Continuous Glucose Monitoring Research Articles

Practical Considerations for Using Continuous Glucose Monitoring in Patients with Gestational Diabetes Mellitus.

Gestational diabetes mellitus (GDM) is the most prevalent metabolic complication during pregnancy. GDM is associated with adverse perinatal, neonatal, and long-term health consequences. Studies have demonstrated that the use of continuous glucose monitoring (CGM) reduces the incidence of maternal and neonatal complications in pregnant women with type 1 diabetes. Although the use of CGM in GDM has not been well studied, a growing body of evidence is showing potential benefits in the GDM population. This article discusses the advantages and challenges of CGM and provides practical guidelines for using this technology in the GDM population.

Type 1 Diabetes and Pregnancy: Challenges in Glycemic Control and Maternal-Fetal Outcomes.

Managing type 1 diabetes during pregnancy presents significant challenges due to physiological and hormonal changes. These factors contribute to major changes in insulin sensitivity, complicating efforts to achieve and sustain optimal blood glucose levels. Poorly controlled glucose levels during pregnancy can result in diabetic embryopathy and elevate the risks of maternal complications such as hypertensive disorders and diabetic ketoacidosis. Fetal complications may include preterm birth, fetal demise, and admission to neonatal intensive care units. It is essential to recognize that there is no universal approach to managing glycemic control in pregnant women with T1DM and care should be individualized. Effective management requires a multidisciplinary approach involving regular monitoring, adjustments in insulin therapy, dietary modifications, and consistent prenatal care. Continuous glucose monitoring has emerged as a valuable tool for real-time glucose monitoring, facilitating tighter glycemic control. Education and support for self-management are important in addressing these challenges. Future developments in technology and personalized approaches to care show promising potential for advancing diabetes management during pregnancy. This provides a comprehensive overview of current literature on the challenges with the management of T1DM during pregnancy, focusing on its impact on maternal and neonatal outcomes and highlighting effective strategies for achieving optimal glycemic control.

Transitioning from Self-Monitoring of Blood Glucose to Continuous Glucose Monitoring in Combination with a mHealth App Improves Glycemic Control in People with Type 1 and Type 2 Diabetes.

Introduction: Integrating mobile health (mHealth) apps into daily diabetes management allows users to monitor and track their health data, creating a comprehensive system for managing daily diabetes activities and generating valuable real-world data. This analysis investigates the impact of transitioning from traditional self-monitoring of blood glucose (SMBG) to real-time continuous glucose monitoring (rtCGM), alongside the use of a mHealth app, on users' glycemic control. Methods: Data were collected from 1271 diabetes type 1 and type 2 users of the mySugr® app who made a minimum of 50 SMBG logs 1 month before transitioning to rtCGM and then used rtCGM for at least 6 months. The mean and coefficient of variation of glucose, along with the proportions of glycemic measurements in and out of range, were compared between baseline and 1, 2, 3, and 6 months of rtCGM use. A mixed-effects linear regression model was built to quantify the specific effects of transitioning to a rtCGM sensor in different subsamples. A novel validation analysis ensured that the aggregated metrics from SMBG and rtCGM were comparable. Results: Transitioning to a rtCGM sensor significantly improved glycemic control in the entire cohort, particularly among new users of the mySugr app. Additionally, the sustainability of the change in glucose in the entire cohort was confirmed throughout the observation period. People with type 1 and type 2 diabetes exhibited distinct variations, with type 1 experiencing a greater reduction in glycemic variance, while type 2 displayed a relatively larger decrease in monthly averages.

Somatic and mental symptoms associated with dysglycaemia, diabetes-related complications and mental conditions in people with diabetes: Assessments in daily life using continuous glucose monitoring and ecological momentary assessment.

To analyse the potential drivers (glucose level, complications, diabetes type, gender, age and mental health) of diabetes symptoms using continuous glucose monitoring (CGM) and ecological momentary assessment. Participants used a smartphone application to rate 25 diabetes symptoms in their daily lives over 8 days. These symptoms were grouped into four blocks so that each symptom was rated six times on 2 days (noon, afternoon and evening). The symptom ratings were associated with the glucose levels for the previous 2 hours, measured with CGM. Linear mixed-effects models were used, allowing for nested random effects and the conduct of N = 1 analysis of individual associations. In total, 192 individuals with type 1 diabetes and 179 with type 2 diabetes completed 6380 app check-ins. Four symptoms showed a significant negative association with glucose values, indicating higher ratings at lower glucose (speech difficulties, P = .003; coordination problems, P = .00005; confusion, P = .049; and food cravings, P = .0003). Four symptoms showed a significant positive association with glucose values, indicating higher scores at higher glucose (thirst, P = .0001; urination, P = .0003; taste disturbances, P = .021; and itching, P = .0120). There were also significant positive associations between microangiopathy and eight symptoms. Elevated depression and diabetes distress were associated with higher symptom scores. N = 1 analysis showed highly idiosyncratic associations between symptom reports and glucose levels. The N = 1 analysis facilitated the creation of personalized symptom profiles related to glucose levels with consideration of factors such as complications, gender, body mass index, depression and diabetes distress. This approach can enhance precision monitoring for diabetes symptoms in precision medicine.

The Diabetes Technology Society Error Grid and Trend Accuracy Matrix for Glucose Monitors.

