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Open Access Research article

Profound influence of different methods for determination of the ankle brachial index on the prevalence estimate of peripheral arterial disease

Stefan F Lange19*, Hans-Joachim Trampisch1, David Pittrow2, Harald Darius3, Matthias Mahn4, Jens R Allenberg5, Gerhart Tepohl6, Roman L Haberl7, Curt Diehm8 and the getABI Study Group

Author Affiliations

1 Department of Medical Informatics, Biometry and Epidemiology, University of Bochum, Universitätsstr., 150D-44801 Bochum, Germany

2 Department of Clinical Pharmacology, Medical Faculty, Technical University of Dresden, Dresden, Germany

3 Department of Medicine I, Vivantes Berlin-Neukölln Medical Centre, Berlin, Germany

4 Medical Department, Sanofi-Aventis, Geneva, Switzerland

5 Department of Vascular Surgery, Ruprecht-Karls University, Heidelberg, Germany

6 Internist/Vascular Medicine, Munich, Germany

7 Department of Neurology, Municipal Hospital Munich-Harlaching, Germany

8 Department of Internal Medicine/Vascular Medicine, Klinikum Karlsbad-Langensteinbach, Affiliated Teaching Hospital of the Ruprecht-Karls University of Heidelberg, Germany

9 Institut für Qualitätssicherung und Wirtschaftlichkeit im Gesundheitswesen (IQWiG), Köln, Germany

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BMC Public Health 2007, 7:147  doi:10.1186/1471-2458-7-147

Published: 6 July 2007

Abstract

Background

The ankle brachial index (ABI) is an efficient tool for objectively documenting the presence of lower extremity peripheral arterial disease (PAD). However, different methods exist for ABI calculation, which might result in varying PAD prevalence estimates. To address this question, we compared five different methods of ABI calculation using Doppler ultrasound in 6,880 consecutive, unselected primary care patients ≥65 years in the observational getABI study.

Methods

In all calculations, the average systolic pressure of the right and left brachial artery was used as the denominator (however, in case of discrepancies of ≥10 mmHg, the higher reading was used). As nominators, the following pressures were used: the highest arterial ankle pressure of each leg (method #1), the lowest pressure (#2), only the systolic pressure of the tibial posterior artery (#3), only the systolic pressure of the tibial anterior artery (#4), and the systolic pressure of the tibial posterior artery after exercise (#5). An ABI < 0.9 was regarded as evidence of PAD.

Results

The estimated prevalence of PAD was lowest using method #1 (18.0%) and highest using method #2 (34.5%), while the differences in methods #3–#5 were less pronounced. Method #1 resulted in the most accurate estimation of PAD prevalence in the general population. Using the different approaches, the odds ratio for the association of PAD and cardiovascular (CV) events varied between 1.7 and 2.2.

Conclusion

The data demonstrate that different methods for ABI determination clearly affect the estimation of PAD prevalence, but not substantially the strength of the associations between PAD and CV events. Nonetheless, to achieve improved comparability among different studies, one mode of calculation should be universally applied, preferentially method #1.