Lower Prevalence of Diabetic Ketoacidosis at Diagnosis in Research Participants Monitored for Hyperglycemia.

CONTEXT: In Colorado children, the prevalence of diabetic ketoacidosis (DKA) at diagnosis of type 1 diabetes (T1D) has been increasing over time. OBJECTIVE: Evaluate the prevalence of and factors involved in DKA at T1D diagnosis among participants followed in monitoring research studies before diagnosis compared to patients from the community. SETTING AND PARTICIPANTS: Patients < 18 years diagnosed with T1D between 2005 and 2021 at the Barbara Davis Center for Diabetes. OUTCOME: Prevalence of and factors associated with DKA at diagnosis among participants in preclinical monitoring studies compared to those diagnosed in the community. RESULTS: Of 5049 subjects, 164 were active study participants, 42 inactive study participants, and 4843 were community patients. Active study participants, compared to community patients, had lower HbA1c (7.3% vs 11.9%]; P < 0.001) and less frequently experienced DKA (4.9% vs 48.5%; P < 0.001), including severe DKA (1.2% vs 16.2%; P < 0.001). Inactive study participants had intermediate levels for both prevalence and severity of DKA. DKA prevalence increased in community patients, from 44.0% to 55%, with less evidence for a temporal trend in study participants. DKA prevalence was highest in children <2 years (13% in active study participants vs 83% in community patients). In community patients, younger age (P = 0.0038), public insurance (P < 0.0001), rural residence (P < 0.0076), higher HbA1c (P < 0.0001), and ethnicity minority status (P < 0.0001) were associated with DKA at diagnosis. CONCLUSIONS: While DKA prevalence increases in community patients over time, it stayed <5% in active research participants, who have a 10 times lower prevalence of DKA at diagnosis, including in minorities.

Body mass index moderates the association between diabetes distress and objective self-management behaviours in adolescents with type 1 diabetes and elevated A1Cs.

OBJECTIVE: To examine the cross-sectional associations between diabetes distress, BMI (zBMI; BMI z-score), objectively measured mean daily blood glucose readings and insulin boluses administered, and A1C in adolescents with type 1 diabetes (T1D) using insulin pumps. METHODS: T1D self-management behaviour data were downloaded from adolescents' (N = 79) devices and mean daily frequency of blood glucose readings and insulin boluses were calculated. Diabetes distress was measured (Problem Areas in Diabetes-Teen questionnaire [PAID-T]), A1C collected, and zBMI calculated from height and weight. Three multiple linear regressions were performed with blood glucose readings, insulin boluses, and A1C as the three dependent variables and covariates (age, T1D duration), zBMI, diabetes distress, and the diabetes distress x zBMI interaction as independent variables. RESULTS: Participants (55.7% female) were 14.9 ± 1.9 years old with T1D for 6.6 ± 3.4 years. zBMI moderated the relationship between diabetes distress and mean daily insulin boluses administered (b = -0.02, p = 0.02); those with higher zBMI and higher diabetes distress administered fewer daily insulin boluses. zBMI was not a moderator of the association between diabetes distress and blood glucose readings (b = -0.01, p = 0.29) or A1C (b = 0.002, p = 0.81). CONCLUSIONS: Using objective behavioural data is useful for identifying how adolescent diabetes distress and zBMI affect daily bolusing behaviour amongst adolescent insulin pump users. Although distinct interventions exist to improve T1D self-management or diabetes distress, none addresses them together while considering zBMI. Decreasing diabetes distress could be especially important for youth with high zBMI.

The Design of the Electronic Health Record in Type 1 Diabetes Centers: Implications for Metrics and Data Availability for a Quality Collaborative.

BACKGROUND: Systematic and comprehensive data acquisition from the electronic health record (EHR) is critical to the quality of data used to improve patient care. We described EHR tools, workflows, and data elements that contribute to core quality metrics in the Type 1 Diabetes Exchange Quality Improvement Collaborative (T1DX-QI). METHOD: We conducted interviews with quality improvement (QI) representatives at 13 T1DX-QI centers about their EHR tools, clinic workflows, and data elements. RESULTS: All centers had access to structured data tools, nine had access to patient questionnaires and two had integration with a device platform. There was significant variability in EHR tools, workflows, and data elements, thus the number of available metrics per center ranged from four to 17 at each site. Thirteen centers had information about glycemic outcomes and diabetes technology use. Seven centers had measurements of additional self-management behaviors. Centers captured patient-reported outcomes including social determinants of health (n = 9), depression (n = 11), transition to adult care (n = 7), and diabetes distress (n = 3). Various stakeholders captured data including health care professionals, educators, medical assistants, and QI coordinators. Centers that had a paired staffing model in clinic encounters distributed the burden of data capture across the health care team and was associated with a higher number of available data elements. CONCLUSIONS: The lack of standardization in EHR tools, workflows, and data elements captured resulted in variability in available metrics across centers. Further work is needed to support measurement and subsequent improvement in quality of care for individuals with type 1 diabetes.