An error grid compares measured versus reference glucose concentrations to assign clinical risk values to observed errors. Widely used error grids for blood glucose monitors (BGMs) have limited value because they do not also reflect clinical accuracy of continuous glucose monitors (CGMs). Diabetes Technology Society (DTS) convened 89 international experts in glucose monitoring to (1) smooth the borders of the Surveillance Error Grid (SEG) zones and create a user-friendly tool-the DTS Error Grid; (2) define five risk zones of clinical point accuracy (A-E) to be identical for BGMs and CGMs; (3) determine a relationship between DTS Error Grid percent in Zone A and mean absolute relative difference (MARD) from analyzing 22 BGM and nine CGM accuracy studies; and (4) create trend risk categories (1-5) for CGM trend accuracy. The DTS Error Grid for point accuracy contains five risk zones (A-E) with straight-line borders that can be applied to both BGM and CGM accuracy data. In a data set combining point accuracy data from 18 BGMs, 2.6% of total data pairs equally moved from Zones A to B and vice versa (SEG compared with DTS Error Grid). For every 1% increase in percent data in Zone A, the MARD decreased by approximately 0.33%. We also created a DTS Trend Accuracy Matrix with five trend risk categories (1-5) for CGM-reported trend indicators compared with reference trends calculated from reference glucose. The DTS Error Grid combines contemporary clinician input regarding clinical point accuracy for BGMs and CGMs. The DTS Trend Accuracy Matrix assesses accuracy of CGM trend indicators.

8122 CGM use and its Impact on Diabetes Self-Management Behaviors: Insights from a Pilot Study Paving the Way for Targeted Interventions in Specialty Clinic

Abstract Disclosure: K.M. St. Jean: None. P. Underwood: None. R. Longo: None. J. Keels: None. J. Upadhyay: None. Introduction: Continuous glucose monitor (CGM) use is increasing. While the relationship between CGM use and improved diabetes (DM) clinical outcomes is well known (lower hemoglobin A1c (A1c), reduced hypoglycemia events), the mechanisms underlying this association are unknown. Further, the factors that contribute to successful CGM use by patients and whether CGM use influences diabetes self-management behavior (DMB) change are still undetermined. Objective: To understand the extent to which personal CGM use influences DMB versus user-blind CGM. In addition, patient satisfaction and provider perspective were evaluated during the study. Methodology: A pilot-randomized control trial (RCT) was conducted on 30 patients (15 libre-professional CGM and 15 libre 2 CGM). Adults (18-80y) with an ICD-10 diagnosis of DM within the past 12 months, A1c >8% measured within the past 6 months, stable weight within the past 3 months (BMI ± 2 units), able to speak, read, and write English, and a negative pregnancy test for women of childbearing age seen in the Lahey Endocrine Clinic were included.Exclusion criteria included evidence of personal CGM device use prior to the trial, addition of new oral or injectable hypoglycemic agents within 3 months of trial start, current or anticipated acute use of steroids, pregnancy, medical conditions that, per investigator determination, make it unsafe to target A1c <8%, severe psychiatric illness, homeless, current substance abuse, vision or hearing impairment that may affect the use of CGM. All participants were given information on how to incorporate the CGM into daily DM management and general diabetes self-management education (DSME). DMB was measured using the Diabetes Self Care Inventory-Revised scale, which was provided at visit 1 (baseline), visit 2 (2 weeks), and visit 3 (3-6 months). Additional outcome variables, including Diabetes Distress, sleep quality, exercise habits, and nutrition, were obtained. A1c was measured at baseline and at the end of visit 3. Provider satisfaction with patient CGM use was also evaluated using a newly developed provider satisfaction survey. Results: This study remains active for enrollment. Thus far, 14 patients have been recruited (7 in Libre 2 arm and 7 in Libre Pro User blind arm). Based on initial results, personal-CGM use supports improved diabetes self-care. Further, improvements in A1c, glucose time in target range, and lower rates of hypoglycemia are evident in all CGM users. Further, 100% of the providers felt that CGM use by their patients was very effective and helped their clinical decision-making during the visit. Providers did not feel that reviewing CGM data prolonged their visit. Conclusion: This pilot suggests that personal CGM use improves diabetes self-management behavior and reaffirms that CGM use in patients with poorly controlled diabetes is associated with improved DM clinical outcomes. Presentation: 6/3/2024

6521 Continuation of Continuous Glucose Monitors in Hospitalized Diabetic Patients Decreased the Incidence of Hypoglycemia

Abstract Disclosure: C.E. DeClue: None. S. Bao: None. Objective: Glycemic control improves patient outcomes in hospitalized patients, and this is generally achieved by monitoring point-of-care (POC) capillary glucose levels. In the outpatient setting, continuous glucose monitoring (CGM) devices have shown improved glycemic control in diabetic patients, but their evidence in hospitalized patients is lacking. Methods: To evaluate CGM accuracy and safety of use in the inpatient setting, 567 consults were analyzed in a single-center inpatient diabetes service between November 2022 and January 2023. Patients were included if managed with multiple daily injections or insulin pump therapy and separated into four groups based on CGM continuation and type of diabetes. POC glucose and incidence of hypoglycemia were recorded over 72 hours from the time of consultation. Results: Forty-six Type-1 (T1DM) and 72 Type-2 (T2DM) diabetic patients were included in this study, with average hemoglobin A1c of 8.6% and 9.0%, respectively. Both diabetic groups showed an average POC glucose that was not statistically different based on CGM continuation (185 mg/dL for T1DM and 187 mg/dL for T2DM continued on CGM versus 183 mg/dL for T1DM and 188 mg/dL for T2DM not using CGM). However, only 2 of the 18 T1DM patients continued on CGM experienced hypoglycemia, whereas 17 of the 28 T1DM patients not using CGM experienced at least 1 hypoglycemic event. Similarly, only 2 of the 20 T2DM patients continued on CGM experienced hypoglycemia, whereas 13 of the 52 patients not using CGM experienced at least 1 hypoglycemic event. Additionally, less glucose variability was observed in both T1DM and T2DM patients continued on CGM (average standard deviation of 64 mg/dL and 55 mg/dL, respectively) compared to those not using CGM (average standard deviation of 84 mg/dL and 62 mg/dL, respectively). Conclusion: CGM has great potential to improve patient care and nursing workload in the hospital, but the safety and accuracy are not yet well-established. In this study, there seemed to be a statistically insignificant trend to less glycemic variability for CGM users despite no glycemic difference on average POC testing. Importantly, significantly fewer hypoglycemic events were observed in patients continued on CGM devices regardless of type of diabetes or baseline glycemic control. This study adds further evidence to the growing body of work supporting improved safety of CGM utilization in hospitalized diabetic patients. Presentation: 6/1/2024

