ORIGINAL ARTICLE Year : 2004  Volume : 5  Issue : 2  Page : 4454 What does my patient's coronary artery calcium score mean? combining information from the coronary artery calcium score with information from conventional risk factors to estimate coronary heart disease risk Mark J Pletcher^{1}, Jeffrey A Tice^{2}, Michael Pignone^{3}, Charles McCulloch^{4}, Tracy Q Callister^{5}, Warren S Browner^{6}, ^{1} Department of Epidemiology and Biostatistics, University of California, San Francisco 500 Parnassus Ave, MU 420 West, Box 0560, San Francisco, CA 941430560 ; Division of General Internal Medicine, University of California, San Francisco, CA, USA ^{2} Division of General Internal Medicine, University of California, San Francisco, CA; Department of Epidemiology and Biostatistics, University of California, San Francisco 500 Parnassus Ave, MU 420 West, Box 0560, San Francisco, CA 941430560, USA ^{3} Division of General Internal Medicine and Clinical Epidemiology, University of North Carolina  Chapel Hill School of Medicine, Chapel Hill, NC, USA ^{4} Department of Epidemiology and Biostatistics, University of California, San Francisco 500 Parnassus Ave, MU 420 West, Box 0560, San Francisco, CA 941430560, USA ^{5} EBT Research Foundation, Nashville, TN, USA ^{6} Research Institute, California Pacific Medical Center, San Francisco, CA; Department of Epidemiology and Biostatistics, University of California, San Francisco 500 Parnassus Ave, MU 420 West, Box 0560, San Francisco, CA 941430560; Department of Medicine, University of California, San Francisco, CA, USA Correspondence Address: Background: The coronary artery calcium (CAC) score is an independent predictor of coronary heart disease. We sought to combine information from the CAC score with information from conventional cardiac risk factors to produce posttest risk estimates, and to determine whether the score may add clinically useful information. Methods: We measured the independent crosssectional associations between conventional cardiac risk factors and the CAC score among asymptomatic persons referred for noncontrast electron beam computed tomography. Using the resulting multivariable models and published CAC scorespecific relative risk estimates, we estimated posttest coronary heart disease risk in a number of different scenarios. Results: Among 9341 asymptomatic study participants (age 3588 years, 40% female), we found that conventional coronary heart disease risk factors including age, male sex, selfreported hypertension, diabetes and high cholesterol were independent predictors of the CAC score, and we used the resulting multivariable models for predicting posttest risk in a variety of scenarios. Our models predicted, for example, that a 60yearold nonsmoking nondiabetic women with hypertension and high cholesterol would have a 47% chance of having a CAC score of zero, reducing her 10year risk estimate from 15% (per Framingham) to 69%; if her score were over 100, however (a 17% chance), her risk estimate would be markedly higher (2551% in 10 years). In low risk scenarios, the CAC score is very likely to be zero or low, and unlikely to change management. Conclusion: Combining information from the CAC score with information from conventional risk factors can change assessment of coronary heart disease risk to an extent that may be clinically important, especially when the pretest 10year risk estimate is intermediate. The attached spreadsheet makes these calculations easy.
Background Aggressive primary prevention of coronary heart disease (CHD) is most appropriate in patients at relatively high risk of CHD events [1],[2] . The coronary artery calcium (CAC) score is an independent predictor of coronary heart disease risk [3],[4],[5],[6],[7] , and therefore may help in deciding how aggressively to pursue cholesterollowering, antiplatelet therapy and other primary prevention strategies. To use a given CAC score result, however, one must know how that score compares with the score of an average person of the same sex, age and CHD risk factor profile. A CAC score of 50, for example, may be unusually high for a 40yearold woman without other CHD risk factors, but unusually low for a 70yearold man with hypertension. The same score, therefore, affects risk assessment in opposite directions for these two patients. How should a clinician use this CAC score (or any other) when assessing the CHD risk of a more typical patient, say a 60yearold woman with hypertension and high cholesterol? To answer this question, we need to know the effects of age, sex and other CHD risk factors on the expected distribution of CAC scores. Several large crosssectional studies have described the prevalence and extent of CAC among different age/sex groups [6],[8],[9],[10] without accounting for conventional CHD risk factors that may strongly influence predicted CAC scores. Five previous studies examined how CAC relates to conventional CHD risk factors [11],[12],[13],[14],[15] . Only one of these was adequately powered [15] , none adequately accounted for the abnormal distribution of CAC scores, and none yielded estimates usable for clinical decisionmaking. We identified a large sample of men and women without clinical CHD who presented for electron beam computed tomography scanning. Using questionnaire data collected from these patients about smoking habits and medical history (hypertension, high cholesterol and