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Am J Epidemiol. Feb 1, 2010; 171(3): 368–376.
Published online Dec 30, 2009. doi:  10.1093/aje/kwp382
PMCID: PMC2842203

The Relevance of Different Methods of Calculating the Ankle-Brachial Index

The Multi-Ethnic Study of Atherosclerosis

Abstract

The authors aimed to determine differences in the prevalence of peripheral arterial disease (PAD) and its associations with cardiovascular disease (CVD) risk factors, using different methods of calculating the ankle-brachial index (ABI). Using measurements taken in the bilateral brachial, dorsalis pedis, and posterior tibial arteries, the authors calculated ABI in 3 ways: 1) with the lowest ankle pressure (dorsalis pedis artery or posterior tibial artery) (“ABI-LO”), 2) with the highest ankle pressure (“ABI-HI”), and 3) with the mean of the ankle pressures (“ABI-MN”). For all 3 methods, the index ABI was the lower of the ABIs calculated from the left and right legs. PAD was defined as an ABI less than 0.90. Among 6,590 subjects from a multiethnic cohort (baseline examination: 2000–2002), in comparison with ABI-HI, the relative prevalence of PAD was 3.95 times higher in women and 2.74 times higher in men when ABI-LO was used. The relative magnitudes of the associations were largest between PAD and both subclinical atherosclerosis and CVD risk factors when ABI-HI was used, except when risk estimates for PAD were less than 1.0, where the largest relative magnitudes of association were found using ABI-LO. PAD prevalence and its associations with CVD risk factors and subclinical atherosclerosis measures depend on the ankle pressure used to compute the ABI.

Keywords: ankle brachial index, cardiovascular diseases, continental population groups, ethnic groups, peripheral vascular diseases

Among persons without significant atherosclerotic disease in the extremities, systolic blood pressure in the posterior tibial or dorsalis pedis artery is higher than that in the brachial artery. In this case, the ratio of the pressure in the posterior tibial artery or dorsalis pedis artery to that in the brachial artery, the “ankle-brachial index” (ABI), is greater than 1.00 (1). The typical mean value for persons without clinical or subclinical cardiovascular disease (CVD) is approximately 1.10 (2).

The accumulation of atherosclerosis in the arteries of the lower extremities can result in flow-limiting stenosis and a drop in systolic blood pressure distal to the obstruction (3). Since the most common location for significant atherosclerosis in the leg is the superficial femoral artery (4, 5), this may reduce systolic blood pressure in the posterior tibial or dorsalis pedis artery and consequently decrease the ABI. In this regard, ABI values less than 0.90 are typically considered evidence of significant atherosclerotic peripheral arterial disease (PAD) (6) and are associated with significantly increased morbidity and mortality (79).

The ABI is a valid (10) and reproducible (11, 12) method of detecting subclinical PAD. Since multiple blood pressures may be obtained in the lower extremities (i.e., using the posterior tibial and dorsalis pedis arteries), there are several possible methods of calculating the ABI. For example, the ABI could be calculated using the highest, lowest, or mean ankle pressure in a given leg. In clinical practice, the highest arm and leg pressures are used to compute the ABI. To be conservative, the American College of Cardiology and the American Heart Association recommend using the highest pressure obtained in each ankle (1). However, the choice of which pressure to use may have implications for associations between ABI and the underlying burden of atherosclerosis. In this regard, we hypothesized that use of the other available pressures (i.e., the lowest or mean pressure) to calculate the ABI would result in different prevalence rates of PAD, as well as differential associations with measures of CVD, such as risk factors. Accordingly, using data from a multiethnic cohort, and among persons with an ABI less than 1.40, we determined differences in the prevalence of PAD using 3 different methods of calculating the ABI. We also determined differences in the magnitudes of the associations between an ABI less than 0.90 and both CVD risk factors and selected measures of subclinical CVD, according to the ABI calculation method used.

