Faculty Bibliography

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

Introduction: Children and adolescents with pre-existing type 1 diabetes (T1D) diagnosed with COVID-19 are at risk of adverse outcomes such as hospitalizations and diabetic ketoacidosis (DKA). There is limited data on the association between the presence of one or more comorbidities and the risk of adverse outcomes for patients with preexisting T1D and COVID19.
Objective(s): This study's aim is to determine if pediatric and adolescent patients with T1D and other pre-existing comorbidities were more likely to experience adverse outcomes than T1D patients with COVID-19 who did not have any other comorbidities.
Method(s): Data from 592 patients with previously established T1D aged <24 years with COVID-19 were analyzed from the T1Dx COVID-19 Surveillance Registry. Data were collected from 52 endocrinology clinics across the US using an online survey tool. Each clinic completed the survey using electronic medical record (EMR) data between April 2020 and May 2021. Descriptive statistics were used to describe the study population, and multivariate logistic regression models were used to analyze the relationship between age, insurance type, use of diabetes technology, presence of comorbidities, adverse outcomes, and hospitalization.
Result(s): The most frequent comorbidities were obesity (14%), asthma (11%), celiac disease (9%), and hypothyroidism (7%). T1D patients with at least one other comorbidity had a higher DKA presentation (16% vs 12%, p = 0.03) and a higher all-cause hospitalization rate (24% vs 15%, p = 0.02) compared to T1D patients without additional comorbidities. T1D Patients with comorbidities and COVID-19 were almost twice as likely to be hospitalized than those with no comorbidities (Odds Ratio 1.94, 95% CI: 1.23-3.03). The most frequent comorbidities were obesity (14%), asthma (11%), celiac disease (9%), and hypothyroidism (7%). T1D patients with at least one other comorbidity had a higher DKA presentation (16% vs 12%, p = 0.03) and a higher all-cause hospitalization rate (24% vs 15%, p = 0.02) compared to T1D patients without additional comorbidities. T1D Patients with comorbidities and COVID-19 were almost twice as likely to be hospitalized than those with no comorbidities (Odds Ratio 1.94, 95% CI: 1.23-3.03).
Conclusion(s): Our data reveal higher rates of hospitalizations and adverse outcomes among children and adolescents with T1D with at least one more comorbidities and COVID-19 in comparison with T1D patients without additional comorbidities. (Table Presented)

Introduction: The COVID-19 pandemic has had far-reaching consequences for individuals with type 1 diabetes (T1D) and has laid bare inequities in health care.
Objective(s): We sought to examine the United States (US) trends in diabetic ketoacidosis (DKA) across the lifespan during the COVID-19 pandemic and factors associated with these trends, compared to DKA rates the year prior to the pandemic.
Method(s): The T1D Exchange Quality Improvement Collaborative (T1DX-QI) collected aggregate data on the incidence of DKA among children and adults with established and new-onset T1D from 7 large medical centers in the US (total T1D population >15,000). We compared DKA rates during COVID-19 Wave 1 (March-May 2020) and COVID-19 Wave 2 (August-October 2020) to the same periods in 2019. Descriptive statistics were used to summarize data. Chi-square tests were used to compare differences in patient characteristics.
Result(s): DKA rates were higher in patients with established T1D during COVID-19 Wave 1 compared to the same period in 2019 (6.15% vs 4.71%, p=<0.001). DKA rates were also higher in patients with established T1D during COVID-19 Wave 2 compared to 2019 (5.55% vs 4.90%, p=0.02). There were no differences in rates of DKA by age or DKA severity. DKA rates were lower among individuals on insulin pumps during both COVID-19 waves compared to 2019 (Wave 1: 6.43% vs 10.25%, p=0.008; Wave 2: 8.14% vs 11.21%, p=0.03). Consistent with known T1D inequities, DKA rates were exacerbated for NH Black patients in 2020, with 18% of NH Blacks with T1D experiencing DKA compared to 6% of NH Whites.
Conclusion(s): DKA rates rose among patients with T1D during US COVID-19 Waves 1 and 2, with the highest rates among NH Blacks. These findings highlight the urgent need for improved strategies to decrease the risk of DKA in individuals with T1D under pandemic conditions, especially among populations most affected by health inequities

