Prediction of Ischemic Stroke Risk in the Atherosclerosis Risk in Communities Study

Lloyd E. Chambless1 , Gerardo Heiss2, Eyal Shahar3, Mary Jo Earp1 and James Toole4

1 Department of Biostatistics, School of Public Health, University of North Carolina, Chapel Hill, NC.
2 Department of Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, NC.
3 Division of Epidemiology, School of Public Health, University of Minnesota, Minneapolis, MN.
4 Department of Neurology, Bowman Gray School of Medicine, Wake Forest University, Winston-Salem, NC.

Received for publication October 8, 2003; accepted for publication February 13, 2004.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
The authors assessed the increase in the predictivity of ischemic stroke (IS) resulting from the addition of nontraditional risk factors and markers of subclinical disease to a basic model containing only traditional risk factors (current smoking, diabetes mellitus, systolic blood pressure, antihypertensive therapy, prior coronary disease, and left ventricular hypertrophy) among 14,685 middle-aged persons in the Atherosclerosis Risk in Communities Study. Participants were recruited from four US communities in 1987–1989. Risk prediction scores for IS through 2000 were estimated from Cox models. The ability to predict which persons would develop IS was assessed by means of the area under the receiver operating characteristic curve—the probability that persons with IS had a higher risk score than those without IS. Among 22 nontraditional factors considered, the joint addition of body mass index, waist:hip ratio, high density lipoprotein cholesterol, albumin, von Willebrand factor, alcohol consumption, peripheral arterial disease, and carotid artery wall thickness modestly and statistically significantly improved prediction of future IS over a risk score that included traditional factors. Further improvement was obtained by adding age and race. For women, the area under the receiver operating characteristic curve went from 0.79 to 0.83 to 0.84; for men, it went from 0.76 to 0.78 to 0.80. These modest improvements are not enough to influence clinical and public health efforts to reduce the community burden of IS.

cerebrovascular accident; risk factors; ROC curve

Abbreviations: Abbreviations: ARIC, Atherosclerosis Risk in Communities; AUC, area under the ROC curve; CI, confidence interval; FEV1, forced expiratory volume in 1 second; HDL, high density lipoprotein; ROC, receiver operating characteristic.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
A major goal of epidemiology and preventive medicine is to develop tools to predict risk of disease. A well-known example is the Framingham Study risk score for prediction of coronary heart disease (1). We sought to develop a risk score for ischemic stroke, similar to a Framingham risk score for stroke (2), using data from the Atherosclerosis Risk in Communities (ARIC) Study and including the "established" basic risk factors age, current smoking, diabetes mellitus, systolic blood pressure, antihypertensive therapy, prior coronary heart disease, and left ventricular hypertrophy, as well as race. Investigators in the Cardiovascular Health Study also produced a stroke risk score (3) for persons aged 65 years or older, considering additional risk factors beyond the traditional factors. The Framingham Study population was drawn from a single location with mainly White residents (Framingham, Massachusetts) and the multicenter Cardiovascular Health Study population was older, so it is important to expand the inquiry into stroke risk prediction in another middle-aged US population that includes multiple localities and a large African-American sample, as in the ARIC Study.

We explored how well additional ischemic stroke risk factors already found in the ARIC Study or markers of subclinical disease improved prediction of individual risk beyond the basic factors, in terms of statistically significant increases in the area under the receiver operating characteristic (ROC) curve. The EUROSTROKE Project (4) has also explored this issue in men from three European cohorts, but with a limited number of nontraditional risk factors. The ARIC Study is one of the few studies that can address this question with many nontraditional factors. Additionally, at a population level, we assessed how much these nontraditional factors increased the proportion of ischemic stroke risk that may be attributed to nonoptimal levels of known risk factors.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
The ARIC Study is a study of cardiovascular disease in a cohort of 15,792 persons sampled from four US communities in 1987–1989. This report includes follow-up through 2000 (median, 12.3 years). At baseline, 45- to 64-year-old members of sampled households in Minneapolis, Minnesota (selected suburbs), Forsyth County, North Carolina, Washington County, Maryland, and Jackson, Mississippi, were enrolled, the latter group being selected from Black residents only. The sampling procedures have been described previously (5, 6).

