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Review | Clinician's Corner

Ankle Brachial Index Combined With Framingham Risk Score to Predict Cardiovascular Events and Mortality:  A Meta-analysis FREE

[+] Author Affiliations

Ankle Brachial Index Collaboration Authors:F. G. R. Fowkes, G. D. Murray, I. Butcher, C. L. Heald, R. J. Lee (coordinating center); L. E. Chambless, A. R. Folsom, A. T. Hirsch (Atherosclerosis Risk in Communities [ARIC] Study); M. Dramaix, G. deBacker, J-C. Wautrecht, M. Kornitzer (Belgian Physical Fitness Study); A. B. Newman, M. Cushman, K. Sutton-Tyrrell (Cardiovascular Health Study); F. G. R. Fowkes, A. J. Lee, J. F. Price (Edinburgh Artery Study); R. B. d’Agostino, J. M. Murabito (Framingham Offspring Study); P. E. Norman, K. Jamrozik (Health in Men Study); J. D. Curb, K. H. Masaki, B. L. Rodríguez (Honolulu Heart Program); J. M. Dekker, L. M. Bouter, R. J. Heine, G.  Nijpels, C. D. A. Stehouwer (Hoorn Study); L.  Ferrucci, M. M.  McDermott (InCHIANTI Study); H. E. Stoffers, J. D. Hooi, J. A. Knottnerus (Limburg PAOD Study); M. Ogren, B. Hedblad (Men Born in 1914 Study); J. C. Witteman, M. M. B. Breteler, M. G. M. Hunink, A. Hofman (Rotterdam Study); M. H. Criqui, R. D. Langer, A. Fronek (San Diego Study); W. R. Hiatt, R. Hamman (San Luis Valley Diabetes Study); H. E. Resnick (Strong Heart Study); J. Guralnik, M. M.  McDermott (Women's Health and Aging Study).


JAMA. 2008;300(2):197-208. doi:10.1001/jama.300.2.197.
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Context Prediction models to identify healthy individuals at high risk of cardiovascular disease have limited accuracy. A low ankle brachial index (ABI) is an indicator of atherosclerosis and has the potential to improve prediction.

Objective To determine if the ABI provides information on the risk of cardiovascular events and mortality independently of the Framingham risk score (FRS) and can improve risk prediction.

Data Sources Relevant studies were identified. A search of MEDLINE (1950 to February 2008) and EMBASE (1980 to February 2008) was conducted using common text words for the term ankle brachial index combined with text words and Medical Subject Headings to capture prospective cohort designs. Review of reference lists and conference proceedings, and correspondence with experts was conducted to identify additional published and unpublished studies.

Study Selection Studies were included if participants were derived from a general population, ABI was measured at baseline, and individuals were followed up to detect total and cardiovascular mortality.

Data Extraction Prespecified data on individuals in each selected study were extracted into a combined data set and an individual participant data meta-analysis was conducted on individuals who had no previous history of coronary heart disease.

Results Sixteen population cohort studies fulfilling the inclusion criteria were included. During 480 325 person-years of follow-up of 24 955 men and 23 339 women, the risk of death by ABI had a reverse J-shaped distribution with a normal (low risk) ABI of 1.11 to 1.40. The 10-year cardiovascular mortality in men with a low ABI (≤0.90) was 18.7% (95% confidence interval [CI], 13.3%-24.1%) and with normal ABI (1.11-1.40) was 4.4% (95% CI, 3.2%-5.7%) (hazard ratio [HR], 4.2; 95% CI, 3.3-5.4). Corresponding mortalities in women were 12.6% (95% CI, 6.2%-19.0%) and 4.1% (95% CI, 2.2%-6.1%) (HR, 3.5; 95% CI, 2.4-5.1). The HRs remained elevated after adjusting for FRS (2.9 [95% CI, 2.3-3.7] for men vs 3.0 [95% CI, 2.0-4.4] for women). A low ABI (≤0.90) was associated with approximately twice the 10-year total mortality, cardiovascular mortality, and major coronary event rate compared with the overall rate in each FRS category. Inclusion of the ABI in cardiovascular risk stratification using the FRS would result in reclassification of the risk category and modification of treatment recommendations in approximately 19% of men and 36% of women.

Conclusion Measurement of the ABI may improve the accuracy of cardiovascular risk prediction beyond the FRS.

Figures in this Article

Major cardiovascular and cerebrovascular events including myocardial infarction and stroke often occur in individuals without known preexisting cardiovascular disease. The prevention of such events, including the accurate identification of those at risk,1 remains a serious public health challenge. Quiz Ref IDScoring equations to predict those at increased risk have been developed using cardiovascular risk factors, including cigarette smoking, blood pressure, total and high-density lipoprotein cholesterol, and diabetes mellitus. The Framingham risk score (FRS)2,3 is often considered the reference standard but has limited accuracy, tending to overestimate risk in low-risk populations and underestimate risk in high-risk populations.4 The incorporation of other risk markers, such as the metabolic syndrome5 and plasma C-reactive protein,6,7 has had partial success in improving prediction, and attention also is being given to indicators of asymptomatic atherosclerosis, such as coronary artery calcium, carotid intima media thickness, and the ankle brachial index (ABI).1

Quiz Ref IDThe ABI, which is the ratio of systolic pressure at the ankle to that in the arm, is quick and easy to measure and has been used for many years in vascular practice to confirm the diagnosis and assess the severity of peripheral artery disease in the legs. Most commonly the ABI is calculated by measuring the systolic blood pressure in the posterior tibial and/or the dorsalis pedis arteries either in both legs or 1 leg chosen at random (using a Doppler probe or alternative pulse sensor), with the lowest ankle pressure then divided by the brachial systolic blood pressure. In addition to peripheral artery disease, the ABI also is an indicator of generalized atherosclerosis because lower levels have been associated with higher rates of concomitant coronary and cerebrovascular disease, and with the presence of cardiovascular risk factors.8 In population cohort studies in the United States912 and Europe,1317 a low ABI has been related to an increased incidence of mortality (total and cardiovascular), myocardial infarction, and stroke. These increased relative risks have been shown to be independent of baseline cardiovascular disease and risk factors, suggesting that the ABI might have an independent role in predicting cardiovascular events.

The objective of our study was to determine if the ABI provides information on the risk of cardiovascular events and mortality independently of the FRS and can improve risk prediction. To enhance the representativeness of our study and to maximize participant numbers, we formed the Ankle Brachial Index Collaboration with the intent of including all major observational studies that had investigated longitudinally the ABI and incidence of cardiovascular events and mortality in general populations. At the same time we wished to identify a normal (low risk) level of the ABI that could be used in future studies and in clinical practice.

The study design was an individual participant data meta-analysis of population-based cohort studies. The criteria for study inclusion were that the study contained participants of any age and sex derived from a general population (ie, not a specific disease group), ABI was measured at baseline using a technique standardized in each study, and individuals were followed up systematically to detect total and cardiovascular mortality.