Introduction: The use of smart insulin pens has the potential to improve glucose stability, medication adherence, glycemic management, time in range, dose accuracy, quality of visits, and virtual care opportunities for patients living with diabetes.
Objective(s): The purpose of this study was to identify facilitators to smart insulin pen use by assessing provider and care team perceptions.
Method(s): The study was conducted using a mixed-methods approach. Participating endocrinology clinics within the T1D Exchange Quality Improvement Collaborative (T1Dx-QI) were recruited for this study. Four pediatric centers participated in focus group sessions while 17 clinics completed an online survey. Focus groups were transcribed, coded, and analyzed for common themes using NVivo qualitative analysis software. The online survey responses were summarized using R software.
Result(s): Smart insulin pens (SIPs) were seen as a tool to engage patients in their diabetes self-management and increase accountability for insulin administration. Improvements were noted in patient engagement. SIPs were viewed as an acceptable alternative to pump therapy. Major facilitators for smart insulin pen use are shown in Figure 1. Majority of participants reported improvement in medication adherence and glycemic management in patients using SIPs. Testimonials from providers include "Especially for the kids who didn't want to go on a pump, that they can still have a lot [benefits of dose calculator] without using a pump." "I've had a couple of teenagers really take ownership of their diabetes in sending in those reports." "It can really help to structure the clinic visits and make them more productive."
Conclusion(s): All respondents saw SIPs as a beneficial tool with provider-reported benefits to the patient, caregivers, provider, and clinic. Reports from smart insulin pens were seen as useful tools for both patients and providers. (Table Presented)

In the pediatric population, insulin pump therapy, or CSII, is often considered the gold standard for intensive diabetes management. Insulin pump technology offers families and caregivers many beneficial features including a calculator for insulin dosing and the ability to review diabetes management data to provide data-driven diabetes management. However, for those who find CSII challenging or choose to use multiple daily injections (MDI) there is an option that offers similar features called the Smart Insulin Pen (SIP). Even though SIP technology provides a safe and data-driven diabetes self-management tool for the pediatric population using MDI, there is limited pediatric specific literature. This article will describe current options, data-driven diabetes management, benefits, challenges and clinical use of SIP technology in the pediatric population.

BACKGROUND:Diabetes is a risk factor for poor COVID-19 outcomes, but pediatric patients with type 1 diabetes are poorly represented in current studies. METHODS:T1D Exchange coordinated a US type 1 diabetes COVID-19 registry. Forty-six diabetes centers submitted pediatric cases for patients with laboratory confirmed COVID-19. Associations between clinical factors and hospitalization were tested with Fisher's Exact Test. Logistic regression was used to calculate odds ratios for hospitalization. RESULTS:Data from 266 patients with previously established type 1 diabetes aged <19 years with COVID-19 were reported. Diabetic ketoacidosis (DKA) was the most common adverse outcome (n = 44, 72% of hospitalized patients). There were four hospitalizations for severe hypoglycemia, three hospitalizations requiring respiratory support (one of whom was intubated and mechanically ventilated), one case of multisystem inflammatory syndrome in children, and 10 patients who were hospitalized for reasons unrelated to COVID-19 or diabetes. Hospitalized patients (n = 61) were more likely than nonhospitalized patients (n = 205) to have minority race/ethnicity (67% vs 39%, P < 0.001), public insurance (64% vs 41%, P < 0.001), higher A1c (11% [97 mmol/mol] vs 8.2% [66 mmol/mol], P < 0.001), and lower insulin pump and lower continuous glucose monitoring use (26% vs 54%, P < 0.001; 39% vs 75%, P < 0.001). Age and gender were not associated with risk of hospitalization. Higher A1c was significantly associated with hospitalization, with an odds ratio of 1.56 (1.34-1.84) after adjusting for age, gender, insurance, and race/ethnicity. CONCLUSIONS:Higher A1c remained the only predictor for hospitalization with COVID-19. Diabetic ketoacidosis is the primary concern among this group.