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The coronavirus disease 2019 (COVID-19) pandemic necessitated using telehealth to bridge the clinical gap, but could increase health disparities. This article reports on a chart review of diabetes telehealth visits occurring before COVID-19, during shelter-in-place orders, and during the reopening period. Visits for children with public insurance and for those who were non-English speaking were identified. Telehealth visits for children with public insurance increased from 26.2% before COVID-19 to 37.3% during shelter-in-place orders and 34.3% during reopening. Telehealth visits for children who were non-English speaking increased from 3.5% before COVID-19 to 17.5% during shelter-in-place orders and remained at 15.0% during reopening. Pandemic-related telehealth expansion included optimization of workflows to include patients with public insurance and those who did not speak English. Increased participation by those groups persisted during the reopening phase, indicating that prioritizing inclusive telehealth workflows can reduce disparities in access to care.

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)

Previous studies have shown that CGM use and frequent contact with the care team is associated with improved A1c. We hypothesized that youth with new onset T1D started on CGM with weekly CGM data review by a CDE would have improved outcomes compared to those on CGM alone. Youth with newly diagnosed T1D from 7/2018 to 3/2019 were started on CGM alone (control, n=46) while those from 3/2019 to 5/2020 were started on CGM with weekly data review by a CDE (GluVue, n=76). Dose adjustments were made via secure messaging. We visualized A1c trajectories using locally estimated scatter plot smoothing (Fig) and assessed for differences in A1c trajectory by GluVue status via cohort*time and cohort*time2 interaction terms in a linear mixed model regression adjusted for baseline characteristics (age, sex, race and insurance type). The mean (SD) A1c at diagnosis was 12.0% (1.8) in the control group and 12.3% (2.3) in the GluVue group. Baseline characteristics of control vs. GluVue: male 25% vs. 39%, Non-Hispanic White 41.3% vs. 35.5%, private insurance 80.4% vs. 73.7%. Mean A1c of the GluVue cohort was lower at 6 months (-0.45%), 9 months (-0.07%), and 12 months (-0.52%) post-diagnosis. A1c trajectories were significantly different between groups via regression analysis (p=0.004). Early initiation of CGM with remote monitoring was associated with lower A1c compared to CGM alone. These data support early CGM initiation plus care team contact in youth with T1D. Disclosure P. Prahalad: 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. J. Leverenz: None. P. Sagan: None. A. Martinez-singh: None. A. Freeman: None. D. P. Zaharieva: None. Funding National Institutes of Health (P30DK116074, R18DK122422)

Due to the SARS CoV-2 pandemic, fewer in-person clinic visits have resulted in fewer point-of-care (POC) A1c measurements in youth with T1D. Therefore, there is an increased need to use alternate methods to assess A1c, including continuous glucose monitoring-derived Glucose Management Indicator (GMI) and home kit A1c. The University of Minnesota’s home kit A1c (n=59), GMI (n=56), and POC A1c (n=16) were collected from youth with T1D (age 10.0 [5.3, 13.0] years, 42% female, and baseline A1c 12.4 ± 2.2%). Matched pairs were used for Bland Altman analyses and Lin’s concordance correlation coefficient (pc) to evaluate the agreement between A1c measures. GMI data (up to 90 days) was captured using Dexcom Clarity. In relation to POC A1c, both home kit A1c (panel A) and GMI (panel B) showed a slight positive bias (mean difference 0.13 and 0.22%, respectively). Home kit A1c and GMI showed strong concordance to POC A1c (pc = 0.987 [0.963, 0.995] and 0.930 [0.835, 0.971], respectively). GMI (panel C) also showed a slight positive bias (mean difference 0.26%) and good concordance (pc = 0.803 [0.703, 0.871]) to home kit A1c. These data demonstrate that home kit A1c and GMI show strong concordance with POC A1c. Overall, home kit A1c and GMI may be potential solutions to glycemic assessment for telehealth visits, including during the SARS CoV-2 pandemic. Disclosure D. Zaharieva: None. A. Addala: None. P. Prahalad: None. B. Leverenz: None. V. Ding: None. M. Desai: None. A. B. Karger: Research Support; Self; Siemens Corporation. D. M. Maahs: Advisory Panel; Self; Abbott Diabetes, Dompe, Eli Lilly and Company, Medtronic, Novo Nordisk, Consultant; Self; aditxt. Funding National Institutes of Health (1R18DK122422 01A1); Stanford Diabetes Research Center (P30DK116074); The Leona M. and Harry B. Helmsley Charitable Trust (G-2002-04251-1)

