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Investigating kidney disease clinical epidemiology using routinely collected administrative data and proteomics
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 (CK...
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| Format: | Thesis |
| Language: | English |
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Department of Medicine
2024
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| _version_ | 1867613149513908224 |
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| access_status_str | Open Access |
| author | Aylward, Ryan Edward |
| author2 | Rayner, Brian |
| author_browse | Aylward, Ryan Edward Rayner, Brian |
| author_facet | Rayner, Brian Aylward, Ryan Edward |
| author_sort | Aylward, Ryan Edward |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/40786 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:31.816Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Department of Medicine |
| publisherStr | Department of Medicine |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/40786 Investigating kidney disease clinical epidemiology using routinely collected administrative data and proteomics Aylward, Ryan Edward Rayner, Brian kidney disease clinical epidemiology 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. 2024-12-20T05:29:53Z 2024-12-20T05:29:53Z 2024 2024-12-20T05:26:36Z Thesis / Dissertation Doctoral PhD http://hdl.handle.net/11427/40786 eng application/pdf Department of Medicine Faculty of Health Sciences University of Cape Town |
| spellingShingle | kidney disease clinical epidemiology Aylward, Ryan Edward Investigating kidney disease clinical epidemiology using routinely collected administrative data and proteomics |
| thesis_degree_str | Doctoral |
| title | Investigating kidney disease clinical epidemiology using routinely collected administrative data and proteomics |
| title_full | Investigating kidney disease clinical epidemiology using routinely collected administrative data and proteomics |
| title_fullStr | Investigating kidney disease clinical epidemiology using routinely collected administrative data and proteomics |
| title_full_unstemmed | Investigating kidney disease clinical epidemiology using routinely collected administrative data and proteomics |
| title_short | Investigating kidney disease clinical epidemiology using routinely collected administrative data and proteomics |
| title_sort | investigating kidney disease clinical epidemiology using routinely collected administrative data and proteomics |
| topic | kidney disease clinical epidemiology |
| url | http://hdl.handle.net/11427/40786 |
| work_keys_str_mv | AT aylwardryanedward investigatingkidneydiseaseclinicalepidemiologyusingroutinelycollectedadministrativedataandproteomics |