Error traps in the perioperative management of children with type 1 diabetes.

Patients with type 1 diabetes mellitus (T1D) require insulin administration at all times to maintain euglycemia and metabolic stability. Insulin administration in the perioperative period is complicated by fasting requirements and perioperative stressors that can change the patient's insulin needs. In addition, many anesthesia providers are not familiar with insulin dosing strategies and technology, such as insulin pumps and continuous glucose monitors (CGMs), that are commonly used by patients with T1D. Errors in perioperative insulin administration can lead to hypoglycemia, hyperglycemia, and diabetic ketoacidosis. This article reviews common errors of associated with the perioperative management of patients with T1D, including failure to assess and coordinate patient care preoperatively; failure to understand diabetes management and technology; failure to monitor blood glucose and recognize dysglycemia; and failure to appropriately administer basal insulin.

Association Between Diabetes Technology Use and Glycemic Outcomes in Adults With Type 1 Diabetes Over a Decade.

OBJECTIVE: To evaluate change in mean clinic HbA1c from 2014 to 2021 with diabetes technology use in adults with type 1 diabetes. RESEARCH DESIGN AND METHODS: In this single-center study, we analyzed diabetes technology use and mean clinic HbA1c among unique adults (age ≥18 years) with type 1 diabetes (last visit of the year per patient) between 1 January 2014 and 31 December 2021 from the electronic medical record. Diabetes technology use was defined as the use of continuous glucose monitors (CGMs) without an automated insulin delivery (AID) system or an AID system. Diabetes technology use and HbA1c over time were analyzed using mixed models adjusted for age, sex, and visit year. RESULTS: A total of 15,903 clinic visits over 8 years (mean 1,988 patients per year, 4,174 unique patients, 52.7% female, 80.0% Non-Hispanic White) showed significant increases in CGM and AID use (P < 0.001 for both), resulting in an increase of diabetes technology use from 26.9% in 2014 to 82.7% in 2021. These increases were associated with a lower mean clinic HbA1c (7.7-7.5%, P < 0.001) and a higher percentage of adults achieving an HbA1c <7.0% (32.3-41.7%, P < 0.001) from 2014 to 2021. The HbA1c difference between technology users and nonusers increased over time from 0.36% (95% CI 0.26-0.47%, P < 0.001) in 2014 to 0.93% (95% CI 0.80-1.06%, P < 0.001) in 2021. CONCLUSIONS: Adopting diabetes technology in adults with type 1 diabetes decreased HbA1c and increased the number of people achieving an HbA1c <7.0%, supporting the current international recommendation to offer AID systems to most individuals with type 1 diabetes.

A randomized controlled clinical trial to improve health outcomes in youth with type 1 diabetes: Study design and baseline characteristics.

Most adolescents with T1D do not meet glycemic recommendations or consistently perform the required self-management behaviors to prevent acute- and long-term deleterious health outcomes. In addition, most youth with T1D do not have access to behavioral health services to address T1D management barriers. Thus, delivering behavioral interventions during routine medical appointments may hold promise for improving T1D outcomes in adolescents. The overall objective of this study was to examine the effect of behavioral interventions, either a Personalized T1D Self-Management Behaviors Feedback Report or Problem-Solving Skills, delivered by a T1D behavioral health provider and a T1D medical provider during a joint, fully integrated appointment to improve health outcomes in youth with T1D. This paper describes the study rationale, design, and baseline characteristics for the 109 adolescent-caregiver dyads who participated. Primary and secondary outcomes include hemoglobin A1c (A1C), T1D self-management behaviors, and biological indicators of complications.

Increased Technology Use Associated With Lower A1C in a Large Pediatric Clinical Population.