Baseline examination
Participants were asked to fast for 12 hours before undergoing a clinical examination. Detailed methods have been reported elsewhere for blood collection (7, 8) and for centralized measurement of plasma total cholesterol, apolipoproteins AI and B (9, 10), triglycerides (9, 11), high density lipoprotein (HDL) cholesterol (9), lipoprotein(a) (12), fibrinogen (1316), factor VII, factor VIII, and von Willebrand factor (1416), and serum creatinine and albumin (17). Estimates of intraindividual variability in blood measurements have been reported previously (1820). White blood cell counts were obtained in the four study communities. Total cholesterol level was categorized as in the Framingham risk score for coronary heart disease (1). Multiple HDL cholesterol categories were not used, since there was no clear monotonic association in the ARIC Study (21) between these categories and ischemic stroke but rather a protective association with high HDL cholesterol, with cutpoints for "high" being 60 mg/dl in women and 50 mg/dl in men.

Methods used for ascertainment of body mass index (weight (kg)/height (m)2) and waist:hip ratio (22), systolic and diastolic blood pressure (23), a sport activity index (22, 24, 25), and heart rate (26) have been reported elsewhere. Forced expiratory volume in 1 second (FEV1), measured with spirometry (27), was used to calculate "residual FEV1" as the difference from the value predicted from age, height, and sex. A centrally read 12-lead electrocardiogram (26) was used to define left ventricular hypertrophy, using the Cornell score (28). A 2-minute rhythm strip was used to assess atrial fibrillation (26). Preexisting coronary heart disease at baseline was defined by self-reported prior physician diagnosis of myocardial infarction or coronary revascularization or evidence of myocardial infarction on the ARIC electrocardiogram (26).

Use of antihypertensive medication within the 2 weeks prior to baseline was self-reported (22). Hypertension was defined as systolic blood pressure ≥140 or diastolic blood pressure ≥90 or use of antihypertensive medication. Categories of hypertension were defined according to levels specified by the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (29). Peripheral arterial disease was defined as an ankle-brachial index (the ratio of ankle systolic blood pressure to brachial systolic blood pressure) less than 0.90 for men and less than 0.85 for women. Smoking status (current, former, or never smoking) and pack-years of cigarette smoking were estimated from the interview (22). Prevalent diabetes mellitus was defined as a fasting glucose level ≥126 mg/dl, a nonfasting glucose level ≥200 mg/dl, a self-reported physician diagnosis, or pharmacologic treatment.

Ultrasound measurements in the ARIC Study used a validated technique (30), a scanning protocol common to the four field centers (31, 32), and standardized central reading of scans (33). The analyses used the mean intima-media thickness of the far wall for 1-cm lengths of the carotid bifurcation and the internal and common carotid arteries, right and left, adjusted for reader differences and measurement drifts in intima-media thickness over the baseline visit (34).

Ascertainment and validation of incident events
Incidence of stroke was ascertained by contacting participants annually, identifying hospitalizations, and surveying discharge lists from local hospitals and death certificates from state vital statistics offices for potential cerebrovascular events (22, 35, 36). Of persons alive at the 14th annual contact, 94 percent were successfully contacted. Data from hospital records were abstracted by trained personnel. Each eligible case was classified by both computer algorithm and physician reviewer, according to criteria adapted from the National Survey of Stroke (37). Differences in diagnoses were adjudicated by another reviewer. Details on quality assurance for ascertainment and classification of events are presented elsewhere (36).

The category "ischemic stroke" includes validated definite or probable hospitalized embolic or thrombotic stroke, with classification based on combinations of symptom type, duration, and severity, results of neuroimaging and other diagnostic procedures, and autopsy evidence when available (35, 36).

Exclusions
Persons of races other than Black or White (n = 48) and Blacks in Minneapolis and Washington County (n = 55) were excluded. Participants were also excluded for prior stroke at baseline (n = 282) (self-reported prior physician diagnosis of stroke), missing data on prior stroke at baseline (n = 43), or missing data on one of the basic risk factors or body mass index (n = 674).