MATERIALS AND METHODS

Subjects

Details about the aims and design of the Multi-Ethnic Study of Atherosclerosis (MESA) have been published elsewhere (13). In brief, between July 2000 and August 2002, 6,814 men and women aged 45–84 years who identified themselves as Caucasian, African-American, Hispanic, or Chinese-American and were free of clinically apparent CVD were recruited at 6 US study centers and participated in the baseline examination, which included measurement of the ABI. Persons with a history of clinical CVD, including having undergone an invasive procedure for CVD (coronary artery bypass graft, angioplasty, valve replacement, or pacemaker placement), were excluded. This included revascularizations for lower-extremity occlusive atherosclerotic disease. The institutional review boards at all participating centers approved the study, and all participants gave written informed consent.

Data collection

At the baseline examination, standardized questionnaires were used to obtain information on health and sociodemographic factors. Cigarette smoking was defined as current, former, or never smoking. Body mass index was calculated as weight in kilograms divided by height in meters squared. Resting blood pressure was measured 3 times in seated participants with a Dinamap model Pro 100 automated oscillometric sphygmomanometer (Wipro GE Healthcare, Waukesha, Wisconsin). Hypertension was defined as systolic blood pressure greater than or equal to 140 mm Hg, diastolic blood pressure greater than or equal to 90 mm Hg, or current use of antihypertensive medication.

Laboratory procedures

At the baseline examination, blood was collected after a 12-hour fast. The proportion of low density lipoprotein cholesterol was calculated by means of the Friedewald equation (14). Dyslipidemia was defined as a total: high density lipoprotein cholesterol ratio greater than 5.0 or use of cholesterol-reducing medication. Diabetes was defined as a fasting glucose concentration greater than or equal to 126 mg/dL or use of hypoglycemic medication. Impaired fasting glucose was defined as a fasting glucose concentration of 100–125 mg/dL (15). The estimated glomerular filtration rate (mL/minute/1.73 m2) was calculated according to the formula used in the Modification of Diet in Renal Disease Study (16).

ABI protocol

At the baseline MESA examination, systolic blood pressure measurements for calculation of the ABI were obtained using a hand-held Doppler instrument with a 5-mHz probe (Nicolet Vascular, Golden, Colorado) in the bilateral brachial, dorsalis pedis, and posterior tibial arteries. We then calculated the ABI in each leg with 3 distinct methods: using the highest ankle pressure (“ABI-HI”), using the lowest ankle pressure (“ABI-LO”), and using the mean of the ankle pressures (“ABI-MN”). The method employed for these calculations was as follows. For the numerator, the pressure used depended on the ABI definition of interest. Specifically, for “ABI-HI,” the higher of the dorsalis pedis artery and posterior tibial artery measurements in a given ankle was used; for “ABI-LO,” the lower of these 2 pressures was used; and for “ABI-MN,” the average of the 2 pressures was used. This resulted in each participant's having 6 different ABI values (3 on the right and 3 on the left). From this, 3 index ABI values (ABI-HI, ABI-LO, and ABI-MN) were defined as the lower of the corresponding right and left values for each method. In all cases, the highest brachial artery pressure (right vs. left) was used for the denominator to account for the possible influence of subclavian stenosis (17).

A subset of 384 MESA participants had replicate ABI measurements taken at visit 3. The overall intraclass correlation coefficient for these measurements was 0.93, while the intratechnician coefficient was 0.95 and the intertechnician coefficient was 0.92.

CAC protocol

Computed tomography of the chest for determination of coronary artery calcium (CAC) was performed with cardiac-gated electron-beam scanners at 3 field centers (Imatron C-150; Imatron, Inc., San Francisco, California) (18) or with an electrocardiogram triggered scan acquisition at 50% of the R-R interval with multidetector scanners at the remaining 3 centers (19). Scans were read centrally at Harbor-UCLA Medical Center (Torrance, California) for quantification of calcium scores using the Agatston method (20).

CCA-IMT protocol

Images of the near and far walls of the bilateral common carotid arteries were obtained using high-resolution B-mode ultrasonography (21). A Logiq 700 ultrasound machine (GE Medical Systems, Waukesha, Wisconsin) was used at all centers. Central reading of common carotid artery intima-media thickness (CCA-IMT) was conducted at the Tufts-New England Medical Center (Boston, Massachusetts) (22).