OBJECTIVE: While continuous glucose monitors (CGMs), insulin pumps, and hybrid closed-loop (HCL) systems each improve glycemic control in type 1 diabetes, it is unclear how the use of these technologies impacts real-world pediatric care. RESEARCH DESIGN AND METHODS: We found 1,455 patients aged <22 years, with type 1 diabetes duration >3 months, and who had data from a single center in between both 2016-2017 (n = 2,827) and 2020-2021 (n = 2,731). Patients were grouped by multiple daily injections or insulin pump, with or without an HCL system, and using a blood glucose monitor or CGM. Glycemic control was compared using linear mixed-effects models adjusting for age, diabetes duration, and race/ethnicity. RESULTS: CGM use increased from 32.9 to 75.3%, and HCL use increased from 0.3 to 27.9%. Overall A1C decreased from 8.9 to 8.6% (P < 0.0001). CONCLUSIONS: Adoption of CGM and HCL was associated with decreased A1C, suggesting promotion of these technologies may yield glycemic benefits.

Trends in Type 1 Diabetic Ketoacidosis During COVID-19 Surges at 7 US Centers: Highest Burden on non-Hispanic Black Patients.

CONTEXT: The impact of the COVID-19 pandemic on individuals with type 1 diabetes remains poorly defined. OBJECTIVE: We examined United States trends in diabetic ketoacidosis (DKA) among individuals with type 1 diabetes (T1D) during the COVID-19 pandemic at 7 large US medical centers and factors associated with these trends. METHODS: We compared DKA events among children and adults with T1D during COVID-19 surge 1 (March-May 2020) and COVID-19 surge 2 (August-October 2020) to the same periods in 2019. Analysis was performed using descriptive statistics and chi-square tests. RESULTS: We found no difference in the absolute number of T1D patients experiencing DKA in 2019 vs 2020. However, a higher proportion of non-Hispanic Black (NHB) individuals experienced DKA in 2019 than non-Hispanic White (NHW) individuals (44.6% vs 16.0%; P < .001), and this disparity persisted during the COVID-19 pandemic (48.6% vs 18.6%; P < .001). DKA was less common among patients on continuous glucose monitor (CGM) or insulin pump in 2020 compared to 2019 (CGM: 13.2% vs 15.0%, P < .001; insulin pump: 8.0% vs 10.6%, P < .001). In contrast to annual DKA totals, a higher proportion of patients had DKA during COVID-19 surges 1 and 2 compared to the same months in 2019 (surge 1: 7.1% vs 5.4%, P < .001; surge 2: 6.6% vs 5.7%, P = .001). CONCLUSION: DKA frequency increased among T1D patients during COVID-19 surges with highest frequency among NHB patients. DKA was less common among patients using CGM or insulin pumps. These findings highlight the urgent need for improved strategies to prevent DKA among patients with T1D-not only under pandemic conditions, but under all conditions-especially among populations most affected by health inequities.

Continuous Glucose Monitoring Initiation Within First Year of Type 1 Diabetes Diagnosis Is Associated With Improved Glycemic Outcomes: 7-Year Follow-Up Study.

OBJECTIVE: To evaluate long-term glycemic outcomes of continuous glucose monitoring (CGM) initiation within the first year of type 1 diabetes diagnosis. RESEARCH DESIGN AND METHODS: Patients with type 1 diabetes (N = 396) were divided into three groups: 1) CGM (CGM use within 1 year of diabetes diagnosis and continued through the study), 2) no-CGM (no CGM use throughout the study), and 3) new-CGM (CGM use after 3 years since diabetes diagnosis). Patients were followed up to 7 years. RESULTS: A1c was significantly lower in the CGM compared with the no-CGM group throughout 7 years of follow-up (least squares mean A1c values: 6 months, 7.3% vs. 8.1%; 1 year, 7.4% vs. 8.6%; 2 years, 7.7% vs. 9.1%; 3 years, 7.6% vs. 9.3%; 4 years, 7.4% vs. 9.6%; 5 years, 7.6% vs. 9.7%; 6 years, 7.5% vs. 10.0%; and 7 years, 7.6% vs. 9.8%; for all, P < 0.001) adjusting for age at diagnosis, sex, and insulin delivery method. CONCLUSIONS: CGM initiation within first year of type 1 diabetes diagnosis results in long-term improvement in A1c.

Making Diabetes Electronic Medical Record Data Actionable: Promoting Benchmarking and Population Health Improvement Using the T1D Exchange Quality Improvement Portal.

