How much is short-term glucose prediction in type 1 diabetes improved by adding insulin delivery and meal content information to CGM data? A Proof-of-Concept Study
Open accessAbstract
BACKGROUND: In type 1 diabetes (T1D) management, short-term glucose prediction can allow to anticipate therapeutic decisions when hypo/hyperglycemia is imminent. Literature prediction methods mainly use past continuous glucose monitoring (CGM) readings. Sophisticated algorithms can use information on insulin delivered and meal carbohydrate (CHO) content. The quantification of how much insulin and CHO information improves glucose prediction is missing in the literature and is investigated, in an open-loop setting, in this proof-of-concept study. METHODS: We adopted a versatile literature prediction methodology able to utilize a variety of inputs. We compared predictors that use (1) CGM; (2) CGM and insulin; (3) CGM and CHO; and (4) CGM, insulin, and CHO. Data of 15 T1D subjects in open-loop setup were used. Prediction was evaluated via absolute error and temporal gain focusing on meal/night periods. The relative importance of each individual input of the predictor was evaluated with a sensitivity analysis. RESULTS: For a prediction horizon (PH) ≥ 30 minutes, insulin and CHO information improves prediction accuracy of 10% and double the temporal gain during the 2 hours following the meal. During the night the 4 methods did not give statistically different results. When PH ≥ 45 minutes, the influence of CHO information on prediction is 5-fold that of insulin. CONCLUSIONS: In an open-loop setting, with PH ≥ 30 minutes, information on CHO and insulin improves short-term glucose prediction in the 2-hour time window following a meal, but not during the night. CHO information improves prediction significantly more than insulin.
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Algorithms that use CGM data plus external inputs, such as ingested carbohydrates, injected insulin and physical activity, have been shown to enhance prediction performance compared to predictions using only CGM data.
“While, others use CGM data plus external inputs, such as the amount of ingested carbohydrates, injected insulin and physical activity [52]. These have been shown to enhance the performance of predictions when compared to predictions using only CGM data [53].”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [53]
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Continuous glucose monitoring sensors: past, present and future algorithmic challenges
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- Authors
- Zecchin, C., Facchinetti, A., Sparacino, G., & Cobelli, C.
- Affiliations
- University of Padua (IT)
- Corresponding author
- Cobelli, Claudio
- Venue
- Journal of Diabetes Science and Technology
- Publisher
- SAGE Publications
- Published
- 2016-07-10
- Volume
- 10
- Issue
- 5
- Pages
- 1149-1160
- ISSN
- 1932-2968 (ISSN-L)
- PMC
- PMC5032963
- OpenAlex
- W2474805097
- Cited by
- 64
- References
- 33
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 7 · 2023: 7 · 2022: 7 · 2021: 12 · 2020: 9 · 2019: 6 · 2018: 7 · 2017: 4 · 2016: 1
- FWCI
- 6.03
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Diabetes Science and Technology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Padua Research Archive (University of Padova) (submittedVersion)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
- Keywords
- type 1 diabetes, continuous glucose monitoring, insulin delivery, prediction horizon, sensitivity analysis
- MeSH terms
- Algorithms, Blood Glucose, Diabetes Mellitus, Type 1, Humans, Hypoglycemic Agents, Insulin, Insulin Infusion Systems, Blood Glucose Self-Monitoring, Meals
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