Statistical methods
We fitted sex-specific Cox regression models for incident ischemic stroke (38). From the coefficients of the models, we calculated a risk score for each person by multiplying risk factor coefficients by risk factor levels and then summing these products. Our measure of individual risk predictivity was the area under the ROC curve (AUC) (39), the probability that a person who had an incident stroke event within 10 years would have a higher risk score than a person who did not have an event by that time. We used Kaplan-Meier-like methods to calculate the relevant probabilities in a recursive formula for AUC in the face of censoring. The AUC has a range of values between 0.5 and 1. Tests of difference in AUC between risk scores were carried out by bootstrapping (39, 40). We also present the ROC curves (39), plots of the sensitivity of the risk score versus 1 – specificity, calculated for various cutpoints for the risk score, above which an ischemic stroke endpoint is predicted. The predicted probability of an incident ischemic stroke event within the first 10 years of follow-up is plotted against percentiles of risk score, with the set of persons at given percentiles differing between different risk scores. Improved prediction is indicated by moving more of the predicted events out of lower percentiles of the risk score into upper percentiles.

The population attributable risk for exposure defined by a risk score above the bottom risk decile was computed as 100(p p*)/p, where p is the overall predicted 10-year probability of an event and p* is the predicted 10-year probability in the bottom decile (41). Thus, the population attributable risk is the percentage of 10-year cumulative incidence of ischemic stroke that is associated with not having an overall risk factor score in the bottom decile. The increases in population attributable risk resulting from inclusion of nontraditional risk factors were calculated.

All models were adjusted for age and race in estimation of the coefficients of the factors included in the risk score. Since our interest was in the potential increase in predictivity from the addition of potentially modifiable risk factors to a score function using the basic risk factors, we calculated risk scores with age set to 55 years for everyone and race set to the proportion of Black participants in the ARIC Study (equivalent to not including age or race in the risk score). Only at the last step, after inclusion of all nontraditional factors, did we consider the effect of additionally including (actual) age and race in the risk score.

For Cox models, the predicted probability P of incident ischemic stroke within 10 years can be calculated from the risk score (RS) by (38), where P0 is the predicted probability of stroke within 10 years for persons with the reference-level risk score RS0. This reference-level RS0 is arbitrary, and we chose it as the low risk levels for the categorical risk factors and the median (over both sexes) values for continuous risk factors. In the basic model, RS0 is for persons who are not current smokers, do not have diabetes, are not taking antihypertensive medication, do not have left ventricular hypertrophy, do not have previous coronary heart disease, have a systolic blood pressure of 119 mmHg, are aged 54 years, and are White. Values of P0 and RS0 are given in the Appendix, and the website www.aricnews.net has a user-friendly ischemic stroke risk calculator based on the results presented below.

We compared the predictivity of the Framingham and ARIC basic risk scores in the ARIC cohort. To remove the advantage created by assessing predictivity with the same data set used to estimate the coefficients, we derived an ARIC score from randomly chosen subsets containing three fourths of the ARIC participants and calculated the AUC using the remaining one fourth, for both this ARIC score and the Framingham score. We repeated this process 1,000 times, each time on a random sample chosen with replacement and of the same size as the original data set, and we calculated the mean AUC for the ARIC and Framingham risk scores and the middle 95 percent of the ARIC-minus-Framingham AUC differences, with the latter being expressed as a bootstrap 95 percent confidence interval for the differences.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
The sample size was 14,685 (8,113 women and 6,572 men), and the number of incident ischemic stroke events was 434 (185 in women and 249 in men). Age- and race-adjusted event rates were 1.9 and 3.3 per 1,000 person-years of follow-up for women and men, respectively. There were only two stroke events with atrial fibrillation (both in men), so this factor was excluded. The interaction of systolic blood pressure with antihypertensive therapy (included in the Framingham score for women) was not significant for men and was significant for women (p = 0.035). The interaction’s addition to the AUC was less than 0.001 when age and race were included in the risk score and 0.002 when age and race were not included, so the interaction was excluded. The age- and race-adjusted sex-specific hazard rate ratios and 95 percent confidence intervals for incident ischemic stroke (table 1) are not strictly comparable with the Framingham results (table 3 in the article by Wolf et al. (2)), because the Framingham Study included hemorrhagic stroke in the outcome variable; included atrial fibrillation and, for women, the interaction between systolic blood pressure and antihypertensive therapy as risk factors; and had a somewhat different definition of prior coronary heart disease. The hazard rate ratios for diabetes were much larger for ARIC than for Framingham, and for men the hazard rate ratio for systolic blood pressure was smaller in ARIC than in Framingham. There were no statistically significant differences in hazard rate ratios between ARIC men and women. Note that the coefficient for Black race is likely to reflect residual confounding by socioeconomic status or other factors not included in the model and is based predominantly on one site (Jackson).


