Coronary heart disease risk prediction in the Atherosclerosis Risk in Communities (ARIC) study

Journal of Clinical Epidemiology 56 (2003) 880–890

Coronary heart disease risk prediction in the Atherosclerosis Risk in Communities (ARIC) study Lloyd E. Chamblessa,*, Aaron R. Folsomb, A. Richey Sharrettc, Paul Sorliec, David Coupera, Moyses Szklod, F. Javier Nietoe a

Department of Biostatistics, University of North Carolina, CB #8300, 137 East Franklin Street, Suite 400, Bank of America Center, Chapel Hill, NC 27514-4145, USA b University of Minnesota, School of Public Health, Division of Epidemiology, Suite 300, 1300 South Second Street, Minneapolis, MN 55454-1015, USA c National Heart, Lung, and Blood Institute, National Institutes of Health, Two Rockledge Center, Mail Station 7934, 6701 Rockledge Drive, Bethesda, MD 20892-7934, USA d Johns Hopkins School of Hygiene and Public Health, Room 6009, 615 North Wolfe Street, Baltimore, MD 21205, USA e University of Wisconsin Medical School, Department of Population Health Sciences, 610 Walnut Street, 707C WARF, Madison, WI 53705-2397, USA Accepted 16 December 2002

Abstract Risk prediction functions for incident coronary heart disease (CHD) were estimated using data from the Atherosclerosis Risk in Communities (ARIC) Study, a prospective study of CHD in 15,792 persons recruited in 1987–1989 from four U.S. communities, with follow-up through 1998. Predictivity of which individuals had incident CHD was assessed by increase in area under ROC curves resulting from adding nontraditional risk factors and markers of subclinical disease to a basic model containing only traditional risk factors. We also assessed the increase in population attributable risk. The additional factors were body mass index; waist–hip ratio; sport activity index; forced expiratory volume; plasma fibrinogen, factor VIII, von Willebrand factor, and Lp(a); heart rate; Keys score; pack-years smoking; and subclinical disease marker carotid intima-media thickness. These factors substantially improved prediction of future CHD for men, less for women, and also increased attributable risks. 쑖 2003 Elsevier Inc. All rights reserved. Keywords: Coronary heart disease; Prediction; Risk factors; Subclinical disease; Population attributable risk; ROC curves

1. Introduction A major goal of epidemiology and preventive medicine is to develop tools to predict risk of disease. A well-known example is the Framingham risk score to predict coronary heart disease (CHD) [1]. The objective of this article is to explore how well nontraditional risk factors or markers of subclinical disease improve prediction of individual risk of incident CHD beyond the traditional risk factors used in the Framingham risk score, total cholesterol, HDLcholesterol, current smoking status, diabetes status, and blood pressure. The nontraditional factors considered were chosen from among the new risk factors already found by the ARIC Study and other recent CHD epidemiology studies to be significantly associated with incident CHD, and included additional lipoproteins, hemostatic and hematologic factors, other blood factors likely related to inflammation,

* Corresponding author. Tel.: ⫹919-962-3264; fax: ⫹919-962-3265. E-mail address: [email protected] (L.E. Chambless). 0895-4356/03/$ – see front matter 쑖 2003 Elsevier Inc. All rights reserved. doi: 10.1016/S0895-4356(03)00055-6

body mass and body fat distribution parameters, a dietary score predictive of plasma total cholesterol levels, cumulative cigarettes smoked, heart rate, measures of kidney and lung function, and measures of peripheral arterial disease and arterial wall thickening and left ventricular hypertrophy. Additionally, on a population level we assess how much these nontraditional factors increase the proportion of CHD risk we can attribute to nonoptimal levels of known risk factors.

2. Methods The ARIC Study is a prospective study of cardiovascular disease in a cohort of 15,792 persons sampled from four U.S. communities in 1987–1989. The cohort continues to be followed for morbidity and mortality, and this report includes follow-up through 1998, for a median of 10.2 years. The ARIC study sample consisted at baseline of 45–64-year-old members of samples of households in selected Minneapolis suburbs (Minnesota), Forsyth County (North Carolina),