Objective To develop and scale algorithm-enabled patient prioritization to improve population-level management of type 1 diabetes (T1D) in a pediatric clinic with fixed resources, using telemedicine and remote monitoring of patients via continuous glucose monitor (CGM) data review. Research design and methods We adapted consensus glucose targets for T1D patients using CGM to identify interpretable clinical criteria to prioritize patients for weekly provider review. The criteria were constructed to manage the number of patients reviewed weekly and identify patients who most needed provider contact. We developed an interactive dashboard to display CGM data relevant for the patients prioritized for review. Results The introduction of the new criteria and interactive dashboard was associated with a 60% reduction in the mean time spent by diabetes team members who remotely and asynchronously reviewed patient data and contacted patients, from 3.2 ± 0.20 to 1.3 ± 0.24 minutes per patient per week. Given fixed resources for review, this corresponded to an estimated 147% increase in weekly clinic capacity. Patients who qualified for and received remote review (n = 58) have associated 8.8 percentage points (pp) (95% CI = 0.6-16.9 pp) greater time-in-range (70-180 mg/dL) glucoses compared to 25 control patients who did not qualify at twelve months after T1D onset. Conclusions An algorithm-enabled prioritization of T1D patients with CGM for asynchronous remote review reduced provider time spent per patient and was associated with improved time-in-range. This article is protected by copyright. All rights reserved.

ObjectiveTo develop and scale algorithm-enabled patient prioritization to improve population-level management of type 1 diabetes (T1D) in a pediatric clinic with fixed resources, using telemedicine and remote monitoring of patients via continuous glucose monitor (CGM) data review.Research Design and MethodsWe adapted consensus glucose targets for T1D patients using CGM to identify interpretable clinical criteria to prioritize patients for weekly provider review. The criteria were constructed to manage the number of patients reviewed weekly and identify patients who most needed provider contact. We developed an interactive dashboard to display CGM data relevant for the patients prioritized for review.ResultsThe introduction of the new criteria and interactive dashboard was associated with a 60% reduction in the mean time spent by diabetes team members who remotely and asynchronously reviewed patient data and contacted patients, from 3.2±0.20 to 1.3±0.24 minutes per patient per week. Given fixed resources for review, this corresponded to an estimated 147% increase in weekly clinic capacity. Patients who qualified for and received remote review (n=58) have associated 8.8 percentage points (pp) (95% CI = 0.6–16.9pp) greater time-in-range (70-180 mg/dL) glucoses compared to 25 control patients who did not qualify at twelve months after T1D onset.ConclusionsAn algorithm-enabled prioritization of T1D patients with CGM for asynchronous remote review reduced provider time spent per patient and was associated with improved time-in-range.

Aim This study aimed to capture the experience of parents of youth with recent onset Type 1 diabetes who initiated use of continuous glucose monitoring (CGM) technology soon after diagnosis, which is a new practice. Methods Focus groups and individual interviews were conducted with parents of youth with Type 1 diabetes who had early initiation of CGM as part of a new clinical protocol. Interviewers used a semi-structured interview guide to elicit feedback and experiences with starting CGM within 30 days of diagnosis, and the benefits and barriers they experienced when adjusting to this technology. Groups and interviews were audio recorded, transcribed and analysed using content analysis. Results Participants were 16 parents (age 44.13 ± 8.43 years; 75% female; 56.25% non-Hispanic White) of youth (age 12.38 ± 4.15 years; 50% female; 50% non-Hispanic White; diabetes duration 10.35 ± 3.89 months) who initiated CGM 11.31 ± 7.33 days after diabetes diagnosis. Overall, parents reported high levels of satisfaction with starting CGM within a month of diagnosis and described a high level of reliance on the technology to help manage their child's diabetes. All participants recommended early CGM initiation for future families and were committed to continue using the technology for the foreseeable future, provided that insurance covered it. Conclusion Parents experienced CGM initiation shortly after their child's Type 1 diabetes diagnosis as a highly beneficial and essential part of adjusting to living with diabetes.