At initial meetings of epidemiologists interested in the ABI, studies fulfilling the inclusion criteria were identified. A search was conducted of MEDLINE from 1950 to February 2008 and EMBASE from 1980 to February 2008. Reference lists and conference proceedings also were searched to identify possible additional studies. The following search terms were used: ABPI.tw, ABI.tw, AAI.tw, ankle brachial pressure index $.tw, ankle brachial pressure$.tw, ankle brachial index$.tw. (or ankle brachial index/), ankle arm index$.tw, ankle arm blood pressure$.tw, ankle arm blood pressure index$.tw, ankle blood pressure$.tw, follow up stud$.tw, follow up studies/ or follow up/, epidemiological stud$.tw, epidemiological studies/ or epidemiology/, cohort$.tw, cohort analysis/ or cohort studies/.

Further studies and unpublished data were sought by discussion between collaborators, cardiovascular epidemiologists, and vascular physicians and by correspondence with the Asia Pacific Cohort Studies Collaboration. Possible studies for inclusion were independently assessed for suitability by 2 collaborators (G.F. and J.P.) and any lack of clarity or disagreement was resolved by discussion.

The principal authors or lead investigators of studies were invited to join the ABI Collaboration and, following acceptance, were sent a questionnaire enquiring about the availability of specific study data. On reviewing responses to these questionnaires, a set of data that were commonly available was agreed on, and each study transferred their relevant data to the coordinating center.

Requested data included individual demographic characteristics (eg, sex, age, height, and weight), baseline clinical cofactors (eg, systolic and diastolic blood pressure, cholesterol, diabetes, and cigarette smoking), details of baseline ABI measurements, and information on nonfatal and fatal events during follow-up. For these analyses, the participants included had no previous history of coronary heart disease (CHD) as defined in each study, a value for ABI recorded at baseline, and follow-up dates or times to events. Data from collaborators were extracted and analyzed using SPSS version 14 (SPSS Inc, Chicago, Illinois) and SAS version 9.1 (SAS Institute Inc, Cary, North Carolina).

A FRS was derived for each individual using the sex-specific prediction formulas proposed by Wilson et al3 based on conventional cardiovascular risk factors (age, total and high-density lipoprotein cholesterol categories, blood pressure categories, diabetes, and smoking status). When data on some of the variables necessary to calculate the FRS were incomplete, missing values, amounting to 3.9% of total values, were imputed using the expectation-maximization procedure for multivariate normal data, which is implemented in SPSS.

Overall (all studies combined) hazard ratios (HRs) for ABI, subdivided into 10 categories compared with a reference range of 1.11 to 1.20, were obtained for men and women for each of 3 outcomes of total mortality, cardiovascular mortality, and major coronary events (ie, coronary death, nonfatal myocardial infarction), and patterns of risk examined. Coronary revascularization and angina were not included as end points. The HRs for low vs normal ABI, which was categorized into 4 groups for the 3 outcomes of total mortality, cardiovascular mortality, and major coronary events were obtained from a proportional hazards model stratified by sex and study, both unadjusted and adjusted for FRS (categorized into 5 strata for men and 4 for women). These HRs were then pooled using a random-effects model and summarized using forest plots (Review Manager version 4.2.9, Cochrane Collaboration, Oxford, England).

Kaplan-Meier estimates and standard errors for outcome rates (total mortality, cardiovascular mortality, and major coronary events) at 10 years were obtained for each study stratified by sex and categories for FRS and ABI. Outcome rates for studies within strata were combined to provide overall summaries using random-effects pooling.18 Area under receiver operating characteristic curves were calculated for the prediction of events using the FRS alone and with the addition of the ABI.

The literature search and information from experts identified 1075 citations from which 20 studies that fulfilled the inclusion criteria were identified (Figure 1). Selected investigators from 16 of these studies917,1925 agreed to participate in the ABI Collaboration and provided data prior to the analysis. The participating studies and investigators are listed at the end of this article. The studies were based in Australia, Belgium, Italy, Netherlands, Sweden, the United Kingdom, and the United States and comprised predominantly white populations except for the Honolulu Heart Program (Japanese Americans)11 and the Strong Heart Study (American Indians).12 The populations in the Cardiovascular Health Study10 and the Atherosclerosis Risk in Communities Study9 comprised 15% and 26% blacks, respectively. In the San Luis Valley Diabetes Study,24 the included healthy population without diabetes was 42% Hispanic. Eleven studies included both sexes, 4 included only men, and 1 included only women.

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Figure 1. Flow Diagram of Selection of Studies for Inclusion in Meta-analysis
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The characteristics of the participants in the studies at baseline when the ABI was measured are shown in Table 1. A total of 24 955 men and 23 339 women without a history of CHD were included. They were late middle aged to elderly with a mean age in the studies ranging from 47 to 78 years. The 10-year mean (SD) incidence of CHD predicted by the FRS at baseline varied across studies from 11.0% (6.1%) to 31.6% (14.1%) in men and from 7.1% (6.1%) to 14.5% (10.1%) in women. The mean (SD) ABI was greater than 1.00 in all studies and ranged from 1.02 (0.13) to 1.21 (0.13) in men and 1.01 (0.16) to 1.15 (0.17) in women; most of the studies comprising both sexes had higher mean values in men than in women, as previously reported.24

Table Graphic Jump LocationTable 1. Baseline Characteristics of Individuals in Studies in the Ankle Brachial Index (ABI) Collaboration

Table 2 and Table 3 show the total mortality, cardiovascular mortality, and major coronary events occurring during follow-up in each of the studies for men and women, respectively. Median duration of follow-up ranged from 3 to 16.7 years, with 9 of the 16 studies having more than 10 years of follow-up. Overall, 9924 deaths occurred during 480 325 person-years of follow-up with around one-quarter of deaths due to CHD or stroke in both men and women. The annual rates of deaths and events varied considerably between the studies. For example, men in the Belgian Physical Fitness Study had a mean (SD) age of 47 (4.4) years and the annual mortality was 0.37% (95% confidence interval [CI], 0.29%-0.45%), whereas men in the Honolulu Heart Program had a mean (SD) age of 78 (4.6) years and the annual mortality was 4.91% (95% CI, 4.59%-5.22%) (Table 2). Likewise, for women annual mortality varied between 0.55% (95% CI, 0.42%-0.68%) in the Framingham Offspring Study and 7.34% (95% CI, 6.39%-8.29%) in the Women's Health and Aging Study (Table 3).

Table Graphic Jump LocationTable 2. Total Mortality, Cardiovascular Mortality, and Major Coronary Events for Men in Studies in the Ankle Brachial Index Collaboration
Table Graphic Jump LocationTable 3. Total Mortality, Cardiovascular Mortality, and Major Coronary Events for Women in Studies in the Ankle Brachial Index Collaboration

The HRs for death for different levels of ABI compared with a reference ABI of 1.11 to 1.20 in all studies combined formed a reverse J-shaped curve for both men and women (Figure 2). Quiz Ref IDFor levels of ABI below 1.11, the HRs increased consistently with decreasing ABI. For an ABI of greater than 1.40, the HRs also were increased in men (1.38; 95% CI, 1.17-1.62) and in women (1.23; 95% CI, 1.00-1.52). For levels of ABI from 1.11 to 1.40, only small and mostly nonsignificant differences in HRs were found.Table 4 and Table 5 show the HRs for total and cardiovascular mortality and major coronary events by ABI in men and women, respectively. The patterns of risk for cardiovascular mortality and major coronary events were similar to that for total mortality; for levels of ABI below 1.11, the HRs for cardiovascular mortality were consistently higher than for total mortality.

