Theses and Dissertations

Date of Award

5-2025

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Applied Statistics and Data Science

First Advisor

Kristina Vatcheva

Second Advisor

Santanu Chakraborty

Third Advisor

Zhuanzhuan Ma

Abstract

Progressive declines in estimated glomerular filtration rate (eGFR) often precede acute kidney injury (AKI), yet the relationship between eGFR trends and AKI risk remains unclear. This study investigates longitudinal eGFR changes and their association with AKI in 459 lung transplant patients followed for up to 7 years (n = 6419). We applied a piecewise linear mixed-effects model to evaluate eGFR trajectories and a Cox proportional hazards model to assess time to AKI. A joint model was used to explore the interplay between longitudinal and survival processes. Key covariates included gender, age at transplantation, antibody-mediated rejection (AMR), and pre-transplant eGFR. Males showed higher baseline eGFR, while older age was linked to greater decline. Pre-transplant eGFR was protective against AKI, and AMR increased AKI risk in separate models. However, the joint model revealed that pre-transplant eGFR influenced AKI risk indirectly via its effect on eGFR trajectories. Gender was significantly associated with AKI risk only in the joint model. These results highlight the added value of joint modeling in uncovering interdependencies between kidney function decline and AKI risk, supporting early intervention strategies post-transplant.

Comments

Copyright 2025 Samiha Zakir. https://proquest.com/docview/3240627865

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