Benchmarking Diabetes Technology Use Among 21 U.S. Pediatric Diabetes Centers.

The American Diabetes Association's Standards of Care in Diabetes recommends the use of diabetes technology such as continuous glucose monitoring systems and insulin pumps for people living with type 1 diabetes. Unfortunately, there are multiple barriers to uptake of these devices, including local diabetes center practices. This study aimed to examine overall change and center-to-center variation in uptake of diabetes technology across 21 pediatric centers in the T1D Exchange Quality Improvement Collaborative. It found an overall increase in diabetes technology use for most centers from 2021 to 2022 with significant variation.

Implementation of Psychosocial Screening into Diabetes Clinics: Experience from the Type 1 Diabetes Exchange Quality Improvement Network.

PURPOSE OF REVIEW: Although advances in diabetes technology and pharmacology have significantly and positively impacted diabetes management and health outcomes for some, diabetes care remains burdensome and can be challenging to balance with other life priorities. The purpose of this article is to review the rationale for assessment of psychosocial domains in diabetes care settings and strategies for the implementation of psychosocial screening into routine practice. Survey data from the Type 1 Diabetes Exchange Quality Improvement Network is highlighted. RECENT FINDINGS: Implementation of psychosocial screening requires identifying the population; selecting validated tools to assess target domains; determining frequency of screening and mode of survey delivery; and scoring, interpreting, documenting, and facilitating referrals such that these processes are part of clinical workflows. Recognizing the influence of psychosocial factors for people with diabetes (PWD), professional society guidelines for comprehensive diabetes care recommend the integration of psychosocial screening into routine care.

Roadmap to Continuous Glucose Monitoring Adoption and Improved Outcomes in Endocrinology: The 4T (Teamwork, Targets, Technology, and Tight Control) Program.

Glucose monitoring is essential for the management of type 1 diabetes and has evolved from urine glucose monitoring in the early 1900s to home blood glucose monitoring in the 1980s to continuous glucose monitoring (CGM) today. Youth with type 1 diabetes struggle to meet A1C goals; however, CGM is associated with improved A1C in these youth and is recommended as a standard of care by diabetes professional organizations. Despite their utility, expanding uptake of CGM systems has been challenging, especially in minoritized communities. The 4T (Teamwork, Targets, Technology, and Tight Control) program was developed using a team-based approach to set consistent glycemic targets and equitably initiate CGM and remote patient monitoring in all youth with new-onset type 1 diabetes. In the pilot 4T study, youth in the 4T cohort had a 0.5% improvement in A1C 12 months after diabetes diagnosis compared with those in the historical cohort. The 4T program can serve as a roadmap for other multidisciplinary pediatric type 1 diabetes clinics to increase CGM adoption and improve glycemic outcomes.

'Much more convenient, just as effective': Experiences of starting continuous glucose monitoring remotely following Type 1 diabetes diagnosis.

AIM: Initiating continuous glucose monitoring (CGM) shortly after Type 1 diabetes diagnosis has glycaemic and quality of life benefits for youth with Type 1 diabetes and their families. The SARS-CoV-2 pandemic led to a rapid shift to virtual delivery of CGM initiation visits. We aimed to understand parents' experiences receiving virtual care to initiate CGM within 30 days of diagnosis. METHODS: We held focus groups and interviews using a semi-structured interview guide with parents of youth who initiated CGM over telehealth within 30 days of diagnosis during the SARS-CoV-2 pandemic. Questions aimed to explore experiences of starting CGM virtually. Groups and interviews were audio-recorded, transcribed and analysed using thematic analysis. RESULTS: Participants were 16 English-speaking parents (age 43 ± 6 years; 63% female) of 15 youth (age 9 ± 4 years; 47% female; 47% non-Hispanic White, 20% Hispanic, 13% Asian, 7% Black, 13% other). They described multiple benefits of the virtual visit including convenient access to high-quality care; integrating Type 1 diabetes care into daily life; and being in the comfort of home. A minority experienced challenges with virtual care delivery; most preferred the virtual format. Participants expressed that clinics should offer a choice of virtual or in-person to families initiating CGM in the future. CONCLUSION: Most parents appreciated receiving CGM initiation education via telehealth and felt it should be an option offered to all families. Further efforts can continue to enhance CGM initiation teaching virtually to address identified barriers.

Psychosocial Needs for Newly Diagnosed Youth with Type 1 Diabetes and Their Families.

