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A mixed-effect model for the evolution of unbalanced longitudinal hematocrit levels in chronic kidney failure
Yemane Hailu Fissuh1, Getachew Beyene Nega2, Azmera Hailay2
1Biostatistics, Department of Statistics, College of Natural and Computational Sciences, Aksum University, Axum, Tigray, Ethiopia.
Background:
Chronic kidney disease, or renal failure, is a public health problem with an estimated prevalence of 8%-16% worldwide. This study was conducted to investigate the evolution of hematocrit levels over time in renal patients after transplantation and to determine how the evolution of hematocrit levels depends on the patients' age, sex, and other factors.
Objective:
The main objective of this study was to employ a mixed-effects model to examine the unbalanced longitudinal evolution of hematocrit levels in chronic kidney failure patients.
Methodology:
This longitudinal study included 1160 patients who received a renal transplant. These patients were followed for at most 10 years. The hematocrit level was considered the response, while the covariates were time in years, sex, and age of the patients. Different statistical methods, such as explanatory analysis, multivariate regression, two-stage analysis, and linear mixed-effects models, were employed to explore the evolution of hematocrit over time.
Results:
The results revealed that hematocrit levels in kidney transplant patients evolve. The sex and age of the patient significantly affect the evolution of hematocrit levels. Males tend to have a greater increase in hematocrit levels over time than females do. Hematocrit levels tend to increase with increasing age. Furthermore, cardiovascular problems before transplant and rejection symptoms did not significantly affect the evolution of hematocrit levels.
Conclusions:
Hematocrit levels evolve, and this evolution follows a quartic time effect. The change in hematocrit levels varies according to the sex and age of the patient after a kidney transplant. Patients with low hematocrit levels tend to have a greater increase over time.
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