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Calibration of blood pressure measurements in the Jackson Heart Study

Seals, Samantha R.a,c; Colantonio, Lisandro D.d; Tingle, Jonathan V.e; Shimbo, Daichif; Correa, Adolfob; Griswold, Michael E.a; Muntner, Pauld

doi: 10.1097/MBP.0000000000000379
Analytical Methods
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SDC

Background In longitudinal research studies with follow-up examinations, the devices used to measure phenotypes may change over time. When a device change occurs, the two devices should be calibrated to each other to ensure that measurements are comparable. This paper details the Jackson Heart Study (JHS) blood pressure (BP) comparability study.

Participants and methods During its second clinic exam (2005–2008), the JHS switched from a random-zero sphygmomanometer (RZS) BP measurement device to an oscillometric device (OD). During this exam, BP measurements from both an RZS and an OD were taken simultaneously in 2117 participants for the purpose of calibration. Five methods for calibrating systolic BP (SBP) and diastolic BP (DBP) were considered: ignoring the change, ordinary least squares regression, adding the average difference, Deming regression, and robust regression.

Results Using the RZS and OD, the mean (SD) SBP was 125.5 (19.2) and 126.5 (19.9), respectively, and the mean (SD) DBP was 76.4 (10.6) and 74.0 (11.0), respectively. The correlation between RZS and the OD was 0.90 for SBP and 0.80 for DBP. The prevalence of high BP and hypertension and associations with albuminuria were similar when applying each of the five calibration methods. Robust regression was chosen for calibration, giving the following equations:

These equations had a higher R2 statistic than using calibration equations from the Coronary Artery Risk Development in Young Adults Study and the Heinz Nixdorf Recall Study.

Conclusions The JHS BP data have been calibrated using the above equations for use in future analyses.

aDepartment of Data Science, Center of Biostatistics and Bioinformatics

bDepartment of Medicine, University of Mississippi Medical Center, Jackson, Mississippi

cDepartment of Mathematics and Statistics, University of West Florida, Pensacola, Florida

dDepartment of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama

eSchool of Medicine, Emory University, Atlanta, Georgia

fDepartment of Medicine, Columbia University Medical Center, Columbia University, New York City, New York, USA

Correspondence to Paul Muntner, PhD, Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, 1665 University Boulevard, Suite 140J, Birmingham, 35294 AL, USA Tel: +1 205 975 8077; e-mail: pmuntner@uab.edu

Received January 28, 2019

Accepted March 16, 2019

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