Outpatient Randomized Crossover Comparison of Zone Model Predictive Control Automated Insulin Delivery with Weekly Data Driven Adaptation Versus Sensor-Augmented Pump: Results from the International Diabetes Closed-Loop Trial 4.

Background: Automated insulin delivery (AID) systems have proven effective in increasing time-in-range during both clinical trials and real-world use. Further improvements in outcomes for single-hormone (insulin only) AID may be limited by suboptimal insulin delivery settings. Methods: Adults (≥18 years of age) with type 1 diabetes were randomized to either sensor-augmented pump (SAP) (inclusive of predictive low-glucose suspend) or adaptive zone model predictive control AID for 13 weeks, then crossed over to the other arm. Each week, the AID insulin delivery settings were sequentially and automatically updated by an adaptation system running on the study phone. Primary outcome was sensor glucose time-in-range 70-180 mg/dL, with noninferiority in percent time below 54 mg/dL as a hierarchical outcome. Results: Thirty-five participants completed the trial (mean age 39 ± 16 years, HbA1c at enrollment 6.9% ± 1.0%). Mean time-in-range 70-180 mg/dL was 66% with SAP versus 69% with AID (mean adjusted difference +2% [95% confidence interval: -1% to +6%], P = 0.22). Median time <70 mg/dL improved from 3.0% with SAP to 1.6% with AID (-1.5% [-2.4% to -0.5%], P = 0.002). The adaptation system decreased initial basal rates by a median of 4% (-8%, 16%) and increased initial carbohydrate ratios by a median of 45% (32%, 59%) after 13 weeks. Conclusions: Automated adaptation of insulin delivery settings with AID use did not significantly improve time-in-range in this very well-controlled population. Additional study and further refinement of the adaptation system are needed, especially in populations with differing degrees of baseline glycemic control, who may show larger benefits from adaptation.

Improved CGM Glucometrics and More Visits for Pediatric Type 1 Diabetes Using Telemedicine During 1 Year of COVID-19.

PURPOSE: The COVID-19 pandemic led to rapid adoption of telemedicine for the care of youth with type 1 diabetes (T1D). We assessed the utility of a primarily virtual care model by comparing glucometrics from a pediatric sample with T1D using continuous glucose monitoring (CGM) both before and during the pandemic. METHODS: Pediatric patients aged 1 to 17 years with T1D duration ≥ 1 year if ≥ 6 years old or ≥ 6 months if < 6 years old, with ≥ 1 visit with recorded CGM data both prepandemic (April 1, 2019-March 15, 2020) and during the pandemic (April 1, 2020-March 15, 2021) were included. Data were extracted from the electronic health record. RESULTS: Our sample comprised 555 young people (46% male, 87% White, 79% pump-treated), mean age 12.3 ±â€…3.4 years, T1D duration 5.9 ±â€…3.5 years, baseline glycated hemoglobin A1c 8.0 ±â€…1.0% (64 ±â€…10.9 mmol/mol). Diabetes visit frequency increased from 3.8 ±â€…1.7 visits/prepandemic period to 4.3 ±â€…2.2 visits/pandemic period (P < 0.001); during pandemic period, 92% of visits were virtual. Glucose management indicator (GMI) improved slightly from 7.9% (63 mmol/mol) prepandemic to 7.8% (62 mmol/mol) during the pandemic (P < 0.001). Those with equal or greater visit frequency (n = 437 [79% of sample]) had significant improvement in GMI (8.0% to 7.8% [64 to 62 mmol/mol], P < 0.001), whereas those with lower visit frequency did not (7.8 [62 mmol/mol], P = 0.86). CONCLUSIONS: Children and adolescents with T1D using CGM before and during the pandemic showed an overall increase in visit frequency using primarily telemedicine-based care and improved CGM glucometrics. Further research is needed to understand factors associated with successful use of telemedicine for pediatric T1D.

Is Glycemic Control Better in Adults with Type 1 Diabetes When Office Visits are Supplemented with Telehealth Visits?

