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Published Online: 9 November 2020

Predicting and Preventing Nocturnal Hypoglycemia in Type 1 Diabetes Using Big Data Analytics and Decision Theoretic Analysis

Publication: Diabetes Technology & Therapeutics
Volume 22, Issue Number 11

Abstract

Background: Despite new glucose sensing technologies, nocturnal hypoglycemia is still a problem for people with type 1 diabetes (T1D) as symptoms and sensor alarms may not be detected while sleeping. Accurately predicting nocturnal hypoglycemia before sleep may help minimize nighttime hypoglycemia.
Methods: A support vector regression (SVR) model was trained to predict, before bedtime, the overnight minimum glucose and overnight nocturnal hypoglycemia for people with T1D. The algorithm was trained on continuous glucose measurements and insulin data collected from 124 people (22,804 valid nights of data) with T1D. The minimum glucose threshold for announcing nocturnal hypoglycemia risk was derived by applying a decision theoretic criterion to maximize expected net benefit. Accuracy was evaluated on a validation set from 10 people with T1D during a 4-week trial under free-living sensor-augmented insulin-pump therapy. The primary outcome measures were sensitivity and specificity of prediction, the correlation between predicted and actual minimum nocturnal glucose, and root-mean-square error. The impact of using the algorithm to prevent nocturnal hypoglycemia is shown in-silico.
Results: The algorithm predicted 94.1% of nocturnal hypoglycemia events (<3.9 mmol/L, 95% confidence interval [CI], 71.3–99.9) with an area under the receiver operating characteristic curve of 0.86 (95% CI, 0.75–0.98). Correlation between actual and predicted minimum glucose was high (R = 0.71, P < 0.001). In-silico simulations showed that the algorithm could reduce nocturnal hypoglycemia by 77.0% (P = 0.006) without impacting time in target range (3.9–10 mmol/L).
Conclusion: An SVR model trained on a big data set and optimized using decision theoretic criterion can accurately predict at bedtime if overnight nocturnal hypoglycemia will occur and may help reduce nocturnal hypoglycemia.

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cover image Diabetes Technology & Therapeutics
Diabetes Technology & Therapeutics
Volume 22Issue Number 11November 2020
Pages: 801 - 811
PubMed: 32297795

History

Published online: 9 November 2020
Published in print: November 2020
Published ahead of print: 14 May 2020
Published ahead of production: 16 April 2020

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Clara Mosquera-Lopez [email protected]
Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA.
Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, Oregon, USA.
Robert Dodier
Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA.
Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, Oregon, USA.
Nichole S. Tyler
Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA.
Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, Oregon, USA.
Leah M. Wilson
Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA.
Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, Oregon, USA.
Joseph El Youssef
Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA.
Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, Oregon, USA.
Jessica R. Castle
Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA.
Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, Oregon, USA.
Peter G. Jacobs [email protected]
Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA.
Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, Oregon, USA.

Notes

This work was previously presented and published as an abstract at the Advanced Technologies and Treatments on February 20–23, 2019, conference in Berlin (ATTD19-0388).
Address correspondence to: Peter G. Jacobs, PhD, Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, 3303 SW Bond Avenue, Portland, OR 97239, USA [email protected]
Clara Mosquera-Lopez, PhD, Artificial Intelligence for Medical Systems (AIMS) Lab, Department of Biomedical Engineering, Oregon Health & Science University, 3303 SW Bond Avenue, Portland, OR 97239, USA [email protected]

Author Disclosure Statement

P.G.J. and J.R.C. have a financial interest in Pacific Diabetes Technologies Inc., a company that may have a commercial interest in the results of this research and technology. For all other authors, no competing interests exist.

Funding Information

This work was supported by The Leona M. and Harry B. Helmsley Charitable Trust, grant 2018PG-T1D001 and a grant from NIH/NIDDK 1DP3DK101044-01.

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