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Washington County (Maryland), and Jackson (Mississippi), the latter sample from Black residents only. Details of the sampling procedures have been described [2,3]. There were also three follow-up examinations, in 1990–1992, 1993–1995, and 1996–1998. Of persons still alive at annual follow-up due date, 95.6% in the 11th annual contact were successfully contacted. 2.1. Baseline examination Participants were asked to fast for 12 hr before the clinical examination. Details have been reported for blood collection [4,5] and for centralized measurement of plasma total cholesterol, apolipoproteins AI and B [6,7], triglycerides [6,8], high-density lipoprotein cholesterol (HDL-cholesterol) [6], calculated low-density lipoprotein cholesterol (LDLcholesterol) [9], Lp(a) [10], fibrinogen [11–14], factor VII, factor VIII, von Willebrand factor (vWF) [12–14], and serum glucose, creatinine, and albumin [15]. Estimates of intraindividual variability in blood measurements have been reported [16–18]. Counts of white blood cells (WBC) were given by Coulter counters in hospital laboratories in the four communities. Methods have been reported for ascertainment of body mass index (BMI, kg/m2) and waist–hip ratio [19], systolic blood pressure (SBP) and diastolic blood pressure [20], a sport activity index [19,21,22], Keys score from diet [19,23], and heart rate [24]. Forced expiratory volume at 1 sec (FEV1) was assessed by spirometry [25], from which “residual FEV1” was calculated as the difference from the value predicted from age, height, and sex. A 12-lead electrocardiogram [24] was used to define left ventricular hypertrophy (LVH), using the Cornell score [26]. Use of antihypertensive medications within the past 2 weeks of baseline interview was self-reported [19]. The ankle/brachial index (ABI) was defined as the ratio of ankle to brachial SBP, and peripheral arterial disease (PAD) was defined as ABI ⭐0.90 for men and ⭐0.85 for women. Current, ex-, or never smoking and pack-years of cigarettes smoked were estimated from interview [19]. Prevalent diabetes mellitus was defined as a fasting glucose level ⭓126 mg/dL, nonfasting level ⭓200 mg/dL, self-reported physician diagnosis of diabetes, or pharmacologic treatment for diabetes. ARIC’s ultrasound measurements were based on the technique validated by Pignoli et al. [27], using a scanning protocol common to the four field centers [28,29], and standardized central reading of scans [30,31]. Analyses were based on mean intima-media thickness (IMT) of the far wall for 1-cm lengths of the carotid bifurcation and the internal and common carotid, right and left, adjusted for site-specific reader differences and downward measurement drifts in IMT over the baseline visit, with the means at missing sites imputed by maximal likelihood methods [32]. The means at the six sites were combined in an unweighted average to

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produce an overall mean IMT. In some analyses extreme IMT was defined as ⭓1.0 mm. 2.2. Ascertainment of incident events CHD incidence in ARIC was ascertained by contacting participants annually, identifying hospitalizations and deaths during the prior year, and by surveying discharge lists from local hospitals and death certificates from state vital statistics offices for potential cardiovascular events [19,33,34]. Trained abstractors obtained hospital charts and recorded presenting symptoms and related clinical information, including cardiac enzymes, and photocopied up to three 12lead ECGs for central reading [24,35]. Out-of-hospital deaths were investigated by means of death certificates and, in most cases, by an interview with one or more next of kin (98%) and a questionnaire filled out by the patient’s physician (85%). Coroner reports or autopsy reports, when available, were abstracted for use in validation. A CHD event was defined as a validated definite or probable hospitalized MI, a definite CHD death, an unrecognized MI defined by ARIC ECG readings, or coronary revascularization. The criteria for definite or probable hospitalized MI were based on combinations of chest pain symptoms, ECG changes, and cardiac enzyme levels [33,34]. The criteria for definite fatal CHD were based on chest pain symptoms, underlying cause of death from the death certificate, and any other associated hospital information or medical history, including that from the ARIC clinic visit [33,34]. Unrecognized incident MI was determined by ARIC follow-up examination ECG (a major Q-wave or a minor Q-wave with ischemic ST-T changes, or an MI by computerized NOVACODE criteria [36] confirmed by a side-by-side visual comparison of baseline and follow-up ECGs). 2.3. Exclusions Races other than Black or White (n ⫽ 48) and Blacks in Minneapolis and Washington County (n ⫽ 55) were excluded. Persons were also excluded for pre-existing CHD at baseline (n ⫽ 763) (self-reported prior physician diagnosis of MI or coronary revascularization, or MI by ARIC ECG), missing data on baseline pre-existing CHD (n ⫽ 339), or missing data on one of the basic risk factors or BMI (n ⫽ 533). 2.4. Statistical methods The continuous traditional risk factors of total cholesterol, HDL-cholesterol, and blood pressure were first categorized as in a recent article from the Framingham study [1]. We fit race–sex-specific Cox regression models [37] and compared the coefficients with sex-specific coefficients from the Framingham study, testing the difference between coefficients assuming approximate Gaussian distributions. Next sex-specific race comparisons were implemented using similar models in the ARIC populations, but replacing the Joint

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National Committee blood pressure level (JNC-V) [38] variables with SBP and antihypertensive medication use jointly.

including age in the risk score). Only at the last step after including all nontraditional factors did we consider the effect of additionally including (actual) age in the risk score.