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Figure 2. Hazard Ratios for Total Mortality in Men and Women by Ankle Brachial Index at Baseline for All Studies Combined in the ABI Collaboration
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Hazard ratios are not adjusted for age or cardiovascular risk factors.

Table Graphic Jump LocationTable 4. Hazard Ratios (HRs) for Total Mortality, Cardiovascular Mortality, and Major Coronary Events by Ankle Brachial Index (ABI) at Baseline for Men in All Studies Combined in the ABI Collaboration
Table Graphic Jump LocationTable 5. Hazard Ratios (HRs) for Total Mortality, Cardiovascular Mortality, and Major Coronary Events by Ankle Brachial Index (ABI) at Baseline for Women in All Studies Combined in the ABI Collaboration

Values of the ABI less than 0.90 have been taken traditionally as a measure of increased risk. In nearly all the studies in men (Figure 3), the HRs for total mortality were statistically significantly higher in individuals with an ABI of 0.90 or less compared with individuals with normal ABI values of 1.11 to 1.40 (HR, 3.33; 95% CI, 2.74-4.06). In women, the results were more heterogeneous (Figure 4), but the HR of 2.71 (95% CI, 2.03-3.62) was comparable with that in men. Likewise, significantly increased HRs were found in men and in women both for cardiovascular mortality (men: 4.21 [95% CI, 3.29-5.39]; women: 3.46 [95% CI, 2.36-5.08]), and for major coronary events (men: 2.97 [95% CI, 2.33-3.78]; women: 3.05 [95% CI, 2.25-4.15]). Adjustment of the HRs for individuals with an ABI of 0.90 or less relative to an ABI of 1.11 to 1.40 for FRS reduced the HRs but they were still elevated substantially and significantly. The adjusted HRs for total mortality were 2.34 (95% CI, 1.97-2.78) in men vs 2.35 (95% CI, 1.76-3.13) in women; cardiovascular mortality, 2.92 (95% CI, 2.31-3.70) in men vs 2.97 (95% CI, 2.02-4.35) in women; and major coronary events, 2.16 (95% CI, 1.76-2.66) in men vs 2.49 (95% CI, 1.84-3.36) in women.

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Figure 3. Random Hazard Ratios for Total Mortality for Low (≤0.90) Compared With Normal (1.11-1.40) Ankle Brachial Index (ABI) in Men in Studies in the ABI Collaboration
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Hazard ratios are not adjusted for age or cardiovascular risk factors. Area of each square is proportional to weight of the study in the meta-analysis. ARIC indicates Atherosclerosis Risk in Communities; CI, confidence interval; InCHIANTI, Invecchiare in Chianti; PAOD, peripheral arterial occlusive disease.

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Figure 4. Random Hazard Ratios for Total Mortality for Low (≤0.90) Compared With Normal (1.11-1.40) Ankle Brachial Index (ABI) in Women in Studies in the ABI Collaboration
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Hazard ratios are not adjusted for age or cardiovascular risk factors. Area of each square is proportional to weight of the study in the meta-analysis. ARIC indicates Atherosclerosis Risk in Communities; CI, confidence interval; InCHIANTI, Invecchiare in Chianti; PAOD, peripheral arterial occlusive disease.

Table 6 and Table 7 show the effect of inclusion of an ABI measurement on the apparent risk of 10-year total mortality, cardiovascular mortality, and major coronary events over the range of FRS categories in men and women, respectively. Compared with the overall rates without ABI included, an ABI of 0.90 or less was associated with a greatly increased risk of mortality (total and cardiovascular) and major coronary events across all FRS categories in both men and women, but more so in the lower than in the higher FRS categories. Women had especially high mortality and event rates in the lowest FRS category. Men and women with an ABI from 0.91 to 1.10 also had higher mortality and event rates compared with those with a normal ABI (1.11-1.40) but the magnitudes of the increase were much less than for those with an ABI of 0.90 or less. Those with an ABI greater than 1.40 also had higher rates across most FRS categories.

Table Graphic Jump LocationTable 6. 10-Year Total Mortality, Cardiovascular Mortality, and Major Coronary Event Rates in Men by Framingham Risk Category and Ankle Brachial Index (ABI) at Baseline for All Studies Combined in the ABI Collaborationa
Table Graphic Jump LocationTable 7. 10-Year Total Mortality, Cardiovascular Mortality, and Major Coronary Event Rates in Women by Framingham Risk Category and Ankle Brachial Index (ABI) at Baseline for All Studies Combined in the ABI Collaborationa

Inclusion of the ABI had an overall effect on the prediction of events, especially in women. When predicting major coronary events using only the FRS, the area under the receiver operating characteristic curve was 0.646 (95% CI, 0.643-0.657) and with the addition of the ABI was 0.655 (95% CI, 0.643-0.666) in men vs 0.605 (95% CI, 0.590-0.619) and 0.658 (95% CI, 0.644-0.672), respectively, in women.

The FRS is mostly used to predict risk of total CHD (including coronary death, myocardial infarction, and angina) and Table 8 shows the effect of including the ABI on this prediction. The calibration of the FRS categories was reasonable because the overall CHD rate in each FRS category was within the range predicted, except for low-risk women in which the overall CHD rate of 11% was higher than predicted. Likewise, the ability of the FRS to discriminate between risk categories was good, except that the overall CHD rate in women in the low-risk group was only slightly lower than those in the intermediate-risk group (11% vs 13%). In each category of FRS in both men and women, a low ABI (≤0.90) was associated with an increased risk of future CHD. Normal levels of the ABI (1.11-1.40) were associated with a slightly reduced risk from the overall rates but levels greater than 1.40 did not differ consistently from the overall rates, although this may have been influenced by the relatively low numbers of participants.

Table Graphic Jump LocationTable 8. 10-Year Total Coronary Heart Disease (CHD) Rates in Men and Women by Framingham Risk Score (FRS) Category and Ankle Brachial Index (ABI) at Baseline for All Studies Combined in the ABI Collaborationa

The results in Table 8 also indicate in which categories of FRS the ABI is likely to change individuals' clinical risk levels (ie, between <10%, 10%-19%, and ≥20%). In men, the greatest effect would be in high-risk individuals (≥20%) with a normal ABI (1.11-1.40) in whom the risk level would be reduced to intermediate (10%-19%). All men with a low ABI (≤0.90) had a relatively high risk but their clinical risk level would not change from that predicted overall by the FRS. In women, the main effect of the ABI would be to change all women in the low FRS category (<10%) with an abnormal ABI (≤0.90 or 0.91 to 1.10 or >1.40) to a higher risk level. Also women in the intermediate FRS category (10%-19%) with a low ABI (≤0.90) would become high risk (≥20%). Table 8 also shows that the number of men changing risk category (shaded numbers) would be 4106 of 21 433 (19%) and in women would be 8154 of 22 486 women (36%).

Predicting future CHD and mortality accurately in individuals in the community who have no prior history of cardiovascular disease has proven difficult when based solely on traditional risk factors and scoring systems. In a recent systematic review of 27 studies using the Framingham risk equation, the predicted-to-observed ratios ranged from an underprediction of 0.43 in a high-risk population to an overprediction of 2.87 in a low-risk population.4 We found that the ABI provided independent risk information compared with the FRS and, when combined with the FRS, a low ABI (≤0.90) approximately doubled the risk of total mortality, cardiovascular mortality, and major coronary events across all Framingham risk categories.

