title: The Relationship Between High Pulse Pressure and Low Ankle-Brachial Index. Potential Utility in Screening for Peripheral Artery Disease in Population-Based Studies.
abstract: BACKGROUND
  The ankle-brachial index (ABI) is a reliable screening procedure for peripheral artery disease detection. However, ABI testing is time-consuming and requires trained personnel, which may preclude its routine use in population-based surveys. Preliminary data suggest a relationship between ABI values and pulse pressure (PP) levels.
  
  
  OBJECTIVE
  To assess whether PP calculation might help to detect persons who need ABI screening in population-based studies.
  
  
  METHODS
  All Atahualpa residents aged ≥60 years were identified during a door-to-door survey and invited to undergo ABI testing. Non-consented persons and those with ABI ≥1.4 were excluded. Using generalized linear and logistic regression models adjusted for demographics and cardiovascular risk factors, as well as receiver operator characteristics curve analysis, we evaluated the association between PP values and ABI, as well as the reliability of PP to identify candidates for ABI testing.
  
  
  RESULTS
  Out of 239 participants (mean age 70 ± 8 years, 62 % women), 46 (19 %) had an ABI ≤0.9 and 136 (57 %) had PP >65 mmHg, with a negative relationship between them (R = -0.386, p < 0.0001). A PP >65 mmHg was associated with an ABI ≤ 0.9 in the logistic regression model (OR 3.46, 95 % CI 1.07-11.2, p = 0.038). Continuous PP levels also correlated negatively with ABI (β -0.0014, 95 % CI -0.0024 to -0.0004, p = 0.005). The sensitivity of a PP >65 mmHg to predict a low ABI was 85 %, and the specificity was 50 %. In contrast, the sensitivity of blood pressure ≥140/90 mmHg was 27 % and the specificity was 10 %. The area under the curve for the predictive value of a PP >65 mmHg was 0.673 (95 % CI 0.609-0.736), and that of a blood pressure ≥140/90 mmHg was 0.371 (95 % CI 0.30-0.443), with a significant difference between them (p < 0.0001).
  
  
  CONCLUSIONS
  PP calculation may be a simple tool to detect candidates for ABI testing in population-based studies.
authors.affiliation: School of Medicine, Universidad Espíritu Santo, Ecuador, Guayaquil, Ecuador, oscardelbrutto@hotmail.com.
authors.forename: Oscar H
authors.initials: OH
authors.lastname: Del Brutto
authors.forename: Robertino M
authors.initials: RM
authors.lastname: Mera
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Sedler
authors.forename: Jadry A
authors.initials: JA
authors.lastname: Gruen
authors.forename: Kelsie J
authors.initials: KJ
authors.lastname: Phelan
authors.forename: Elizabeth H
authors.initials: EH
authors.lastname: Cusick
authors.forename: Mauricio
authors.initials: M
authors.lastname: Zambrano
authors.forename: David L
authors.initials: DL
authors.lastname: Brown
mesh_terms: D000368:Aged; D055109:Ankle Brachial Index; D019540:Area Under Curve; D001794:Blood Pressure; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D058729:Peripheral Arterial Disease / Q000175:diagnosis* / Q000503:physiopathology; D011237:Predictive Value of Tests; D012372:ROC Curve