6894 Disparities in Continuous Glucose Monitor Utilization and Real-Time Remote Sharing in Patients with Type 2 Diabetes Treated with Insulin on Medicare

Abstract Disclosure: K.L. Flint: None. T. Ting: None. K. Rivera: None. P. Tamang: None. C.A. Colling: None. J.H. Li: None. M.S. Putman: Consulting Fee; Self; Synspira Therapeutics. Research Investigator; Self; Dexcom. Other; Self; Vertex Pharmaceuticals Incorporated. Introduction: Continuous glucose monitors (CGM) are FDA-approved for the management of diabetes and have been shown to improve glycemic control. For optimal utilization, CGM can be connected to cloud-based clinic portals for real-time sharing of glycemic data with clinicians to guide clinical care. Although studies have previously identified disparities in access to CGM for patients who receive Medicare, limited data are available examining disparities in CGM utilization and data sharing. Hypothesis: We hypothesized that racial and socioeconomic disparities in CGM access exist both in utilization and in real-time remote sharing of CGM data. Methods: This was a retrospective cohort study examining patients with type 2 diabetes on insulin using Medicare as primary or secondary insurance in a single diabetes clinic affiliated with a tertiary care medical center. Clinical data extracted from the electronic health record (EHR) included age, self-reported race and ethnicity, sex, preferred language, education level, ZIP code-based median household income, and enrollment in the EHR-patient portal. The most recent diabetes clinic note for each patient as of October 2023 was reviewed to assess whether each patient was using CGM at the time of the visit. The clinic’s CGM portal accounts were reviewed to assess whether the patient was connected to and actively sharing CGM data with the clinic. Two sample t-tests and chi-square tests were used to evaluate continuous and categorical predictors of CGM use and real-time remote sharing, respectively. Results: Of the 847 patients who qualified for inclusion, 420 (49.6%) were using CGM and 213 (25.1%) were sharing CGM data in real-time. Compared to patients not using CGM (n=427), patients using CGM were younger (70.8 years vs 73.5 years, p<0.001) and more often enrolled in the EHR-based patient portal (91.9% vs 84.1%, p<0.001); however, there were no differences in racial, ethnic, or socioeconomic factors between the groups (p>0.05 for all). Of the patients using CGM, remote sharing of data was associated with younger age (69.5 years vs 72.3 years, p<0.01), identifying as White (74.2% vs 65.0%, p=0.04), using English as preferred language (92.5% vs 84.2%, p=0.02), higher levels of education (p=0.01), and using the EHR-based patient portal (97.2% vs 86.7%, p<0.001). Conclusions: In this cohort of patients with type 2 diabetes on insulin receiving Medicare, there were no significant socioeconomic disparities in CGM utilization. However, racial and socioeconomic disparities were pronounced in real-time remote sharing of CGM data, suggesting that patients from minoritized racial backgrounds, patients who do not use English as their preferred language, and patients with less education may benefit from additional support and training to connect and share their CGM with their providers. Presentation: 6/2/2024

12420 Effects Of A Continuous Glucose Monitoring (CGM) Optimization Program On Prescribing Behavior And Glycemic Outcomes

Abstract Disclosure: A. Sterbenz: None. J. Fuentes Rosales: None. S. Abraham: None. P. Siroya: None. R. Middelbeek: None. J. Mitri: None. F. Rossitto: None. E. Cabana: None. A. Gharse: None. L. Collins: None. L. Aliaga: None. A. Millan Ferro: None. J. Cifuni: None. A. Schwartz: None. E. Kelley: None. C. Gomez: None. M. Aballay: None. Introduction: Continuous glucose monitoring (CGM) improves glucose control in patients with diabetes, as demonstrated in clinical trials. There is limited information available on the real-world application of CGM adoption in a resource-limited setting or safety net outpatient hospital setting. This intervention's aim was to assess the impact of a multifaceted approach on improving CGM utilization and on glycemic control among patients with diabetes. Methods: Wyckoff Heights Medical Center, located in Brooklyn, NY is a resource-limited setting that in partnership with the Joslin Diabetes Center, Boston, MA launched a CGM Clinical Care Optimization Program. This quality improvement (QI) initiative included establishing a diabetes technology team (DTT) which met weekly (n=24 sessions) to establish a highly specialized interprofessional group. The DTT activities included: 1) Establishing a CGM workflow; 2) Expanding Medical Assistant responsibilities related to diabetes technology and training; 3) Creating five bilingual (English-Spanish) patient handouts; 4) Building CGM vendor relationships; 5) Developing a CGM manual for data download, billing and coding, and education to increase CGM knowledge across the system. Data were collected prior to CGM initiation and at most recent visit. Results: The intervention occurred between February and November 2023. Prior to this intervention, 49 patients had a CGM prescription. After 9 months, CGM prescriptions were written for 333 patients (680% increase) with the following baseline characteristics (mean ± SD): age (61.2 ± 14.7 yrs.); 56% female; 44% male; 71% Latino; Race: 67% Other (63% Hispanic or Latino), 25% Black or African American, 4% White, Asian 2%, American Indian or Alaska Native 2%; 38% Spanish-speaking, 59% English-Speaking, 3% Other; 81% government insurance (63% Medicaid, 18% Medicare), 19% commercial insurance. The majority of patients had been diagnosed with type 2 diabetes (91%) and 9% had type 1 diabetes. Baseline HbA1C was 9.7 ± 1.5%. Of note, prior to CGM, 34% of patients had A1C>9%. The impact of CGM use was evaluated on 116 patients that met the following criteria: baseline A1C value within 60 days before or up-to 30 days after prescription, and a A1C >90 days after. After an average duration of 154 days of CGM use, HbA1c decreased significantly by -0.51% (p=0.001), demonstrating improved glycemic control post-CGM use. In those with A1c >9%, HbA1c decreased from 10.5% to 8.8% (p<0.01). Conclusion: Creating a CGM optimization program in a resource-limited setting is critical in improving patient self-awareness and provider insight in glycemic control in patients diagnosed with type 1 and type 2 diabetes. These data demonstrate the feasibility of implementing a CGM optimization program in a resource-limited setting, which is critically important in improving clinical care for people with diabetes. Presentation: 6/2/2024

