Abstract:
Background: Hypertension is persistent elevation of BP above the normal range or taking
anti hypertensive medication. It is a major worldwide healthy problem and becoming risk factor
for stroke, myocardial infarction, vascular disease, and chronic kidney disease. The purpose of
this study was to model the longitudinal measurements of diastolic blood pressure and systolic
blood pressure using the Bayesian approach for hypertensive patients.
Methodology: The data was obtained from Wachemo University Nigist Elleni Mohamed
Memorial Teaching and Referral Hospital records. The data included basic demographic and
clinical characteristics of 200 hypertensive patients, who started antihypertensive treatment at
any time between September, 2015 and March, 2020. To analyze our data we employed
descriptive method, Linear mixed effect model for the longitudinal outcomes. Linear mixed effect
modeling for the longitudinal measurements of diastolic blood pressure and systolic blood
pressure were used Bayesian approach using JMbayes package in R software.
Results: The result from this study revealed that from the bivariate linear mixed model age, time,
family history and related disease were significantly associated with the mean change of SBP
measurements. Time, residence, related disease and the interaction of time with sex and diabetic
had significant effect on the mean change DBP measurements of antihypertensive patients. With
bivariate linear mixed model the association of the evolution (AE) between SBP and DBP was
investigated and obtained as 0.8565.
Conclusion: Based on the study result we can summarized that age, time, family history and
related disease had a significant effect on the mean change of SBP and time, residence, related
disease and the interaction of time with sex and diabetic had significant effect on the mean
change DBP. There is strong association between the evolution of SBP and the evolution of
DBP
