Joint Modeling of Bivariate Longitudinal Changes of Blood Pressure and Time to Remission of Hypertensive Patients Receiving Treatment: Bayesian Approach.

Tilahun, Frezer

dc.contributor.author	Tilahun, Frezer
dc.date.accessioned	2021-07-27T12:09:53Z
dc.date.available	2021-07-27T12:09:53Z
dc.date.issued	2021-07-27
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/12281
dc.description.abstract	Introduction: Hypertension (high blood pressure) is a widespread condition in which the blood's long-term force on the artery walls is high enough to develop health problems such as heart disease. The main aim of this paper was to jointly model the longitudinal change of blood pressures (SBP and DBP) and time to the first remission of hypertensive outpatients receiving treatment. Methods: A retrospective study design was used to collect relevant data on longitudinal changes in blood pressure and time-to-event from the medical charts of 301 hypertensive outpatients under follow-up at Felege Hiwot referral hospital. The data were explored using basic descriptive statistics, individual and mean profile plots, Kaplan-Meier plots, and log-rank tests. To get wide-ranging information about the progression of the disease, bivariate linear mixed, Cox PH regression, and joint models were employed. Results: A sample of 301 hypertensive patients who take treatment was taken from FHRH recorded from September 2017 to February 2021. With an estimated median survival time of 11 months, 80.7% of patients had their first remission time. The patient’s mean SBP and DBP under all the visit times were 148.89mmHg and 87.78mmHg respectively. There is a 0.89-fold decrease in risk of the first remission, per doubling of the current true value of SBP. The evolution/change of SBP and DBP has a strong positive correlation. Conclusion: A patient who had a good follow-up, lower BUN, lower serum calcium, lower serum sodium, lower hemoglobin, and take the treatment enalapril shows an opportunity in decreasing the BPs, consequently, this compels patients to experience the first remission early. Furthermore, Age, patient’s history of DM, patient's history of CKD, and treatment type were the joint determinant factors for the longitudinal change of blood pressure and the first remission time. In conclusion, the Bayesian joint bivariate model approach provides specific dynamic predictions, provides wide-ranging information about the disease transitions, and better knowledge of disease etiology.	en_US
dc.language.iso	en_US	en_US
dc.subject	Statistics	en_US
dc.title	Joint Modeling of Bivariate Longitudinal Changes of Blood Pressure and Time to Remission of Hypertensive Patients Receiving Treatment: Bayesian Approach.	en_US
dc.type	Thesis	en_US