title: Profound influence of different methods for determination of the ankle brachial index on the prevalence estimate of peripheral arterial disease.
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 > or = 65 years in the observational get ABI 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 > or = 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Medical Informatics, Biometry and Epidemiology, University of Bochum, Universitätsstr., 150D-44801 Bochum, Germany. stefan.lange@iqwig.de
authors.forename: Stefan F
authors.initials: SF
authors.lastname: Lange
authors.forename: Hans-Joachim
authors.initials: HJ
authors.lastname: Trampisch
authors.forename: David
authors.initials: D
authors.lastname: Pittrow
authors.forename: Harald
authors.initials: H
authors.lastname: Darius
authors.forename: Matthias
authors.initials: M
authors.lastname: Mahn
authors.forename: Jens R
authors.initials: JR
authors.lastname: Allenberg
authors.forename: Gerhart
authors.initials: G
authors.lastname: Tepohl
authors.forename: Roman L
authors.initials: RL
authors.lastname: Haberl
authors.forename: Curt
authors.initials: C
authors.lastname: Diehm
mesh_terms: D000368:Aged; D000842:Ankle / Q000098:blood supply*; D001795:Blood Pressure Determination / Q000379:methods*; D001916:Brachial Artery / Q000503:physiopathology*; D002318:Cardiovascular Diseases / Q000150:complications; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D008297:Male; D016017:Odds Ratio; D016491:Peripheral Vascular Diseases / Q000150:complications / Q000175:diagnosis / Q000453:epidemiology*; D015995:Prevalence