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. 2023 Nov 27;9(12):e22984.
doi: 10.1016/j.heliyon.2023.e22984. eCollection 2023 Dec.

Bayesian longitudinal modeling of blood pressure measurements of hypertensive patients at Wachemo University Nigist Elleni Mohamed Memorial Teaching and Referral Hospital Hosanna, Southern Ethiopia

Anteneh Asmare Godana  1 , Bahirnesh Teshome Molla  2 , Dawit Abatihun  1
Affiliations

Bayesian longitudinal modeling of blood pressure measurements of hypertensive patients at Wachemo University Nigist Elleni Mohamed Memorial Teaching and Referral Hospital Hosanna, Southern Ethiopia

Anteneh Asmare Godana et al. Heliyon. .

Abstract

Hypertension is characterized by the persistent elevation of blood pressure (BP) above the normal range or the use of antihypertensive medication. It represents a major global health issue and serves as a significant risk factor for conditions such as stroke, myocardial infarction, vascular disease, and chronic kidney disease. This study aimed to model the longitudinal measurements of diastolic blood pressure and systolic blood pressure in hypertensive patients using a Bayesian approach. The data were obtained from the records of Wachemo University Nigist Elleni Mohamed Memorial Teaching and Referral Hospital. It encompassed the basic demographic and clinical characteristics of 200 hypertensive patients who commenced antihypertensive treatment between September 2015 and March 2020. Descriptive analysis and a Linear mixed-effects model were employed to analyze the data, utilizing the Bayesian approach with the JMbayes package in R software. The results of this study revealed significant associations between age, time, family history, related diseases, and the mean change of systolic blood pressure (SBP) measurements within the bivariate linear mixed model. Likewise, time, residence, related diseases, and the interaction of time with sex and diabetes demonstrated a significant effect on the mean change of diastolic blood pressure (DBP) measurements in antihypertensive patients. By employing a bivariate linear mixed model, an association of 0.8565 was observed between the evolution (AE) of SBP and DBP. Based on the study findings, we can summarize that age, time, family history, and related diseases have a significant impact on the mean change of SBP, while time, residence, related diseases, and the interaction of time with sex and diabetes affect the mean change of DBP. Furthermore, a strong association exists between the evolution of SBP and DBP.

Keywords: Bayesian; Blood pressure; Hypertension; Linear mixed model.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1
Figure 1
Individual profile plot of longitudinal SBP of hypertensive patients by follow-up time.
Figure 2
Figure 2
Individual profile plot of longitudinal DBP of hypertensive patients by follow-up time.
Figure 3
Figure 3
Individual profile plot of SBP with sex of hypertensive patients by follow-up time.
Figure 4
Figure 4
Individual profile plot of DBP with sex of hypertensive patients by follow-up time.
Figure 5
Figure 5
Mean profile plot of SBP and DBP with gender under the hypertensive follow-up.
Figure 6
Figure 6
Observed variance of hypertensive patients' both SBP and DBP by gender.
See this image and copyright information in PMC

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