Investigating kidney disease clinical epidemiology using routinely collected administrative data and proteomics

Abstract

Data collected routinely during healthcare visits and additional biospecimens collected as part of cohort study activities are invaluable to better understand kidney disease epidemiology. This thesis explores the detection and characterization of acute kidney injury (AKI), chronic kidney disease (CKD), acute-on-chronic kidney disease (A-on-CKD) and kidney disease progression using rule-based laboratory- and database-embedded algorithms and proteomic analysis.
The research includes three components. Firstly, an internal validation of the National Health Services England (NHSE) AKI detection algorithm-generated alerts received by the United Kingdom Renal Registry. Secondly, a description of the clinical epidemiology of AKI, CKD and A-on-CKD in Cape Town, South Africa, within the Provincial Health Data Centre, a health information exchange that houses administrative and clinical data about clients accessing public healthcare in the province. Lastly, proteins and biological pathways in association with CKD progression in older European adults were investigated (European Quality Study).
The implementation of the NHSE AKI detection algorithm in English laboratories was largely successful, though further investigation is required for alerts in people with CKD and alerts from a few outlying laboratories. Overall, the epidemiological findings in Cape Town shed light on the burden and characteristics of AKI, CKD and A-on-CKD in the region and challenges to research with routinely collected data in complex health systems like South Africa. In the EQUAL study, three proteins were associated with eGFR decline, potentially serving as markers of CKD progression and targets for treatment.
In conclusion, the digitome (administrative data) and proteome provided unique opportunities for detecting and understanding kidney disease, but limitations such as misclassification, missing data and inability to establish causal relationships were identified, requiring future refinements.

Date of Award	19 Mar 2024
Original language	English
Awarding Institution	University of Bristol
Sponsors	University of Cape Town
Supervisor	Fergus J Caskey (Supervisor), Yoav Ben-Shlomo (Supervisor), Kate Birnie (Supervisor), Brian Rayner (Supervisor) & Nicki Tiffin (Supervisor)