In predicting the 10-year risk of total CHD, our results (Table 8) indicate that Quiz Ref IDapproximately 1 of 5 men would have their broad category of risk (<10, 10-19, ≥20%) changed from that predicted by FRS alone to that found on inclusion of the ABI. These changes from higher to lower categories of risk would likely have an effect on decisions to commence preventive treatment, such as lipid-lowering therapy, as recommended in the Adult Treatment Panel III guidelines.27 In contrast, the main effect in women of inclusion of the ABI would be that many at low risk with the FRS (<10%) would change to a higher risk level. In total, around 1 in 3 women would be affected. It should be recognized, however, that the proportion of men and women affected by inclusion of the ABI is approximate due to the method of estimating total CHD end points and possible residual confounding within the FRS categories.

Our results also confirm the recent findings of the Strong Heart Study,12 Cardiovascular Health Study,28 and Multi-ethnic Study of Atherosclerosis29 that the relationship between ABI and cardiovascular disease is nonlinear and varies across the range of ABI. High values (>1.40) could be related to poor arterial compressibility resulting from stiffness and calcification, which may occur more commonly in those with diabetes,29,30 and may be 1 explanation why those with an ABI greater than 1.40 are at increased risk. The differences in risk between ABI values from 1.11 to 1.40 in both men and women were so small that, for practical purposes, an ABI within this range could be considered normal. Individuals with an ABI from 0.91 to 1.10 were at slightly increased risk. These results would suggest that the widely accepted high-risk cut point of 0.90 is reasonable. However, for ABI values from 0.91 to 1.10 and greater than 1.40, individuals might be advised that their risk may be slightly higher than normal levels.

The ABI Collaboration includes 16 international cohort studies. The consistency of results, especially in men (Figure 3), across a diverse spectrum of populations strengthens the validity of our findings. This consistency also was apparent despite some differences in methods of measuring the ABI and in ascertaining outcome events. We did not recalibrate the FRS, as has been suggested in populations very different from that in Framingham,31 because in our collaboration there was no evidence that particular studies had substantially worse calibration than others and also the FRS when used in routine clinical practice is not usually calibrated to the local population. Although the area under receiver operating characteristic curves examining the added effect of the ABI are presented, from a clinical perspective, the added value of the ABI is the extent to which its inclusion reclassifies patient risk at an individual level.32

Other indicators of asymptomatic atherosclerosis, notably coronary artery calcium score and carotid intima media thickness have been evaluated as incremental risk predictors to the FRS. Population studies of apparently healthy individuals have suggested that coronary artery calcium score may provide added value,33,34 particularly in discriminating high- and low-risk individuals with an intermediate FRS (predicted 10-year coronary event risk between 10% and 20%).35 In the Atherosclerosis Risk in Communities study,36 inclusion of carotid intima media thickness had a modest effect on the area under the receiver operating characteristic curve for the prediction of CHD using traditional risk factors. Likewise, in patients with dyslipidaemia37 and diabetes,38 a combination of carotid intima media thickness and FRS improved prediction compared with FRS alone. We are not aware, however, of reports of any direct comparisons in the same study of the additional values in which different measures of asymptomatic atherosclerosis (eg, coronary artery calcium vs carotid intima media thickness) make to FRS prediction in the general population.

Quiz Ref IDThe ABI is potentially a useful tool for prediction of cardiovascular risk. In contrast to measurement of coronary artery calcium and carotid intima media thickness, it has the advantage of ease of use in the primary care physician's office and in community settings. The equipment is inexpensive—a handheld Doppler costs less than $600. The procedure is simple, taking less than 10 to 15 minutes,39,40 and can be performed by a suitably trained nurse or other health care professional. Technological advances to make the test quicker and easier to apply are being investigated, including automatic pressure measurement at the ankle.41 Given the noninvasiveness of the test and minimal discomfort, patient acceptability is high. Variability is comparable with that of routine blood pressure42,43 and individuals with borderline results may benefit from a repeated measure at a different visit.43

Although widely used in specialist vascular clinics, the ABI is rarely applied in routine clinical practice. Barriers to its use include: (1) most clinicians are not aware that a low ABI is a marker of cardiovascular risk; (2) it is perceived as a specialist test for use only by vascular surgeons and physicians; and (3) most clinicians would not know how to perform the test. Physician education would be essential in promoting use of the ABI in practice. Furthermore, in a survey of physicians primed to use the ABI in 1 program in the United States, time constraints, lack of reimbursement, and staff availability were barriers to use of the ABI, each reported by around half the physicians.40

The yield of a screening test also is important. Our results indicate that a proportion of men and women having an ABI test would be placed in a different risk category. However, this proportion may vary considerably by age because the prevalence of a low ABI is known to increase substantially with age. For example, in the United States in 2000, the prevalence of an ABI lower than 0.90 in non–Hispanic white men aged 40 to 49 years was 1.4% but was 22.6% in those aged 80 years or older.44 Significantly higher prevalences were found in blacks. In 12 300 men free of cardiovascular disease in the general population in Scotland, the prevalence of an ABI of 0.90 or less in those aged 50 to 54 years was 3.7% but was 12.7% in those aged 75 years or older.45 While recognizing that most risk factors also increase with age, it is likely that the added yield of a low ABI is age-related.

Recently published guidelines by the American Heart Association and the American College of Cardiology,46 the Transatlantic Inter-Society Consensus Working Group,47 and the Fourth Joint European Task Force48 have suggested that the ABI should be considered for the purposes of cardiovascular risk assessment. The results of our study indicate that, when using the FRS, this may indeed be justified to improve prediction of cardiovascular risk and provision of advice on ways to reduce that risk. A new risk equation incorporating the ABI and relevant Framingham risk variables could more accurately predict risk and our intention is to develop and validate such a model in our combined data set. Cost-effectiveness modeling of the effect of using the ABI on long-term clinical outcomes also would be of interest, as has been recommended recently by an American Heart Association expert working group on screening for atherosclerotic peripheral vascular disease (Michael H. Criqui, MD, University of California San Diego, written communication, January 2008). A cost-effectiveness analysis also would be useful because successful implementation of the ABI in programs for assessment of cardiovascular risk would require a change in reimbursement regulations in some countries.

Corresponding Author: Gerry Fowkes, PhD, Wolfson Unit for Prevention of Peripheral Vascular Diseases, Public Health Sciences, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, Scotland (Gerry.Fowkes@ed.ac.uk).

Author Contributions: Drs Fowkes and Murray had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Fowkes, Murray, Newman, Bouter, Stehouwer, Stoffers, Hooi, Hofman, Criqui, Fronek, Hiatt, Guralnik.

Acquisition of data: Fowkes, Butcher, Heald, Chambless, Folsom, Hirsch, DeBacker, Wautrecht, Kornitzer, Cushman, Sutton-Tyrrell, D’Agostino, Murabito, Norman, Jamrozik, Curb, Masaki, Rodríguez, Dekker, Heine, Nijpels, Stehouwer, Ferrucci, McDermott, Stoffers, Hooi, Knottnerus, Ogren, Hedblad, Witteman, Breteler, Hunink, Criqui, Langer, Hiatt, Hamman, Guralnik.

