Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes
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...
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| Published in: | Nature medicine Vol. 26; no. 9; pp. 1380 - 1384 |
|---|---|
| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Nature Publishing Group US
01.09.2020
Nature Publishing Group |
| Subjects: | |
| ISSN: | 1078-8956, 1546-170X, 1546-170X |
| Online Access: | Get full text |
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| Summary: | 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.
The randomized-controlled trial ADVICE4U demonstrates non-inferiority of an automated AI-based decision support system compared with advice from expert physicians for optimal insulin dosing in youths with type 1 diabetes. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Undefined-3 |
| ISSN: | 1078-8956 1546-170X 1546-170X |
| DOI: | 10.1038/s41591-020-1045-7 |