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

被引：0
作者：
Godana, Anteneh Asmare [1 ]
Molla, Bahirnesh Teshome [2 ]
Abatihun, Dawit [1 ]
机构：
[1] Univ Gondar, Coll Nat & Computat Sci, Dept Stat, POB 196, Gondar, Ethiopia
[2] Dilla Univ, Dept Stat, Dilla, Ethiopia
来源：
HELIYON | 2023年 / 9卷 / 12期
关键词：
Bayesian; Linear mixed model; Blood pressure; Hypertension; PREVALENCE; OUTCOMES;
D O I：
10.1016/j.heliyon.2023.e22984
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
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.
引用
收藏
页数：16
相关论文
共 30 条
  • [1] Multivariate Longitudinal Analysis with Bivariate Correlation Test
    Adjakossa, Eric Houngla
    Sadissou, Ibrahim
    Hounkonnou, Mahouton Norbert
    Nuel, Gregory
    [J]. PLOS ONE, 2016, 11 (08):
  • [2] Prevalence and associated factors of hypertension among adults in Gondar, Northwest Ethiopia: a community based cross-sectional study
    Awoke, Akilew
    Awoke, Tadesse
    Alemu, Shitaye
    Megabiaw, Berihun
    [J]. BMC CARDIOVASCULAR DISORDERS, 2012, 12
  • [3] Brooks S, 2011, CH CRC HANDB MOD STA, P1, DOI 10.1201/b10905
  • [4] Brown H., 2014, Applied mixed models in medicine, V3rd
  • [5] Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure
    Chobanian, AV
    Bakris, GL
    Black, HR
    Cushman, WC
    Green, LA
    Izzo, JL
    Jones, DW
    Materson, BJ
    Oparil, S
    Wright, JT
    Roccella, EJ
    [J]. HYPERTENSION, 2003, 42 (06) : 1206 - 1252
  • [6] A Semiparametric Approach to Simultaneous Covariance Estimation for Bivariate Sparse Longitudinal Data
    Das, Kiranmoy
    Daniels, Michael J.
    [J]. BIOMETRICS, 2014, 70 (01) : 33 - 43
  • [7] A Bayesian semiparametric model for bivariate sparse longitudinal data
    Das, Kiranmoy
    Li, Runze
    Sengupta, Subhajit
    Wu, Rongling
    [J]. STATISTICS IN MEDICINE, 2013, 32 (22) : 3899 - 3910
  • [8] Diggle P.D., 2002, A Nalysis of Longitudinal Data
  • [9] Comparison of methodologies to assess the convergence of Markov chain Monte Carlo methods
    El Adlouni, Salaheddine
    Favre, Anne-Catherine
    Bobee, Bernard
    [J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2006, 50 (10) : 2685 - 2701
  • [10] A high-dimensional joint model for longitudinal outcomes of different nature
    Faes, Christel
    Aerts, Marc
    Molenberghs, Geert
    Geys, Helena
    Teuns, Greet
    Bijnens, Luc
    [J]. STATISTICS IN MEDICINE, 2008, 27 (22) : 4408 - 4427
123