Analysis and interpretation of data: Fowkes, Murray, Butcher, Heald, R. Lee, Dramaix, Kornitzer, Newman, A. Lee, Price, Murabito, Stehouwer, Ferrucci, Hooi, Knottnerus, Ogren, Witteman, Criqui, Fronek, Hiatt, Resnick.

Drafting of the manuscript: Fowkes, Murray, Newman, A. Lee.

Critical revision of the manuscript for important intellectual content: Fowkes, Murray, Butcher, Heald, R. Lee, Chambless, Folsom, Hirsch, Dramaix, DeBacker, Wautrecht, Kornitzer, Newman, Cushman, Sutton-Tyrrell, Price, D’Agostino, Murabito, Norman, Jamrozik, Curb, Masaki, Rodríguez, Dekker, Bouter, Heine, Nijpels, Stehouwer, Ferrucci, McDermott, Stoffers, Hooi, Knottnerus, Ogren, Hedblad, Witteman, Breteler, Hunink, Hofman, Criqui, Langer, Fronek, Hiatt, Hamman, Resnick, Guralnik.

Statistical analysis: Murray, Butcher, R. Lee, Dramaix, D’Agostino, Dekker, Stehouwer, Knottnerus.

Obtained funding: Fowkes, Sutton-Tyrrell, D’Agostino, Norman, Curb, Rodríguez, Dekker, Knottnerus, Breteler, Hofman, Criqui.

Administrative, technical, or material support: Fowkes, Heald, Kornitzer, Newman, Cushman, A. Lee, Masaki, Stehouwer, Hedblad, Hofman, Criqui, Guralnik.

Study supervision: Fowkes, Murray, DeBacker, Price, Dekker, Bouter, Heine, Stehouwer, Knottnerus, Ogren, Hofman, Criqui, Fronek, Guralnik.

Financial Disclosures: Drs Fowkes and McDermott reported receiving honoraria and consulting fees and Dr Norman reported receiving a research grant from Sanofi-Aventis/BMS for purposes other than for this research. Dr McDermott reported receiving consulting fees from Hutchison Technology and educational honoraria from Otsuka Pharmaceuticals. Dr Ogren is an employee of AstraZeneca's Research and Development. No other authors reported financial disclosures.

Funding/Support: Sanofi-Aventis/BMS provided an unrestricted educational grant for data processing and initial statistical analysis. The Framingham Offspring Study is supported by National Institutes of Health grant NOI-HC-25195. The InCHIANTI and Women's Health and Aging studies are supported by the Intramural Research Program, National Institute on Aging, National Institutes of Health.

Role of the Sponsor: Sanofi-Aventis/BMS had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