PURPOSE OF REVIEW: To synthesize findings from studies published within the last 5 to 10 years and recruiting families of children with new-onset type 1 diabetes (T1D). RECENT FINDINGS: Children can establish glycated hemoglobin (HbA1c) trajectories in the new-onset period that may persist for up to a decade. Demographic factors, family conflict, and diabetic ketoacidosis at the time of diagnosis may be risk factors for sub-optimal child HbA1c, while new immune modulating therapies and a treatment approach that combines advanced technologies and remote patient monitoring may improve child HbA1c. Nonetheless, recent trials in the new-onset period have largely overlooked how treatments may impact families' psychosocial functioning and longitudinal observational studies have been limited. The new-onset period of T1D is an important time for research and clinical intervention, though gaps exist specific to families' psychosocial needs. Filling these gaps is essential to inform clinical management and standard of care guidelines and improve outcomes.

Hemoglobin A1c trajectories in the first 18 months after diabetes diagnosis in the SWEET diabetes registry.

AIM: A majority of youth with type 1 diabetes do not meet recommended hemoglobin A1c (HbA1c) targets. The SWEET diabetes registry is a multi-national registry of youth with diabetes. We used data from this registry to identify characteristics associated with glycemic control. METHODS: Patients in the SWEET diabetes registry with at least one HbA1c value within 10 days of diagnosis and three follow up measurements in the first 18 months of diagnosis were included (~10% of the SWEET diabetes registry). Locally weighted scatterplot smoothing was used to generate curves of HbA1c. Wilcoxon, Kruskal-Wallis, or χ2-tests were used to calculate differences between groups. RESULTS: The mean HbA1c of youth in the SWEET diabetes registry is highest at diagnosis and lowest between months 4 and 5 post-diabetes diagnosis. HbA1c continues to increase steadily through the first 18 months of diagnosis. There are no differences in HbA1c trajectories based on sex or use of diabetes technology. Youth in North America/Australia/New Zealand had the highest HbA1c throughout the first 18 months of diagnosis. The trajectory of youth from countries with nationalized health insurance was lower than those countries without nationalized health insurance. Youth from countries with the highest gross domestic product (GDP) had the highest HbA1c throughout the first 18 months of diagnosis. CONCLUSIONS: In this subset of patients, the trajectory of youth from countries with nationalized health insurance was lower than those countries without nationalized health insurance. High GDP and high use of technology did not seem to protect from a higher trajectory.

Closing Disparities in Pediatric Diabetes Telehealth Care: Lessons From Telehealth Necessity During the COVID-19 Pandemic.

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.

'I was ready for it at the beginning': Parent experiences with early introduction of continuous glucose monitoring following their child's Type 1 diabetes diagnosis.

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.

Population-level management of type 1 diabetes via continuous glucose monitoring and algorithm-enabled patient prioritization: Precision health meets population health.

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 min 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 12 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.

Lower HbA1c targets are associated with better metabolic control.

Previous studies have suggested that clear HbA1c target setting by the diabetes team is associated with HbA1c outcomes in adolescents. The aim of this study was to evaluate whether this finding is consistent in a larger cohort of children from centers participating in the SWEET international diabetes registry. A questionnaire was sent out to 76 SWEET centers, of which responses from 53 pediatric centers were included (70%). Descriptive outcomes were presented as median with lower and upper quartile. The association between the centers' target HbA1c and mean outcome HbA1c was calculated using linear regression adjusted for age, diabetes duration, sex, and gross domestic product. Median age of the children in the studied centers (n = 35,483) was 13.3 [12.6-14.6] years (49% female). Of the 53 centers, 13.2% reported an HbA1c target between 6.0 and 6.5%, 32.1% had a target between ≥ 6.0 and 7.0%, 18.9% between ≥ 7.0 and 7.5%, and 3.8% between ≥ 7.5 and 8.5%. No specific target value was reported by 32.1% of all centers. Median HbA1c across all centers was 7.9 [7.6-8.3] %. Adjusted regression analysis showed a positive association between HbA1c outcome and target HbA1c (p = 0.005).Conclusions: This international study demonstrated that a lower target for HbA1c was associated with better metabolic control. It is unclear whether low target values result in better metabolic control, or lower HbA1c values actually result in more ambitious target values. This target setting could contribute to the differences in HbA1c values between centers and could be an approach for improving metabolic outcomes. What is Known: • Target setting of HbA1c is important in children and adolescents with type 1 diabetes. • The optimal therapeutic approach of children with type 1 diabetes requires a trained multidisciplinary team. What is New: • Lower HbA1c targets are associated with better metabolic control. • No associations between the composition of the diabetes teams and metabolic control could be demonstrated.

Multi-Clinic Quality Improvement Initiative Increases Continuous Glucose Monitoring Use Among Adolescents and Young Adults With Type 1 Diabetes.