Objective: Since the COVID-pandemic began, instead of in-office care alone, many institutions implemented hybrid care (in-office + telemedicine) . It is not known if hybrid care is as effective as in-office visits in regards to achieving glycemic goals. Methods: Clinical characteristics of adults with type 1 diabetes (T1D) (age ≥40 years) were retrieved from electronic health records from two periods: in-office model before the pandemic (April 2019-March 2020) and hybrid-care model during the pandemic (September 20pril 2021) . Patients were stratified by age. Results: Overall, 1,820 patients were evaluated, 66% younger (40-64 years: mean age 52±7yrs, 52% female, 53% CGM users, 56% pump users) and 34% older (≥65 years: mean age 72±5yrs, 55% female, 53% CGM users, 38% pump users) . A1c using hybrid-care improved in both younger (7.8±1.2 vs. 7.6±1.2%;p=0.005) and older adults (7.6±0.9 vs. 7.4±1%;p=0.02) , compared to in-office care. Within the hybrid-care model period, poor glycemic control was associated with a higher number of hybrid visits, and more in-office missed appointments, while pump use was associated with lower A1c. Conclusion: Compared with in-office care, hybrid care was effective at maintaining glycemic control in both younger and older adults with T1D. Prospective studies are needed to understand the use of hybrid-care for the management of adults with T1D.

Equitable Glycemic Improvement in Youth with T1D during COVID-19

Aim: Despite disruptions caused by the COVID-pandemic, prior studies suggest some improvements in glycemic control. We investigated whether this improvement was equitable and seen across socioeconomic status (SES) groups in youth with T1D. Method: Using EHR-extracted visit and CGM data, we geocoded patient addresses linked with census-tract derived education from the 20American Community Survey and a composite measure of SES, the Neighborhood Deprivation Index (NDI) . Analyses included youth ≤18 years old using CGM with T1D duration ≥6 months (age <6 yrs) or ≥1 yr (age ≥6 yrs) . We performed t-tests and regressions comparing SES and CGM metrics during the pandemic (4/1/20-3/15/21) with pre-pandemic (4/1/19-3/15/20) . Results: The pre-pandemic sample had 641 youth (52% female, age 12.5±3.5, T1D 6.2±3.5 yrs) and the pandemic sample had 650 youth (52% female, age 13.5±3.6, T1D 6.8±3.8 yrs) ;86% were common to both samples. Addresses allowed for geocoding of 98%;44% of youth lived in low education census tracts where ≥30% of adults in the census tract had no more than a high school education. Mean CGM-derived glucose management indicator (GMI) improved during the pandemic for both those living in lower (8.07±0.05% pre vs. 7.91±0.% during, p<0.05) and higher SES education tracts (7.82±0.% pre vs. 7.69±0.% during) . There was similar improvement in GMI in lower vs. higher SES education tracts (0.16±.vs. 0.13±.06) . Other CGM metrics similarly improved during the pandemic, mean CGM glucose decreased by 6.7 mg/dL and 5.4 mg/dL in low and high SES education tract patients respectively (both p<.05) . Those living in the most deprived NDI areas had the highest GMI both pre and during the pandemic (p<0.05) and demonstrated similar or greater improvements than those from Iess deprived areas. Conclusion: Equitable improvements in CGM metrics during the pandemic was evident in youth with T1D. Future studies can assess how changes in healthcare delivery during the pandemic can reduce disparities and sustain benefits to all patients.

Characteristics Associated with Diabetes Device Use Before and During the Pandemic in Youth with T1D

Introduction: The impact of the Covid-pandemic on diabetes device uptake and use in youth with T1D is unclear. We assessed demographic, diabetes, and SES characteristics associated with diabetes technology (pump, CGM) use in youth with T1D, aged ≤18 years, pre- and during the pandemic. Methods: EHR provided ∼18 months of data pre- and during the pandemic. Pump and CGM use were defined as device use at ≥50% of visits. Geocoding provided census tract-specific SES measures from the 20American Community Survey. Logistic regression models analyzed the probability of device use vs. non-use in the pre- and pandemic periods by geocoded SES status, adjusted for age, gender, baseline A1c, and T1D duration. Results: Pre-pandemic sample had 734 youth (male 49%, aged 13.8±3.3 years, T1D duration 7.3±3.4 years) ;pandemic sample had 800 youth (male 51%, aged 13.4±3.7 years, T1D duration 6.6±3.9 years) ;689 were in both periods. Pre- and during pandemic, respectively, there were 25% and 22% device non-users;19% and 21% pump only;13% and 16% CGM only;43% and 41% both. Pre-pandemic baseline A1c differed by the 4 device use groups: non-users 9.1±1.7%;pump 8.5±1.2%;CGM 8.2±1.2%;both 8.1±1.0%;p<.05.In both periods, the odds of device non-users starting a device were reduced in those from low-income tracts (≤$40,000) (pre: OR 0.60, 95%CI 0.40-0.91;during: OR 0.59, 95%CI 0.39-0.88;respectively, both p=.01) and low education tracts (≥30% high school or less) (pre: OR 0.57, 95%CI 0.39-0.85;during: OR 0.65, 95%CI 0.44-0.97;both p≤.03) . While the odds of CGM uptake were significantly reduced by ∼40% in presence of unfavorable census SES factors (p≤.01) , the odds of pump start were not (p≥.05) . Odds of uptake of both devices were significantly reduced by ∼45% in presence of unfavorable census SES factors (p≤.02) in both periods. Conclusion: During the pandemic, there remained decreased uptake and less continued use of diabetes device in lower SES patients. Further work is needed to decrease the inequality gap.

