AbstractKidney disease is a growing problem within the UK and worldwide. Progression of kidney disease can result in the need for dialysis, a method of renal replacement therapy (RRT). In the UK 68,111 individuals are receiving RRT. Of these 24,365 have in-centre haemodialysis (ICHD) with 6540 (28%) due to a primary diagnosis of diabetes. The combination of haemodialysis dependence and diabetes is life limiting and a significant management challenge.
This thesis initially describes qualitative data gathered using an anonymous questionnaire among diabetic patients undergoing haemodialysis. We show a high degree of perceived confidence in self-management, yet actions that raise clinical concerns. Most significantly no, or low frequency, glucose testing and lack of rechecking highlight the need for further monitoring and education. Chapters 3 to 6 then describe the DRIVE-HD study, a large observational study conducted at the Wessex Kidney Centre. This study generated the largest dataset of interstitial glucose levels in haemodialysis patients to date:
•Chapter 3 describes glucose variability using data gathered by Continuous Glucose Monitoring (CGM) sensors. Interquartile ranges (IQR) are used to generate ambulatory glucose profiles (AGP).
•Chapter 4 uses Clarke Error Grid Analysis to determine the clinical validity of the Libre FreeStyle Pro sensor, identifying that sensor application can be on a dialysis day.
•Chapter 5 describes IQR correlation with other markers of glycaemic variability (mean and %CV). Overall IQR is not influenced by time of dialysis treatment, but is increased by insulin regime used.
•Chapter 6 reviews data against Time in Range (TIR) as established in consensus recommendations. A very high burden of hypoglycaemia was identified, with 69.9% not meeting target of <1% of time with a glucose <3.9mmol/L. 70% of AGPs generated were experiencing glycaemia of a clinical concern.
The work described in this thesis has revealed the need for future efforts using Ambulatory Glucose Profile (AGP) as a tool to aid patient education and understanding. Personalisedcare for this high-risk group of patients should aim to identify and modify risk of clinically dangerous hypoglycaemic events. I suggest that pairing CGM with cardiac monitoring could help identify
dysglycaemia induced arrhythmias in this population, thus potentially becoming an important new clinical tool that can be used to help prevent sudden cardiac death.
|Date of Award||23 May 2023|
|Supervisor||Simon Kolstoe (Supervisor), Adam Kirk (Supervisor) & Rebecca Stores (Supervisor)|