2.5. Measure of model predictivity From the coefficients of a proportional hazards model of time to incident CHD, a risk score was calculated for each person by multiplying each model coefficient times the person’s level for the risk variable associated with that coefficient, then summing all these products. Our measure of individual risk predictivity of a model is the area under the ROC curve (AUC) [39], which is the probability that a person who had an incident event within a specified time (10 years here) had a higher risk score than a person who did not have an event by that time. We used Kaplan-Meierlike methods to calculate the relevant probabilities of event by 10 years in the face of censoring. When the variables in the model are unrelated to the event of interest, the expected AUC would be 0.5. Thus, the AUC has range between 0.5 and 1. When we consider percentage increase in AUC it will be as the percentage increase in the area above 0.5. A test of the hypothesis “risk score A yields a higher AUC than risk score B” has been presented [39], but is not directly applicable to use with the curves derived from censored data through a proportional hazards model. Our approach to this test was bootstrapping [39,40]. We also present the ROC curves [39], plots of sensitivity of the risk score vs. one minus specificity, calculated for various cut points for the risk score, above which a CHD end point is predicted. As an alternative visual measure of the increase in predictivity due to additional variables, we present graphs of the predicted probability of CHD event within the first 10 years of followup, by decile of risk score. The predicted probability of incident CHD within 10 years follow-up was calculated by decile as the mean predicted probability at 10 years over all persons in the decile. Improved prediction would be indicated by moving more of the predicted events out of the lower deciles of risk score into the upper deciles. Means or proportions for various risk factors were also calculated by these race/sex-specific deciles of risk score. Race/sex-specific population attributable risks for exposure defined by risk score in the top k deciles (k ⫽ 1, 2, … , or 9) were computed as PAR ⫽ 100(p ⫺ p*)/p for p the overall predicted 10-year probability of event and p* the predicted 10 year probability of event in the bottom (10 ⫺ k) deciles [41]. Thus, PAR is the percentage of 10-year cumulative incidence of CHD, which is associated with not having overall risk factor score at a low level, i.e., in the bottom (10 ⫺ k) deciles. The increases in PAR resulting from inclusion of nontraditional risk factors were calculated. All models adjusted for age in estimating the coefficients of the factors included in the risk score. However, because our interest was in the potential increase in predictivity from adding potentially modifiable risk factors to a score function using the basic risk factors, risk scores were calculated with age set to 55 for everyone (equivalent to not

3. Results The sample size was 14,054 (2,297 Black women, 5,686 White women, 1,394 Black men, 4,677 White men), and number of incident CHD events 1,064 (113, 232, 133, 586, respectively). Age-adjusted event rates were much higher for men than for women (data not shown). Black women had higher rates than White women (P ⫽ .03), and Black men had lower rates than White men (P ⫽ .05). In preliminary models to compare sex-specific incident CHD hazard rate ratios (HRR) from the ARIC study with those from the mostly White Framingham population (Table 2 of [1]), for ARIC Black women the HRRs for hypertension and current smoking were statistically significantly higher than for Framingham women. For ARIC White women the HRRs were statistically significantly higher for diabetes and current smoking than for Framingham women. Neither for ARIC Black men nor for ARIC White men were there statistically significant differences in HRRs compared to Framingham men, except for the diabetes HRR for ARIC White men, which was statistically significantly higher than for Framingham men. For the basic, or reference, models that served as the comparison models for investigating the addition of variables beyond the basic risk set, we collapsed two adjacent HDL-cholesterol categories for White women to make CHD risk estimates decrease monotonically, and used the same categories also for Blacks (Table 1). We collapsed two adjacent total cholesterol categories for White men and two for Black women for similar reasons. White women had statistically significantly larger HRRs related to total cholesterol than did Black women. White men had a larger HRR related to SBP than Black men, but had a smaller HRR related to antihypertensive medications, and White women had a smaller HRR related to SBP than Black women (these comparisons statistically significant at P ⬍ .05). Other race differences were not statistically significant. The comparison of associations between incident CHD and SBP and antihypertensive medications are perhaps better considered jointly, as is done below in the context of ROC curves. In evaluating the effect of dropping basic risk factors from or adding nontraditional risk factors to the basic model, 1,700 persons, including 157 CHD events, were excluded if missing a value for any of the risk factors considered. Comparing the basic risk factor model to the models omitting one risk factor (Table 2), clearly “SBP ⫹ meds” made by far the biggest contribution to CHD risk prediction among the basic factors added last, in all race/sex groups. For example, AUC ⫽ 0.70 for Black women for the basic model minus SBP and antihypertensive medications, but when