9391 Clinical Accuracy Of Continuous Glucose Monitoring In Post-kidney Transplant Patients With Type 2 Diabetes

Abstract Disclosure: Q. Aziz: None. K. Batra: None. S. Fatima: None. J. Splinter: None. A.L. Champion: None. A.M. Kumar: None. K.E. Izuora: Research Investigator; Self; Novo Nordisk. Background: Continuous glucose monitoring (CGM) devices provide real time blood glucose data to guide therapy in patients with diabetes mellitus. Diabetic nephropathy is a major complication of diabetes which can eventually lead to End Stage Renal Disease (ESRD). Kidney transplant is a treatment option for ESRD and patients who undergo kidney transplant are exposed to anti-rejection therapy, including high-dose steroids, resulting in significant fluctuations in blood glucose. Having a reliable system for monitoring blood glucose continuously, such as a CGM, can provide valuable information to guide optimal diabetes management. Objective This study aims to compare the clinical accuracy of the CGM data with the Point of Care Testing (POCT) glucose values as a reference among post kidney transplant patients with Type 2 diabetes mellitus (T2DM). Methodology This study was conducted at an academic medical center from September 2023 to January 2024, enrolling subjects ≥ 18 years, diagnosed with T2DM , admitted for kidney transplant. Following informed consent, a blinded CGM (Freestyle Libre Pro®) was applied. We collected all POCT and serum glucose values and downloaded corresponding CGM glucose data. Using matched pairs between CGM and POCT glucose values, bias and absolute relative differences (ARD) were calculated. Based on the ARD, the Clarke error grid (CEG) analysis was conducted to quantify the clinical accuracy of CGM and POCT by assigning glucose data points into 5 zones A, B, C, D, and E. Summary statistics for numeric variables included mean and standard deviation, whereas categorical variables were described as counts and proportions. All analyses were performed using SAS, 9.4 version. Results: A total of 22 subjects were analyzed after excluding one patient due to CGM malfunction. Subjects were 58±9.69 years old, and 82% were males. The mean body mass index was 30±6.41 kg/m2 and mean HbA1c was 6.7 ± 1.07%, at baseline. The mean duration of diabetes and ESRD were 19±10.6 years and 3±2.27 years respectively. Average CGM wear time was 76±24.5 hours. The CEG comparing CGM values with POCT values showed 83.79% values in zone A, 15.29% in zone B (combined =99.08%) with the Mean Absolute Relative Difference (MARD) of 13.24%. The CEG comparing CGM values with serum glucose showed 83.1% values in zone A, 16.9% in zone B (combined =100%) with the MARD of 13.10%. Conclusion: Our results indicate that CGM values are comparable to the POCT and serum glucose values in post kidney transplant patients with T2DM receiving high dose steroids. Therefore, use of CGMs should be considered as a useful tool, providing timely information to guide prompt interventions and improve outcomes in this patient population. Presentation: 6/3/2024

8118 Effect of Rurality on Diabetes Technology Use Among Medicaid-Insured Youth Enrolled in a Mid-Western Accountable Care Organization

Abstract Disclosure: E.E. Bell-Sambataro: None. J. Kilbarger: None. A. Lahoti: None. J.M. Ladd: None. Background: Diabetes technology (continuous glucose monitors (CGM) and insulin pumps), is associated with improved glycemic control and quality of life in children with type 1 diabetes (T1D), but technology use is not equitable. We explored whether rural/urban disparities exist in CGM and pump use among pediatric patients with T1D enrolled in an Accountable Care Organization (ACO) and thus on Medicaid. Methods: We conducted a retrospective cohort study using claims data from the central Ohio ACO database from January 1, 2018 to September 30, 2022. Individuals aged 1 to <21years were identified using T1D-specific billing codes. CGM or pump use was defined using established prescription codes. Rurality was based on zip codes of residence linked to RUCA codes. For descriptive analyses, we calculated proportions, means and standard deviations. T-tests were used to compare continuous variables; chi-square tests were used to compare categorical variables. Multivariable Poisson regression was used to model the association between rurality and CGM or pump use, adjusted for age, sex, race, and era (pre-/post-COVID pandemic). Results: We identified 1079 enrolled individuals with T1D during the study period; 878 (81.4%) were using CGM and 259 (24%) were using insulin pumps. In descriptive analysis, CGM users were younger than non-users (users 11.4±4.4years vs non-users 15.3±3.8years, p<0.01). The distribution of rurality between CGM users and non-users was similar (users 73.5% urban, 26.3% rural vs non-users 66.6% urban, 33.3% rural, p=0.05). There were no age or rurality differences between pump users and non-users (users 12.2±3.8years vs non-users 12.1±4.8years, p=0.86; users 72.6% urban vs non-users 72.1% urban, p=0.92). In regression modeling, rurality was not significantly associated with CGM use (adjusted risk ratio (aRR) 0.95, 95% confidence interval (CI) 0.89-1.02) whereas age, race, era, and pump use were significantly associated. Rurality was significantly associated with pump use (aRR 0.79, 95% CI 0.63-0.99) as were age, race and era. Conclusions: In this cohort limited to pediatric patients with T1D on Medicaid, rural residence did not have a significant effect on CGM use but did make pump use less likely. Those of older age or non-White race were significantly less likely to use CGM and pumps. Future efforts targeting these disparities should be undertaken to increase use of these standard of care technologies among all children. Presentation: 6/2/2024

