Glucose Variability Analysis in Two Large-Scale and Real-World Data Sets of Open-Source Automated Insulin Delivery Systems
Authors' conclusion
Both the OPEN and OpenAPS Data Commons data sets show TOR < 70, TIR, and TOR > 180 within recommended goals, adding additional evidence of real-world efficacy of OS-AID. Future research should evaluate in more detail potential data set differences and relationships between individual patterns of user behaviors and GV outcomes.
Study context
- Population
- People with insulin-requiring diabetes using open-source automated insulin delivery (OS-AID) systems
- Participants
- 75 vs 122
- Exposure
- OPEN data set vs OpenAPS Data Commons
- OPENn = 75
- OpenAPS Data Commonsn = 122reference
- Outcome
- Glycemic variability (GV) outcomes
Methodological detailsunsupervised machine learning clustering, Glucose data clustered with unsupervised machine learning; Glycemic variability (GV) metrics compared with statistical tests for distribution comparison
- Unsupervised machine learning clustering
- Glucose data clustered with unsupervised machine learning; GV metrics compared with statistical tests for distribution comparison
Main finding
As reported in the abstract
GV in OPEN data set (36 827 days of data)
| Group | Value | TIR | TOR < 70 | TOR > 180 |
|---|---|---|---|---|
| OPEN | mean 82.08% | 82.08% | 3.66% | 14.3% |
OPEN
Valuemean 82.08%
TIR82.08%
TOR < 703.66%
TOR > 18014.3%
- Between OPEN and OpenAPS Data Commons: P < 0.05 — GV metrics (except TOR < 70, LBGI) differed significantly
Observational data: associations, not effects.
Further findings
LBGI and HBGI by gender (OPEN)
- LBGI differs by gender
- P < 0.05
- HBGI distributions similar by gender
- P > 0.05
Original publication
Glucose Variability Analysis in Two Large-Scale and Real-World Data Sets of Open-Source Automated Insulin Delivery Systems. J Diabetes Sci Technol. Published 26 September 2023. doi:10.
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