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Table 1 Multivariable adjusteda hazard rate ratios (HRR) of CHD, by sex and race: The Atherosclerosis Risk in Communities Study 1987–1998 Black women

White women

Black men

Risk factors

HRR

95% CI

HRR

95% CI

HRR

95% CI

HRR

95% CI

Systolic blood pressure (20 mmHg) Hypertension medications Total cholesterol (mg/dL) (200, 240) vs. ⬍ 200

1.65

1.45–1.87

1.35

1.17–1.56

1.05

0.88–1.24

1.31

1.19–1.45

2.25

1.48–3.42

1.80 1.91

1.35–2.40 1.31–2.79

2.00 1.40

1.41–2.84 0.93–2.10

1.13 1.56

0.93–1.38 1.28–1.90

1.13b

0.73–1.73 2.25

1.50–3.37

1.46

0.89–2.38 2.17b

1.75–2.68

3.57

2.29–5.56

2.50

1.61–3.88

1.92

1.18–3.13

Total cholesterol (mg/dL) (240, 280) vs. ⬍ 200 Total cholesterol (mg/dL) ⭓ 280 vs. ⬍ 200 HDL cholesterol (mg/dL) ⬍ 35 vs. ⭓60 HDL cholesterol (mg/dL) (35, 45) vs. ⭓60

2.26

1.29–3.95

2.54

1.59–4.06

2.01

1.11–3.63

2.92

1.45–5.88

3.35

2.05–5.49

2.21

1.20–4.06

1.49b HDL cholesterol (mg/dL) (45, 50) vs. ⭓60 HDL cholesterol (mg/dL) (50, 60) vs. ⭓60 Diabetes Current smoker a b

White men

0.91–2.44

2.49b

1.70–3.65

1.54b

0.92–2.58

1.26

0.74–2.14

1.77

1.16–2.69

1.32

0.74–2.37

1.85

1.15–2.98

1.86 2.75

1.24–2.80 1.86–4.05

2.95 3.01

2.16–4.04 2.30–3.94

1.60 1.88

1.07–2.38 1.32–2.66

2.19 1.46

1.77–2.71 1.22–1.75

Adjusted for age and the other variables in the model. Collapsed adjacent categories.

these were added AUC rose to 0.81. Note also that the increases to AUC due to “SBP ⫹ meds” were larger for Blacks than for Whites, for women and men. On the other hand, none of the other basic risk factors singly contributed a large amount to predictivity when added after the other basic risk variables. Which of the additional risk factors gave the biggest increase in AUC beyond the basic model varied among race– sex groups, but albumin, fibrinogen, and LP(a) were most frequently the biggest contributors (⭓0.005 AUC increase in at least two groups), followed by Factor VII, sport activity index, Factor VIII, vWF, waist–hip ratio, WBC, pack-years of cigarettes, and residual FEV1 (⭓0.005 AUC increase in only one group), and then BMI, fasting triglycerides, creatinine, HR, and Keys score (⬍0.005 AUC increase in all groups but some AUC increase ⭓0.0025). The lowest contributors were apolipoprotein AI and apolipoprotein B, with AUC increases ⬍0.0025 in all groups. Thus, apolipoprotein AI and apolipoprotein B were dropped from further consideration, as was fasting triglycerides, because of their small contribution to predictivity and high correlation with other lipoprotein variables in the score function. The particular decision rules chosen for judging the importance of these increases in AUC are arbitrary, and as with any variable selection procedure, different cutoff levels to judge importance could result in different variables being chosen for inclusion in the risk equation. Factor VIII was also dropped from further consideration because of its high correlation with vWF and lower contribution than vWF. Comparing the

“Basic ⫹ Multiple Risk Factors” model, with 14 additional factors, to the basic model, both still not including age in the risk score, the AUC beyond 0.5 was increased in all groups over the basic model, by 19–25% for men and 4– 10% for women. Age added little to further increase predictivity for White or Black females, but more for Black men. Similar results are given in Table 3 for adding subclinical disease markers to the basic model predicting CHD, this time for the persons with no missing values on any of the markers being considered (1,309 persons were excluded, of which 110 were CHD events). For women of neither race did any of the markers individually increase predictivity of CHD much, although carotid IMT brought increases of at least 0.005 in AUC for each race. For Black men IMT brought a sizable increase to AUC, by 0.03. For White men, only IMT and IMT ⭓1 brought notable increases. Because LVH by ECG did not increase AUC in any group, it was dropped from further consideration. The model adding IMT and PAD simultaneously did not bring much of an AUC increase for women, but for Black men there was a 21% increase in AUC beyond 0.5, and for White men there was a 15% increase. The additional inclusion of age in the risk score brought little increase in AUC. Most of these potential risk factors and markers of subclinical disease have been addressed in other manuscripts [42–47] from the ARIC study, and we do not attempt here to give each a full analysis, adjusting or not for traditional risk factors or other important confounders. All the variables considered in Tables 2 and 3 in the “basic ⫹ one factor”