Statistical analysis

In this analysis, we utilized data from the baseline MESA visit only. Participants were dichotomized according to whether or not they had an ABI less than 0.90 (presence or absence of PAD, respectively). We performed chi-squared tests of independence to determine statistical differences in the proportion with PAD between females and males, both overall and within each ethnic group (Caucasian, Chinese-American, African-American, and Hispanic).

Since the prevalence of CAC in this study was higher than is typically accepted for the rare-disease assumption of logistic regression (i.e., 10%), we assessed the association between PAD and CAC by means of prevalence ratio regression, using the presence of CAC as the dependent variable and adjusting for age, gender, and ethnicity. We assessed the association between PAD and CAC among persons with nonzero CAC by means of linear regression, using the natural log of nonzero CAC as the dependent variable and adjusting for age, gender, and ethnicity. This linear model estimates the mean difference in CAC score between a person with PAD and a person without PAD as a function of PAD status, age, gender, and ethnicity, specifying normal errors. Compared with the lowest 3 quartiles, the top quartile of CAC was also modeled in a logistic regression analysis with PAD as the independent variable, with adjustment for age, gender, and ethnicity. Additionally, we assessed the association between PAD and CCA-IMT by modeling presence in the top quartile (as compared with the lowest 3 quartiles) in a logistic regression analysis, using PAD status as the independent variable and adjusting for age, gender, and ethnicity. Confounding was determined by observation of a change of at least 10% in the effect of PAD upon the addition of the possible confounder to these models. Since it was determined that there was no confounding by other risk factors, the analyses described here were adjusted for age, gender, and ethnicity only.

The sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of PAD (as determined by ABI-HI, ABI-LO, and ABI-MN) to detect a top-quartile CAC score, a nonzero CAC score, a top-quartile CCA-IMT, and a CCA-IMT greater than 1 mm were determined. Sensitivity was calculated as the proportion of persons with the outcome of interest (e.g., CAC score > 0) who had PAD, while specificity was the proportion of persons without the outcome of interest (e.g., CAC score = 0) who did not have PAD. The positive predictive value was the proportion of persons with PAD who had the outcome of interest, while the negative predictive value was the proportion of persons without PAD who did not have the outcome of interest. Overall accuracy (“percent agreement”) was calculated as the sum of persons with the outcome of interest and PAD and persons without the outcome of interest and no PAD, divided by the total number of participants studied. All statistical analyses were carried out using SAS, version 9.1 (SAS Institute Inc., Cary, North Carolina).

RESULTS

There were 6,814 participants recruited into the MESA. After exclusions for missing data or relevant measurements, as well as having an ABI greater than 1.40, 6,590 participants were available for analysis. The exclusion of persons with an ABI greater than 1.40 was applied to all analyses (ABI-HI, ABI-MN, and ABI-LO). The characteristics of these participants are shown in Table 1. For all ethnic groups, the mean age was approximately 62 years, and approximately 53% were women. African Americans had the highest values for nearly all of the risk factors, except for dyslipidemia, which was highest in Hispanics. Hispanics and African Americans had similar prevalences of diabetes mellitus.

Table 1.
Characteristics of the Cohort by Ethnicity, Multi-Ethnic Study of Atherosclerosis, 2000–2002

Using the ABI-HI method, the mean baseline ABI for the entire cohort was 1.10. ABI-HI was lowest in African Americans (1.07), highest in Hispanics (1.12), and intermediate for Caucasians and Chinese Americans (1.11 and 1.10, respectively). When ABI-LO was used, the corresponding ABI values were 1.03, 0.99, 1.06, 1.03, and 1.04, whereas these values were 1.07, 1.04, 1.09, 1.07, and 1.07, respectively, when ABI-MN was used (Table 1). For all 3 methods of calculating the ABI, African Americans had the highest prevalence of PAD, followed by Caucasians, Hispanics, and Chinese Americans.

We then compared the prevalences of PAD between men and women by ABI method within each ethnic group (Table 2). In general, the prevalence of PAD was higher among women than among men, regardless of ethnic group. When the ABI-LO method was used, a significantly higher prevalence of PAD was identified among women for Caucasians, Chinese Americans, and African Americans (Table 2). When the ABI-MN method was used, a significantly higher prevalence of PAD was identified among Caucasians and Chinese Americans. When the ABI-HI method was used, there were no significant gender differences in PAD prevalence within any ethnic group.