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TABLE 1. Multivariable adjusted beta coefficients and hazard rate ratios for ischemic stroke, by sex, Atherosclerosis Risk in Communities Study, 1987–2000
 
When the ARIC model was fitted to three fourths of the data (from table 2) and the predictivity of the remaining one fourth was assessed, mean AUCs over 1,000 random repetitions were 0.808 (95 percent confidence interval (CI): 0.729, 0.882) and 0.796 (95 percent CI: 0.723, 0.866) for ARIC and Framingham women, respectively, and 0.783 (95 percent CI: 0.714, 0.842) and 0.781 (95 percent CI: 0.710, 0.841) for ARIC and Framingham men, respectively. The differences in AUC between the ARIC and Framingham risk scores for the ARIC population were not statistically significant.


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TABLE 2. Area under the receiver operating characteristic curve for ischemic stroke in the basic model and the basic model minus or plus one or more risk factors, by sex, Atherosclerosis Risk in Communities Study, 1987–2000
 
In an evaluation of the effect of dropping or adding risk factors to the basic model, 1,547 persons (64 incident ischemic stroke events) were excluded if they were missing a value for any risk factor. In comparing the basic risk factor model with the models omitting one risk factor and considering the risk score without inclusion of age or race (table 2), omitting systolic blood pressure had by far the greatest effect on the AUC; diabetes had the next-greatest effect, and left ventricular hypertrophy had the least. Omitting systolic blood pressure and hypertension medication jointly had a very large effect on the AUC. Alternatively (data not shown), we considered the effect of one risk factor alone added to age and race. Again, systolic blood pressure plus use of hypertension medication had by far the largest effect on the AUC, increasing it by 0.07, with diabetes having the second strongest effect (increasing the AUC by 0.05 for women and 0.04 for men). All other basic factors increased the AUC by at least 0.01 for men, but for women only current smoking had this effect.

None of the additional risk factors increased the AUC beyond the basic model by 0.005 or more in both sexes. The factors contributing at least a 0.005 increase in the AUC in only one sex were apolipoprotein B, HDL cholesterol, total cholesterol, lipoprotein(a), FEV1, and von Willebrand factor for women and albumin and waist:hip ratio for men. With regard to other factors, only heart rate, pack-years of cigarette smoking, and factor VIII (women) and factor VII (men) increased the AUC by at least 0.003 in at least one sex. Thus, apolipoprotein AI was dropped because of its small contribution to predictivity and its high correlation with HDL cholesterol. Factor VIII was dropped because of its high correlation with von Willebrand factor and its lower contribution than von Willebrand factor. Apolipoprotein B was also dropped because of its high correlation with total cholesterol and the fact that omitting it from a preliminary "multiple risk factors" model led to a drop of only 0.001 in the AUC for men and 0.003 for women. When the other risk factors were retained in a "basic + multiple risk factors" model with 17 additional factors and this model was compared with the basic model, with neither model including age or race in the risk score, the AUC increased by 0.028 for women and 0.012 for men—a 10 percent increase for women and a 5 percent increase for men in the area beyond 0.5.

We considered separately the addition of fasting triglyceride levels, since the fasting restriction resulted in many missing values. The addition of fasting triglycerides increased the AUC above the basic model by 0.005 for women and did not increase it for men. Similar separate consideration was given to parental history of stroke and to hormone use among women (current use of estrogen only, current use of estrogen plus progestin, former hormone use, never use). Neither factor added more than 0.001 to the AUC, so both factors were dropped. Finally, the blood pressure categories of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (29) and their interaction with use of antihypertensive medication were considered in place of systolic blood pressure and hypertension medication use but did not improve the AUC.

In the addition of subclinical disease markers to the basic model predicting ischemic stroke, persons with missing values for any of the markers were excluded (1,292 persons with 45 incident ischemic stroke events). Peripheral arterial disease did not increase the AUC for women, though it increased the AUC by 0.002 for men. Intima-media thickness (continuous variable) increased the AUC by 0.013 for women and 0.015 for men (table 3). The model adding intima-media thickness and peripheral arterial disease jointly yielded a 5 percent increase in the AUC beyond 0.5 for women and a 6 percent increase for men.


