PMID- 37134305 OWN - NLM STAT- MEDLINE DCOM- 20230622 LR - 20230925 IS - 1935-5548 (Electronic) IS - 0149-5992 (Print) IS - 0149-5992 (Linking) VI - 46 IP - 7 DP - 2023 Jul 1 TI - A Randomized Crossover Trial to Compare Automated Insulin Delivery (the Artificial Pancreas) With Carbohydrate Counting or Simplified Qualitative Meal-Size Estimation in Type 1 Diabetes. PG - 1372-1378 LID - 10.2337/dc22-2297 [doi] AB - OBJECTIVE: Qualitative meal-size estimation has been proposed instead of quantitative carbohydrate (CHO) counting with automated insulin delivery. We aimed to assess the noninferiority of qualitative meal-size estimation strategy. RESEARCH DESIGN AND METHODS: We conducted a two-center, randomized, crossover, noninferiority trial to compare 3 weeks of automated insulin delivery with 1) CHO counting and 2) qualitative meal-size estimation in adults with type 1 diabetes. Qualitative meal-size estimation categories were low, medium, high, or very high CHO and were defined as <30 g, 30-60 g, 60-90 g, and >90 g CHO, respectively. Prandial insulin boluses were calculated as the individualized insulin to CHO ratios multiplied by 15, 35, 65, and 95, respectively. Closed-loop algorithms were otherwise identical in the two arms. The primary outcome was time in range 3.9-10.0 mmol/L, with a predefined noninferiority margin of 4%. RESULTS: A total of 30 participants completed the study (n = 20 women; age 44 (SD 17) years; A1C 7.4% [0.7%]). The mean time in the 3.9-10.0 mmol/L range was 74.1% (10.0%) with CHO counting and 70.5% (11.2%) with qualitative meal-size estimation; mean difference was -3.6% (8.3%; noninferiority P = 0.78). Frequencies of times at <3.9 mmol/L and <3.0 mmol/L were low (<1.6% and <0.2%) in both arms. Automated basal insulin delivery was higher in the qualitative meal-size estimation arm (34.6 vs. 32.6 units/day; P = 0.003). CONCLUSIONS: Though the qualitative meal-size estimation method achieved a high time in range and low time in hypoglycemia, noninferiority was not confirmed. CI - (c) 2023 by the American Diabetes Association. FAU - Haidar, Ahmad AU - Haidar A AD - 1Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada. AD - 2The Research Institute of McGill University Health Centre, Montreal, Quebec, Canada. FAU - Legault, Laurent AU - Legault L AD - 3Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada. FAU - Raffray, Marie AU - Raffray M AD - 4Institut de Recherches Cliniques de Montreal, Montreal, Quebec, Canada. FAU - Gouchie-Provencher, Nikita AU - Gouchie-Provencher N AD - 2The Research Institute of McGill University Health Centre, Montreal, Quebec, Canada. FAU - Jafar, Adnan AU - Jafar A AD - 1Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada. FAU - Devaux, Marie AU - Devaux M AD - 4Institut de Recherches Cliniques de Montreal, Montreal, Quebec, Canada. FAU - Ghanbari, Milad AU - Ghanbari M AD - 1Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada. FAU - Rabasa-Lhoret, Remi AU - Rabasa-Lhoret R AUID- ORCID: 0000-0003-4706-5170 AD - 4Institut de Recherches Cliniques de Montreal, Montreal, Quebec, Canada. AD - 5Nutrition Department, Faculty of Medicine, Universite de Montreal, Montreal, Quebec, Canada. AD - 6Montreal Diabetes Research Center and Endocrinology Division Centre Hospitalier de l'Universite de Montreal, Saint-Denis Montreal, Quebec, Canada. LA - eng SI - figshare/10.2337/figshare.22595938 PT - Journal Article PT - Randomized Controlled Trial PL - United States TA - Diabetes Care JT - Diabetes care JID - 7805975 RN - 0 (Insulin) RN - 0 (Hypoglycemic Agents) RN - 0 (Blood Glucose) RN - 0 (Insulin, Regular, Human) SB - IM CIN - Diabetes Care. 2023 Oct 1;46(10):e207-e208. PMID: 37729499 MH - Adult MH - Humans MH - Female MH - Insulin/therapeutic use MH - *Diabetes Mellitus, Type 1/drug therapy MH - Hypoglycemic Agents/therapeutic use MH - *Pancreas, Artificial MH - Cross-Over Studies MH - Blood Glucose MH - Insulin, Regular, Human/therapeutic use MH - Insulin Infusion Systems PMC - PMC10300520 COIS- Duality of Interest. A.H. received research support from Eli Lilly, Dexcom, Adocia, Tandem, and AgaMatrix; consulting fees from Eli Lilly; and has pending patents in the field of automated insulin delivery. L.L. received consulting fees from Dexcom and received research support from Merck, AstraZeneca, and Sanofi. R.R.-L. received research grants from AstraZeneca, Eli Lilly, Merck, Novo Nordisk, and Sanofi; has been a consultant or member on advisory panels for Abbott, Amgen, AstraZeneca, Boehringer, Carlina Technology, Eli Lilly, Janssen, Medtronic, Merck, Neomed, Novo Nordisk, Roche, Sanofi, and Takeda; received honoraria for conferences from Abbott, AstraZeneca, Eli Lilly, Janssen, Medtronic, Merck, Novo Nordisk, and Sanofi; received in-kind contributions related to automated insulin delivery studies from Animas, Medtronic, and Roche; benefits from unrestricted grants for clinical and educational activities from Eli Lilly, Lifescan, Medtronic, Merck, Novo Nordisk, and Sanofi; and holds intellectual property in the field of type 2 diabetes risk biomarkers, infusion-set catheter life, and automated insulin delivery system. R.R.L., A.H., and L.L. have received purchase fees from Eli Lilly in relation to automated insulin delivery algorithms. No other potential conflicts of interest relevant to this article were reported. EDAT- 2023/05/03 18:41 MHDA- 2023/06/22 06:42 PMCR- 2023/05/03 CRDT- 2023/05/03 16:32 PHST- 2022/11/25 00:00 [received] PHST- 2023/04/02 00:00 [accepted] PHST- 2023/06/22 06:42 [medline] PHST- 2023/05/03 18:41 [pubmed] PHST- 2023/05/03 16:32 [entrez] PHST- 2023/05/03 00:00 [pmc-release] AID - 148823 [pii] AID - 222297 [pii] AID - 10.2337/dc22-2297 [doi] PST - ppublish SO - Diabetes Care. 2023 Jul 1;46(7):1372-1378. doi: 10.2337/dc22-2297.