Greenland P, Smith SC Jr, Grundy SM. Improving coronary heart disease risk assessment in asymptomatic people: role of traditional risk factors and noninvasive cardiovascular tests.  Circulation. 2001;104(15):1863-1867
PubMed   |  Link to Article
Anderson KM, Odell PM, Wilson PW, Kannel WB. Cardiovascular disease risk profiles.  Am Heart J. 1991;121(1 pt 2):293-298
PubMed   |  Link to Article
Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories.  Circulation. 1998;97(18):1837-1847
PubMed   |  Link to Article
Brindle P, Beswick AD, Fahey T, Ebrahim SB. Accuracy and impact of risk assessment in the primary prevention of cardiovascular disease: a systematic review.  Heart. 2006;92(12):1752-1759
PubMed   |  Link to Article
Wannamethee SG, Shaper AG, Lennon L, Morris RW. Metabolic syndrome vs Framingham risk score for prediction of coronary heart disease, stroke, and type 2 diabetes mellitus.  Arch Intern Med. 2005;165(22):2644-2650
PubMed   |  Link to Article
Cushman M, Arnold AM, Psaty BM,  et al.  C-reactive protein and the 10-year incidence of coronary heart disease in older men and women: the Cardiovascular Health Study.  Circulation. 2005;112(1):25-31
PubMed   |  Link to Article
Tsimikas S, Willerson JT, Ridker PM. C-reactive protein and other emerging blood biomarkers to optimize risk stratification of vulnerable patients.  J Am Coll Cardiol. 2006;47(8):(suppl)  C19-C31
PubMed   |  Link to Article
Newman AB, Siscovick DS, Manolio TA,  et al; Cardiovascular Heart Study (CHS) Collaborative Research Group.  Ankle-arm index as a marker of atherosclerosis in the Cardiovascular Health Study.  Circulation. 1993;88(3):837-845
PubMed   |  Link to Article
Weatherley BD, Nelson JJ, Heiss G,  et al.  The association of the ankle-brachial index with incident coronary heart disease: the Atherosclerosis Risk in Communities (ARIC) study, 1987-2001.  BMC Cardiovasc Disord. 2007;7:3
PubMed   |  Link to Article
Newman AB, Shemanski L, Manolio TA,  et al; Cardiovascular Health Study Group.  Ankle-arm index as a predictor of cardiovascular disease and mortality in the Cardiovascular Health Study.  Arterioscler Thromb Vasc Biol. 1999;19(3):538-545
PubMed   |  Link to Article
Abbott RD, Petrovitch H, Rodriguez BL,  et al.  Ankle/brachial blood pressure in men >70 years of age and the risk of coronary heart disease.  Am J Cardiol. 2000;86(3):280-284
PubMed   |  Link to Article
Resnick HE, Lindsay RS, McDermott MM,  et al.  Relationship of high and low ankle brachial index to all-cause and cardiovascular disease mortality: the Strong Heart Study.  Circulation. 2004;109(6):733-739
PubMed   |  Link to Article
Leng GC, Fowkes FG, Lee AJ, Dunbar J, Housley E, Ruckley CV. Use of ankle brachial pressure index to predict cardiovascular events and death: a cohort study.  BMJ. 1996;313(7070):1440-1444
PubMed   |  Link to Article
Hooi JD, Kester AD, Stoffers HE, Rinkens PE, Knottnerus JA, van Ree JW. Asymptomatic peripheral arterial occlusive disease predicted cardiovascular morbidity and mortality in a 7-year follow-up study.  J Clin Epidemiol. 2004;57(3):294-300
PubMed   |  Link to Article
Ogren M, Hedblad B, Isacsson SO, Janzon L, Jungquist G, Lindell SE. Non-invasively detected carotid stenosis and ischaemic heart disease in men with leg arteriosclerosis.  Lancet. 1993;342(8880):1138-1141
PubMed   |  Link to Article
van der Meer IM, Bots ML, Hofman A, del Sol AI, van der Kuip DA, Witteman JC. Predictive value of noninvasive measures of atherosclerosis for incident myocardial infarction: the Rotterdam Study.  Circulation. 2004;109(9):1089-1094
PubMed   |  Link to Article
Kornitzer M, Dramaix M, Sobolski J, Degre S, De Backer G. Ankle/arm pressure index in asymptomatic middle-aged males: an independent predictor of ten-year coronary heart disease mortality.  Angiology.  1995;46(3):211-219
PubMed   |  Link to Article
Whitehead A. Meta-Analysis of Controlled Clinical Trials. Chichester, England: John Wiley & Sons Ltd; 2002
Murabito JM, Evans JC, Nieto K, Larson MG, Levy D, Wilson PW. Prevalence and clinical correlates of peripheral arterial disease in the Framingham Offspring Study.  Am Heart J. 2002;143(6):961-965
PubMed   |  Link to Article
Fowler B, Jamrozik K, Norman P, Allen Y. Prevalence of peripheral arterial disease: persistence of excess risk in former smokers.  Aust N Z J Public Health. 2002;26(3):219-224
PubMed   |  Link to Article
Jager A, Kostense PJ, Ruhe HG,  et al.  Microalbuminuria and peripheral arterial disease are independent predictors of cardiovascular and all-cause mortality, especially among hypertensive subjects: five-year follow-up of the Hoorn Study.  Arterioscler Thromb Vasc Biol. 1999;19(3):617-624
PubMed   |  Link to Article
McDermott MM, Guralnik JM, Albay M, Bandinelli S, Miniati B, Ferrucci L. Impairments of muscles and nerves associated with peripheral arterial disease and their relationship with lower extremity functioning: the InCHIANTI Study.  J Am Geriatr Soc. 2004;52(3):405-410
PubMed   |  Link to Article
Criqui MH, Langer RD, Fronek A,  et al.  Mortality over a period of 10 years in patients with peripheral arterial disease.  N Engl J Med. 1992;326(6):381-386
PubMed   |  Link to Article
Hiatt WR, Hoag S, Hamman RF. Effect of diagnostic criteria on the prevalence of peripheral arterial disease: the San Luis Valley Diabetes Study.  Circulation. 1995;91(5):1472-1479
PubMed   |  Link to Article
McDermott MM, Fried L, Simonsick E, Ling S, Guralnik JM. Asymptomatic peripheral arterial disease is independently associated with impaired lower extremity functioning: the women's health and aging study.  Circulation. 2000;101(9):1007-1012
PubMed   |  Link to Article
Grundy SM, Pasternak R, Greenland P, Smith S, Fuster V. Assessment of cardiovascular risk by use of multiple-risk-factor assessment equations: a statement for health care professionals from the American Heart Association and the American College of Cardiology.  J Am Coll Cardiol. 1999;34(4):1348-1359
PubMed   |  Link to Article
Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults.  Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III).  JAMA. 2001;285(19):2486-2497
PubMed   |  Link to Article
O’Hare AM, Katz R, Shlipak MG, Cushman M, Newman AB. Mortality and cardiovascular risk across the ankle-arm index spectrum: results from the Cardiovascular Health Study.  Circulation. 2006;113(3):388-393
PubMed   |  Link to Article
McDermott MM, Liu K, Criqui MH,  et al.  Ankle brachial index and subclinical cardiac and carotid disease: the Multi-Ethnic Study of Atherosclerosis.  Am J Epidemiol. 2005;162(1):33-41
PubMed   |  Link to Article
Everhart JE, Pettitt DJ, Knowler WC, Rose FA, Bennett PH. Medial arterial calcification and its association with mortality and complications of diabetes.  Diabetologia. 1988;31(1):16-23
PubMed
D'Agostino RB Sr, Grundy S, Sullivan LM, Wilson P.CHD Risk Prediction Group.  Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation.  JAMA. 2001;286(2):180-187
PubMed   |  Link to Article
Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction.  Circulation. 2007;115(7):928-935
PubMed   |  Link to Article
Greenland P, LaBree L, Azen SP, Doherty TM, Detrano RC. Coronary artery calcium score combined with Framingham score for risk prediction in asymptomatic individuals.  JAMA. 2004;291(2):210-215
PubMed   |  Link to Article
Arad Y, Goodman KJ, Roth M, Newstein D, Guerci AD. Coronary calcification, coronary disease risk factors, C-reactive protein, and atherosclerotic cardiovascular disease events: the St Francis Heart Study.  J Am Coll Cardiol. 2005;46(1):158-165
PubMed   |  Link to Article
Greenland P, Bonow RO, Brundage BH,  et al; American College of Cardiology Foundation Clinical Expert Consensus Task Force; Society of Atherosclerosis Imaging and Prevention; Society of Cardiovascular Computed Tomography.  ACCF/AHA 2007 clinical expert consensus document on coronary artery calcium scoring by computed tomography in global cardiovascular risk assessment and in evaluation of patients with chest pain: a report of the American College of Cardiology Foundation Clinical Expert Consensus Task Force.  Circulation. 2007;115(3):402-426
PubMed   |  Link to Article
Chambless LE, Folsom AR, Sharrett AR,  et al.  Coronary heart disease risk prediction in the Atherosclerosis Risk in Communities (ARIC) study.  J Clin Epidemiol. 2003;56(9):880-890
PubMed   |  Link to Article
Baldassarre D, Amato M, Pustina L,  et al.  Measurement of carotid artery intima-media thickness in dyslipidemic patients increases the power of traditional risk factors to predict cardiovascular events.  Atherosclerosis. 2007;191(2):403-408
PubMed   |  Link to Article
Bernard S, Serusclat A, Targe F,  et al.  Incremental predictive value of carotid ultrasonography in the assessment of coronary risk in a cohort of asymptomatic type 2 diabetic subjects.  Diabetes Care. 2005;28(5):1158-1162
PubMed   |  Link to Article
Farkouh ME, Oddone EZ, Simel DL. Improving the clinical examination for a low ankle-brachial index.  Int J Angiol. 2002;11(1):1067-1711
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Mohler ER III, Treat-Jacobson D, Reilly MP,  et al.  Utility and barriers to performance of the ankle-brachial index in primary care practice.  Vasc Med. 2004;9(4):253-260
PubMed   |  Link to Article
Jönsson B, Laurent C, Eneling M, Skau T, Lindberg L-G. Automatic ankle pressure measurements using PPG in ankle-brachial pressure index determination.  Eur J Vasc Endovasc Surg. 2005;30(4):395-401
PubMed   |  Link to Article
Baker JD, Dix DE. Variability of Doppler ankle pressures with arterial occlusive disease: an evaluation of ankle index and brachial-ankle pressure gradient.  Surgery. 1981;89(1):134-137
PubMed
Fowkes FGR, Housley E, Macintyre CCA, Prescott RJ, Ruckley CV. Variability of ankle and brachial systolic pressures in the measurement of atherosclerotic peripheral arterial disease.  J Epidemiol Community Health. 1988;42(2):128-133
PubMed   |  Link to Article
Allison MA, Ho E, Denenberg JO,  et al.  Ethnic-specific prevalence of peripheral arterial disease in the United States.  Am J Prev Med. 2007;32(4):328-333
PubMed   |  Link to Article
Price JF, Stewart MC, Douglas AF, Murray GD, Fowkes GF. Frequency of a low ankle brachial index in the general population by age, sex and deprivation: cross-sectional survey of 28 980 men and women.  Eur J Cardiovasc Prev Rehabil. 2008;15(3):370-375
PubMed   |  Link to Article
Hirsch AT, Haskal ZJ, Hertzer NR,  et al; Vascular Disease Foundation.  ACC/AHA 2005 practice guidelines for the management of patients with peripheral arterial disease (lower extremity, renal, mesenteric, and abdominal aortic): a collaborative report from the American Association for Vascular Surgery/Society for Vascular Surgery, Society for Cardiovascular Angiography and Interventions, Society for Vascular Medicine and Biology, Society of Interventional Radiology, and the ACC/AHA Task Force on Practice Guidelines (Writing Committee to Develop Guidelines for the Management of Patients With Peripheral Arterial Disease): endorsed by the American Association of Cardiovascular and Pulmonary Rehabilitation; National Heart, Lung, and Blood Institute; Society for Vascular Nursing; TransAtlantic Inter-Society Consensus; and Vascular Disease Foundation.  Circulation. 2006;113(11):e463-e654
PubMed   |  Link to Article
Norgren L, Hiatt WR, Dormandy JA,  et al.  Inter-Society Consensus for the Management of Peripheral Arterial Disease (TASC II).  Eur J Vasc Endovasc Surg. 2007;33:(suppl 1)  S1-S70
PubMed   |  Link to Article
Graham I, Atar D, Borch-Johnsen K,  et al; European Society of Cardiology (ESC); European Association for Cardiovascular Prevention and Rehabilitation (EACPR); Council on Cardiovascular Nursing; European Association for Study of Diabetes (EASD); International Diabetes Federation Europe (IDF-Europe); European Stroke Initiative (EUSI); Society of Behavioural Medicine (ISBM); European Society of Hypertension (ESH); WONCA Europe (European Society of General Practice/Family Medicine); European Heart Network (EHN); European Atherosclerosis Society (EAS).  European guidelines on cardiovascular disease prevention in clinical practice: full text: Fourth Joint Task Force of the European Society of Cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of nine societies and by invited experts).  Eur J Cardiovasc Prev Rehabil. 2007;14:(suppl 2)  S1-S113
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure 1. Flow Diagram of Selection of Studies for Inclusion in Meta-analysis
Graphic Jump Location
Place holder to copy figure label and caption
Figure 2. Hazard Ratios for Total Mortality in Men and Women by Ankle Brachial Index at Baseline for All Studies Combined in the ABI Collaboration
Graphic Jump Location

