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CGM use is associated with improvements in A1c. We hypothesized that initiation of CGM in the 1st month after T1D diagnosis would improve A1c. Youth with newly diagnosed T1D from July 2018 to May 2020 (pilot cohort, n=122) were offered CGM initiation in the 1st month after T1D diagnosis (119 started CGM). We compared A1c outcomes in the pilot cohort with those diagnosed from 2014-2016 (controls, n=272) who were not offered early CGM. We visualized A1c trajectories using locally estimated scatter plot smoothing (Fig). We and assessed for differences in A1c trajectory by cohort via interaction terms in a linear mixed model adjusted for baseline characteristics (age, sex, race and insurance type). The mean A1c at diagnosis was higher in the pilot cohort (12.2 vs. 10.7%). The median age of diagnosis was 9.5 years [6.8, 13.3], 64% male, 38% non-Hispanic White, and 76% with private insurance in the pilot cohort. In this pilot, 89% initiated CGM in the first 30 days after diagnosis compared to 2% in the control cohort. After adjusting for baseline characteristics, the mean A1c of the pilot cohort was lower at 6 months (-0.81, p = 0.019), 9 months (-1.43, p = 0.013), and 12 months (-2.05, p = 0.012) post-diagnosis compared to the historic cohort. Early initiation of CGM was associated with a lower A1c compared to those in a historic cohort. These data support early initiation of CGM as standard of care in youth with T1D. Disclosure P. Prahalad: None. J. Senaldi: None. A. Freeman: None. A. Addala: None. D. P. Zaharieva: None. K. K. Hood: Consultant; Self; Cecelia Health, Cercacor, LifeScan Diabetes Institute. D. Scheinker: Advisory Panel; Self; Carta Healthcare. M. Desai: None. D. M. Maahs: Advisory Panel; Self; Abbott Diabetes, Dompe, Eli Lilly and Company, Medtronic, Novo Nordisk, Consultant; Self; aditxt. V. Ding: None. B. Leverenz: None. J. Hooper: None. A. Chmielewski: None. B. P. Conrad: Advisory Panel; Self; Abbott Diabetes. J. Leverenz: None. A. Martinez-singh: None. P. Sagan: None. Funding National Institutes of Health (R18DK122422, P30DK116074)

CGM use is associated with lower HbA1c in people with T1D. We hypothesize early CGM initiation will improve longer term HbA1c. Youth with newly diagnosed T1D from July 2018 to May 2019 (pilot cohort, n=65), were offered CGM initiation in the 1st month of T1D diagnosis with 62 initiating CGM. We compared HbA1c outcomes in this pilot cohort with those diagnosed in 2014-2016 (control cohort, n=272) with follow-up duration restricted to 15 months. HbA1c trajectories were visualized using locally estimated scatter plot smoothing (Figure 1) and compared using a generalized mixed effects regression model adjusted for baseline characteristics (HbA1c at onset, age, sex, race and insurance type). Mean HbA1c at diagnosis was higher in the pilot cohort (12.0% vs. 10.7%). Over the first 15 months of T1D, CGM was initiated in 95% of the pilot cohort (78.5% within 30 days), whereas 56% initiated in the control cohort (1.8% within 30 days). Adjusting for baseline characteristics, mean HbA1c levels were 0.5% lower (95%CI: -1.0, -0.1) in the pilot cohort at 3 months and 1.7% lower (95% CI: -2.6, -0.8) at 6 months compared to the control cohort. Differences beyond 6 months were also in the expected direction and statistically significant at the 0.05 level. Early initiation of CGM in the new onset period was associated with lower HbA1c compared to historic controls. These data suggest that early CGM initiation should be considered a part of standard new onset T1D care. Disclosure P. Prahalad: None. V. Ding: None. A. Addala: None. C. New: None. B.P. Conrad: None. A. Chmielewski: None. E. Geels: None. J. Leverenz: None. A. Martinez-Singh: None. P. Sagan: None. J. Senaldi: None. A. Freeman: None. D. Scheinker: None. K.K. Hood: Research Support; Self; Dexcom, Inc. Speaker’s Bureau; Self; LifeScan, Inc., MedIQ. M. Desai: None. D.M. Maahs: Advisory Panel; Self; Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk A/S. Consultant; Self; Abbott, Sanofi. Research Support; Self; Bigfoot Biomedical, Dexcom, Inc., Roche Diabetes Care, Tandem Diabetes Care.