diabetes), we determined how conventional CHD risk factors, along with age and sex, affect CAC scores. We then developed a method for combining information from conventional risk factors and the CAC score (easy spreadsheet calculator attached), and we present several examples illustrating how that method may be applied in common clinical situations. Methods Study sample All persons referred by their physician to an electron beam computed tomography (EBCT) scanning center in Nashville, Tennessee for measurement of coronary artery calcification between May 15, 1995 and December 31, 1997 were eligible for inclusion. Subjects with a history of CHD or complaining currently of any chest pain were excluded, as were subjects for whom CHD risk factor data were incomplete or missing. Only the first CAC score was included for those who received more than one EBCT scan. Measurement of coronary heart disease risk factors Current age, sex and presence of CHD risk factors were elicited by questionnaire from subjects and referring physicians. Each subject was labeled with hypertension, high cholesterol and/or diabetes mellitus if they answered affirmatively to the question, "Has your physician ever told you that you needed medicine for X?", or if their physician confirmed that such a condition was documented in their medical records. Patients were labeled as smokers if they currently smoked or had quit smoking within the preceding 3 months. No direct measurements of blood pressure, lipids or glucose were taken for the purposes of this study. Estimation of the 10year risk of coronary heart disease events We estimated the 10year risk of a first CHD event using published mathematical models based on the Framingham study [16] . For this purpose, we assumed that subjects reporting hypertension had systolic blood pressures of 140160 mmHg and/or diastolic blood pressures of 90100 mmHg (Stage I hypertension), and that subjects without hypertension had systolic pressures of 120130 and diastolic pressures of 8085 mmHg. We also assumed that patients with high cholesterol had lowdensity lipoprotein (LDL) cholesterol levels of 130159 mg/dl and high density lipoprotein (HDL) cholesterol levels of 3544 mg/dl, whereas patients without high cholesterol had LDL cholesterol levels of 100129 mg/dl and HDL cholesterol levels of 4549 mg/dl (for men) or 5059 mg/dl (for women). Smoking and diabetes mellitus were dichotomous variables in both Framingham models [16] and our data set. We then used published model coefficients [16] to estimate the 10year risk for each patient in our study. Measurement of the CAC score Each subject underwent electron beam computed tomography scanning with an Imatron C100 or C150 scanner (Imatron, South San Francisco, California) after giving written informed consent. During a single breath hold, 40 consecutive slices of 3 mm thickness were obtained starting at the level of the carina and proceeding to the level of the diaphragm. Scans were obtained within 100 ms and were electrocardiographically triggered at 6080% of the RR interval. Coronary calcification was defined as a plaque of at least 3 consecutive pixels (area = 1.03 mm 2 ) with density Ž 130 Hounsfield units. The CAC score was calculated according to the method described by Agatston [17] . Statistical analysis We categorized patients according to age and sex, and examined histograms, quantile plots and box plots in each category to evaluate distributional normality. The CAC score is fundamentally not normally distributed because of the large percentage of zero measurements, and hence is not amenable to a normalizing transformation, as noted by others [13] . We also considered a censored normal distribution, which would have allowed a onestep Tobit regression analysis. However, even after square and cuberoot transformations, the zero scores were distributed in a manner inconsistent with the Tobit regression model. After exclusion of zero values, however, the logtransformed CAC score was approximately normally distributed [Figure 1]. This led us naturally to a twostage modeling approach. We first applied logistic regression to model the probability of a nonzero score, and then used linear regression to model the actual CAC score, logtransformed, for the subset of patients with nonzero values. Using this methodology, we assessed the independent effects of CHD risk factors on both the presence and extent of CAC. We considered three sets of predictors: 1) age and sex, 2) age, sex, hypertension, high cholesterol, smoking, and diabetes, and 3) the Framingham 10year CHD risk estimate. We examined whether the effects of age were linear (as opposed to Jshaped, for example) by testing a quadratic term in the model containing only age and sex. We evaluated the ability of each logistic model to discriminate subjects at high and low risk for CAC using the Cstatistic, and estimated the proportion of variability in the extent of CAC explained in each linear regression model using the adjustedR 2 statistic. Finally, we used coefficients, intercepts and residual variance from logistic and linear models to estimate the probability that the CAC score of an individual with known risk factors would fall into each of four standard CAC score categories (0, 1100, 101400, and >400). We estimated these probabilities, using models containing the 10year risk estimate as the only predictor, for a range of 10year risk