Table 2.
Prevalence of an Ankle-Brachial Index Less Than 0.90 by Ethnicity, Gender, and Method of Ankle-Brachial Index Calculation, Multi-Ethnic Study of Atherosclerosis, 2000–2002

Among all ethnic groups and in both genders, the prevalence of PAD differed by method of calculation. Overall and compared with ABI-HI, the prevalence of PAD was 3.95 times higher among women when ABI-LO was used, while among men the prevalence was 2.74 times higher when this pressure was used. When the ABI-LO method was used, the corresponding (female and male) prevalences among Caucasians, Chinese Americans, African Americans, and Hispanics were 4.71 and 3.19, 3.44 and 3.15, 3.39 and 2.56, and 3.94 and 2.04 times higher, respectively. When ABI-MN was used, the prevalence of PAD was intermediate between the prevalences for the highest and lowest ankle pressures.

Table 3 provides the age-, gender-, and ethnicity-adjusted associations between selected CVD risk factors and PAD by method of calculating the ABI. Almost uniformly, the magnitudes of the associations were largest between the risk factors and the ABI when the ABI-HI method was used, except for the variables gender and ethnicity. In these cases, the largest magnitudes of association were typically found using the ABI-LO method. For example, the odds ratio for the association between hypertension and PAD was 1.90 when using the highest ankle pressure, 1.71 when using the mean ankle pressure, and 1.64 when using the lowest ankle pressure. Conversely, and in comparison with Caucasians, the odds of PAD among Hispanics were 0.76, 0.71, and 0.46 using the highest, mean, and lowest ankle pressures, respectively.

Table 3.
Odds Ratios for Peripheral Arterial Disease by Method of Ankle-Brachial Index Calculation, Multi-Ethnic Study of Atherosclerosis, 2000–2002

The associations between different definitions of the ABI and 4 selected subclinical measures of atherosclerosis are shown in Figure 1. The outcome categories for these subclinical measures included prevalent CAC (“CAC > 0”), mean difference in CAC among persons with a CAC score greater than 0 (“CAC difference”), being in the highest (fourth) quartile of CAC (“Q4 CAC”), and being in the highest quartile of CCA-IMT (“Q4 CCA-IMT”). With adjustment for age, gender, ethnicity, smoking status, hypertension, dyslipidemia, and diabetes, an ABI less than 0.90 was significantly associated with all 4 subclinical measures regardless of how the ABI was defined. However, the associations were strongest when the ABI-HI method was used and weakest when the ABI-LO method was used. This finding was uniform across all definitions of the ABI. Furthermore, although the magnitudes of the associations between the ABI and the subclinical measures were different, the relations between the magnitudes of the associations and how the ABI was defined were essentially unchanged when the analyses were conducted separately in men and women. That is, the associations were strongest when the highest ankle pressure was used and weakest when the lowest ankle pressure was used.

Figure 1.
Associations between peripheral arterial disease (PAD), determined using different methods of calculating the ankle-brachial index (ABI), and selected subclinical measures of atherosclerosis, Multi-Ethnic Study of Atherosclerosis, 2000–2002. “CAC ...

We then examined the sensitivity, specificity, positive and negative predictive values, and overall accuracy for each definition of the ABI and both CAC and CCA-IMT (Table 4). Outcome categories for CAC included being in the top quartile and having a score greater than zero. For CCA-IMT, these categories were being in the top quartile and having an intima-media thickness greater than 1.0 mm. For all definitions of CAC and CCA-IMT, use of ABI-LO resulted in the highest sensitivity and negative predictive values while having the lowest specificity and positive predictive values. Conversely, use of ABI-HI resulted in the lowest sensitivity and negative predictive values while having the highest specificity and positive predictive values. Use of ABI-MN produced intermediate results. In general, use of ABI-HI was associated with the greatest overall accuracy for all measures of subclinical CVD.

