All blood glucose without any grouping. [n = all BG readings of target population within specified time-period]
| Pros |
Cons |
- Can be appropriate to detect occasional glucose excursions outside of any narrow target range
- Greatest statistical power since the largest sample size
|
- Single patient with 10 hypoglycaemic events vs. 10 patients with a single episode of hypoglycaemia
|
All BGs grouped by single patient [n = no. of admissions admitted during specified time-period in target population]
| Pros |
Cons |
- Minimises the bias of BG testing frequency
- “What is my chance of becoming hypoglycaemic in a hospital?"
|
- Undervalues the relative impact of patients with extended lengths of stay
- Single patient with 50 hypoglycaemic events vs. patient with a single episode of hypoglycaemia
|
All BGs grouped by calendar days [n = total no. of calendar days patients were admitted during specified time-period in target population]
| Pros |
Cons |
- “Intermediate model” - optimal balance of BG frequency and patient length of stay
- Most clinically useful
|
- Can be viewed as more complicated to generate and interpret
|
For further information, please refer to the following studies:
Goldberg PA, Bozzo JE, Thomas PG, Mesmer MM, Sakharova OV, Radford MJ, et al. "Glucometrics"--assessing the quality of inpatient glucose management. Diabetes Technol Ther. 2006;8(5):560-9. doi: 10.1089/dia.2006.8.560.
Kyi M, Colman PG, Rowan LM, Marley KA, Wraight PR, Fourlanos S. Glucometric benchmarking in an Australian hospital enabled by networked glucose meter technology. Med J Aust. 2019;211(4):175-80. doi: 10.5694/mja2.50247.
Barmanray RD, Kyi M, Colman PG, Fourlanos S. Longitudinal Digital Glucometric Benchmarking to Evaluate the Impact of Institutional Diabetes Care Initiatives in Adults With Diabetes Mellitus Over the 2016-2020 Period. J Diabetes Sci Technol. 2022 May;18(3):610-17. doi: 10.1177/19322968221140126.