Evaluating Community COVID-19 Vaccination Status and Diabetes Visit Frequency during the Pandemic

Aim: The COVID-19 pandemic highlighted potential healthcare misalignment as seen by vaccine rates. We investigated pediatric diabetes visit frequency in patients living in lower vs. higher COVID 19 vaccination areas. Method: We obtained EHR-extracted visit data for youth with T1D, ≤18 years old, with ≥1 visit in both the pre-pandemic (9/15/18-3/15/20) and pandemic (4/1/20-12/31/21) period. Residential addresses were geocoded and linked with CDC county level COVID vaccination data from 2/2/2022. High vaccine counties were defined as those in which ≥75% of the population ages 12 and older were fully vaccinated. We performed paired t-tests and logistic regression. Models were adjusted for age, gender, pre-pandemic A1c, and diabetes duration. Results: There were 1,029 youth (49% female, pre-pandemic age 13.7±3.8 yrs, T1D duration 6.7±4.3 yrs) who had clinical encounters in both periods. 541 youth were from high vaccine counties. There was no difference in age, gender, T1D duration, CGM or pump use, or baseline A1c between youth from high vs. low vaccine counties. Overall there was increased visit frequency in the pandemic (5.1±3.0) vs. pre-pandemic (4.7±2.6) (p=0.03) . The mode of visits shifted from in person to virtual during the pandemic (p<0.001) . In adjusted models, there was no association between county-level COVID vaccination coverage and visit frequency (OR 1.03, 95% CI 0.99-1.07) . Conclusion: The COVID 19 pandemic resulted in a shift in care delivery to telehealth. Telehealth allowed for increased visit frequency. Patients coming from low COVID 19 vaccination counties attended diabetes visits as frequently as those coming from high COVID vaccination counties. County COVID vaccine status did not impact visit frequency of a chronic condition.

Favorable Shift in CGM Glucose in Youth with T1D during COVID-19 Pandemic

Aim: COVID-19 caused disruption with potential for changes in lifestyle and T1D care behaviors, especially for youth. We compared CGM-derived glucometrics during the COVID-19 pandemic with the previous year in a clinic-based pediatric sample with T1D using CGM. Method: We used EHR-extracted data to compare CGM metrics during the pandemic (3/16/20 -10/29/20) with the same calendar months in 2019. The sample comprised youth using CGM, aged 1-18 years, with T1D duration ≥6 months (age <6 years) or ≥1 year (age ≥6 years). Results: The pre-pandemic sample comprised 578 youth (53% female, age 12.4±3.5, T1D 6.2±3.5 years). The pandemic sample comprised 605 youth (54% female, age 13.4±3.6, T1D 6.8±3.8 years). Over 80% of youth were common to both samples. Mean CGM glucose was 7 mg/dL lower during the pandemic (186±35) vs. pre-pandemic (193±33) (p<.001). The proportion of youth with mean glucose <145 and 145-154 mg/dL increased 80-100% during the pandemic (5 to 9% and 5 to 10%, respectively), while the proportion with mean glucose ≥210 mg/dL decreased by 29% (31 to 22%). The % of youth with glucose management indicator (GMI) <7% increased by >110% (Figure). The 7-12y age group showed the largest shift in GMI: 8.0 pre- vs. 7.7% during pandemic (p=.003). Conclusion: There was a beneficial shift in glucose levels from pre to during the pandemic. Future studies are needed to assess how changes in healthcare during the pandemic may have improved glycemic outcomes.

