Faculty Bibliography

BACKGROUND/UNASSIGNED: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/UNASSIGNED:We conducted interviews with quality improvement (QI) representatives at 13 T1DX-QI centers about their EHR tools, clinic workflows, and data elements. RESULTS/UNASSIGNED: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/UNASSIGNED: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.

OBJECTIVES:We sought to examine in individuals with SARS-CoV-2 infection whether risk for thrombotic and thromboembolic events (TTE) is modified by presence of a diabetes diagnosis. Furthermore, we analysed whether differential risk for TTEs exists in type 1 diabetes mellitus (T1DM) versus type 2 diabetes mellitus (T2DM). DESIGN:Retrospective case-control study. SETTING:COVID-19 database is a deidentified, nationwide database containing electronic medical record (EMR) data from 87 US-based health systems. PARTICIPANTS:We analysed EMR data for 322 482 patients >17 years old with suspected or confirmed SARS-CoV-2 infection who received care between December 2019 and mid-September 2020. Of these, 2750 had T1DM; 57 811 had T2DM; and 261 921 did not have diabetes. OUTCOME:TTE, defined as presence of a diagnosis code for myocardial infarction, thrombotic stroke, pulmonary embolism, deep vein thrombosis or other TTE. RESULTS:Odds of TTE were substantially higher in patients with T1DM (adjusted OR (AOR) 2.23 (1.93-2.59)) and T2DM (AOR 1.52 (1.46-1.58)) versus no diabetes. Among patients with diabetes, odds of TTE were lower in T2DM versus T1DM (AOR 0.84 (0.72-0.98)). CONCLUSIONS:Risk of TTE during COVID-19 illness is substantially higher in patients with diabetes. Further, risk for TTEs is higher in those with T1DM versus T2DM. Confirmation of increased diabetes-associated clotting risk in future studies may warrant incorporation of diabetes status into SARS-CoV-2 infection treatment algorithms.

Background and Aims: Evidence from clinical trials suggest that use of CGM devices decreases hypoglycemia, but no realworld studies have demonstrated efficacy of real-time CGM vs. flash CGM device use in improving CGM derived glycemic outcomes. A flash or intermittently scanning CGM (isCGM) provides glucose levels immediately upon scanning sensor; whereas real-time CGM (rtCGM) device automatically transmits a continuous stream of glucose data to the user. We examined efficacy of isCGM vs. rtCGM device use using real-world EMR data from 19 endocrinology clinics participating in the T1DX-QI Collaborative.
Method(s): Main outcomes were a) mean time in range (TIR: 70-180 mg/dL), b) time above range (TAR: >=250mg/dL) and c) time below range (TBR: <70 mg/dL). Patients >=6 years with T1D from 2018 to 2022 were included. Discriptive differences between isCGM and rtCGM groups were assessed using chisquare and Mann-Whitney U tests. Bootstrapped point estimates and 95% CIs were reported. Linear mixed models examined association between type of CGM and TIR adjusting for covariates.
Result(s): This analysis included 6234 people in the rtCGM group and 412 people in the isCGM group. In the overall study population, mean TIR was higher for rtCGM users relative to isCGM users (Mean(95% CI): 50 (49-51) vs. 40 (38-43)) [p = 0.0001], mean TBR was lower for rtCGM users relative to isCGM users (Mean (95% CI): 1.9 (1.8-2.0) vs. 2.6 (2.2-3.0)) [p = 0.001] and mean TAR was also lower for rtCGM users (Mean(95% CI): 19 (18-20) vs. 26 (23-30)) [p < 0.001].
Conclusion(s): We found improved CGM derived glycemic outcomes for rtCGM relative to the isCGM grroup

Background and Aims: Diabetes technology (DT) use is associated with lower HbA1c in type 1 diabetes (T1D). Non- Hispanic Black and Hispanic populations are more likely to have lower DT use and higher HbA1c compared to non-Hispanic White populations. We examined the extent to which differential DT use explains outcome disparities at an outpatient pediatric diabetes center in NYC.
Method(s): Patients identifying as non-White, Hispanic, or non-English language preference were grouped (minority race/ language; MRL) and compared to non-Hispanic White, Englishpreferred patients. HbA1c >9% was categorized as high. T-test and chi-square statistics compared patient characteristics by HbA1c category. Binomial regression with generalized estimating equations estimated associations (risk ratios, RR; 95% confidence intervals, CI) between MRL and high HbA1c. First, models were adjusted for insurance type and Child Opportunity Index (COI), then additionally for CGM and pump use.
Result(s): Patients (n = 331) aged 2-25 years with T1D >= 3 months attended 709 visits (mean 2.2, SD 1.2) from 2020-2021; 32% identified as MRL. At the most recent visit, 16% had HbA1c>9% (MRL 29%, non-MRL 10%), 87% used CGMs (MRL 77%, non-MRL 92%), and 78% used pumps (MRL 72%, non-MRL 81%). MRL youth were 2.5 (95% CI 1.6-4.0) times more likely to have HbA1c>9% as compared to non-MRL youth, adjusted for insurance and COI. After adjusting for DT use, MRL youth remained twice as likely to have HbA1c>9% (RR 2.0, 95% CI 1.2-3.3).
Conclusion(s): While the disparity in HbA1c between MRL and non-MRL youth can be partially attributed to DT use, disparity persists even after accounting for DT use

