PS-P04-8: COMPARISON OF INDICES OF WITHIN-VISIT BLOOD PRESSURE VARIABILITY IN MAY MEASUREMENT MONTH 2019. (January 2023)

• Record Type: Journal Article
• Authors: Kerr, Gabriele Keziah; Schlaich, Markus; Schutte, Aletta E; Stergiou, George; Poulter, Neil; Beaney, Thomas
• Is Part Of: Journal of hypertension. Volume 41(2023)Supplement 1
• Page Start: e248
• Publication Date: 2023-01
• Subjects: Hypertension -- Periodicals; Hypertension -- Periodicals; 616.132005
• DOI: 10.1097/01.hjh.0000915256.08547.3d
• Languages: English
• ISSNs: 1473-5598

Abstract

Abstract : Objective: There is a lack of consensus on the optimal mathematical indices for quantifying blood pressure variability (BPV). We sought to compare four established indices of BPV across six age strata among the substantial international population screened during May Measurement Month (MMM) 2019. Design and method: Within visit BPV of readings taken at 1 minute intervals was calculated for each of the 1, 133, 008 participants with three readings on a single occasion. Indices of BPV were the standard deviation (SD), absolute difference in BP from the first to the second reading (AR), coefficient of variation (CV), and variation independent of the mean (VIM). Mixed effects regression models were fit with a random intercept for country of screening site to account for clustering. Three models were created for each systolic (sbp) and diastolic (dbp) BPV metric: (1) unadjusted, (2) adjusted for average BP, and (3) adjusted for average BP, sex, and use of antihypertensive medication (multifactor). Results: Unadjusted models of sbpSD and sbpAR showed increased BPV with age from 40 to 49 years onwards. In average BP and multifactor adjusted models of all indices and unadjusted models of sbpVIM and sbpCV, systolic BPV decreased with age from 18 to 49 years but was similar across the remaining age categories (Fig.1A). Diastolic BPV displayed a "U shaped" association with age such that it was lowest in the 40 to 49 year group and relatively higher in the youngest and oldest age categories (Fig.1B). Adjustment for average BP significantly altered coefficient estimates for SD and AR (Fig.1). Compared to unadjusted models, adjustment for average BP modestly affected effect estimates for sbpCV, dbpCV, sbpVIM, and dbpVIM (Fig.1). Compared to models only adjusted for average BP, multifactor adjusted models resulted in increases in the relative differences in systolic within visit BPV across age categories but estimates for diastolic BPV across age categories were slightly attenuated on multifactor adjustment. Conclusions: Unlike AR and SD, CV and VIM were largely independent of average BP levels. Unlike VIM, to calculate CV for an individual solely requires parameters derived from their own BP readings. Models of CV produced similar outputs those produced by models of VIM, which suggests CV may have utility in clinical practice settings as an index of within visit BPV that is relatively independent of average BP while simple to calculate. The relative clinical utility and prognostic implications of these metrics of BPV remain to be assessed.