estimates. We also estimated these probabilities, using models with all CHD risk factor predictors, for the specific clinical scenario described in the Introduction (a 60yearold woman with hypertension and high cholesterol) and for several other scenarios. We compared the actual distribution of CAC scores among 5862yearold women with hypertension and high cholesterol in our sample (n = 130) with predictions from 1) our twostage model, 2) a onestage model using Ln(CAC score + 1) as a continuous outcome in a linear regression model, and 3) a onestage model using a censored normal distribution of cuberoot transformed CAC scores (a Tobit regression model). This comparison was made both graphically and statistically, using X2 tests with 3 degrees of freedom to compare the expected frequencies based on each model with the observed frequencies. Lower p values, in this case, indicate a poorer fit of the model to the observed data. All statistical analyses were performed with Stata 7.0 (College Station, Texas). Combining information from conventional risk factors and the CAC score First, we calculated the Framingham 10year CHD risk estimate (and corresponding 1year risk estimate assuming an equal event rate each year) according to published models [16] . Next, we calculated the probability, as described above, that that individual's CAC score would fall into each one of four standard CAC score categories [15],[18],[19] (0, 1100, 101400, and >400). We obtained risk factoradjusted relative risk (RR) estimates from a metaanalysis [7] comparing the risk of a CHD event among persons with CAC scores of 1100 (RR = 2.1), 101400 (RR = 5.4) and 400). The posttest CHD risk estimates for each CAC score category were then calculated algebraically by assuming that the overall 1year CHD risk estimate represents an average of the 1year risk estimates from the four CAC score categories, weighted by the probabilities that an individual's score would fall into each category. A spreadsheet that automates these calculations is attached. Results Study Population We identified 9341 persons without chest pain or a history of CHD presenting for their first EBCT scan between 4/15/95 and 12/31/97. Our sample was mostly middleaged, but included persons as young as 35 years and as old as 88 years of age. Forty percent were women. The proportion with cardiac risk factors was high, though only 9% were diabetic [Table 1]. Framingham 10year CHD risk estimates ranged widely, mostly dependent on age, but most were between 7% and 15%. Coronary artery calcium score distributions Coronary artery calcium scores ranged from 0 to 4058. The mean score (± standard deviation) was 135 (± 377), and the median was 4 (25th75th percentile: 0  87). The prevalence of zero scores ranged from 80% among women younger than 50 years to 5% among men 70 years old or older. After excluding zero scores, logtransformed CAC scores were approximately normally distributed, and appeared to be strongly associated with age and sex [Figure 1]. Predictors of the presence and extent of coronary artery calcification Age and sex were strong predictors of the presence of CAC in logistic regression models [Table 2]. There was no evidence that the effects of age were nonlinear (i.e. J or Ushaped) (pvalue=0.32 for a quadratic age term). Conventional CHD risk factors were also independent predictors of the presence of CAC (p Coronary artery calcium distribution predictions Using these models, we estimated the probability of measuring a CAC score in each of four standard CAC score categories (0, 1100, 101400, and >400) using the Framingham 10year CHD risk estimate, a value easily calculated from conventional CHD risk factors using accessible web or handheld computerbased software. These probabilities ranged widely based on the value of the 10year risk estimate, with the probability of measuring a zero CAC score going from 75% (at a 10year risk of 2.5%) to 13% (at a 10year risk of 25%) [Table 4]. Risk integration example Using the case example presented in the Background section, we calculated that a 60yearold woman with Stage I hypertension (140/90 mmHg) and high cholesterol (LDL cholesterol = 155 mg/dl, HDL cholesterol = 40 mg/dl) will have a 15% risk of suffering a CHD event in 10 years, according to the Framingham equation. If this woman undergoes EBCT scanning, our models predict a 47% chance that her CAC score will be zero, a 36% chance that it will be between 1100, a 12% chance that it will be between 101400, and a 5% chance that it will be greater than 400. By integrating this information with previously published relative risk estimates (see Additional File 1) we estimate her 10year CHD risk to be as low as 6% (if her CAC score is 0), or as high as 51% (if her CAC score is >400). These estimates are only moderately sensitive to variation in the relative risk assumptions [Table 5], and may be easily calculated in any clinical scenario in which CHD risk factor data is available; see [Table 5] for several other examples. Comparing predictions from different modeling strategies Our strategy outperformed two other modeling strategies in predicting the actual CAC distribution among the 5862yearold nonsmoking nondiabetic women with hypertension and high cholesterol in our study sample (n=127) [Figure 2]. The onestage regression model using Ln(CAC score +1) as the outcome, which has been utilized extensively in previous research [11],[12],[14],[20] , performed particularly poorly. Discussion In this article, we present a clinically useful method of combining information from the CAC score with pretest coronary risk estimates. To fully appreciate the utility of this analysis, it may be worthwhile to discuss the example from the Background section further. According to current guidelines, this 60yearold woman, whose 10year CHD risk estimate is about 15%, should receive both aspirin and cholesterollowering drug therapy, aiming for a goal LDL cholesterol of 130 mg/dl1,2. After measuring her CAC score, however, there is a good chance (64%) that our recommendations would change. If her CAC score were zero (47% chance), our estimate of her 10year CHD risk would be approximately halved (69%). Given this information, we would continue to recommend a healthy diet and exercise, but might decide that cholesterollowering medication is unnecessary [1] , and that the benefits of aspirin in terms of CHD prevention do not outweigh the risk of hemorrhagic stroke associated with aspirin use [2] . On the other hand, if her CAC score were over 100 (17% chance), our estimate of her CHD risk would be approximately doubled (2531% if CAC score = 101400) or tripled (3451% if CAC score > 400). In such a case, we would certainly recommend both aspirin [2] and cholesterollowering medication1 and would probably aim for a more aggressive LDL cholesterol goal of [1] . The probability that her treatment plan would be altered by measurement of her CAC score, therefore, is approximately 64% (the probability that her score is either 0 or >100 = 47% + 17%), indicating likely usefulness of the test in this situation. The third and fourth clinical scenarios presented in Table 5, on the other hand, provide examples where the test is unlikely to change management. The 40yearold woman who smokes, for example, has a very low pretest 10year CHD risk (3%). It is very likely her CAC score will be zero (89%) or less than 100 (10%), in which case her posttest 10year CHD risk will still be low (. 5%) and her management would not change. The 80yearold man with high cholesterol has a high pretest 10year CHD risk (26%) and a high probability of having a high CAC score (70% will have a score > 100), in which case his posttest 10year CHD risk would remain over 20% and his management would have to remain aggressive. In these cases, and others in which the risk factor profile indicates very low or very high pretest risk, the test is not likely to provide useful results, and the clinician might decide not to order the test. We have provided a simple spreadsheet (see Additional File 1) that may be used by readers of this article to replicate these analyses and apply our models to other clinical scenarios. While others have proposed similar Bayesian approaches to use of the CAC score for coronary risk prediction [6],[21],[22],[23],[24] , ours has advantages. Previous approaches do generally take into account the pretest probability of coronary heart disease, but none consider the expected distribution of CAC scores in the tested population after adjustment for conventional CHD risk factors. Raggi et al advocate use of an age and sexadjusted calcium score percentile, but this ignores both persons with zero scores and the strong effects of other risk factors such as hypertension and hypercholesterolemia [6] . Some approaches use only sensitivity and specificity from dichotomized CAC score cutoffs [21],[23] , and others use CAC scorespecific relative risks generated from a single study population [6],[24] . Only two provide actual posttest risk estimates for specific clinical situations [23],[24] . Our approach takes into account the pretest coronary risk, the expected distribution of CAC scores adjusted for all conventional CHD risk factors, and summary adjusted relative risks from a recent metaanalysis, and provides clinically relevant posttest risk estimates that may be directly useful to primary care physicians, cardiologists and patients as they decide whether or not to take medications for primary prevention of CHD. This analysis confirms that conventional risk factors for CHD (hypertension, diabetes, smoking and high cholesterol, as well as increasing age and male sex) are independent predictors of coronary artery calcification. This finding is consistent with previous studies [11],[12],[13],[14],[15] . We also present expected CAC score distributions for a variety of clinical situations, which are not easily calculated from other studies, via [Table 4] and [Table 5] and the attached spreadsheet calculator. Our finding that high cholesterol was less strongly associated with the extent of CAC than other CHD risk factors is consistent with the other large study addressing this issue [15] , and perhaps reflects effective medical treatment for hypercholesterolemia. Male sex was a very strong predictor of the presence and extent of CAC  women with the same CHD risk factor profile would be expected to develop CAC approximately 12 years later than men, and remain approximately 11 years behind men in the extent of their calcification. Finally, our analysis provides a guide for how to use the CAC score as a surrogate outcome when studying causes of coronary artery disease (a widely used study design [25],[26],[27] . The central problem with this approach is the fundamentally nonnormal distribution of CAC scores, which makes