OBJECTIVES/OBJECTIVE:We evaluated COVID-19 outcomes in children and young adults with type 1 diabetes (T1D) to determine if those with comorbidities are more likely to experience severe COVID-19 compared to those without. RESEARCH DESIGN AND METHODS/METHODS:This cross-sectional study included questionnaire data on patients <25 years of age with established T1D and laboratory-confirmed COVID-19 from 52 sites across the US between April 2020 and October 2021. We examined patient factors and COVID-19 outcomes between those with and without comorbidities. Multivariate logistic regression analysis examined the odds of hospitalization among groups, adjusting for age, HbA1c, race and ethnicity, insurance type and duration of diabetes. RESULTS:Six hundred fifty-one individuals with T1D and COVID-19 were analyzed with mean age 15.8 (SD 4.1) years. At least one comorbidity was present in 31%, and more than one in 10%. Obesity and asthma were the most frequently reported comorbidities, present in 19% and 17%, respectively. Hospitalization occurred in 17% of patients and 52% of hospitalized patients required ICU level care. Patients with at least one comorbidity were almost twice as likely to be hospitalized with COVID-19 than patients with no comorbidities (Odds ratio 2.0, 95% CI: 1.3-3.1). This relationship persisted after adjusting for age, HbA1c, race and ethnicity (minority vs nonminority), insurance type (public vs. private), and duration of diabetes. CONCLUSIONS:Our findings show that comorbidities increase the risk for hospitalization with COVID-19 in children and young adults highlighting the need for tailored COVID-19 prevention and treatment strategies in T1D.

OBJECTIVE:We examined United States (US) trends in diabetic ketoacidosis (DKA) among individuals with type 1 diabetes (T1D) during the COVID-19 pandemic at seven 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 Blacks (NHB) experienced DKA in 2019 than non-Hispanic Whites (NHW) (44.6% vs 16.0%; p<0.001), and this disparity persisted during the COVID-19 pandemic (48.6% vs 18.6%; p<0.001). 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<0.001; insulin pump: 8.0% vs 10.6%, p<0.001). 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<0.001; surge 2: 6.6% vs 5.7%, p=0.001). CONCLUSIONS:DKA frequency increased among T1D patients during COVID-19 surges with highest frequency among NHB. 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.

AIMS/OBJECTIVE:Individuals with type 1 diabetes (T1D) face increased risk for cardiovascular disease (CVD). Controlling individual cardiovascular risk factors can prevent or slow the onset of CVD. Ideal cardiovascular health is associated with a lower incidence of CVD. Identifying areas of suboptimal cardiovascular health can help guide CVD prevention interventions. To assess cardiovascular health and explore the barriers and facilitators to achieving ideal cardiovascular health in a sample of young adults with T1D. METHODS AND RESULTS/RESULTS:We used a sequential mixed-method design to assess the seven factors of cardiovascular health according to American Heart Association. Qualitative interviews, guided by Pender's Health Promotion Model, were used to discuss participant's cardiovascular health results and the barriers and facilitators to achieving ideal cardiovascular health. We assessed the frequency of ideal levels of each factor. The qualitative data were analysed using content analysis. Qualitative and quantitative data were integrated in the final analysis phase. The sample (n = 50) was majority female (70%), White (86%), with a mean age of 22 ± 2.4 and diabetes duration of 10.7 ± 5.5 years. Achievement of the seven factors of cardiovascular health were: non-smoking (96%); cholesterol <200 mg/dL (76%); body mass index <25 kg/m2 (54%); blood pressure <120/<80 mmHg (46%); meeting physical activity guidelines (38%); haemoglobin A1c <7% (40%); and healthy diet (14%). Emerging qualitative themes related to the perceived benefits of action, interpersonal influences on their diabetes self-management, and perceived self-efficacy. CONCLUSION/CONCLUSIONS:We found areas of needed improvement for cardiovascular health. However, these young adults expressed a strong interest in healthy habits which can be supported by their healthcare providers.