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Table 2 Area under the ROC curve for various models for incident CHD: basic group without one risk factor, basic,a basic plus one risk factor, basic plus multiple risk factors,b adding age to risk score last (N ⫽ sample size, n ⫽ number of CHD events): The Atherosclerosis Risk in Communities Study 1987–1998 Black women

White women

Black men

White men

Variables in risk scorec

(N ⫽ 1,886; n ⫽ 81)

(N ⫽ 5,150; n ⫽ 203)

(N ⫽ 1,118; n ⫽ 103)

(N ⫽ 4,200; n ⫽ 520)

Basic–(SBP ⫹ Meds) Basic–Cholesterol Basic–HDL Basic–Diabetes Basic–Current smoker Basica Basic ⫹ BMI Basic ⫹ Fasting triglycerides Basic ⫹ Lp(a) Basic ⫹ ApoAI Basic ⫹ ApoB Basic ⫹ Albumin Basic ⫹ Fibrinogen Basic ⫹ Factor VII Basic ⫹ WBC Basic ⫹ Sport activity index Basic ⫹ Residual FEV1 Basic ⫹ Creatinine Basic ⫹ Heart rate Basic ⫹ Factor VIII Basic ⫹ vWF Basic ⫹ Keys score Basic ⫹ Cigarette pack/years Basic ⫹ Waist-hip ratio Basic ⫹ age Basic ⫹ Multiple risk factorsb Basic ⫹ Multiple risk factorsb ⫹ age

0.704 0.807 0.798 0.805 0.796 0.808 0.809 0.809 0.811 0.809 0.808 0.816 0.815 0.808 0.808 0.811 0.814 0.810 0.808 0.815 0.814 0.809 0.808 0.811 0.818 0.834 0.838

0.769 0.776 0.778 0.775 0.770 0.793 0.794 0.795 0.798 0.794 0.794 0.795 0.795 0.793 0.794 0.793 0.795 0.796 0.796 0.794 0.794 0.790 0.794 0.790 0.800 0.804 0.810

0.655 0.686 0.692 0.693 0.676 0.699 0.703 0.700 0.699 0.698 0.699 0.703 0.698 0.704 0.699 0.705 0.701 0.698 0.698 0.697 0.703 0.697 0.701 0.704 0.734 0.737 0.749

0.662 0.666 0.669 0.675 0.683 0.688 0.691 0.691 0.693 0.690 0.688 0.693 0.699 0.688 0.694 0.689 0.691 0.687 0.688 0.689 0.691 0.692 0.693 0.688 0.697 0.721 0.723

a

Total cholesterol, HDL cholesterol, systolic blood pressure, antihypertensive medications, current smoking, diabetes. BMI, waist–hip ratio, Keys score, albumin, WBC, residual FEV1, fibrinogen, factor VII, vWF, Lp[a], HR, pack/years cigarette smoking, sport activity index, creatinine. c Age is used in all survival models to derive coefficients for risk function, but is used in the risk score only where indicated. b

models were statistically significant (P ⬍ .05) for at least one race/sex group except for fasting triglycerides, apolipoprotein AI, apolipoprotein B, sport activity level, and heart rate. Of these latter, all were significant in at least one group when adjusting only for age.

We next fit a model for each race–sex group predicting CHD using both additional risk factors and subclinical disease markers, dropping some of these variables as indicated above, and also sequentially dropping one variable at a time if there resulted no decline in AUC greater than 0.0025 in

Table 3 Area under the ROC curve for various models for incident CHD: basic,a basic plus one marker of subclinical disease, basic plus multiple markers,b adding age to risk score last (N ⫽ sample size, n ⫽ number of CHD events): The Atherosclerosis Risk in Communities Study 1987–1998 Black women

White women

Black men

White men

Variables in risk scorec

(N ⫽ 1,981; n ⫽ 97)

(N ⫽ 5,252; n ⫽ 213)

(N ⫽ 1,225; n ⫽ 113)

(N ⫽ 4,287; n ⫽ 531)