12242 Enhancing Continuous Glucose Monitor Utilization In Lower Economic Community Hospital Outpatient Clinics: A Quality Improvement Initiative

Abstract Disclosure: V. Mehta: None. S. Deshpande: None. M. Nieman: None. K. Pereira: None. B. Agrawal: None. S.R. Russell: None. Continuous glucose monitors (CGM) measure the blood glucose level (BGL) and have been used for better glucose control, especially in patients requiring insulin. Primary care physicians (PCP) manage diabetic patients on a large scale in the US, especially in rural settings, and data shows that CGMs are prescribed less in PCP settings than in endocrine clinics. Our study aimed to understand internal medicine (IM) residents' knowledge about CGMs and their application in a community hospital outpatient clinic serving lower-economic populations and demonstrates how a clinical pharmacist (PharmD) led educational intervention successfully increased the adoption of CGMs. A survey was done using a form with multiple-choice questions. 50 residents participated and responded anonymously. Data was analyzed by calculating percentages based on answers, providing understanding of their perspectives on CGMs. 78% knew about and correctly identified CGM. When asked to name indications of CGM, answers varied and included uncontrolled diabetes (34%), insulin-dependent diabetes (34%), elderly and non-compliant patients (14%), any diabetic (12%), and others. Only 14% reported to have had formal training in use and interpretation of CGMs, 70% had prescribed, 34% knew how to apply the device, and 42% knew how to interpret the BGL. More than 95% self-reported having either poor/beginner or fair knowledge about the usage, application, and interpretation, whereas more than 80% professed to need more training to be more confident about prescribing the device. After the survey, our PharmD conducted session demonstrating the indications, types, usage, application, and interpretation of CGM. Following this, an impactful increase in patients who were appropriately prescribed and received CGM was observed. Technologies used in medicine have increased, CGM, being one of them. More than 100 million Americans have prediabetes or diabetes, with more than 10 million using insulin. Hence, access to CGM is crucial for patients for better management. Despite the benefits, many factors play a role in limiting their use, including prescription, cost, insurance coverage, social factors, willingness of the patients, discomfort, and decreased knowledge. Non-compliance, difficulty in application and picking the device from pharmacy was also observed in our clinic. A study by Sherrill et al. on use of CGMs (2014-2020) showed that use of CGM increased almost ten times in 2020 (v/s2014); most patients were younger, employed, and had better insurance coverage.With advances in technology, easier and more efficient devices are available to assess patients' glycemic control. Pharmacists play key role in ensuring continued education among practitioners and trainees. Continued efforts to increase awareness is required to ensure that latest advances are adequately incorporated into graduate medical education. Presentation: 6/3/2024

12314 Effectiveness Of Continuous Glucose Monitors In Patients With Type 2 Diabetes On Washington Medicaid Or Financial Assistance

Abstract Disclosure: A. Deng: None. A. Thirumalai: None. J. Lin: None. M. Weber: None. S. Zuo: None. Continuous glucose monitors (CGMs) have become more accessible for patients with type 2 diabetes mellitus (T2DM) on one insulin injection per day who have either commercial or Medicare plans. However, there are still strict requirements for patients with Washington State Medicaid, creating disparities in diabetes care for vulnerable populations. The aim of this study is to assess the effectiveness and adherence of CGMs in patients with T2DM on Washington State Medicaid plans or institutional financial assistance who are prescribed at least one insulin injection daily, similar to Medicare requirements. This ongoing prospective cohort study enrolled 40 adult patients with T2DM, baseline A1C greater than 7%, and prescribed at least one insulin injection per day from three Harborview Medical Center clinics. Through grant funding, patients were provided with Freestyle Libre 2 CGMs for 6 months. Since this is an ongoing study, we present preliminary results of patients who completed 3 months of CGM use. Effectiveness was measured by the change in A1C from baseline to 3 months after initiation of the CGM (primary outcome) and change in CGM glycemic metrics on ambulatory glucose profile (AGP) from baseline to 3 months after initiation of CGM (secondary outcomes). Assessment of adherence to the CGMs involved tracking the percentage of CGM data captured from the AGP reports recorded at each visit and monitoring dispense history. Twenty patients reached the 3-month follow-up with a median age of 59 years; 60% female, 70% non-English speaking; 45% with Medicaid, and 55% with Harborview Financial Assistance as insurance coverage. Median baseline A1C was 9.2%, duration of diabetes was 10 years, number of diabetes medications was 3, daily insulin injections of 2, and number of blood glucose checks per day was 1. At first CGM download (2 weeks), the median time in range (TIR, glucose 70-180 mg/dL) was 53.5%, coefficient of variation was 35.3% and percent of data captured was 80%. At 3-month follow-up, the median reduction in A1c was –1.2% (IQR -1.8, -0.5). On CGM, the median TIR had increased to 60.5%, coefficient of variation had improved to 31.5% and percent of data captured was 79%. In this ongoing study, preliminary findings show encouraging early evidence of improved glycemic control after CGMs were initiated and patients sustained greater than 70% CGM data captured at 3 months. These results should be interpreted with caution given the small number of participants with results at 3 months. Nonetheless, once the study is complete, the outcomes may be used to inform eligibility requirements for continuous glucose monitors in patients with Washington Medicaid or Harborview Financial Assistance. Presentation: 6/3/2024

7296 Gestational Diabetes RT-Continuous Glucose Monitoring Users Utilized Fingerstick less in Pregnancy than Recommended Compared to Fingerstick Monitoring Alone with No Difference in A1c Outcomes

