Measurement characteristics of the ankle–brachial index: results from the Action for Health in Diabetes study

Abstract

Many protocols have been used in clinical and research settings for collecting systolic blood pressure (SBP) measurements to calculate the ankle–brachial index (ABI); however, it is not known how useful it is to replicate measurements and which measures best reflect cardiovascular risk. Standardized measurements of ankle and arm SBP from 5140 overweight or obese individuals with type 2 diabetes were used to estimate sources of variation. Measurement characteristics of leg-specific ABI, as calculated using a standard algorithm based on the highest SBP of the dorsalis pedis or posterior tibial arteries, were projected using simulations. Coefficients of variability ranged from 2% to 3% when single SBP measurements were used and ABI was overestimated by 2–3%. Taking two SBP measurements at each site reduced standard errors and bias each by 30–40%. The sensitivity of detecting low ABI ranges exceeded 90% for ABI within 0.05 of the 0.90 clinical cut-point. The average and the minimum of the two (i.e. right and left) leg-specific ABI values had similar U-shaped relationships with Framingham risk scores; however, the average leg ABI had slightly greater precision. Replicating SBP measurements reduces the error and bias of ABI. Averaging leg-specific values may increase power for characterizing cardiovascular disease risk.

Keywords

diagnostic error peripheral arterial disease sensitivity and specificity

References

1 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: 1472–1479.

2 Greenland P Abrams J Aurigemma GP . Beyond secondary prevention: identifying the high-risk patient for primary prevention: noninvasive tests of atherosclerotic burden. Circulation 2000; 101: e16–e222.

3 Hiatt WR . Medical treatment of peripheral arterial disease and claudication. N Engl J Med 2001; 344: 1608–1621.

4 Murabito JM Evans JC Larson MG Nieto K Levy D Wilson PWF . The ankle–brachial index in the elderly and risk of stroke, coronary disease, and death. The Framingham Study. Arch Intern Med 2003; 163: 1939–1942.

5 Resnick HE Lindsay RS McDermott MM . Relationship of high and low ankle brachial index to all-cause and cardiovascular disease mortality: the Strong Heart Study. Circulation 2004; 109: 733–739.

6 Lee AJ Price JF Russell MJ Smith FB van Wijk MCW Fowkes FGR . Improved prediction of fatal myocardial infarction using the ankle brachial index in addition to conventional risk factors. The Edinburgh Artery Study. Circulation 2004; 110: 3075–3080.

7 Koji Y Tomiyama H Ichihashi H . Comparison of ankle–brachial pressure index and pulse wave velocity as markers of the presence of coronary disease in subjects with high risk of atherosclerotic disease. Am J Cardiol 2004; 94: 868–872.

8 Lange S Trampisch HJ Haberl R . Excess 1-year cardiovascular risk in elderly primary care patients with a low ankle–brachial index (ABI) and high homocysteine level. Atherosclerosis 2005; 178: 351–357.

9 McDermott MM Liu K Criqui MH . Ankle–brachial index and subclinical cardiac and carotid disease. The Multi-Ethnic Study of Atherosclerosis. Am J Epidemiol 2005; 162: 33–41.

10.

10 Kweon SS Shin MH Park KS . Distribution of the ankle–brachial index and associated cardiovascular risk factors in a population of middle-aged and elderly Koreans. J Korean Med Sci 2005; 20: 373–378.

11.

11 Look AHEAD Research Group . Look AHEAD: Action for Health in Diabetes. Design and methods for a clinical trial of weight loss for the prevention of cardiovascular disease in type 2 diabetes. Control Clin Trials 2003; 24: 610–628.

12.

12 Look AHEAD Research Group . The Look AHEAD Study: a description of the lifestyle intervention and the evidence supporting it. Obesity 2006; 14: 737–752.

13.

13 D’Agostino RB Russell MW Huse DM . Primary and subsequent coronary risk appraisal: new results from The Framingham Study. Am Heart J 2000; 139: 272–281.

14.

14 Gilks WR Richardson S Spiegelhalter DJ . Introducing Markov chain Monte Carlo. In: WR Gilks S Richardson DJ Spiegelhalter (eds). Markov Chain Monte Carlo in practice. London: Chapman and Hall; 1996. p. 1–21.

15.

15 Fitzmaurice GM Laird NM Ware JH . Applied longitudinal analysis. New York: Wiley and Sons; 2004. p. 200–205.

16.

16 Spiegelhalter D Thomas A Best N . BUGS 0.5: Bayesian inference Using Gibbs Sampling manual (Version ii). Cambridge, England: MRC Biostatistics Unit; 1996.