Predictive ability of visit-to-visit glucose variability on diabetes complications
1
Issued Date
2022
Copyright Date
2022
Resource Type
Language
eng
File Type
application/pdf
No. of Pages/File Size
ix, 114 leaves : ill.
Access Rights
open access
Rights
ผลงานนี้เป็นลิขสิทธิ์ของมหาวิทยาลัยมหิดล ขอสงวนไว้สำหรับเพื่อการศึกษาเท่านั้น ต้องอ้างอิงแหล่งที่มา ห้ามดัดแปลงเนื้อหา และห้ามนำไปใช้เพื่อการค้า
Rights Holder(s)
Mahidol University
Bibliographic Citation
Thesis (M.Sc. (Data Science for Healthcare and Clinical Informatics))--Mahidol University, 2022)
Suggested Citation
Teh, Xin Rou, 1991- Predictive ability of visit-to-visit glucose variability on diabetes complications. Thesis (M.Sc. (Data Science for Healthcare and Clinical Informatics))--Mahidol University, 2022). Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/113816
Title
Predictive ability of visit-to-visit glucose variability on diabetes complications
Author(s)
Abstract
Prevalence of Type 2 diabetes is increasing worldwide and it is expected that diabetes complications will follow. Glucose variability (GV) measures fluctuations of glucose homeostasis either by measuring intra-day, between-day, or visit-to-visit variations. There are limited studies that include the use of GV measures to predict diabetes complications using machine learning models. This study aimed to explore the predictive ability of different visit-to-visit GV measures (HbA1c-CV, HbA1c-SD, FPG-CV, FPG-SD, time-varying HbA1c and time-varying FPG) on three diabetes complications (CVD, DR, and CKD), by using Cox proportional hazard regression and machine learning models (random survival forest (RSF) and LTRC forest). This retrospective cohort study included 40,662 Type 2 diabetes patients from Ramathibodi Hospital, Thailand from 2010 to 2019. The results found that RSF models of DR complications had the highest C-index. All the GV measures also ranked the top in DR models. HbA1c-CV, HbA1c-SD, FPG-CV and FPG-SD were associated with all the three diabetes complications of interest (CVD, DR and CKD) whereas time-varying of HbA1c and FPG were only associated with CVD and DR. LTRC forest models showed the worst performance, with C-index ranging from 0.468-0.678 across all the models. In addition, it was observed that models developed using longitudinal dataset had higher C-index than baseline dataset. In conclusion, GV measures can be an additional marker for disease monitoring. HbA1c and FPG GV measures models had similar performance, which showed that FPG GV measures can be a good measure in resource-limited settings, when HbA1c cannot be performed. IMPLICATION OF THE THESIS: GV measures, in terms of HbA1c or FPG, can be used as additional monitoring parameters in diabetes management. Models can be deployed as clinical decision support tool/prompt by auto-calculation of GV and risk prediction of diabetes complications.
Degree Name
Master of Science
Degree Level
Master's degree
Degree Department
Faculty of Medicine Ramathibodi Hospital
Degree Discipline
Data Science for Healthcare and Clinical Informatics
Degree Grantor(s)
Mahidol University