Hazard ratios are not adjusted for age or cardiovascular risk factors.

Place holder to copy figure label and caption
Figure 3. Random Hazard Ratios for Total Mortality for Low (≤0.90) Compared With Normal (1.11-1.40) Ankle Brachial Index (ABI) in Men in Studies in the ABI Collaboration
Graphic Jump Location

Hazard ratios are not adjusted for age or cardiovascular risk factors. Area of each square is proportional to weight of the study in the meta-analysis. ARIC indicates Atherosclerosis Risk in Communities; CI, confidence interval; InCHIANTI, Invecchiare in Chianti; PAOD, peripheral arterial occlusive disease.

Place holder to copy figure label and caption
Figure 4. Random Hazard Ratios for Total Mortality for Low (≤0.90) Compared With Normal (1.11-1.40) Ankle Brachial Index (ABI) in Women in Studies in the ABI Collaboration
Graphic Jump Location

Hazard ratios are not adjusted for age or cardiovascular risk factors. Area of each square is proportional to weight of the study in the meta-analysis. ARIC indicates Atherosclerosis Risk in Communities; CI, confidence interval; InCHIANTI, Invecchiare in Chianti; PAOD, peripheral arterial occlusive disease.

Tables

Table Graphic Jump LocationTable 1. Baseline Characteristics of Individuals in Studies in the Ankle Brachial Index (ABI) Collaboration
Table Graphic Jump LocationTable 2. Total Mortality, Cardiovascular Mortality, and Major Coronary Events for Men in Studies in the Ankle Brachial Index Collaboration
Table Graphic Jump LocationTable 3. Total Mortality, Cardiovascular Mortality, and Major Coronary Events for Women in Studies in the Ankle Brachial Index Collaboration
Table Graphic Jump LocationTable 4. Hazard Ratios (HRs) for Total Mortality, Cardiovascular Mortality, and Major Coronary Events by Ankle Brachial Index (ABI) at Baseline for Men in All Studies Combined in the ABI Collaboration
Table Graphic Jump LocationTable 5. Hazard Ratios (HRs) for Total Mortality, Cardiovascular Mortality, and Major Coronary Events by Ankle Brachial Index (ABI) at Baseline for Women in All Studies Combined in the ABI Collaboration
Table Graphic Jump LocationTable 6. 10-Year Total Mortality, Cardiovascular Mortality, and Major Coronary Event Rates in Men by Framingham Risk Category and Ankle Brachial Index (ABI) at Baseline for All Studies Combined in the ABI Collaborationa
Table Graphic Jump LocationTable 7. 10-Year Total Mortality, Cardiovascular Mortality, and Major Coronary Event Rates in Women by Framingham Risk Category and Ankle Brachial Index (ABI) at Baseline for All Studies Combined in the ABI Collaborationa
Table Graphic Jump LocationTable 8. 10-Year Total Coronary Heart Disease (CHD) Rates in Men and Women by Framingham Risk Score (FRS) Category and Ankle Brachial Index (ABI) at Baseline for All Studies Combined in the ABI Collaborationa