Abstract Disclosure: N.M. Ehrhardt: Advisory Board Member; Self; Bayer, Inc., Novo Nordisk, Dexcom. Grant Recipient; Self; Novo Nordisk. Research Investigator; Self; Novo Nordisk, Dexcom. P. Mandava: None. E. Fay: None. G. Abdalla: None. S. Fonda: None. Background: The Federal Drug Agency recently approved continuous glucose monitoring (CGM) use in gestational diabetes (GDM). However, self-monitoring of blood glucose (SMBG) with fingersticks is still recommended as an adjunct to CGM use. Here we report on SMBG with and without CGM and A1c, as part of a randomized controlled trial (RCT) of CGM for women with GDM. Methods: This is an RCT of 104 participants with GDM who were randomized to real-time CGM (RT-CGM) (N=48) or SMBG alone (control group; N=56). Sixty-one percent in the control group (N=34) withdrew after randomization because they desired a CGM device. Data collection was completed remotely given COVID-19 pandemic. A1c data was collected at baseline and 36 weeks gestation. Fingerstick data was collected at each of 4 study visits (2 weeks apart) until 38 weeks or delivery. All participants were advised to perform SMBG by fingerstick as instructed by their obstetric provider. This analysis is of participants who completed the study (control N=22; RT-CGM N=47). Results: The average age was 33.77 (+/- 5.26) years, 68.11% had a college degree or greater, 69.57% had an income of >$50,000, 95.65% had health insurance, 30.43% reported being Asian and 42.03% reported being white. Baseline mean A1c (+/- SD) was 5.39 (+/- 0.39) in the RT-CGM group and 5.41 (+/- 0.37) in in the control group. Mean A1c at ∼36 weeks of pregnancy was 5.44 (+/-0.43) in the RT-CGM group and 5.37 (+/- 0.44) in the control group. In the RT-CGM group, 20/47 (42.55%) provided SMBG data for ≥3 study visits, 18/47 (38.30%) had data for 1-2 visits, and 9/47 (19.15%) provided no SMBG data at all 4 visits. The mean (+/- SD) tests/day for the RT-CGM group who provided any meter data was 1.31 (+/- 1.89). In the control group, 16/22 (72.73%) provided SMBG data for ≥3 visits, 2/22 (9.09%) had data for 1-2 visits, and 4/22 (18.18%) never provided SMBG data. The mean (+/- SD) tests/day for the control group was 3.4 (+/- 0.73). For the RT-CGM group, simple correlation analyses found no association between number of visits in which fingerstick data were provided and A1c at visit 3 (r=-0.14; p=0.40) or average fingerstick tests/day and A1c at visit 3 (r=0.10; p=0.40). For the control group, there was no association between number of study visits in which fingerstick data were provided and A1c at visit 3 (r=0.38; p=0.11). However, for the control group, more fingerstick tests/day was associated with lower A1c at visit 3 (r=-0.45; p=0.07). Conclusions. Data is limited by missing data as participants did not always supply fingerstick data. Overall A1c in both groups remained stable, despite less SMBG testing in the CGM group. This suggests that SMBG data may add little in GDM management in conjunction with CGM use. More data is necessary to remove the recommended requirement for fingerstick with CGM in pregnancy. Presentation: 6/2/2024

12395 Continuous Glucose Monitoring In Pseudohypoparathyroidism Type 1a

Abstract Disclosure: J. Tamaroff: None. A.E. Lee: None. A.H. Shoemaker: None. Background: Pseudohypoparathyroidism type 1A (PHP1A), due to heterozygous mutations in GNAS, is a rare disorder that leads to multi-hormone resistance, cognitive impairment, and early-onset obesity. Individuals with PHP1A have dysglycemia on oral glucose tolerance tests and increased rates of type 2 diabetes (T2DM). Individuals with rare diseases, like PHP1A, may travel significant distances for specialized clinical or research care. Wearable technology may alleviate barriers and provide useful information for clinical care or for use as potential biomarkers. The objective of this study is to evaluate glycemia in individuals with PHP1A and feasibility of utilizing continuous glucose monitors (CGMs) in this population. Methods: Observational study of individuals with PHP1A (3R01DK118407-03S1). CGMs (Dexcom Pro, Dexcom Inc, San Diego, CA) were placed on participants’ abdomens, typically by a family member, over secure-video call with study staff. CGMs collected data every 5 minutes for up to 10 days. Results: Seven participants (all female) with PHP1A without a diagnosis of diabetes had available data. Median age was 17 years (IQR: 14, 19; range: 8-23). Body mass index (BMI) categories (by percent of the 95th percentile for those under 18 years and BMI for adults) spanned from overweight to class 3 obesity. In terms of feasibility, 1 individual had to replace a CGM sensor due to an upcoming MRI and 1 individual had a “sensor failure” of unclear etiology. Both were able to place new CGMs. Individuals participated across 5 states in the United States. Median length of wear was 9.8 days (IQR 8.6, 9.9). Participants had a median average glucose of 104mg/dL (IQR 102, 111) and COV of 16% (15, 21). Percent of glucose > 140mg/dL was 5.0% (IQR: 1.8, 10.9; range:1.2-19.4%). One individual had 3.2% of glucose values ≥ 200mg/dL despite a normal hemoglobin A1c (HbA1c) level (5.5%). This level of hyperglycemia is qualitatively higher than published CGM data in healthy individuals. One study found individuals (6-24 years of age) had median percent of glucose values over 140mg/dL ranging from 1.2 to 2.4% with IQR values ranging from 0.3 to 4.4%, based on age categories.1Conclusions: In females with PHP1A, with overweight or obesity, CGM placement at home is a feasible way to collect real-world data. Participants with rare diseases often must travel for clinical and research care and CGMs may provide insight into glycemia without requiring an in-person visit. This “real-world” data mirrors research data reporting glycemia with normal HbA1c levels. The rate of hyperglycemia is higher than expected in some individuals with PHP1A though more data is needed. Ongoing studies will evaluate matched control participants and assess drivers of glycemia including inactivity and decreased sleep duration, as measured by actigraphy.