Table 4.
Results From Screening Test Analyses of Different Methods of Calculating Ankle-Brachial Index and Selected Subclinical Measures of Atherosclerosis, Multi-Ethnic Study of Atherosclerosis, 2000–2002

DISCUSSION

In this study of a large, multiethnic US cohort, and after exclusion of persons with an ABI greater than 1.40, calculations of the ABI that used different ankle blood pressure values resulted in differences in the prevalence of PAD. The prevalence rate was almost 3-fold higher when ABI-LO was used, compared with the use of ABI-HI. These results were similar across ethnic groups.

Our results support earlier findings from 2 European studies (23, 24) and extend the literature by providing data on a multiethnic cohort. Specifically, for each gender and ethnic group, use of the highest ankle pressure resulted in the lowest prevalence of PAD, while the lowest pressure was associated with the highest prevalence. Since guidelines regarding the management of PAD also include asymptomatic patients with low (<0.90) ABIs (1), selection of the method of calculating the ABI is important with respect to the public health and economic burdens of this disease.

Several attempts have been made to compare different methods of calculating the ABI. Based on its superior reproducibility in repeated tests and closer statistical association with leg function, the results of 2 studies support the use of ABI-MN (24, 25). Conversely, using color Duplex as the reference, Schröder et al. (26) reported improved diagnostic performance when the lower ankle pressure was used instead of the higher one, with sensitivities of 89% versus 68% and specificities of 93% versus 99%, respectively, for PAD. Similar findings were reported by Niazi et al. (27), who reviewed ABI results for 208 limbs versus digital subtraction angiography and found that in comparison with ABI-HI, ABI-LO was more sensitive (69% vs. 84%) and less specific (83% vs. 64%) and had higher diagnostic accuracy overall (72% vs. 84%).

Importantly, the ABI not only is a diagnostic method for PAD screening but also is used as a marker of generalized atherosclerosis (28). From this perspective, our study was the first to assess the associations of different methods of calculating the ABI with atherosclerosis in multiple vascular areas. That is, we found that different methods of calculating the ABI resulted in differences in the magnitudes of the associations with both CVD risk factors and subclinical measures of atherosclerosis. Specifically, use of the highest ankle pressure resulted in the largest magnitudes of associations with these variables, except for the variables gender and ethnicity. In these cases, ABI calculations using the lowest ankle pressure resulted in the largest magnitudes of association. Finally, use of the lowest ankle pressure resulted in the best sensitivity and negative predictive value, while the highest ankle pressure gave the best specificity, positive predictive value, and overall accuracy for selected measures of subclinical atherosclerosis. These results indicate that the use of different methods of calculating the ABI can influence the magnitude and significance of the associations with PAD and other forms of CVD.

These results are consistent with the extent of atherosclerotic disease in the legs. In order to classify a person as having PAD using the ABI-HI method, both ankle arteries must have significant occlusive disease. Conversely, for the ABI-LO method, only 1 of the ankle arteries needs to have significant disease to result in an ABI less than 0.90 for that leg. Therefore, an ABI less than 0.90 obtained using the ABI-HI method represents more extensive and diffuse PAD in comparison with ABI-LO, with ABI-MN being intermediate between the two. This explains the stronger associations between ABI-HI and both CVD risk factors and markers of subclinical CVD. From this context, one would also expect the ABI-HI method to be less sensitive and more specific for atherosclerotic disease, while ABI-LO would be more sensitive and less specific. Moreover, since an abnormal result obtained using the ABI-HI method is indicative of more severe disease, use of this method for calculating the ABI would appear to be more appropriate for evaluating perfusion abnormalities in the lower extremities.

The ABI is considered one of the simplest subclinical markers for predicting CVD morbidity and mortality. Recently, in a clinical population of patients with chest pain referred for coronary artery angiography, Espinola-Klein et al. (29) reported the predictive value of ABI after a median follow-up of more than 6 years. As expected, patients with ABI-HI less than 0.90 were found to be at increased risk of CVD events. Notably, those with ABI-HI greater than 0.90 but ABI-LO less than 0.90 were also found to be at increased risk, with a magnitude of association similar to but slightly lower than that found when ABI-HI was used. This means that use of ABI-LO identified an additional group (10% of the population) at higher CVD risk that would have been missed by the use of ABI-HI. The higher sensitivity of ABI-LO to detect events in that study is concordant with our findings of a higher sensitivity for subclinical CVD.