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TABLE 3. Area under the receiver operating characteristic curve for ischemic stroke in the basic model and the basic model plus one or more markers of subclinical disease, by sex, Atherosclerosis Risk in Communities Study, 1987–2000
 
Most of these potential ischemic stroke risk factors and markers of subclinical disease have been addressed in published articles (4249) from the ARIC Study and others. The variables considered in tables 2 and 3 were statistically significant (p < 0.10) in "basic + one factor" models for at least one sex group, except for body mass index, fasting triglycerides, apolipoprotein AI, sport activity level, FEV1, pack-years of cigarette smoking, creatinine, ethanol intake, and serum potassium. Of these latter factors, all were significant in at least one group when data were adjusted only for age and race, except alcohol consumption.

In predicting incident ischemic stroke using both additional risk factors and markers of subclinical disease, we sequentially dropped one variable at a time if the AUC did not decline greater than 0.002 in either sex group. The final model retained body mass index, waist:hip ratio, HDL cholesterol, albumin, von Willebrand factor, alcohol consumption, intima-media thickness, and peripheral arterial disease, in addition to the basic factors. (When the analysis was restricted to persons who had fasted for at least 8 hours, the addition of triglycerides to this final model did not increase the AUC for women or men.) There were 1,524 persons excluded (58 ischemic stroke events) because they were missing data on one of the remaining risk factors or subclinical disease markers. Men started with less ischemic stroke predictivity from the basic risk variables (table 4). The increases in the AUC beyond 0.5 obtained by the addition of nontraditional risk factors and subclinical disease markers were 11 percent for women and 11 percent for men, and these increases were statistically significant (p < 0.05). The inclusion of race in the risk score added little to predictivity, and these increases were not statistically significant, but the inclusion of age did add significantly to predictivity.


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TABLE 4. Area under the receiver operating characteristic curve in various models for incident ischemic stroke, Atherosclerosis Risk in Communities Study, 1987–2000
 
The ROC curves (figures 1 and 2) improve (rise) from the basic model to the full model, with less difference between the full models with or without age and race in the risk score. For the full model, sensitivity and specificity can be read from left to right at the 90th through 10th percentiles of risk. For women, at the 90th percentile sensitivity and specificity were 0.54 and 0.91, and at the 10th percentile they were 0.98 and 0.10; for men, at the 90th percentile they were 0.35 and 0.91, and at the 10th percentile they were 1.00 and 0.10.



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FIGURE 1. Receiver operating characteristic curve for women, Atherosclerosis Risk in Communities Study, 1987–2000. The dots indicate deciles of risk: 90th, 80th, ... 10th percentile (left to right). At baseline, the study sample consisted of 14,685 middle-aged persons recruited from households in Minneapolis, Minnesota (selected suburbs), Forsyth County, North Carolina, Washington County, Maryland, and Jackson, Mississippi. The basic model included systolic blood pressure, use of antihypertensive medication, diabetes mellitus, current smoking, previous coronary heart disease, left ventricular hypertrophy by electrocardiogram, age, and race. The additional model included body mass index, waist:hip ratio, high density lipoprotein cholesterol, albumin, von Willebrand factor, alcohol consumption, intima-media thickness, and peripheral arterial disease.

 


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FIGURE 2. Receiver operating characteristic curve for men, Atherosclerosis Risk in Communities Study, 1987–2000. The dots indicate deciles of risk: 90th, 80th, ... 10th percentile (left to right). At baseline, the study sample consisted of 14,685 middle-aged persons recruited from households in Minneapolis, Minnesota (selected suburbs), Forsyth County, North Carolina, Washington County, Maryland, and Jackson, Mississippi. The basic model included systolic blood pressure, use of antihypertensive medication, diabetes mellitus, current smoking, previous coronary heart disease, left ventricular hypertrophy by electrocardiogram, age, and race. The additional model included body mass index, waist:hip ratio, high density lipoprotein cholesterol, albumin, von Willebrand factor, alcohol consumption, intima-media thickness, and peripheral arterial disease.

 
The probability of incident ischemic stroke within 10 years of follow-up (figures 3 and 4) was only slightly lower in the lower risk percentiles in the full model than in the basic model, but it was notably greater in the full model than in the basic model in the highest percentiles. For example, at the 98th percentile (of the almost 6,000 women), the 10-year probability of an event was 0.10 for the basic model and 0.15 for the full model. At the lowest percentiles of risk, there were so few events that there was little chance for improvement.



