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Algorithms for converting random-zero to automated oscillometric blood pressure values, and vice versa
Andreas Stang1, Susanne Moebus, Stefan Möhlenkamp
1Institute of Medical Epidemiology, Biometry and Informatics, Medical Faculty, University of Halle-Wittenberg, Halle, Germany. andreas.stang@medizin.uni-halle.de
American Journal of Epidemiology
|May 6, 2006
Summary
Automated oscillometric devices (AODs) measure higher blood pressure than random-zero sphygmomanometers (RZS). A conversion algorithm using linear regression can reconcile these differing blood pressure readings for epidemiological studies.
Area of Science:
- Cardiovascular Epidemiology
- Biomedical Instrumentation
Background:
- Random-zero sphygmomanometers (RZS) and automated oscillometric devices (AODs) are used to measure blood pressure.
- AODs systematically yield higher readings than RZS, hindering comparability in epidemiological studies.
- Previous studies using RZS for blood pressure prediction of cardiovascular events are difficult to compare with newer AOD studies.
Purpose of the Study:
- To compare blood pressure measurements between RZS and AOD.
- To develop a conversion algorithm for inter-device comparability in epidemiological research.
- To enable better comparison of blood pressure values across studies using different devices.
Main Methods:
- A randomized comparison of RZS and AOD measurements was conducted in a German cohort study (2000-2003).
- 2,365 subjects aged 45-75 years were measured three times with each device in a randomized order.
- Linear regression models incorporating age, sex, and blood pressure level were used to develop conversion algorithms.
Main Results:
- The mean difference (AOD-RZS) was 3.9 mmHg for systolic and 2.6 mmHg for diastolic blood pressure.
- Linear regression models effectively converted blood pressure values between RZS and AOD.
- The developed algorithm allows for the conversion of RZS to AOD and vice versa.
Conclusions:
- Blood pressure values measured by RZS and AOD can be converted using a linear regression algorithm.
- This method enhances the comparability of blood pressure data across epidemiological studies utilizing different measurement devices.
- The findings facilitate more accurate meta-analyses and comparisons of cardiovascular risk assessed through blood pressure.