Basica Basic ⫹ IMT Basic ⫹ LVH Basic ⫹ PAD Basic ⫹ IMT ⭓1 Basic ⫹ age Basic ⫹ Multiple markersb Basic ⫹ Multiple markersa with age in score

0.829 0.836 0.827 0.829 0.828 0.835 0.836 0.838

0.788 0.796 0.788 0.787 0.794 0.798 0.796 0.802

0.664 0.691 0.661 0.666 0.669 0.692 0.698 0.708

0.681 0.707 0.681 0.683 0.696 0.694 0.708 0.714

a b c

Total cholesterol, HDL cholesterol, systolic blood pressure, antihypertensive medications, current smoking, diabetes. IMT [continuous] and PAD. Age is used in all survival models to derive coefficients for risk function, but is used in the risk score only where indicated.

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Table 4 Area under the ROC curve for various models for incident CHD: basic,a basic ⫹ non-traditional risk factorsb ⫹ subclinical disease markers,c and similar scores adding aged last (N ⫽ sample size; n ⫽ number of CHD events): The Atherosclerosis Risk in Communities Study 1987–1998 Black women

White women

Black men

White men

Model

(N ⫽ 1,798; n ⫽ 90)

(N ⫽ 5,006; n ⫽ 198)

(N ⫽ 1,102; n ⫽ 101)

(N ⫽ 4,082; n ⫽ 504)

Basic Basic ⫹ Risk ⫹ Sube Basic ⫹ Age Basic ⫹ Risk ⫹ Sub ⫹ Agef

0.830 0.845 0.842 0.848

0.793 0.805 0.798 0.809

0.669 0.720 0.700 0.728

0.685 0.733 0.695 0.735

a b c d e f

Total cholesterol, HDL cholesterol, systolic blood pressure, antihypertensive medications, current smoking, diabetes. BMI, waist–hip ratio, Keys score, albumin, residual FEV1, Lp(a), HR, fibrinogen, factor VII, vWF, pack/years smoking, sport activity index. IMT. Age is used in all survival models to derive coefficients for risk function, but is used in the risk score only where indicated. Increases from the basic model are all statistically significant (P ⬍ .05). No increases from the similar score without age are statistically significant.

any race and sex group. We thus dropped PAD, WBC, albumin, and creatinine. In this final model were excluded 2,066 persons with missing values on one of the remaining risk factors or subclinical disease markers, including 171 CHD events. We simultaneously added BMI, waist–hip ratio, sport activity index, Keys score, residual FEV1, fibrinogen, factor VII, vWF, LP(a), HR, pack-years cigarette smoking, and IMT to the traditional risk factors. Men started with less CHD predictability from the basic risk variables (Table 4). The increases in AUC beyond 0.5 by adding nontraditional risk factors and subclinical disease markers were 4–5% for women and 26–30% for men, and all these increases were statistically significant (P ⬍ .05). Inclusion of age in the risk score added little further to predictivity and none of these increases were statistically significant. The ROC curves are given in Fig. 1, by race and sex, for the full (dashed line) and basic (dotted line) models excluding age from the risk score, and for the full model including age in the score (solid line). These are plots of sensitivity vs. one minus specificity, where these are defined by “diagnosing” CHD when risk score is above each possible risk score cutpoint. The improvement (the rise) in the ROC curve from the basic to the full is seen most clearly for White men, and little difference is seen between the full model with and without age in the risk score. Fig. 2 gives a perspective on how absolute risk was affected by consideration of either the basic risk factors or the full set in Table 4. The predicted probability of incident CHD within 10 years of follow-up is plotted vs. decile of risk. The plots excluding age from the risk score were similar to those including age, so only the latter are used. As expected, the probability of event in the lower risk deciles was less in the full model than in the basic model, and greater in the upper deciles. This effect was clearest for White men. Women at lowest deciles of risk had so few events that there is little chance for improvement in that region. Fig. 2 also shows the population risk of incident CHD attributable to not having risk score in the bottom deciles of risk, for the full and basic models. The population risk attributable to not having risk score in the optimal bottom decile increased

from that using the score with basic risk factors to that using the full model: 93 to 95% for Black women, 89 to 91% for White women, 75 to 81% for Black men, and 72 to 75% for White men. Table 5 gives medians or percentages of risk factors by deciles (only three are presented) of risk score. For example, for White females the range of median values or prevalences from the bottom decile of risk to the top decile was 23.1 to 28.0 kg/m2 BMI, 0.58 to 0.77 mm IMT, 29 to 68 µg/mL Lp(a), 264 to 330 mg/dL fibrinogen, 65 to 69 beats/min heart rate, 39 to 44 Keys score, 0 to 26 cigarette pack/ years, 0.83 to 0.95 waist–hip ratio, 69 to 43 mg/dL HDLcholesterol, 183 to 238 mg/dL total cholesterol, 106 to 126 mmHg SBP, 0 to 54% antihypertension medication use, 0 to 42% diabetes, 0 to 61% current smoking, 2.8 to 2.0 sport activity index, 110 to 128% factor VII, 96 to 124% vWF, and 0.11 above predicted FEV1 to 0.33 below.

