BAYESIAN LONGITUDINAL MODELING OF BLOOD PRESSURE MEASUREMENTS OF HYPERTENSIVE PATIENTS AT WACHEMO UNIVERSITY NIGIST ELLENI MOHAMED MEMORIAL TEACHING AND REFERRAL HOSPITAL HOSANNA, SOUTHERN ETHIOPIA.

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