Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes
Abstract
Despite the increasing adoption of insulin pumps and continuous glucose monitoring devices, most people with type 1 diabetes do not achieve their glycemic goals 1. This could be related to a lack of expertise or inadequate time for clinicians to analyze complex sensor-augmented pump data. We tested whether frequent insulin dose adjustments guided by an automated artificial intelligence-based decision support system (AI-DSS) is as effective and safe as those guided by physicians in controlling glucose levels. ADVICE4U was a six-month, multicenter, multinational, parallel, randomized controlled, non-inferiority trial in 108 participants with type 1 diabetes, aged 10-21 years and using insulin pump therapy (ClinicalTrials.gov no. NCT03003806). Participants were randomized 1:1 to receive remote insulin dose adjustment every three weeks guided by either an AI-DSS, (AI-DSS arm, n = 54) or by physicians (physician arm, n = 54). The results for the primary efficacy measure-the percentage of time spent within the target glucose range (70-180 mg dl-1 (3.9-10.0 mmol l-1))-in the AI-DSS arm were statistically non-inferior to those in the physician arm (50.2 ± 11.1% versus 51.6 ± 11.3%, respectively, P < 1 × 10-7). The percentage of readings below 54 mg dl-1 (<3.0 mmol l-1) within the AI-DSS arm was statistically non-inferior to that in the physician arm (1.3 ± 1.4% versus 1.0 ± 0.9%, respectively, P < 0.0001). Three severe adverse events related to diabetes (two severe hypoglycemia, one diabetic ketoacidosis) were reported in the physician arm and none in the AI-DSS arm. In conclusion, use of an automated decision support tool for optimizing insulin pump settings was non-inferior to intensive insulin titration provided by physicians from specialized academic diabetes centers.
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Nimri et al. conducted multicenter and multinational RCTs comparing ML with physicians from specialized academic diabetes centers in optimizing insulin pump doses.
“The researchers conducted multicenter and multinational RCTs to compare ML with physicians from specialized academic diabetes centers in optimizing insulin pump doses.”
From Toward real‐world deployment of machine learning for health care: External validation, continual monitoring, and randomized clinical trials (2024) · cites this paper as [27]
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- Authors
- Nimri, R., Battelino, T., Laffel, L. M., Slover, R. H., Schatz, D., Weinzimer, S. A., Dovc, K., Danne, T., Phillip, M., NextDREAM Consortium, Phillip, M., Nimri, R., Shalitin, S., Bello, R., Nevo-Shenker, M., Fisch-Shvalb, N., Shiovitch-Mantzuri, G., Choresh, O., Drutz, I., Nava, Y., Hemo, A., Hermon, O., Nave, R., Battelino, T., Dovc, K., Bratina, N., Smigoe-Schweiger, D., Mali, B., Gianini, A., Sever, U., Berkopec, B. M., Laffel, L. M., Katz, M., Isganaitis, E., Mehta, S., Quinn, H., Naik, N., Guo, Z., Volkening, L., Slover, R. H., Forlenza, G., Wadwa, R. P., Alonso, G. T., Messer, L., Towers, L., Thivener, K., Berget, C., Lange, S., Jost, E., Rossick-Solis, M., Schatz, D., Haller, M., Hiers, P., Jacobsen, L., Smith, M., O’Neill, A., Hosford, J., Perry, A., Weinzimer, S. A., Cengiz, E., Sherr, J., Gibbons, K., Carria, L., Zgorski, M., Danne, T., Biester, T., Kordonouri, O., von dem Berge, T., Biester, S., & Remus, K.
- Affiliations
- Schneider Children's Medical Center (IL)
- University of Ljubljana (SI)
- Joslin Diabetes Center (US)
- University of Colorado Anschutz (US)
- University of Florida (US)
- Yale University (US)
- Kinderkrankenhaus auf der Bult (DE)
- ORCID
- Shalitin, Shlomit
- Nevo-Shenker, Michal
- Fisch-Shvalb, Naama
- Katz, Michelle
- Isganaitis, Elvira
- Mehta, Sanjeev
- Guo, Zijing
- Volkening, Lisa
- Forlenza, Gregory
- Wadwa, R. Paul
- Alonso, G. Todd
- Messer, Laurel
- Berget, Cari
- Lange, Samantha
- Jost, Emily
- Haller, Michael
- Hiers, Paul
- Jacobsen, Laura
- Smith, Madison
- Cengiz, Eda
- Sherr, Jennifer
- Biester, Torben
- Kordonouri, Olga
- Venue
- Nature Medicine
- Published
- 2020-09
- Volume
- 26
- Issue
- 9
- Pages
- 1380-1384
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W3084252132
- Cited by
- 242
- References
- 27
- Citations by year
- 2026: 18 · 2025: 42 · 2024: 58 · 2023: 52 · 2022: 41 · 2021: 28 · 2020: 3
- FWCI
- 23.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- The Leona M. and Harry B. Helmsley Charitable Trust
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Keywords
- type 1 diabetes, artificial intelligence, clinical decision support systems, insulin pump therapy, continuous glucose monitoring, adolescents, glycemic control, non-inferiority, hypoglycemia, diabetic ketoacidosis
- MeSH terms
- Adolescent, Artificial Intelligence, Blood Glucose, Child, Diabetes Mellitus, Type 1, Humans, Insulin, Insulin Infusion Systems, Blood Glucose Self-Monitoring, Treatment Outcome, Decision Support Systems, Clinical, Young Adult
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