parametric statistic testing (including both simple ttests and multivariable linear regression) invalid. In dealing with this issue, some researchers have used the Ln(CAC score +1) as an outcome in linear regression analyses [11],[12],[14],[20] . This approach is not ideal, as the Ln(CAC score +1) is still grossly nonnormal  there are too many zero scores. Adding 1 to the CAC score makes the logtransformation possible (yielding zeroes instead of negative infinity), but it does not solve the distributional problem, and leads to predictions that misrepresent actual CAC score distributions [Figure 2]. This observation has led others to present only nonparametric percentile data without multivariable modeling [6],[8],[9],[10] , but this approach does not allow adjustment for conventional CHD risk factors that we have shown are strong predictors of the CAC score. One other group used ordinal logistic regression analysis to analyze CAC scores categorized into four ordinal categories (quartiles in their study sample) [13] . While such an approach does allow multivariable modeling with ordinal logistic regression, it does not take full advantage of the continuous nature of the CAC score and may blur the important distinction between zero and nonzero scores. Our analysis suggests that a twostep approach (using first logistic regression to model the risk of having a nonzero score, then linear regression of logtransformed nonzero CAC scores to model the extent of coronary calcification) will allow multivariable analysis of the interval data provided by the CAC score without violating the basic assumptions of parametric statistics. Our analysis has a number of limitations, perhaps the most important being a lack of clinical detail about participants. While we had information about conventional risk factors (hypertension, high cholesterol, diabetes mellitus and tobacco use), the data were only available from a questionnaire, and were not confirmed by direct measurement. Only dichotomous indicators of such conditions were used. Furthermore, other conditions and indicators of high CHD risk such as family history of CHD, obesity, physical activity, income, education, and levels of Creactive protein, triglycerides and Lp(a), for example, were unavailable. Whether such factors are important predictors of the presence and extent of coronary artery calcification is unknown. On the other hand, CHD risk assessment is often based on the same type of limited information we had available on each of our patients, so the models we present are perhaps more easily applicable to common clinical situations than models based on more detailed clinical data. Furthermore, a historical indicator of past exposure to high blood pressure or high cholesterol, as we had access to in this study, may actually be more useful as a predictor of CAC than treated blood pressure measured at one point in time. Another important limitation of this study is our lack of data on race/ethnicity  our results may not apply to all ethnic groups. Finally, our data are limited in application to CAC scores measured by electron beam computed tomography with 3 mm slice thickness and the described protocol. While CAC scores measured by the latest spiral computed tomography scanners appear to be similar to those generated by electron beam computed tomography [28] , we cannot guarantee that our results apply to such scores. Our models should be applied to other similar cohorts for validation, and also applied in cohorts that include different racial/ethnic groups and different ways of measuring the CAC score before being used in these clinical situations. Conclusions The Clinical Research Roundtable at the Institute of Medicine has identified translation of clinical research findings into improvements in medical care as the "next scientific frontier" [29] . While our analysis has some limitations, it provides methodology that will directly assist in the translation of research into practice. Our models, once validated, can be used directly by patients and clinicians to decide when it might be useful to order this potentially expensive test, and what to do with the results. Additional File 1: File can be accessed at: http://www.biomedcentral.com/content/supplementary/17417015231S1.xls[SUPPORTING:1] This spreadsheet is used for combining information from conventional risk factors and the coronary artery calcium score to estimate coronary heart disease risk in an individual patient. Step 1: Enter your patient's clinical information (the red numbers). Step 2: Choose an assumption about the coronary artery calcium score relative risks (optimistic or conservative). Step 3: Find the following results: 1) "Pretest" 10year risk of coronary heart disease (CHD) based on Framingham equations; 2) The probability of having a coronary artery calcium (CAC) score that falls within 4 standard CAC score categories; and 3) The "posttest" 10year risk of CHD for each CAC score category. Step 4: Use the results to interpret a CAC score,or to decide whether or not to order a coronary artery calcium scan. If a score that would change your management is unlikely to occur, it may not be worth the money. Acknowledgements Dr. Pletcher was supported by funds from the Health Resources and Services Administration, Grant D14 HP00178. The authors would like to thank Paolo Raggi and Joseph Schwartz for their helpful input. References


