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Castle JR, Wilson LM, Tyler NS, Espinoza AZ, Mosquera-Lopez CM, Kushner T, Young GM, Pinsonault J, Dodier RH, Hilts WW, Oganessian SM, Branigan DL, Gabo VB, Eom JH, Ramsey K, Youssef JE, Cafazzo JA, Winters-Stone K, Jacobs PG. Assessment of a Decision Support System for Adults with Type 1 Diabetes on Multiple Daily Insulin Injections. Diabetes Technol Ther 2022; 24:892-897. [PMID: 35920839 PMCID: PMC9700374 DOI: 10.1089/dia.2022.0252] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Introduction: DailyDose is a decision support system designed to provide real-time dosing advice and weekly insulin dose adjustments for adults living with type 1 diabetes using multiple daily insulin injections. Materials and Methods: Twenty-five adults were enrolled in this single-arm study. All participants used Dexcom G6 for continuous glucose monitoring, InPen for short-acting insulin doses, and Clipsulin to track long-acting insulin doses. Participants used DailyDose on an iPhone for 8 weeks. The primary endpoint was % time in range (TIR) comparing the 2-week baseline to the final 2-week period of DailyDose use. Results: There were no significant differences between TIR or other glycemic metrics between the baseline period compared to final 2-week period of DailyDose use. TIR significantly improved by 6.3% when more than half of recommendations were accepted and followed compared with 50% or fewer recommendations (95% CI 2.5%-10.1%, P = 0.001). Conclusions: Use of DailyDose did not improve glycemic outcomes compared to the baseline period. In a post hoc analysis, accepting and following recommendations from DailyDose was associated with improved TIR. Clinical Trial Registration Number: NCT04428645.
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Affiliation(s)
- Jessica R. Castle
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Leah M. Wilson
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Nichole S. Tyler
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Alejandro Z. Espinoza
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Clara M. Mosquera-Lopez
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Taisa Kushner
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Gavin M. Young
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Joseph Pinsonault
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Robert H. Dodier
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Wade W. Hilts
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Sos M. Oganessian
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Deborah L. Branigan
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Virginia B. Gabo
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Jae H. Eom
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Katrina Ramsey
- Biostatistics & Design Program, Oregon Health & Science University, Portland, Oregon, USA
| | - Joseph El Youssef
- Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
| | - Joseph A. Cafazzo
- Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, Canada
- Dalla Lana School of Public Health, Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
| | - Kerri Winters-Stone
- Division of Oncological Sciences, Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA
| | - Peter G. Jacobs
- Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA
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Tyler NS, Mosquera-Lopez CM, Wilson LM, Dodier RH, Branigan DL, Gabo VB, Guillot FH, Hilts WW, El Youssef J, Castle JR, Jacobs PG. An artificial intelligence decision support system for the management of type 1 diabetes. Nat Metab 2020; 2:612-619. [PMID: 32694787 PMCID: PMC7384292 DOI: 10.1038/s42255-020-0212-y] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 04/23/2020] [Indexed: 11/08/2022]
Abstract
Type 1 diabetes (T1D) is characterized by pancreatic beta cell dysfunction and insulin depletion. Over 40% of people with T1D manage their glucose through multiple injections of long-acting basal and short-acting bolus insulin, so-called multiple daily injections (MDI)1,2. Errors in dosing can lead to life-threatening hypoglycaemia events (<70 mg dl-1) and hyperglycaemia (>180 mg dl-1), increasing the risk of retinopathy, neuropathy, and nephropathy. Machine learning (artificial intelligence) approaches are being harnessed to incorporate decision support into many medical specialties. Here, we report an algorithm that provides weekly insulin dosage recommendations to adults with T1D using MDI therapy. We employ a unique virtual platform3 to generate over 50,000 glucose observations to train a k-nearest neighbours4 decision support system (KNN-DSS) to identify causes of hyperglycaemia or hypoglycaemia and determine necessary insulin adjustments from a set of 12 potential recommendations. The KNN-DSS algorithm achieves an overall agreement with board-certified endocrinologists of 67.9% when validated on real-world human data, and delivers safe recommendations, per endocrinologist review. A comparison of inter-physician-recommended adjustments to insulin pump therapy indicates full agreement of 41.2% among endocrinologists, which is consistent with previous measures of inter-physician agreement (41-45%)5. In silico3,6 benchmarking using a platform accepted by the United States Food and Drug Administration for evaluation of artificial pancreas technologies indicates substantial improvement in glycaemic outcomes after 12 weeks of KNN-DSS use. Our data indicate that the KNN-DSS allows for early identification of dangerous insulin regimens and may be used to improve glycaemic outcomes and prevent life-threatening complications in people with T1D.
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Affiliation(s)
- Nichole S Tyler
- Artificial Intelligence for Medical Systems (AIMS) Lab, Oregon Health & Science University, Portland, OR, USA.
| | - Clara M Mosquera-Lopez
- Artificial Intelligence for Medical Systems (AIMS) Lab, Oregon Health & Science University, Portland, OR, USA
| | - Leah M Wilson
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Robert H Dodier
- Artificial Intelligence for Medical Systems (AIMS) Lab, Oregon Health & Science University, Portland, OR, USA
| | - Deborah L Branigan
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Virginia B Gabo
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Florian H Guillot
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Wade W Hilts
- Artificial Intelligence for Medical Systems (AIMS) Lab, Oregon Health & Science University, Portland, OR, USA
| | - Joseph El Youssef
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Jessica R Castle
- Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR, USA
| | - Peter G Jacobs
- Artificial Intelligence for Medical Systems (AIMS) Lab, Oregon Health & Science University, Portland, OR, USA.
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