ArticleHeliyon2023
Bayesian longitudinal modeling of blood pressure measurements of hypertensive patients at Wachemo University Nigist Elleni Mohamed Memorial Teaching and Referral Hospital Hosanna, Southern Ethiopia.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors at 2 institutions in 1 country.
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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.
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