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FIGURE 3. Predicted probability of an ischemic stroke event within 10 years, by percentile of risk, among women, Atherosclerosis Risk in Communities Study, 1987–2000. All variables were included in the risk score. At baseline, the study sample consisted of 14,685 middle-aged persons recruited from households in Minneapolis, Minnesota (selected suburbs), Forsyth County, North Carolina, Washington County, Maryland, and Jackson, Mississippi.

 


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FIGURE 4. Predicted probability of an ischemic stroke event within 10 years, by percentile of risk, among men, Atherosclerosis Risk in Communities Study, 1987–2000. All variables were included in the risk score. At baseline, the study sample consisted of 14,685 middle-aged persons recruited from households in Minneapolis, Minnesota (selected suburbs), Forsyth County, North Carolina, Washington County, Maryland, and Jackson, Mississippi.

 
From the full model, among women, 6.4 percent had a predicted 10-year risk of ischemic stroke above 5 percent, and 2.7 percent had a predicted risk above 10 percent. For men, the estimates were 16.7 percent and 5.9 percent, respectively. The risk of stroke in the top two deciles was 29 times the risk in the bottom two deciles for women and 16 times the risk in the bottom two deciles for men. The population risk attributable to not having a risk score in the optimal bottom decile increased from that using the risk score with basic risk factors to that using the full model: from 86 percent to 91 percent for women and from 82 percent to 85 percent for men.


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
Coefficients in the basic ischemic stroke risk factor model were not significantly different between men and women, but ARIC coefficients sometimes differed from those of the Framingham Study; this suggests a need for caution when applying model coefficients to external populations. While the ARIC Study included a sizable African-American cohort, most of these persons resided in one city, and other ethnic groups were too sparsely represented in the ARIC Study for inclusion in this analysis. Thus, it is possible that the ARIC risk score will not be applicable to other US populations, even populations of the same age. Furthermore, when applying the ARIC-predicted 10-year stroke risk to populations with different overall event rates, one should calibrate predicted risk to the different risk level (see Appendix).

Predictivity (AUC) in the ARIC population differed little between the basic ARIC and Framingham risk scores. The basic risk factors were more predictive for women than for men, whereas the increases in predictivity from the additional factors were similar between the sexes. Few ischemic stroke events occurred in women or men with low risk scores.

In the Cardiovascular Health Study, Lumley et al. (3) published a stroke risk score for any stroke, not just ischemic stroke, for persons aged 65 years or older at entry, considering additional risk factors beyond the traditional factors, although the criteria for retention were based on the statistical significance of the coefficients. Men and women were combined, keeping sex-specific risk factor coefficients only when the differences were large or statistically significant. Lumley et al.’s final model included left ventricular hypertrophy by electrocardiogram, diabetes mellitus, impaired fasting glucose level, creatinine level, 15-foot (3-m) walking time, systolic blood pressure, history of heart disease, atrial fibrillation by electrocardiogram, and age, with sex-specific coefficients for the last three factors. Relative to the ARIC population, both the Cardiovascular Health Study model and the Framingham model have somewhat lower predictivity for the Cardiovascular Health Study population, with AUCs of 0.73–0.79 for women and 0.65–0.69 for men. The probability of incident stroke within 5 years was 14 times higher in the highest quintile of predicted risk than in the lowest.

In the British Regional Heart Study, Coppola et al. published a scoring system for any new or recurrent stroke among men aged 40–59 years drawn at random from general medical practices in Britain, keeping statistically significant risk factors unless they "did not increase the yield of the score" (50, p. 187). The analysis used logistic regression, and the outcome was any stroke within 5 years of study entry. The factors kept in the analysis were age, systolic blood pressure, Rose questionnaire angina, and number of cigarettes smoked per day. No results were given for AUC. The stroke risk rate was 17 times higher in the top quintile than in the lowest, with 82 percent of strokes occurring in the highest quintile of stroke score.

The EUROSTROKE Project derived a stroke prediction rule (4) with a nested case-control design, using data on men from three European cohorts and applying logistic regression. The authors concluded, in agreement with the ARIC findings presented here, that neither fibrinogen nor left ventricular hypertrophy by electrocardiogram added much stroke predictive value, in terms of the AUC.