The use of ABI-LO may be associated with several drawbacks (30). First, in the case of a missing arterial signal, the patient's ABI would be calculated at zero. While this situation is usually related to an arterial occlusion, it may also be due to operator error or arterial hypoplasia, a condition estimated to be as frequent as 4%–12% for the dorsalis pedis artery (31). Second, the use of this numerator results in the lowest reproducibility among the different methods of calculating the ABI (24). Hence, use of ABI-LO may result in some false-positive findings. This was evidenced in our study by the lower specificity of the ABI-LO to predict subclinical atherosclerosis in other vascular beds.

A limitation of this study is that there was no gold standard available with which to define PAD (i.e., angiograms) and that would have allowed for comparisons using the 3 methods of calculating ABI. Additionally, the study design was cross-sectional. Therefore, the results are not applicable to prospective changes in the ABI. The MESA examination protocol did not include duplicate blood pressure measurements of the ABI. This may have led to an increased probability of misclassification. However, Newman et al. (32) previously demonstrated very high correlations (r = 0.97) between duplicate blood pressure measurements for ABI. Additionally, in MESA, the intraclass correlation coefficients for the ABI measurements were 0.92–0.95. Given the high concordance between the blood pressure measurements, we believe the likelihood of significant misclassification is low. Finally, for technical reasons, detection of the dorsalis pedis artery pulse may be more difficult than detection of the pulse in the posterior tibial artery. The subsequent use of this value in calculating the ABI may lead to misclassification. Importantly, in the MESA, the examination used a standardized protocol across all study sites, with consistent retraining and checking for internal biases. This resulted in the very high intraclass correlation coefficient of 0.93.

In conclusion, different methods of calculating the ABI are highly relevant to estimating not only the prevalence of PAD but also the magnitude and significance of the associations between the ABI and different measures of subclinical CVD. Moreover, despite similar accuracies for all 3 methods of calculating the ABI, these methods are associated with different sensitivities and specificities, as well as positive and negative predictive values. Therefore, when choosing the method used to calculate the ABI, investigators should consider the objective of the study. If the objective is to determine the likelihood of the presence of atherosclerosis (regardless of the degree of flow obstruction), ABI-LO is the most sensitive and has the highest overall accuracy, while ABI-HI would be more sensitive to luminal stenosis (i.e., lower-extremity perfusion). Importantly (as is the case with all “screening tests”), increases in sensitivity are typically associated with decreases in specificity and vice versa. These relationships should also be considered when designing a study utilizing the ABI. Finally, since the ABI is a strong predictor of incident CVD, the method used to calculate the ABI will probably influence the number of persons considered at high risk of a future CVD event. Accordingly, future research should consider the scientific, reproducibility, and health policy implications of different methods of calculating the ABI.

Acknowledgments

Author affiliations: Department of Family and Preventive Medicine, School of Medicine, University of California, San Diego, La Jolla, California (Matthew A. Allison, Michael H. Criqui); Department of Cardiovascular Surgery and Vascular Medicine, Dupuytren University Hospital, Limoges, France (Victor Aboyans); Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington (Tanya Granston, Aruna Kamineni); Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois (Mary M. McDermott); and Epidemiology Branch, National Heart, Lung, and Blood Institute, Bethesda, Maryland (Hanyu Ni).

This research was supported by a grant from the American Heart Association (to M. A. A.) and contracts N01-HC-95159 through N01-HC-95165 and N01-HC-95169 from the National Heart, Lung, and Blood Institute.

The authors thank the investigators and staff of the Multi-Ethnic Study of Atherosclerosis (MESA) for their valuable contributions.

A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org.

Conflict of interest: none declared.

Glossary

Abbreviations

ABI
ankle-brachial index
CAC
coronary artery calcium
CCA-IMT
common carotid artery intima-media thickness
CVD
cardiovascular disease
MESA
Multi-Ethnic Study of Atherosclerosis
PAD
peripheral arterial disease

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