Health Care Delivery for Young Adults (YAs) with Type 1 Diabetes (T1D) during the Pandemic

Background: YAs, aged 18-30 years, with T1D have high risk of worsening glycemic control and loss to follow-up. In March 2020, the COVID-19 pandemic required a sudden change in healthcare delivery from in-person to telehealth visits. We aimed to evaluate use of telehealth by YAs with T1D during this time and assess characteristics associated with telehealth usage. Method: Frequency of telehealth visits from 3/16/2020 to 9/30/2020 was compared with in-person visits during the same calendar period before COVID-19 (3/16/2019 to 9/30/2020) in a sample of YAs with T1D seen by pediatric and adult diabetes clinicians. The Electronic Health Record provided demographic and clinical data for YAs aged 18-30 with a pre-pandemic A1c. Descriptive statistics and t-tests compared data before and during COVID-19. Results: Pre-COVID-19, there were 1177 YAs (50% male) with mean±SD age 24.4±3.4 years, T1D duration 12.8±5.9 years, and A1c 8.1±1.6%;46% were pump-treated, 55% used CGM, and 91% had private insurance. In 2019, there were 2961 visits;in 2020, there were 2701 visits. For the two 6-month periods, the mean number of visits per YA declined significantly, from 2.5±2.5 in 2019 to 2.3±3.1 in 2020 (p=.02). Further, 20% (n=240) of YAs had no visits during the pandemic period. YAs with an A1c <7% pre-pandemic (21%, n=247) had fewer visits during the pandemic than before the pandemic (1.6±2.7 vs. 2.2±2.7, p=.001). YA males (p=.01), those using CGM (p=.02), and those on injection based therapy (p=.0002) had fewer visits during COVID-19. Conclusions: The majority of YAs (80%) maintained follow-up T1D care during the initial six months of telehealth implementation due to COVID-19, especially those with less favorable characteristics (e.g., A1c ≥7%, no CGM use). Maintaining telehealth options can become an effective means to improve future health outcomes for at-risk groups, such as YAs with T1D.

Mean CGM Values Before and During COVID-19 Pandemic Demonstrate Noninferiority of Telehealth in Pediatric T1D

Aim: The COVID-19 pandemic called for rapid deployment of telehealth. To assess utility of telehealth in pediatric T1D, we compared CGM glucose values in youth with T1D using CGM before and during the pandemic. Methods: The EHR provided visit frequency and CGM metrics. Data during pandemic (3/16/20 to 10/29/20) were compared with data from the same calendar months in 2019. Youth (N=469) using CGM and seen in both periods were included based on pre-pandemic characteristics: age 1-18 yr, T1D duration ≥6 mo (if age <6) or ≥1 yr (if age ≥6 yr). CGM metrics included mean and SD glucose, coefficient of variation (CV), and glucose management index (GMI). Each youth's CGM data were averaged in each time period and compared in paired analyses. Results: Youth (46% male) had a mean age 12.4±3.5 yr, T1D duration 6.1±3.6 yr, HbA1c 7.8±0.9%;80% were pump-treated. Pre-pandemic, 0.1% of visits were telehealth, increasing to 99.6% during pandemic. Mean number of visits during each 7-mo period increased from 2.5 in 2019 to 2.7 in 2020 (p=.006). CGM metrics generally improved during the pandemic period (Table). Those whose visit frequency increased with telehealth had the greatest improvement in CGM glucose, from 192±72 to 183±67 (p<.001). Conclusions: Telehealth allowed increased visit frequency and improved CGM glucose metrics, supporting non-inferiority and potential benefit of telehealth for youth with T1D using CGM.

The Digital/Virtual Diabetes Clinic: The Future Is Now-Recommendations from an International Panel on Diabetes Digital Technologies Introduction.

The increasing prevalence of diabetes, combined with a growing global shortage of health care professionals (HCP), necessitates the need to develop new approaches to diabetes care delivery to expand access to care, lessen the burden on people with diabetes, improve efficiencies, and reduce the unsustainable financial liability on health systems and payers. Use of digital diabetes technologies and telehealth protocols within a digital/virtual diabetes clinic has the potential to address these challenges. However, several issues must be resolved to move forward. In February 2020, organizers of the Advanced Technologies & Treatments for Diabetes Annual Conference convened an international panel of HCP, researchers, patient advocates, and industry representatives to review the status of digital diabetes technologies, characterize deficits in current technologies, and identify issues for consideration. Since that meeting, the importance of using telehealth and digital diabetes technologies has been demonstrated amid the global coronavirus disease (COVID-19) pandemic. This article summarizes the panel's discussion of the opportunities, obstacles, and requisites for advancing the use of these technologies as a standard of care for the management of diabetes.