We considered the effect of additional risk factors before including age and race in the risk score, because our interest was in the potential increase in predictivity from the addition of potentially modifiable risk factors to a score function using the basic risk factors. Only at the last step, after including all nontraditional risk factors, did we consider the effect of additionally including age and race in the risk score. When assessing absolute risk, we always included age and race. Age is unquestionably associated with risk of incident ischemic stroke (table 1); however, it is probably not simply years since birth that contributes directly to increased risk but rather changes in other factors that are associated with age but nevertheless are potentially modifiable. This point is discussed further elsewhere (5153). In comparisons of AUCs between risk prediction from the basic risk factors (and not race) and risk prediction with the "basic + race" model (tables 3 and 4), race did not improve predictivity when added. Age, however, improved predictivity in the basic and full models.

With the exception of blood pressure, no single factor that we considered provided a large increase in the ability to predict which persons will experience incident ischemic stroke. Small incremental predictability of future ischemic stroke events for individuals should not be interpreted as small relative risk—observed rates among persons with high levels of many of these factors can be three times the rates in the reference group (4249). Adding body mass index, waist:hip ratio, HDL cholesterol, serum albumin, von Willebrand factor, peripheral arterial disease, alcohol consumption, and intima-media thickness to the basic risk function did somewhat improve prediction of new-onset ischemic stroke.

We also considered the effect of inclusion of nontraditional risk factors on population attributable risk, the percentage of 10-year cumulative risk of ischemic stroke that is attributed to not having an overall risk factor score at a low level. Even with traditional risk factors alone, 82 percent of the ischemic stroke risk for men (and 86 percent for women) is attributable to not being in the bottom (optimal) decile of the risk score derived from these factors. This suggests a sizable potential for population stroke prevention. These already high population attributable risks are further increased by inclusion of the nontraditional risk factors and markers of subclinical disease.

The so-called "basic risk factors" were treated preferentially in our analyses (entered first) because they reflect predictors that happened to be discovered first, can be measured easily, and are often measured in clinical practice. We constructed prediction models here by a hybrid of biologic reasoning, chronology of discovery of causes, practicality, and statistical criteria; therefore, no model necessarily includes only biologic causes or even the most important ones. Within the population at hand, the basic risk factors performed reasonably well, and because the other candidate risk factors considered provided only modest incremental value, prediction of risk from nontraditional risk factors does not emerge as a priority for clinical and public health intervention, especially in light of the persistently low rates of hypertension control in the United States (54). Of course, it is possible that different results will be found in other populations, such as patients at medium risk, older persons, and persons of ethnicities other than Black or White.


    ACKNOWLEDGMENTS
 
The Atherosclerosis Risk in Communities (ARIC) Study was carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts N01-HC-55015, N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, and N01-HC-55022.

The authors thank the staff of the ARIC Study for their important contributions.


    APPENDIX
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
From the basic models (table 1), the risk score, RS0, for persons at the reference risk factor levels was 5.79944 for women and 6.55671 for men, for which the 10-year ischemic stroke risk, P0, is 0.00609426 and 0.0109276, respectively. To apply this probability calculation to a population with a different overall 10-year risk of ischemic stroke than the Atherosclerosis Risk in Communities Study population but assuming that the same model coefficients apply and also allowing for a different reference RS0,new (calculated from different median values for continuous risk factors), a P0,new applicable to the different overall risk level is needed, and then the similar formula (Cox proportional hazards model assumption (36)) can be applied. If a representative cohort sample is available, P0,new can be approximated by solving the equation Poverall,new = cohort mean , where Poverall,new can be taken as 1 minus the survival probability estimated by Kaplan-Meier methods and "cohort mean" signifies the mean value for the entire cohort.

The risk score and 10-year predicted risk can also be calculated from the full model shown in table 4. Beta coefficients and median values for continuous risk factors are given in Appendix table A1.


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APPENDIX TABLE 1. Beta coefficients for risk factors included in the full model shown in table 4, median values for continuous risk factors, RS0, and P0*
 


    NOTES
 
Reprint requests to Dr. Lloyd E. Chambless, Department of Biostatistics, University of North Carolina, CB #8030, 137 East Franklin Street, Suite 400, Chapel Hill, NC 27514-4145 (e-mail: wchambless{at}unc.edu). Back


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 

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