Continuous glucose monitoring (CGM) use is associated with improved A1C outcomes and quality of life in adolescents and young adults with diabetes; however, CGM uptake is low. This article reports on a quality improvement (QI) initiative of the T1D Exchange Quality Improvement Collaborative to increase CGM use among patients in this age-group. Ten centers participated in developing a key driver diagram and center-specific interventions that resulted in an increase in CGM use from 34 to 55% in adolescents and young adults over 19-22 months. Sites that performed QI tests of change and documented their interventions had the highest increases in CGM uptake, demonstrating that QI methodology and sharing of learnings can increase CGM uptake.

Uninterrupted continuous glucose monitoring access is associated with a decrease in HbA1c in youth with type 1 diabetes and public insurance.

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 body mass index 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.

Effect of Continuous Glucose Monitoring on Glycemic Control in Adolescents and Young Adults With Type 1 Diabetes: A Randomized Clinical Trial.

Importance: Adolescents and young adults with type 1 diabetes exhibit the worst glycemic control among individuals with type 1 diabetes across the lifespan. Although continuous glucose monitoring (CGM) has been shown to improve glycemic control in adults, its benefit in adolescents and young adults has not been demonstrated. Objective: To determine the effect of CGM on glycemic control in adolescents and young adults with type 1 diabetes. Design, Setting, and Participants: Randomized clinical trial conducted between January 2018 and May 2019 at 14 endocrinology practices in the US including 153 individuals aged 14 to 24 years with type 1 diabetes and screening hemoglobin A1c (HbA1c) of 7.5% to 10.9%. Interventions: Participants were randomized 1:1 to undergo CGM (CGM group; n = 74) or usual care using a blood glucose meter for glucose monitoring (blood glucose monitoring [BGM] group; n = 79). Main Outcomes and Measures: The primary outcome was change in HbA1c from baseline to 26 weeks. There were 20 secondary outcomes, including additional HbA1c outcomes, CGM glucose metrics, and patient-reported outcomes with adjustment for multiple comparisons to control for the false discovery rate. Results: Among the 153 participants (mean [SD] age, 17 [3] years; 76 [50%] were female; mean [SD] diabetes duration, 9 [5] years), 142 (93%) completed the study. In the CGM group, 68% of participants used CGM at least 5 days per week in month 6. Mean HbA1c was 8.9% at baseline and 8.5% at 26 weeks in the CGM group and 8.9% at both baseline and 26 weeks in the BGM group (adjusted between-group difference, -0.37% [95% CI, -0.66% to -0.08%]; P = .01). Of 20 prespecified secondary outcomes, there were statistically significant differences in 3 of 7 binary HbA1c outcomes, 8 of 9 CGM metrics, and 1 of 4 patient-reported outcomes. The most commonly reported adverse events in the CGM and BGM groups were severe hypoglycemia (3 participants with an event in the CGM group and 2 in the BGM group), hyperglycemia/ketosis (1 participant with an event in CGM group and 4 in the BGM group), and diabetic ketoacidosis (3 participants with an event in the CGM group and 1 in the BGM group). Conclusions and Relevance: Among adolescents and young adults with type 1 diabetes, continuous glucose monitoring compared with standard blood glucose monitoring resulted in a small but statistically significant improvement in glycemic control over 26 weeks. Further research is needed to understand the clinical importance of the findings. Trial Registration: ClinicalTrials.gov Identifier: NCT03263494.

A Machine Learning Model for Week-Ahead Hypoglycemia Prediction From Continuous Glucose Monitoring Data.

BACKGROUND: Remote patient monitoring (RPM) programs augment type 1 diabetes (T1D) care based on retrospective continuous glucose monitoring (CGM) data. Few methods are available to estimate the likelihood of a patient experiencing clinically significant hypoglycemia within one week. METHODS: We developed a machine learning model to estimate the probability that a patient will experience a clinically significant hypoglycemic event, defined as CGM readings below 54 mg/dL for at least 15 consecutive minutes, within one week. The model takes as input the patient's CGM time series over a given week, and outputs the predicted probability of a clinically significant hypoglycemic event the following week. We used 10-fold cross-validation and external validation (testing on cohorts different from the training cohort) to evaluate performance. We used CGM data from three different cohorts of patients with T1D: REPLACE-BG (226 patients), Juvenile Diabetes Research Foundation (JDRF; 355 patients) and Tidepool (120 patients). RESULTS: In 10-fold cross-validation, the average area under the receiver operating characteristic curve (ROC-AUC) was 0.77 (standard deviation [SD]: 0.0233) on the REPLACE-BG cohort, 0.74 (SD: 0.0188) on the JDRF cohort, and 0.76 (SD: 0.02) on the Tidepool cohort. In external validation, the average ROC-AUC across the three cohorts was 0.74 (SD: 0.0262). CONCLUSIONS: We developed a machine learning algorithm to estimate the probability of a clinically significant hypoglycemic event within one week. Predictive algorithms may provide diabetes care providers using RPM with additional context when prioritizing T1D patients for review.