References

Greenland P, Smith SC Jr, Grundy SM. Improving coronary heart disease risk assessment in asymptomatic people: role of traditional risk factors and noninvasive cardiovascular tests.  Circulation. 2001;104(15):1863-1867
PubMed   |  Link to Article
Anderson KM, Odell PM, Wilson PW, Kannel WB. Cardiovascular disease risk profiles.  Am Heart J. 1991;121(1 pt 2):293-298
PubMed   |  Link to Article
Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories.  Circulation. 1998;97(18):1837-1847
PubMed   |  Link to Article
Brindle P, Beswick AD, Fahey T, Ebrahim SB. Accuracy and impact of risk assessment in the primary prevention of cardiovascular disease: a systematic review.  Heart. 2006;92(12):1752-1759
PubMed   |  Link to Article
Wannamethee SG, Shaper AG, Lennon L, Morris RW. Metabolic syndrome vs Framingham risk score for prediction of coronary heart disease, stroke, and type 2 diabetes mellitus.  Arch Intern Med. 2005;165(22):2644-2650
PubMed   |  Link to Article
Cushman M, Arnold AM, Psaty BM,  et al.  C-reactive protein and the 10-year incidence of coronary heart disease in older men and women: the Cardiovascular Health Study.  Circulation. 2005;112(1):25-31
PubMed   |  Link to Article
Tsimikas S, Willerson JT, Ridker PM. C-reactive protein and other emerging blood biomarkers to optimize risk stratification of vulnerable patients.  J Am Coll Cardiol. 2006;47(8):(suppl)  C19-C31
PubMed   |  Link to Article
Newman AB, Siscovick DS, Manolio TA,  et al; Cardiovascular Heart Study (CHS) Collaborative Research Group.  Ankle-arm index as a marker of atherosclerosis in the Cardiovascular Health Study.  Circulation. 1993;88(3):837-845
PubMed   |  Link to Article
Weatherley BD, Nelson JJ, Heiss G,  et al.  The association of the ankle-brachial index with incident coronary heart disease: the Atherosclerosis Risk in Communities (ARIC) study, 1987-2001.  BMC Cardiovasc Disord. 2007;7:3
PubMed   |  Link to Article
Newman AB, Shemanski L, Manolio TA,  et al; Cardiovascular Health Study Group.  Ankle-arm index as a predictor of cardiovascular disease and mortality in the Cardiovascular Health Study.  Arterioscler Thromb Vasc Biol. 1999;19(3):538-545
PubMed   |  Link to Article
Abbott RD, Petrovitch H, Rodriguez BL,  et al.  Ankle/brachial blood pressure in men >70 years of age and the risk of coronary heart disease.  Am J Cardiol. 2000;86(3):280-284
PubMed   |  Link to Article
Resnick HE, Lindsay RS, McDermott MM,  et al.  Relationship of high and low ankle brachial index to all-cause and cardiovascular disease mortality: the Strong Heart Study.  Circulation. 2004;109(6):733-739
PubMed   |  Link to Article
Leng GC, Fowkes FG, Lee AJ, Dunbar J, Housley E, Ruckley CV. Use of ankle brachial pressure index to predict cardiovascular events and death: a cohort study.  BMJ. 1996;313(7070):1440-1444
PubMed   |  Link to Article
Hooi JD, Kester AD, Stoffers HE, Rinkens PE, Knottnerus JA, van Ree JW. Asymptomatic peripheral arterial occlusive disease predicted cardiovascular morbidity and mortality in a 7-year follow-up study.  J Clin Epidemiol. 2004;57(3):294-300
PubMed   |  Link to Article
Ogren M, Hedblad B, Isacsson SO, Janzon L, Jungquist G, Lindell SE. Non-invasively detected carotid stenosis and ischaemic heart disease in men with leg arteriosclerosis.  Lancet. 1993;342(8880):1138-1141
PubMed   |  Link to Article
van der Meer IM, Bots ML, Hofman A, del Sol AI, van der Kuip DA, Witteman JC. Predictive value of noninvasive measures of atherosclerosis for incident myocardial infarction: the Rotterdam Study.  Circulation. 2004;109(9):1089-1094
PubMed   |  Link to Article
Kornitzer M, Dramaix M, Sobolski J, Degre S, De Backer G. Ankle/arm pressure index in asymptomatic middle-aged males: an independent predictor of ten-year coronary heart disease mortality.  Angiology.  1995;46(3):211-219
PubMed   |  Link to Article
Whitehead A. Meta-Analysis of Controlled Clinical Trials. Chichester, England: John Wiley & Sons Ltd; 2002
Murabito JM, Evans JC, Nieto K, Larson MG, Levy D, Wilson PW. Prevalence and clinical correlates of peripheral arterial disease in the Framingham Offspring Study.  Am Heart J. 2002;143(6):961-965
PubMed   |  Link to Article
Fowler B, Jamrozik K, Norman P, Allen Y. Prevalence of peripheral arterial disease: persistence of excess risk in former smokers.  Aust N Z J Public Health. 2002;26(3):219-224
PubMed   |  Link to Article
Jager A, Kostense PJ, Ruhe HG,  et al.  Microalbuminuria and peripheral arterial disease are independent predictors of cardiovascular and all-cause mortality, especially among hypertensive subjects: five-year follow-up of the Hoorn Study.  Arterioscler Thromb Vasc Biol. 1999;19(3):617-624
PubMed   |  Link to Article
McDermott MM, Guralnik JM, Albay M, Bandinelli S, Miniati B, Ferrucci L. Impairments of muscles and nerves associated with peripheral arterial disease and their relationship with lower extremity functioning: the InCHIANTI Study.  J Am Geriatr Soc. 2004;52(3):405-410
PubMed   |  Link to Article
Criqui MH, Langer RD, Fronek A,  et al.  Mortality over a period of 10 years in patients with peripheral arterial disease.  N Engl J Med. 1992;326(6):381-386
PubMed   |  Link to Article
Hiatt WR, Hoag S, Hamman RF. Effect of diagnostic criteria on the prevalence of peripheral arterial disease: the San Luis Valley Diabetes Study.  Circulation. 1995;91(5):1472-1479
PubMed   |  Link to Article
McDermott MM, Fried L, Simonsick E, Ling S, Guralnik JM. Asymptomatic peripheral arterial disease is independently associated with impaired lower extremity functioning: the women's health and aging study.  Circulation. 2000;101(9):1007-1012
PubMed   |  Link to Article
Grundy SM, Pasternak R, Greenland P, Smith S, Fuster V. Assessment of cardiovascular risk by use of multiple-risk-factor assessment equations: a statement for health care professionals from the American Heart Association and the American College of Cardiology.  J Am Coll Cardiol. 1999;34(4):1348-1359
PubMed   |  Link to Article
Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults.  Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III).  JAMA. 2001;285(19):2486-2497
PubMed   |  Link to Article
O’Hare AM, Katz R, Shlipak MG, Cushman M, Newman AB. Mortality and cardiovascular risk across the ankle-arm index spectrum: results from the Cardiovascular Health Study.  Circulation. 2006;113(3):388-393
PubMed   |  Link to Article
McDermott MM, Liu K, Criqui MH,  et al.  Ankle brachial index and subclinical cardiac and carotid disease: the Multi-Ethnic Study of Atherosclerosis.  Am J Epidemiol. 2005;162(1):33-41
PubMed   |  Link to Article
Everhart JE, Pettitt DJ, Knowler WC, Rose FA, Bennett PH. Medial arterial calcification and its association with mortality and complications of diabetes.  Diabetologia. 1988;31(1):16-23
PubMed
D'Agostino RB Sr, Grundy S, Sullivan LM, Wilson P.CHD Risk Prediction Group.  Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation.  JAMA. 2001;286(2):180-187
PubMed   |  Link to Article
Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction.  Circulation. 2007;115(7):928-935
PubMed   |  Link to Article
Greenland P, LaBree L, Azen SP, Doherty TM, Detrano RC. Coronary artery calcium score combined with Framingham score for risk prediction in asymptomatic individuals.  JAMA. 2004;291(2):210-215
PubMed   |  Link to Article
Arad Y, Goodman KJ, Roth M, Newstein D, Guerci AD. Coronary calcification, coronary disease risk factors, C-reactive protein, and atherosclerotic cardiovascular disease events: the St Francis Heart Study.  J Am Coll Cardiol. 2005;46(1):158-165
PubMed   |  Link to Article
Greenland P, Bonow RO, Brundage BH,  et al; American College of Cardiology Foundation Clinical Expert Consensus Task Force; Society of Atherosclerosis Imaging and Prevention; Society of Cardiovascular Computed Tomography.  ACCF/AHA 2007 clinical expert consensus document on coronary artery calcium scoring by computed tomography in global cardiovascular risk assessment and in evaluation of patients with chest pain: a report of the American College of Cardiology Foundation Clinical Expert Consensus Task Force.  Circulation. 2007;115(3):402-426
PubMed   |  Link to Article
Chambless LE, Folsom AR, Sharrett AR,  et al.  Coronary heart disease risk prediction in the Atherosclerosis Risk in Communities (ARIC) study.  J Clin Epidemiol. 2003;56(9):880-890
PubMed   |  Link to Article
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PubMed   |  Link to Article
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