Objective Continuous glucose monitor (CGM) use is associated with improved glucose control. We describe the effect of continued and interrupted CGM use on hemoglobin A1c (HbA1c) in youth with public insurance. Methods We reviewed 956 visits from 264 youth with type 1 diabetes (T1D) and public insurance. Demographic data, HbA1c and two-week CGM data were collected. Youth were classified as never user, consistent user, insurance discontinuer, and self-discontinuer. Visits were categorized as never-user visit, visit before CGM start, visit after CGM start, visit with continued CGM use, visit with initial loss of CGM, visit with continued loss of CGM, and visit where CGM is regained after loss. Multivariate regression adjusting for age, sex, race, diabetes duration, initial HbA1c, and BMI were used to calculate adjusted mean and delta HbA1c. Results Adjusted mean HbA1c was lowest for the consistent user group (HbA1c 8.6%;[95%CI 7.9,9.3]). Delta HbA1c (calculated from visit before CGM start) was lower for visit after CGM start (-0.39%;[95%CI -0.78,-0.02]) and visit with continued CGM use (-0.29%;[95%CI -0.61,0.02]) whereas it was higher for visit with initial loss of CGM (0.40%;[95%CI -0.06,0.86]), visit with continued loss of CGM (0.46%;[95%CI 0.06,0.85]), and visit where CGM is regained after loss (0.57%;[95%CI 0.06,1.10]). Conclusions Youth with public insurance using CGM have improved HbA1c, but only when CGM use is uninterrupted. Interruptions in use, primarily due to gaps in insurance coverage of CGM, were associated with increased HbA1c. These data support both initial and ongoing coverage of CGM for youth with T1D and public insurance. This article is protected by copyright. All rights reserved.

Despite advances in diabetes technology and treatment, many youth with type 1 diabetes (T1D) continue to have hemoglobin A1c (HbA1c) above target. Consistent use of continuous glucose monitors (CGM) improves HbA1c and time in range (TIR 70-180 mg/dl) glucose, while minimizing hypoglycemia in youth with T1D. A previous study suggested that long-term HbA1c outcomes may be determined early in the course of T1D. Thus, increased awareness of glucose trends and frequent insulin dose adjustments early in the course of T1D is hypothesized to improve long-term HbA1c and health outcomes. We have initiated CGM in the first month of T1D diagnosis. In March 2019, we added weekly remote review of CGM data by a pediatric endocrinologist or certified diabetes educator to provide dose adjustments. Data sharing is facilitated by using a custom platform including Apple HealthKit to transmit data from the Dexcom G6 mobile app to Epic MyChart. Inclusion criteria were diagnosis of T1D within the past 30 days and intention to follow in our clinic. During the initial 8 months of this study, we enrolled 43 youth (age at diagnosis 9.2±4.2 years, 57% male, 78% on private insurance, 42.5% non-Hispanic White, initial HbA1c 12.0±2.0%). Twenty-eight youth had at least 1 documented data review with an average of 15.8±8.1 reviews/patient and 5.4 dose adjustments/youth (∼1 dose adjustment every 3 weeks) via phone calls or secure MyChart messages. Based on CGM downloads, users had a high percentage of time of CGM wear (88±11% of time over 2-week periods), high percentage of TIR glucose (72.2±18.4%), and low incidence of hypoglycemia (32±54 min/day, 2.2±3.8%). This pilot study demonstrates that implementing routine data review as part of a new onset program increases the frequency of insulin dose adjustments. Tools should be developed to allow for efficient data review for population health management. Further follow up is needed to determine long-term outcomes, scalability, and sustainability of this new onset program. Disclosure M. Lee: None. J. Leverenz: None. B. Leverenz: None. N.M. Pageler: None. D. Scheinker: 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. P. Prahalad: None. Funding Stanford Diabetes Research Center (P30DK116074)

Optimal glycemic control is associated with improved T1D outcomes. However, in the U.S. over the last decade, glycemic control has worsened. We aimed to monitor glucose time-in-range (TIR) in the first year after diagnosis in pediatric T1D to identify the time when glucose control worsens. Newly diagnosed patients (N=67) with T1D were commenced on continuous glucose monitoring (CGM). Data was uploaded to Dexcom Clarity and analyzed retrospectively. Sample size differences at each time period vary due to patient enrollment start dates. All data was analyzed by mixed model and Tukey’s multiple comparison test. In 67 children (mean age 10±5 yrs, A1c at onset 12.1±1.9%) glucose TIR decreased from 64±21% to 55±18% at 10±9 days to 361±23 days post-diagnosis (P=0.03, Table). Mean glucose increased from 146±28 mg/dL at nadir to 172±29 mg/dL at 361±23 days post-diagnosis (P=0.001). CGM wear time also increased from 83±20% to 95±9% at 10±9 days to 361±23 days post-diagnosis (P=0.04). Time spent Disclosure D. Zaharieva: Speaker’s Bureau; Self; Ascensia Diabetes Care, Insulet Corporation, Medtronic. P. Prahalad: None. A. Addala: None. D. Scheinker: None. M. Desai: None. K.K. Hood: Research Support; Self; Dexcom, Inc. Speaker’s Bureau; Self; LifeScan, Inc., MedIQ. B. Leverenz: 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.