Smart Start - Designing Powerful Clinical Trials Using Pilot Study Data.

BACKGROUND: Digital health interventions may be optimized before evaluation in a randomized clinical trial. Although many digital health interventions are deployed in pilot studies, the data collected are rarely used to refine the intervention and the subsequent clinical trials. METHODS: We leverage natural variation in patients eligible for a digital health intervention in a remote patient-monitoring pilot study to design and compare interventions for a subsequent randomized clinical trial. RESULTS: Our approach leverages patient heterogeneity to identify an intervention with twice the estimated effect size of an unoptimized intervention. CONCLUSIONS: Optimizing an intervention and clinical trial based on pilot data may improve efficacy and increase the probability of success. (Funded by the National Institutes of Health and others; ClinicalTrials.gov number, NCT04336969.)

Diabetic Ketoacidosis at Diagnosis in Youth with Type 1 Diabetes Is Associated with a Higher Hemoglobin A1c Even with Intensive Insulin Management.

Introduction: Diabetic ketoacidosis (DKA) at diagnosis is associated with short- and long-term complications. We assessed the relationship between DKA status and hemoglobin A1c (A1c) levels in the first year following type 1 diabetes (T1D) diagnosis. Research Design and Methods: The Pilot Teamwork, Targets, Technology, and Tight Control (4T) study offered continuous glucose monitoring to youth with T1D within 1 month of diagnosis. A1c levels were compared between historical (n = 271) and Pilot 4T (n = 135) cohorts stratified by DKA status at diagnosis (DKA: historical = 94, 4T = 67 versus without DKA: historical = 177, 4T = 68). A1c was evaluated using locally estimated scatter plot smoothing. Change in A1c from 4 to 12 months postdiagnosis was evaluated using a linear mixed model. Results: Median age was 9.7 (interquartile range [IQR]: 6.6, 12.7) versus 9.7 (IQR: 6.8, 12.7) years, 49% versus 47% female, 44% versus 39% non-Hispanic White in historical versus Pilot 4T. In historical and 4T cohorts, DKA at diagnosis demonstrated higher A1c at 6 (0.5% [95% confidence interval (CI): 0.21-0.79; P < 0.01] and 0.38% [95% CI: 0.02-0.74; P = 0.04], respectively), and 12 months (0.62% [95% CI: -0.06 to 1.29; P = 0.07] and 0.39% [95% CI: -0.32 to 1.10; P = 0.29], respectively). The highest % time in range (TIR; 70-180 mg/dL) was seen between weeks 15-20 (69%) versus 25-30 (75%) postdiagnosis for youth with versus without DKA in Pilot 4T, respectively. Conclusions: Pilot 4T improved A1c outcomes versus the historical cohort, but those with DKA at diagnosis had persistently elevated A1c throughout the study and intensive diabetes management did not mitigate this difference. DKA prevention at diagnosis may translate into better glycemic outcomes in the first-year postdiagnosis. Clinical Trial Registration: clinicaltrials.gov: NCT04336969.

Equitable implementation of a precision digital health program for glucose management in individuals with newly diagnosed type 1 diabetes.

Few young people with type 1 diabetes (T1D) meet glucose targets. Continuous glucose monitoring improves glycemia, but access is not equitable. We prospectively assessed the impact of a systematic and equitable digital-health-team-based care program implementing tighter glucose targets (HbA1c < 7%), early technology use (continuous glucose monitoring starts <1 month after diagnosis) and remote patient monitoring on glycemia in young people with newly diagnosed T1D enrolled in the Teamwork, Targets, Technology, and Tight Control (4T Study 1). Primary outcome was HbA1c change from 4 to 12 months after diagnosis; the secondary outcome was achieving the HbA1c targets. The 4T Study 1 cohort (36.8% Hispanic and 35.3% publicly insured) had a mean HbA1c of 6.58%, 64% with HbA1c < 7% and mean time in the range (70-180 mg dl-1) of 68% at 1 year after diagnosis. Clinical implementation of the 4T Study 1 met the prespecified primary outcome and improved glycemia without unexpected serious adverse events. The strategies in the 4T Study 1 can be used to implement systematic and equitable care for individuals with T1D and translate to care for other chronic diseases. ClinicalTrials.gov registration: NCT04336969 .