This article describes how the T1D Exchange Quality Improvement Collaborative leverages an innovative web platform, the QI Portal, to gather and store electronic medical record (EMR) data to promote benchmarking and population health improvement in a type 1 diabetes learning health system. The authors explain the value of the QI Portal, the process for mapping center-level data from EMRs using standardized data specifications, and the QI Portal's unique features for advancing population health.

Testing an Automated Approach to Identify Variation in Outcomes among Children with Type 1 Diabetes across Multiple Sites.

Introduction: Efficient methods to obtain and benchmark national data are needed to improve comparative quality assessment for children with type 1 diabetes (T1D). PCORnet is a network of clinical data research networks whose infrastructure includes standardization to a Common Data Model (CDM) incorporating electronic health record (EHR)-derived data across multiple clinical institutions. The study aimed to determine the feasibility of the automated use of EHR data to assess comparative quality for T1D. Methods: In two PCORnet networks, PEDSnet and OneFlorida, the study assessed measures of glycemic control, diabetic ketoacidosis admissions, and clinic visits in 2016-2018 among youth 0-20 years of age. The study team developed measure EHR-based specifications, identified institution-specific rates using data stored in the CDM, and assessed agreement with manual chart review. Results: Among 9,740 youth with T1D across 12 institutions, one quarter (26%) had two or more measures of A1c greater than 9% annually (min 5%, max 47%). The median A1c was 8.5% (min site 7.9, max site 10.2). Overall, 4% were hospitalized for diabetic ketoacidosis (min 2%, max 8%). The predictive value of the PCORnet CDM was >75% for all measures and >90% for three measures. Conclusions: Using EHR-derived data to assess comparative quality for T1D is a valid, efficient, and reliable data collection tool for measuring T1D care and outcomes. Wide variations across institutions were observed, and even the best-performing institutions often failed to achieve the American Diabetes Association HbA1C goals (<7.5%).

Quality Improvement in Diabetes Care: A Review of Initiatives and Outcomes in the T1D Exchange Quality Improvement Collaborative.

Despite immense strides in therapeutic advances, clinical outcomes continue to be less than ideal for people with type 1 diabetes. This discrepancy has prompted an outpouring of quality improvement (QI) initiatives to address the medical, psychosocial, and health equity challenges that complicate ideal type 1 diabetes care and outcomes. This article reviews a framework for QI in diabetes care that guided the development of the T1D Exchange Quality Improvement Collaborative to improve care delivery and health outcomes in type 1 diabetes. Evaluation of the methodology, outcomes, and knowledge gained from these initiatives will highlight the importance of continued QI initiatives in diabetes care.

Increasing Insulin Pump Use Among 12- to 26-Year-Olds With Type 1 Diabetes: Results From the T1D Exchange Quality Improvement Collaborative.

Insulin pump therapy in pediatric type 1 diabetes has been associated with better glycemic control than multiple daily injections. However, insulin pump use remains limited. This article describes an initiative from the T1D Exchange Quality Improvement Collaborative aimed at increasing insulin pump use in patients aged 12-26 years with type 1 diabetes from a baseline of 45% in May 2018 to >50% by February 2020. Interventions developed by participating centers included increasing in-person and telehealth education about insulin pump technology, creating and distributing tools to assist in informed decision-making, facilitating insulin pump insurance approval and onboarding processes, and improving clinic staff knowledge about insulin pumps. These efforts yielded a 13% improvement in pump use among the five participating centers, from 45 to 58% over 22 months.

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.

A Worldwide Perspective on COVID-19 and Diabetes Management in 22,820 Children from the SWEET Project: Diabetic Ketoacidosis Rates Increase and Glycemic Control Is Maintained.