A minority of children and adolescents with T1D meet HbA1c targets as recommended in the ADA guidelines. We have previously shown that in our clinic there is a sharp inflection point in HbA1c trajectory starting between 5 and 6 months of diagnosis with a rise in HbA1c from 7.0 ± 1.5% at 5 months post-diagnosis to 8.0 ± 1.7% at 12 months post-diagnosis. Given the benefits of CGM and improved CGM technology, we started a clinical program to initiate CGM therapy within the first month of diabetes diagnosis aimed at decreasing the rise in HbA1c that occurs over the first year of diagnosis. Since initiating this program in August 2018, 20 youth with T1D were started on the Dexcom G6 CGM system within the first month of diagnosis (average time to start is 8.5 ± 1.3 days post-diagnosis, 3 declined CGM initiation). The average age at T1D onset was 10.1 ± 0.8 years and 50% presented in DKA. Mean HbA1c at diagnosis was 12.0 ± 4.0%. In this cohort, 45% were male, 50% non-Hispanic white, 85% had private insurance, and 85% spoke English as the primary language. A majority of clinic patients used the mobile phone as their CGM receiver (73%) and all of these individuals used the Share feature. A majority of clinic patients (75%) have had at least 2 follow-up visits. By the second follow-up visit (46.1 ± 3.9 days since CGM start), patients were wearing the CGM on average of 94.5 ± 5.9% of the days over the last 2 weeks. Of the 20 individuals initially started on CGM, 3 were no longer using it at the time of their most recent visit. Two of the individuals had issues with insurance coverage and the third had issues with the transmitter. The incidence of hypoglycemia was low (2.2 ± 0.7%) and the patients had a time in range (TIR, glucose 70 - 180 mg/dL) of 71.1 ± 4.6%. In our cohort, patients continued to use CGM with a high percent of wear time. Although we do not have a control or comparison group, our population had a low incidence of hypoglycemia and high percentage of TIR. These data suggest that CGM can be successfully started within 2 weeks of T1D diagnosis with potential benefits on glucose control. Disclosure P. Prahalad: None. D. Scheinker: Advisory Panel; Self; Carta Healthcare. K.K. Hood: Consultant; Self; Lilly Diabetes. Research Support; Self; Dexcom, Inc. Speaker's Bureau; Self; Johnson & Johnson Diabetes Institute. B.A. Buckingham: Advisory Panel; Self; ConvaTec Inc., Novo Nordisk Inc., Profusa, Inc. Consultant; Self; Medtronic MiniMed, Inc. Research Support; Self; Beta Bionics, ConvaTec Inc., Dexcom, Inc., Insulet Corporation, Medtronic MiniMed, Inc., Tandem Diabetes Care. Other Relationship; Self; Insulet Corporation, Tandem Diabetes Care. D.M. Wilson: Advisory Panel; Self; Tolerion, Inc. Research Support; Self; Beta Bionics, Dexcom, Inc., Medtronic. A. Chmielewski: None. B.P. Conrad: None. E. Geels: None. J. Leverenz: None. K. Peterson: None. D.M. Maahs: Advisory Panel; Self; Novo Nordisk Inc. Consultant; Self; Abbott, Sanofi. Research Support; Self; Dexcom, Inc., Tandem Diabetes Care.

Objective: In September 2016, the FDA approved the Medtronic 670G “hybrid” closed-loop system. In “Auto Mode,” this system automatically controls basal insulin delivery based on CGM data, but requires users to enter carbohydrate intake and blood sugar for boluses. Studies show improved time in range associated with the use of Auto Mode. In an effort to track our real-world experience with this first commercial closed-loop device, we followed pediatric and adult patients placed on the 670G. Methods: This was a 1-year prospective observational study, recruiting 5/2017-9/2018, of patients with type 1 diabetes, ≥7 years, starting the 670G system. Results: 84 patients started on the 670G (range 9-61 years), 26 (31%) were Conclusions: While use of closed-loop technology correlates with improved glycemic control, a focus on usability and human factors is necessary to ensure patients stay on treatment. Alarms and sensor calibration are a major patient concern, which next generation technology should alleviate. Disclosure R. Lal: Consultant; Self; Abbott. M. Basina: None. D.M. Maahs: Advisory Panel; Self; Novo Nordisk Inc. Consultant; Self; Abbott, Sanofi. Research Support; Self; Dexcom, Inc., Tandem Diabetes Care. B.A. Buckingham: Advisory Panel; Self; ConvaTec Inc., Novo Nordisk Inc., Profusa, Inc. Consultant; Self; Medtronic MiniMed, Inc. Research Support; Self; Beta Bionics, ConvaTec Inc., Dexcom, Inc., Insulet Corporation, Medtronic MiniMed, Inc., Tandem Diabetes Care. Other Relationship; Self; Insulet Corporation, Tandem Diabetes Care. K.K. Hood: Consultant; Self; Lilly Diabetes. Research Support; Self; Dexcom, Inc. Speaker's Bureau; Self; Johnson & Johnson Diabetes Institute. B.P. Conrad: None. J. Leverenz: None. A. Chmielewski: None. K. Peterson: None. D. Wilson: Advisory Panel; Self; Tolerion, Inc. Research Support; Self; Beta Bionics, Dexcom, Inc., Medtronic. Funding National Institute of Diabetes and Digestive and Kidney Diseases (T32DK007217); Seiler Fund

In less-than-truckload (LTL) terminals, arriving trucks have to be assigned to inbound doors and to suitable time slots for unloading. Simultaneously, waiting trucks have to be allocated to outbound doors. During a couple of hours, shipments from all incoming trucks are unloaded, sorted according to their relation, transported to the right outbound door, and loaded on the outgoing truck. (The term “relation” is an equivalent for destination; it originates from the German logistics vocabulary that uses the term to specify a certain transport offered between a source and a sink.) The first and the most important optimization aim is to minimize the total distance when transshipping units, because this leads to reduction in operational costs, which are usually very high. The second, and minor, aim is to minimize the waiting time for each truck. Usually the operator of an LTL transshipment building works with subcontractors when collecting and delivering goods. Therefore, no penalties have to be paid by the operators in case waiting times are too long. The logistical optimization task is modeled as a time-discrete, multicommodity flow problem with side constraints. Based on the applicable model, a decomposition approach and a modified column-generation approach are developed. In parallel, an evolutionary algorithm (EA) was implemented to tackle the problem at hand. Both algorithms—from the field of discrete mathematics, as well as from the field of computational intelligence—are applied to 10 test scenarios. A comparison of the solution process, as well as a comparison of the solution quality, concludes the work.