Guo, Anni
(2020)
Predicting Outcomes in Lost-to-follow-up Subjects from a 15-year Observational Study of Autosomal Dominant Polycystic Kidney Disease.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
Autosomal dominant polycystic kidney disease (ADPKD) is a common chronic hereditary kidney disease, mainly characterized by kidney volume growth and cyst formation. Chronic Kidney Disease is the predictable result of ADPKD, which is usually defined as 5 stages (CKD, stage 1-5) from mild to severe by estimated Glomerular Filtration Rate (eGFR) values.
The Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) is a study of ADPKD patients’ kidney function decline. The participants were in different CKD stages and typically progress to worse CKD stages over time. CRISP was established, in large part, to describe the natural history of ADPKD. Given the necessary long-term follow-up, CRISP participants are often lost to follow-up (LTF).
In order to better use data from the LTF participants, this study focuses on predicting the CKD status at for the LTF participants in CRISP and assessing whether there is a difference between the LTF participants and the non-LTF participants. To predict the trajectory of eGFR, participants were grouped based on the Mayo imaging classification (MIC), which uses age and height-adjusted total kidney volume (htTKV) to estimate the rate of htTKV growth. Within each MIC, a different mixed model was fit to predict eGFR trajectory; the final status of each LTF participant was then estimated based on that trajectory. Bootstrapping was used to assess the variability of the predictions.
Results described the predicted CKD status and showed a minimal impact of variability on the prediction, allowing us to effectively predict the final outcome of LTF ADPKD patients. Further, the predicted outcomes of the LTF participants were consistent with the observed outcome of the non-LTF participants, which indicated the group of LTF participants was a random subset of the entire cohort.
The findings of the study are significant to public health. Because lack of follow-up will affect the effectiveness of the study, it is important to obtain the information of the LTF participants as much as possible. For ADPKD, an early understanding of the variability in patients with different disease risks could provide specific information for the development of disease, which is of great significance for proper prevention and treatment in the future.
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Details
| Item Type: |
University of Pittsburgh ETD
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| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
30 July 2020 |
| Date Type: |
Publication |
| Defense Date: |
20 April 2020 |
| Approval Date: |
30 July 2020 |
| Submission Date: |
21 June 2020 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
69 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Public Health > Biostatistics |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
ADPKD, CKD, Mayo Classification, Mixed Model, Bootstrap, CRISP |
| Date Deposited: |
31 Jul 2020 02:29 |
| Last Modified: |
31 Jul 2020 02:29 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/39259 |
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