7437 Harnessing Diabetes Technology for Management of Immune Checkpoint Inhibitor Associated Diabetes

Abstract Disclosure: A. Knape: None. A. Kotwal: None. D. Rohlfsen: None. A. Patel: None. W. Goldner: None. Background: Immune checkpoint inhibitors (ICIs) are a key treatment modality for many cancers. One percent of patients who receive ICIs can develop insulin deficient ICI associated diabetes (ICI-DM). These patients often have complex oncologic therapy plans making treatment of newly diagnosed insulin deficient diabetes challenging which often results in additional healthcare-related burden for the patient. Methods: This is a retrospective case series of five adults who developed ICI-DM after receiving either PD-1, CTLA-4 or PDL-1 inhibitors or a combination. Data on demographics, medical history and labs were collected in addition to mode of glucose monitoring and treatment after diagnosis of ICI-DM. Results: Patients were age 44-72 at diagnosis of ICI-DM, three were female. Two were admitted to the hospital in DKA, one was diagnosed in the ER with severe hyperglycemia without DKA, two had outpatient hyperglycemia without DKA. Glucose at diagnosis ranged from 215-510 mg/dL. C-peptide was low for all patients. Fingerstick blood sugar (FSBS) monitoring and basal/bolus insulin was immediately initiated for all patients. All were transitioned from FSBS to continuous glucose monitor (CGM) and 2/5 ultimately transitioned to sensor-augmented insulin pump therapy (AID). Time in range on CGM improved in four patients with percent time in range improving between 10-48%. In two patients, hypoglycemia frequency declined by 6% and 3%. Hyperglycemia frequency declined by 13% and 48% in patients on AID therapy and by 14% and 25% in two of those on basal/bolus insulin therapy. All patients preferred CGM over FSBS and remained on CGM therapy. Those who chose AID preferred it over basal/bolus insulin therapy stating it was less complicated than basal/bolus insulin with CGM. Conclusions: We report successful treatment of five cases of ICI-DM with CGM + basal/bolus insulin or AID. 80% demonstrated improved time in range and decreased overall frequency of hyperglycemia. 40% had decreased frequency of hypoglycemia. CGM allows for dynamic adjustment of insulin therapy in a population with complex medical regimens resulting in fluctuating glucose levels. The integrated pump and sensor technology in AID was less burdensome than basal/bolus in those who chose it. Managing ICI-DM is complicated. The burden of this entity can be lessened by harnessing the power of diabetes technology. Presentation: 6/2/2024

6547 Glycated Hemoglobin is a Better Glycemic Indicator than Glycated Albumin in Mothers with Pregestational Diabetes: Preliminary Results of a CGM study in the 2nd and 3rd Trimesters

Abstract Disclosure: M. Md Amin: None. J. Ratnasingam: None. S.S. Paramasivam: None. L. Ibrahim: None. L. Lim: None. Q. Lim: None. N. Hee Ken Yoong: None. M. Hamdan: None. S. Ganapathy: None. P. Sthaneshwar: None. S.R. Vethakkan: None. Glycemic control is strongly associated with maternofetal outcomes in mothers with pregestational diabetes. Glycated Hemoglobin (HbA1c) is most accurate in the first trimester. The use of HbA1c to monitor glycemia in pregnancy however is controversial, as altered red-cell kinetics and iron deficiency render it less accurate than in the nongravid state, especially in the 2nd or 3rd trimester. Glycated albumin (GA) is an alternative measure of intermediate-term glycemia (2-3 weeks) which is unaffected by red-cell survival that has been used to monitor glycemia in CKD and pregnancy. However, the accuracy of HbA1c as opposed to GA is unclear, especially in later pregnancy and there are no published data comparing it with CGM (Continuous Glucose Monitoring) metrics in pregnancy. We compared the accuracy of HbA1c and GA as a glycemic indicator in later pregnancy using mean glucose from a 6-day CGM as reference, hypothesizing that GA would correlate more strongly with CGM metrics.Twelve pregnant Malaysian mothers with pregestational Type 1 (n=2) and Type 2 DM(n=10) were enrolled in this prospective observational study. Mothers with known hemoglobinopathy, nephrotic syndrome and liver cirrhosis were excluded. Patients underwent blinded 6-day CGM (Medtronic iPro2) followed by blood sampling for GA, HbA1c, albumin and hemoglobin on Day 7 at standardized timepoints in the 2nd and 3rd trimester (20-28 weeks, 30-36 weeks). Mean pre-pregnancy BMI was 28kg/m2(±5). Mean Hb was 12.3g/dL (±1.5), 16.7% of mothers had anemia, while 62.5% had serum albumin < 35g/L. GA did not correlate with mean CGM glucose (2nd trimester: r=0.180 [n=10], 3rd trimester r=0.493 [n=8], p=NS). After excluding obese women (pre-pregnancy BMI>30kg/m2) however, GA correlated well with mean CGM glucose in the 3rd trimester (r=0.870, p<0.05). HbA1c correlated significantly and strongly with mean CGM glucose in later pregnancy (2nd trimester: r=0.621 [n=11], 3rd trimester r=0.718[n=8], p<0.05) and correlated with estimated A1c from CGM (r=0.548, p=0.01). Unexpectedly, our data indicate that HbA1c correlates well with gold standard mean CGM glucose with a strength approximating that observed in the nongravid population with diabetes, thus validating its use as a complementary secondary measure of glycemia to fingerstick-glucose for therapeutic decision-making in later, as well as early pregnancy. Obesity is a known confounder of accuracy of GA, our findings corroborate this. Therefore, utility of GA as an alternative glycemic measure in pregnancy is potentially limited by the increasing prevalence of obesity worldwide and impact of gestational weight gain. A larger sample is needed to confirm these preliminary findings. Presentation: 6/2/2024