Aims: To investigate the short-term effects of the first wave of COVID-19 on clinical parameters in children with type 1 diabetes (T1D) from 82 worldwide centers participating in the Better Control in Pediatric and Adolescent DiabeteS: Working to CrEate CEnTers of Reference (SWEET) registry. Materials and Methods: Aggregated data per person with T1D ≤21 years of age were compared between May/June 2020 (first wave), August/September 2020 (after wave), and the same periods in 2019. Hierarchic linear and logistic regression models were applied. Models were adjusted for gender, age-, and diabetes duration-groups. To distinguish the added burden of the COVID-19 pandemic, the centers were divided into quartiles of first wave COVID-19-associated mortality in their country. Results: In May/June 2019 and 2020, respectively, there were 16,735 versus 12,157 persons, 52% versus 52% male, median age 13.4 (Q1; Q3: 10.1; 16.2) versus13.5 (10.2; 16.2) years, T1D duration 4.5 (2.1; 7.8) versus 4.5 (2.0; 7.8) years, and hemoglobin A1c (HbA1c) 60.7 (53.0; 73.8) versus 59.6 (50.8; 70.5) mmol/mol [7.8 (7.0; 8.9) versus 7.6 (6.8; 8.6) %]. Across all country quartiles of COVID-19 mortality, HbA1c and rate of severe hypoglycemia remained comparable to the year before the first wave, while diabetic ketoacidosis rates increased significantly in the centers from countries with the highest mortality rate, but returned to baseline after the wave. Continuous glucose monitoring use decreased slightly during the first wave (53% vs. 51%) and increased significantly thereafter (55% vs. 63%, P < 0.001). Conclusions: Although glycemic control was maintained, a significant rise in DKA at follow-up was seen during first wave in the quartile of countries with the highest COVID mortality. Trial Registration: NCT04427189.

Worldwide differences in childhood type 1 diabetes: The SWEET experience.

OBJECTIVE: To study worldwide differences in childhood diabetes, comparing relevant indicators among five regions within the SWEET initiative. SUBJECTS: We investigated 26 726 individuals with type 1 diabetes (T1D) from 54 centers in the European region; 7768 individuals from 30 centers in the Asia/Middle East/Africa region; 2642 people from five centers in Australia/New Zealand; 10 839 individuals from seven centers in North America, and 1114 patients from five centers in South America. METHODS: The SWEET database was analyzed based on the following inclusion criteria: T1D, time period 2015-2019, and age < 21 years, with analysis of the most recent documented year of therapy. For the statistical analysis, we used multivariable linear and logistic regression models to adjust for age (<6 years, 6- < 12 years, 12- < 18 years, 18- < 21 years), gender, and duration of diabetes (<2 years, 2- < 5 years, 5- < 10 years, ≥10 years). RESULTS: Adjusted HbA1c means ranged from 7.8% (95%-confidence interval: 7.6-8.1) in Europe to 9.5% (9.2-9.8) in Asia/Middle East/Africa. Mean daily insulin dose ranged from 0.8 units/kg in Europe (0.7-0.8) and Australia/New Zealand (0.6-0.9) to 1.0 unit/kg 0.9-1.1) in Asia/Middle East/Africa. Percentage of pump use was highest in North America (80.7% [79.8-81.6]) and lowest in South America (4.2% [3.2-5.6]). Significant differences between the five regions were also observed with regards to body mass index SD scores, frequency of blood glucose monitoring and presence of severe hypoglycaemia. CONCLUSIONS: We found significant heterogeneity in diabetes care and outcomes across the five regions. The aim of optimal care for each child remains a challenge.

A Clinical Training Program for Hybrid Closed Loop Therapy in a Pediatric Diabetes Clinic.

BACKGROUND: Hybrid closed loop (HCL) therapy is now available in clinical practice for treatment of type 1 diabetes; however, there is limited research on how to educate patients on this new therapy. The purpose of this quality improvement project was to optimize a HCL education program for pediatric patients with type 1 diabetes (T1D). METHODS: Our multidisciplinary team developed a novel HCL clinical training program for current insulin pump users, using a quality improvement process called the Plan-Do-Study-Act model. Seventy-two patients participated in the HCL training program, which included (1) an in-person group class to reinforce conventional insulin pump and CGM use on the new system, (2) a live video conference class to teach HCL use, and (3) three follow-up phone calls in the first 4 weeks after HCL training to assess system use, make insulin adjustments, and provide targeted reeducation. Diabetes educators collected data during follow-up calls, and patients completed a training satisfaction survey. RESULTS: The quality improvement process resulted in a training program that emphasized education on HCL exits, CGM use, and optimizing insulin to carbohydrate ratio settings. Patients successfully sustained time in HCL in the initial weeks of use and rated the trainings and follow-up calls highly. CONCLUSIONS: Ongoing educational support is vital in the early weeks of HCL use. This quality improvement project is the first to examine strategies for implementation of HCL therapy into a large pediatric diabetes center, and may inform best practices for implementation of new diabetes technologies into other diabetes clinics.