APPLICATION OF MULTISTATE MARKOV MODEL IN ANALYZING THE TRANSI TION OF HYPERTENSION AT HAWASSA UNIVERSITY COMPREHENSIVE SPECI ALIZED HOSPITAL, ETHIOPIA

Abstract

Background: Hypertension is the primary cause of cardiovascular death as well as other serious conditions like heart attacks, strokes, chronic heart failure, and kidney failure. In Ethiopia, cardiovascular disease is by far the most common NCD-related cause of death, and hypertension is one of the main risk factors. this study aimed to model hypertension progression and identify factors determining the transition rate between different stages of hypertension among hypertensive patients under follow up at HU-CSH recorded between September 2017 to August 2022 using multistate Markov model by the European Society of Cardiology and the European S ociety of Hypertension's guidelines for blood pressure. Method: Data for this study was obtained from Hawassa university comprehensive specialized Hospital, with a total of 210 hypertensive patients who were under follow up from September 2017 to August 2022 were included in the study. A twenty-four-month transition probability between prehypertension progression stages or state 1 (systolic <140 mm Hg & diast olic <90 mm Hg), grade 1 hypertension or state 2 (systolic 140-159 mm Hg & diastolic 90-99 mm Hg), grade 2 hypertension or state 3 (systolic 160-179 mm Hg diastolic 100-109 mm Hg) and grade 3 or state 4 hypertension (systolic ≥180 mm Hg & diastolic ≥110 mm Hg) and factors determining the rate of progression among patients was estimated. Result and Discussion: Among the total number of patients included in the study, 56.7% were female and 43.3% were male. Among the total number of patients included in the study, 56.7% were female and 43.3% were male. Among them, 22.9% of patients were in state 1 at initial checkup, 36.2 in state 2, 23.8% in state 3 and 17.1% in state 4 hypertension. The estimated 24 m onths transition probability for the hypertensive patients was 15.1 % (95% CI: 0.108, 0.210) from state 1 to state 2, 1.4% (95% CI: 0.009, 0.023) from state 1 hypertension to state 3, 0.2 %(95% CI:0.001, 0.003) from state 1 to state 4, 12.8% (95% CI:0.091, 0.177) from state 2 hypertension to state 3, 2.6% (95% CI:0.017, 0.041) from state 2 hypertension to state 4, 20.5% (95%CI:0.137, 0.290) from state 3 to state 4 and 83.3% (95%CI: 0.768, 0.881), 72.6% (95%CI: 0.660, 0.778), 45.1% (95%CI: 0.368, 0.529) and 67.8% (95%CI: 0.577, 0.777) were the estimate probability of remaining at state 1, state 2, state 3 and state 4 respectively. The mean time a pati ent takes to transition from state to state was estimated and state 1 hypertension had the longest estimated time followed by state 2, while state 3 had the shortest estimated sojourn time. By comparing a likelihood ratio test statistic, the full model fits significantly better than the null model. The observed and expected plots does not have much deviations and assumption of homogeneity of transition rate through the specified time are satisfied. Conclusion: The conditional probability of hypertensive patients from good states to the next worst state are decreasing over time except the first state of hypertension. Being male, older aged, living in urban, taking medication and treatments in the past, history of diabetes (between all states) had high risk of transition from state2 and state 3, state 3and state 4. Being female, younger age, living in rural, and not taking medication in the past between state 1 and state 2 were high risk of hypertension. Having family history of hypertension between state 1 to state 2, state 2 to state 3 and who were not family history of hypertension in state 3 to state 4 had high risk of transition of hypertension.V