4. Discussion We have shown that for neither White men nor Black men in ARIC do the coefficients in a Framingham-type CHD risk score function differ significantly from Framingham men, except that diabetes carried a larger relative risk for ARIC White men than for Framingham men. For ARIC women some risk factors did have larger coefficients than for Framingham women, namely hypertension and current smoking for Black women and diabetes and current smoking for White women. (These results differ slightly from an earlier report using 2 years less follow-up [48]). This does suggest some caution in generalizing Framingham coefficients to other women without further study. Race differences in predictivity and increases to predictivity were minor compared to sex differences, except that SBP plus antihypertensive medications increased AUC more for Blacks than for Whites of either sex. The traditional risk factors were much more predictive for women than for men, and the increase in predictivity from the additional factors was greater for men. This is clear from the increases

Fig. 1. ROC curves comparing risk scores from the basic model excluding age from score (dotted line), the full model excluding age from score (dashed line), and the full model with age in score (solid line): ARIC 1987–1998.

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Fig. 2. Predicted 10-year risk of incident CHD, comparing the basic model (dotted line) to the full model (solid line), both with age in risk score, by race and sex: ARIC 1987–1998.

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in AUC seen in Fig. 1, and can also be seen in the curves (see Fig. 2) showing predicted 10-year risk as a function of risk score, and also from the increases in risks attributable to being above the bottom decile of risk score. Even with only the basic risk factors the curves in Fig. 2 are much flatter at lower levels of risk score for women, indicating that relatively few CHD events occurred in women with low risk score. Men, however, had CHD events across the entire range of risk score. Two recent nested case–control studies have addressed the increase in AUC when adding factors to the traditional ones in deriving a risk score using logistic regression. The Second Northwick Park Heart Study [49] found an increase in AUC from 0.71 to 0.72 when adding fibrinogen to a model with traditional risk factors plus alcohol consumption. AUC increased further to 0.77 with the further addition of coagulation factors VIIa, Xpep, XIIa, and IXpep. The study included 104 new CHD cases over a mean 7.8 years follow-up. The subjects were men aged 50–61 and free of MI at baseline, from nine general medical practices in Britain. The Rotterdam Study [50] found a statistically significant increase in AUC, from 0.65 to 0.72, when common carotid IMT was added to a model containing traditional risk factors plus an indicator for previous cardiovascular disease. The study included 374 MI or stroke cases over a mean 4.2 years follow-up. The subjects were men and women from a suburb of Rotterdam, aged 55 or older at baseline. In our study we chose to consider the effect of additional risk factors before including age in the risk score. Age is unquestionably highly associated with risk of incident CHD, and “indeed, after 50 years, age becomes the predominant risk factor” [51,52]. In Tables 2–4, comparing the AUC

between risk prediction with the basic risk factors (and not age) and risk prediction with “basic ⫹ age,” age improved predictivity when added. However, it is likely not simply years since birth that was contributing directly to increased risk, but rather increases in other factors that were associated with age but nevertheless are potentially modifiable. Grundy states [51] that “although several factors contribute to higher risk for acute coronary syndromes in older persons, the major factor almost certainly is an increasing coronary plaque burden with advancing age.” Grundy goes further to suggest several methods to estimate coronary plaque burden, including ankle-brachial blood pressure index and carotid IMT. Finally, Grundy argues for using one of these methods to replace age in the risk score, as it is these values that are more properly related to incident CHD. We go slightly further and argue for “replacing” age in the risk score by any of several potentially modifiable and possibly yet-to-bediscovered factors associated with onset of CHD. Indeed, age contributed little to increase predictivity after including our expanded list of factors in the risk score. No single factor that we considered provided a large increase in predictivity of which individuals will experience incident CHD. We found that this was also true for any one of the traditional risk factors except for blood pressure. The fact that the factors here considered generally do not singly greatly increase our ability to predict future CHD events for individuals does not contradict the fact that for many of these factors there are large increases in rates of events in groups with high level of the factors compared to low, as is evidenced by the up to tripling of rates indicated by some of the traditional risk factors in Table 2 and by the high relative risks of the nontraditional factors reported

