al. (14), in which patients with CAC scores >1,000 had a 25% 1-year event rate. A recent study also demonstrated risk stratification in 510 uncomplicated type-2 diabetic patients prospectively enrolled and undergoing CAC (15). The ROC analysis demonstrated that CAC predicted cardiovascular events with the best area under the curve (0.92), significantly better than the United Kingdom Prospective Diabetes Study Risk Score (0.74) and Framingham Score (0.60) (p < 0.0001). The RR to predict a cardiovascular event for a CAC score of 101 to 400 was 10.1 and increased to 58.1 for scores >1,000 (p < 0.0001). Our cohort similarly demonstrated unadjusted relative risks from 12.8 to 62.6 for similar CAC groups.
Patients without evidence of CAC in this study experienced a very low event rate, with 12-year survival of 99.4%. This low event rate in patients without CAC has been observed in other studies. Arad et al. (12) demonstrated an annual event rate of 0.1%, Taylor et al. (13) demonstrated an event rate of 0.06%, and Raggi et al. (11) demonstrated an annual event rate of 0.11% for persons with no detectable CAC by EBT. Indeed, even a cohort of approximately 900 diabetic patients followed for 5-year survival was 98.8% in the absence of CAC (16). In that study, diabetic and non-diabetic patients with no CAC demonstrated a similar survival (98.8% and 99.4%, respectively, p = 0.5). In a prospective study of type-2 diabetic patients, no cardiac events or perfusion abnormalities occurred in subjects with a CAC score 
10 through 2 years of follow-up (15). The only study reporting a higher event rate for 0 scores used atypical image acquisition and quantification of CAC, relying on thick slices (6 mm) and large areas of calcification (17), which has been shown to result in data loss (18).
The low risk associated with a 0 score provides further evidence that these patients, despite some high risk attributes, might be at sufficiently low risk to recommend against cholesterol-lowering drug therapy (given the cost) and aspirin therapy (given the risk of hemorrhagic stroke associated with aspirin use) (2). Conversely, a score higher than 100 might lead to the recommendation of continued aspirin use and more aggressive lipid control aiming for a goal low-density lipoprotein-C level of <100 mg/dl, as suggested currently by the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III): "In persons with multiple risk factors, high coronary calcium scores (e.g., >75th percentile for age and gender) denote advanced coronary atherosclerosis and provide a rationale for intensified low-density lipoprotein–lowering therapy" (19). Current recommendations are to use cardiac computed tomography for measuring CAC in individuals determined to be at intermediate clinical risk according to the NCEP-ATP III criteria and in whom decisions concerning prevention strategies might be altered on the basis of the test results (20).
Study limitations. Although the current article includes a rigorous analysis of the prognostic value of CAC, the majority of patients referred for calcium scanning had cardiac risk factors and, as such, are not representative of the general population. We had incomplete information related to cardiovascular risk factors, because these measures were taken by survey rather than being measured. The prevalence of hypercholesterolemia, hypertension, and diabetes in our population was similar to that observed in other large, population-based studies of CHD (21) and in studies on CAC and CHD (7,22–24). Recent large epidemiologic studies (NIH-NHLBI MESA and CARDIA) (25) demonstrate similar prevalence rates for CAC compared with our cohort (Fig. 4). Thus, results from the current cohort seem similar to population-based estimates of atherosclerosis.
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We observed a decrement in predictive capabilities for risk factor models (i.e., the C-index was lower for categorical risk factors as compared with the CAC score), including family history of premature disease, smoking, and diabetes. The reduced C-indexes for traditional cardiac risk factors, we believe, are the result of concurrent treatment for hypertension, diabetes, and hypertension as well as a younger age for screening patients with a family history of premature coronary disease. However, use of categorical risk factors results in an underestimation in their predictive abilities. Despite this limitation, the availability of categorical risk factor data is consistent with current clinical practice where physicians are generally limited by the availability of only partial, self-reported, categorical data. Therefore, there is a possibility that the value of CAC testing is being overinflated when measured with observed risk factors, as compared with measured risk factors.
Information on subsequent therapy after calcium scanning is unknown. We have previously demonstrated that patients with higher calcium burdens are more likely to be placed on statin therapy and more likely to maintain statin therapy (improved adherence) over the subsequent 3 to 5 years (28). Thus, higher calcium scores are confounded by improved anti-atherosclerotic therapies that would possibly lower cardiovascular mortality. However, this confounder would weaken the predictive value of coronary calcification.
Additionally, the National Death Index data do not include the cause of death and, as such, our models include mortality possibly unrelated to atherosclerotic disease. However, the bias resulting from death misclassification does not occur in all-cause mortality models, and in this age group, the prevalence of CHD deaths has been reported to be approximately one-third of death from all-causes (29). If two-thirds of deaths in the American population are unrelated to coronary disease risk factors, it is possible that the addition of standard CAD risk factors to age would reduce the accuracy of the predictive model for all-cause mortality. However, the cardiovascular death rate, in a patient population referred with cardiovascular risk factors, should be much higher than general population estimates. Patients with active cancer, acquired immune deficiency syndrome, congestive heart failure, or advanced lung disease were generally not referred for calcium scoring to assess cardiovascular risk.
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