CONTEXT:COVID-19 morbidity and mortality are increased in type 1 diabetes (T1D), but few data focus on age-based outcomes. OBJECTIVE:This work aimed to quantify the risk for COVID-19-related hospitalization and adverse outcomes by age in people with T1D. METHODS:For this observational, multisite, cross-sectional study of patients with T1D and laboratory-confirmed COVID-19 from 56 clinical sites in the United States, data were collected from April 2020 to March 2021. The distribution of patient factors and outcomes across age groups (0-18, 19-40, and > 40 years) was examined. Descriptive statistics were used to describe the study population, and multivariate logistic regression models were used to analyze the relationship between age, adverse outcomes, and hospitalization. The main outcome measure was hospitalization for COVID-19. RESULTS:A total of 767 patients were analyzed. Fifty-four percent (n = 415) were aged 0 to 18 years, 32% (n = 247) were aged 19 to 40 years, and 14% (n = 105) were older than 40 years. A total of 170 patients were hospitalized, and 5 patients died. Compared to the 0- to 18-years age group, those older than 40 years had an adjusted odds ratio of 4.2 (95% CI, 2.28-7.83) for hospitalization after adjustment for sex, glycated hemoglobin A1c, race, insurance type, and comorbidities. CONCLUSION:Age older than 40 years is a risk factor for patients with T1D and COVID-19, with children and younger adults experiencing milder disease and better prognosis. This indicates a need for age-tailored treatments, immunization, and clinical management of individuals affected by T1D.

Background: Patient outcomes of COVID-19 have improved throughout the pandemic. However, because it is not known whether outcomes of COVID-19 in the type 1 diabetes (T1D) population improved over time, we investigated differences in COVID-19 outcomes for patients with T1D in the United States. Methods: We analyzed data collected via a registry of patients with T1D and COVID-19 from 56 sites between April 2020 and January 2021. We grouped cases into first surge (April 9, 2020, to July 31, 2020, n = 188) and late surge (August 1, 2020, to January 31, 2021, n = 410), and then compared outcomes between both groups using descriptive statistics and logistic regression models. Results: Adverse outcomes were more frequent during the first surge, including diabetic ketoacidosis (32% vs 15%, P< .001), severe hypoglycemia (4% vs 1%, P= .04), and hospitalization (52% vs 22%, P< .001). Patients in the first surge were older (28 [SD,18.8] years vs 18.0 [SD, 11.1] years, P< .001), had higher median hemoglobin A1c levels (9.3 [interquartile range {IQR}, 4.0] vs 8.4 (IQR, 2.8), P< .001), and were more likely to use public insurance (107 [57%] vs 154 [38%], P< .001). The odds of hospitalization for adults in the first surge were 5 times higher compared to the late surge (odds ratio, 5.01; 95% CI, 2.11-12.63). Conclusion: Patients with T1D who presented with COVID-19 during the first surge had a higher proportion of adverse outcomes than those who presented in a later surge.

Background/Objective: The T1D Exchange Quality Improvement Collaborative (T1Dx-QI) is a learning health system of over 40 US type 1 diabetes clinics. Participating clinics benefit from benchmarking insights, share best-practice ideas for quality improvement (QI), and use data for population health research. This study aims to benchmark the HbA1c metric targets across pediatric clinics in the T1Dx-QI network.
Method(s): T1Dx-QI pediatric clinics report monthly on key metrics, including median HbA1c, percent with HbA1c <7%, time in range, depression screening, and other quality metrics. T1Dx-QI uses this data to support, monitor, and sustain improvement efforts. The T1Dx-QI has set numerous collaborative goals, including to (a) Decrease the median HbA1c to <8% and (b) Increase the % of patients with HbA1c <7%and other QI metric goals. Median values for each site were calculated using Lahey P run charts between July 2020 and June 2021.
Result(s): Across 17 T1Dx-QI clinics, median HbA1c values between July 2020 and June 2021 ranged from 7.4% to 9.2% (Figure 1A). In the same time frame, the clinic-specific average monthly percentage of patients with HbA1c <7% ranged from 35.5% to 11.9% (Figure 1B). These results are shared internally in a non-anonymized version among participating T1Dx-QI clinics. The benchmarked data facilitates collaborative learning and advancement.
Conclusion(s): The T1Dx-QI uses benchmarking of key clinical outcomes metrics as a tool to support quality improvement, sharing of best practices, and promote learning across clinics