Table 5 Medians and percentages for risk factors and subclinical disease markers by decile of risk score (“Basic ⫹ Risk ⫹ Sub” model from Table 4): The Atherosclerosis Risk in Communities Study 1987–1998

Risk factor or disease marker BMI (kg/m2) IMT (mm) Lp(a)(µg/mL) Fibrinogen (mg/dL) Heart rate (beats/min) Keys score Pack/years smoking Waist–hip ratio HDL cholesterol (mg/dL) Total cholesterol (mg/dL) Systolic BP (mmHg) Use of hypt. meds (%) Diabetes (%) Current smoker (%) Sport activity index Factor VII (%) VWF (%) FEV1 residual (L)

Black women

White women

Black men

White men

Risk decile

Risk decile

Risk decile

Risk decile

1

5

10

1

5

10

1

5

10

1

5

10

26.4 0.62 123 277 66 41.2 0.0 0.82 67.0 210 108 0.0 0.6 1.1 2.0 107 96 0.18

30.1 0.68 123 313 66 41.5 0.0 0.91 57.0 212 122 43.3 11.7 17.8 2.0 123 121 ⫺0.03

30.6 0.79 176 371 70 42.6 8.1 0.96 48.2 226 154 84.4 53.3 56.1 1.9 131 150 ⫺0.30

23.1 0.58 29 264 65 39.2 0.0 0.83 69.3 183 106 0.2 0.0 0.4 2.8 110 96 0.11

25.1 0.63 43 285 67 41.1 0.0 0.89 55.9 216 113 12.2 1.0 13.8 2.3 122 101 0.03

28.0 0.77 68 330 69 44.1 25.5 0.95 43.3 238 126 53.9 42.1 61.1 2.0 128 124 ⫺0.33

26.9 0.67 110 272 61 42.4 1.6 0.89 60.3 193 120 0.9 2.7 10.9 2.8 95 97 ⫺0.16

26.5 0.72 128 297 62 44.8 15.5 0.93 48.2 209 125 20.7 14.4 41.4 2.0 106 117 ⫺0.29

27.1 0.85 144 327 65 43.3 25.0 0.97 39.5 219 132 82.0 41.4 60.4 2.0 123 157 ⫺0.53

25.2 0.63 31 248 62 39.2 4.0 0.94 55.9 184 110 5.4 0.0 14.7 3.3 104 90 ⫺0.04

26.7 0.71 34 279 64 42.8 17.0 0.96 41.0 207 116 14.4 2.2 23.0 2.5 108 104 ⫺0.24

27.2 0.94 60 336 66 46.8 33.0 0.99 34.7 231 131 36.7 38.1 38.6 2.3 115 124 ⫺0.53

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elsewhere [42–47], nor does this fact suggest that some of these factors are not good targets for intervention for specific subgroups. What we have shown, however, is that adding all of BMI, waist–hip ratio, sport activity index, Keys score, residual FEV1, fibrinogen, factor VII, vWF, LP(a), HR, packyears cigarette smoking, and IMT to the basic risk function did substantially improve prediction of which individual men had new onset CHD, as measured by the gain in area under the ROC curve above the 0.5 minimum. We also considered the effect of inclusion of nontraditional risk factors on PAR, the percentage of 10-year cumulative risk of CHD, which is attributed to not having overall risk factor score at a low level. Even with traditional risk factors alone, 72–75% of CHD risk for men is attributable to not being in the bottom (optimal) decile of risk score derived from these factors, and 89–93% for women. These already high population attributable risks are further increased by inclusion of the nontraditional risk factors and markers of subclinical disease. The estimates of CHD risk attributable to the traditional risk factors are quite similar to those by Stamler and colleagues [53–55] of percent of CHD events attributable to not having low risk factor levels, with low risk defined by no current smoking and total serum cholesterol and blood pressure under specified cutpoints. In summary, whether one considers increases in predictivity in terms of concordance on an individual level between risk score and CHD outcome (ROC approach) or in terms of ability to separate the population into high risk/low risk groups in a way that few of the events are in the low risk group (PAR or Fig. 2 approach), the inclusion of nontraditional risk factors increases predictivity of CHD substantially for men but only slightly for women.

Acknowledgments The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts N01-HC-55015, N01HC-55016, N01-HC-55018, N01-HC-55019, N01-HC55020, N01-HC-55021, and N01-HC-55022. The authors thank the staff and participants of the ARIC study for their efforts, and thank Ernestine Bland for her word processing, Adam Gilbert for assistance in analysis, and Ding-yi Zhao and Yue Shen for their programming.

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