8642 Continuous glucose monitor: reclaiming type 2 diabetes self-efficacy and mitigating disparities

Abstract Disclosure: K. Ni: None. C.A. Tampe: None. K. Sol: Employee; Self; Patient accepted job with Dexcom Jan 2024. L. Cervantes: None. R.I. Pereira: None. Context: The increase in continuous glucose monitor (CGM) use has been notable for widening disparities between the least and most socially marginalized. Given access barriers, there is limited CGM patient experience information that is inclusive of those with type 2 diabetes mellitus from socially marginalized backgrounds. Objective: To understand the CGM usage experience across a U.S. Medicaid population with type 2 diabetes at federally qualified health centers (FQHCs). Methods: This qualitative study used semi-structured phone interviews with 28 English or Spanish speaking participants prescribed CGM who were enrolled in a U.S. Medicaid program that subsidized CGM. Audio-recordings of interviews were transcribed and analyzed by inductive thematic analysis. Results: Participants had a median age 56 (interquartile range 48-60) with varying levels of formal schooling (55% with 9 years or less) and were from diverse racial or ethnic backgrounds (57% Hispanic, 18% non-Hispanic Black, 21% non-Hispanic White). Interviews were conducted in English (68%) or Spanish (32%). We identified six major themes: initial expectations and overcoming initiation barriers (subthemes: initial expectations, overcoming initiation barriers), convenience and ease promote daily use (subthemes: benefits over fingersticks), increased knowledge leads to improved self-management (subthemes: increased understanding and knowledge, glucose level self-awareness, motivating healthy behaviors), collaboration with provider and clinical team, improved self-reported outcomes (subthemes: improved glucose and non-glucose targets, positive emotional effect, essential for management), and barriers and burdens are generally tolerated (subthemes: barriers to monitoring, burden of self-monitoring). Conclusions: CGM was experienced as easy to understand and use and viewed as a tool for diabetes self-efficacy. Expanded CGM access for socially marginalized patients with type 2 diabetes can enhance diabetes self-management to help mitigate diabetes outcome disparities. Presentation: 6/2/2024

6631 A Week With Diabetes: Using Real-Time Continuous Glucose Monitoring In A Simulation Activity With Residents And Fellows

Abstract Disclosure: T. Gallagher: None. C. Donelly: None. R.E. Amori: None. Background: Continuous glucose monitor (CGM) use in managing diabetes mellitus is increasing rapidly. Fellows and residents often prescribe these devices, but most do not themselves have diabetes mellitus and do not use these devices personally. A real-time simulation activity can help physicians in training learn about using the technology in diabetes management and the patient experience living with diabetes mellitus. Objective: Assessing learner’s experiences using a one week real-time CGM simulation. Methods: A real-time simulation using CGM and elements of role playing can enhance knowledge of CGM use and overall diabetes care. Voluntary participants included endocrinology fellows, internal and family medicine residents, and one health professions student. One common structured learning task per day was assigned to all participants over a single seven day period of use in academic year 2022-23. Assessment of each task occurred via personal observations and reflections. Data was collected in an anonymous form and in a group debrief. Results: Eight of 10 participants completed the deidentified post simulation survey and 8/10 participated in the live debrief. Participants reported ease of CGM placement and use. They reported improved insight into the impacts of diabetes on daily activities and increased awareness of diet and exercise impact on glucose levels. Insulin dose calculations were largely correct but errors were seen. All felt the activity was beneficial to their learning. Conclusions: Real-time CGM simulation improves student, resident, and fellow knowledge of the technology and allows the learner to gain insight into a patient’s diabetes self-management. Presentation: 6/3/2024

12288 Diabetes Wearable Technology In Children Less Than Six Years At A Large Urban Diabetes Center

Abstract Disclosure: K. Citron-Zafrin: None. L. Richter: None. K. Williams: None. R. Gandica: None. I. Vargas: None. N. Leibel: None. Introduction/Background: Type 1 diabetes (T1D) is one of the most common chronic diseases in pediatrics. T1D is particularly challenging to manage in children under 6 years old due to unpredictable eating patterns, rapid growth, and risk of extreme blood glucose levels. Diabetes wearable technology (insulin pumps and continuous glucose monitors (CGMs)) has become increasingly prominent, but its use has not been well described in this age group. Objectives: We sought to characterize our experience with technology use in children with T1D less than 6 years old. Design/Methods: Retrospective chart review of 82 youth diagnosed with T1D before 6 years old, seen at our center from 2019-2023. Data was collected on demographic information, insurance type, use of insulin pump and CGM, hemoglobin A1c (A1c), diabetic ketoacidosis (DKA) and hypoglycemic events. We used chi-square statistics to compare variables in our population with reported national data. Results: In this cohort, 80% used insulin pumps and 100% used CGMs, an increase from previously reported data (56% insulin pump use and 44% CGM use in those less than 6yo, p<0.001). 54% had private insurance. There were more individuals with family members with T1D compared to reported data (35% vs. 22%, p=0.0041). The median age at diagnosis was 34 months, median time from diagnosis to pump start was 3.5 months. After diagnosis, 1 pump user and 1 multiple daily injections (MDI) user had DKA. One MDI user had hypoglycemia requiring intervention. Average A1C 12 months after diagnosis was 7.9% in the pump group and 8.2% in the MDI group. Hybrid closed loop pump users had an average A1C 6 months after placement of 7.7%. Conclusions: At our urban academic diabetes center, there was higher use of diabetes wearable technology as compared with previously reported data in this age group. There were more individuals with relatives with T1D compared to reported averages. The low rates of DKA and hypoglycemic events seen in this population support that these technologies can be used safely and soon after diagnosis. Although improved from national data, tight glycemic control remains a challenge in this very young age group. Presentation: 6/1/2024