Multivendor Continuous Glucose Monitor Integration into the Electronic Health Record: Real-World Experience of an Academic Pediatric Endocrinology Clinic.

The Official Journal of ATTD Advanced Technologies & Treatments for Diabetes Conference 22‐25 February 2023 I Berlin & Online

Update on technologies, medicines and treatments.

Abstracts from ATTD 2016 9th International Conference on Advanced Technologies & Treatments for Diabetes Milan, Italy-February 3-6, 2016.

Abstracts from ATTD 20158th International Conference on Advanced Technologies & Treatments for DiabetesParis, France—February 18–21, 2015

Real-World Diabetes Technology: Overcoming Barriers and Disparities.

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

People with diabetes (PWD) benefit from continuous glucose monitoring (CGM), yet CGM uptake in the US remains low. While diabetes care providers are key facilitators of CGM provision, data on provider behavior related to CGM use and CGM generated data is limited. In March 2020, we conducted a national survey of providers caring for PWD on CGM-related opinions, facilitators and barriers to CGM prescription, data review practices, and reimbursement. Descriptive statistics were used to report prevalence of Likert scale answers to a 55-question survey. Of 182 survey respondents caring for PWD using CGM, 75% were at academic medical centers, 66% were endocrinologists, and 70% practiced in urban settings. Nearly 70% of providers reported CGM use in a majority of their patients with type 1 diabetes. CGM use in patients with type 2 diabetes was low, with half the providers reporting <10% CGM use. All respondents believed CGM improved quality of life and could help optimize diabetes control. Nearly all providers reviewed CGM data each visit (94%) and directly involved patients in data review (90%). Only 14% of providers reviewed CGM data outside of a scheduled visit without prompting from patients or their family members. Most providers (81%) reported their CGM data review was valued by patients although only half (55%) reported having adequate time or an efficient process to do so. Most providers included CGM data in the electronic health record but only 41% had an efficient process for data capture. The majority of providers (79%) sought reimbursement for CGM interpretation during a visit, but a minority (41%) sought reimbursement for interpretation outside of visits. Despite uniform support for CGM by providers, inadequate time and inefficient data review processes are ongoing challenges. Improvements in data access and integration, supportive clinical infrastructure, and decreased administrative burden to obtain CGM, are facilitators of effective use of CGM-data by providers and necessitate ongoing attention. Disclosure T. Kompala: Consultant; Self; Eli Lilly and Company, Employee; Self; Livongo. J. C. Wong: Advisory Panel; Self; Provention Bio, Inc., Research Support; Self; Dexcom, Inc., Tandem Diabetes Care. A. B. Neinstein: Consultant; Self; Eli Lilly and Company, Intuity Medical , Medtronic, Roche Diabetes Care, Steady Health.

Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data. We used data from The Maastricht Study, an observational population-based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman's correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6). Models trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%). Machine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.

Introduction and Objective: GluVue is a free application designed to enhance the efficiency of continuous glucose monitoring (CGM) data analysis for healthcare providers. Its primary goal is to improve clinical workflows and patient care in diabetes management. Methods: GluVue is designed to integrate seamlessly with electronic health records (EHR) and provide real-time analysis of continuous glucose monitoring (CGM) data. The application initiates its workflow when a healthcare provider places an order in the patient's EHR, which triggers a connection request in the MyChart patient portal app. Upon patient or proxy acceptance, connectivity is established, allowing CGM data to be shared with Apple Health or the Health Connect app via HealthKit and subsequently integrated into the Epic EHR through MyChart. The system pulls raw glucose data from Epic and transforms it into intuitive visual formats i.e. modal-day reports, daily/weekly trend graphs, and an Ambulatory Glucose Profile display without storing data in Epic, thereby addressing potential security concerns. Results: GluVue significantly reduces the time required for CGM data interpretation, allowing clinicians to access real-time insights without the need for multiple portal logins. Users report enhanced ability to identify glucose management opportunities, leading to more informed, data-driven conversations with patients. This automated handling of CGM data increases the efficiency of reviewing patient data. Conclusion: GluVue represents a significant advancement in diabetes care by streamlining CGM data interpretation and integrating seamlessly within EHR systems. GluVue empowers healthcare providers to make more informed and timely decisions, ultimately improving patient outcomes. The implementation process can be initiated through GluVue's Showroom webpage by submitting a request to the institution's informatics Systems department, enabling widespread adoption of this innovative clinical tool. Disclosure K. Cefalu: Stock/Shareholder; Abbott, Dexcom, Inc.

The current lack of continuous glucose monitor (CGM) data integration into the electronic health record (EHR) is holding back the use of this wearable technology for patient-generated health data (PGHD). This failure to integrate with other healthcare data inside the EHR disrupts workflows, removes the data from critical patient context, and overall makes the CGM data less useful than it might otherwise be. Many healthcare organizations (HCOs) are either struggling with or delaying designing and implementing CGM data integrations. In this article, the current status of CGM integration is reviewed, goals for integration are proposed, and a consensus plan to engage key stakeholders to facilitate integration is presented.

BackgroundClosed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data.MethodsWe used data from The Maastricht Study, an observational population‐based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman’s correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6).ResultsModels trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%).ConclusionsMachine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.

Objective: While it is recognized that there is a strong relationship between the amount of time glucose levels are <70 mg/dL (T<70) and the amount of time <54 mg/dL (T<54), the association has not been well quantified. Methods: Datasets with Dexcom continuous glucose monitoring (CGM) data from nine type 1 diabetes randomized trials were pooled to evaluate the relationship between CGM-measured T<70 and T<54. Penalized B-spline regression lines were fitted to assess the relationship between T<70 and T<54 for blinded CGM use, unblinded CGM use without an automated insulin delivery (AID) system, and unblinded CGM use with an AID system. Results: For blinded data, the T<54 : T<70 ratio varied from 19% when the amount of T<70 was <1% to 44% when the amount of T<70 was ≥7% whereas for unblinded data the ratio varied from 15% to 42%, respectively. When T<70 was 4%, the predicted T<54 was 1.18%, 0.94%, and 0.91% for the blinded, unblinded, and AID data, respectively (P<0.001 comparing blinded versus unblinded and AID). Conclusions: The T<54 : T<70 ratio increases with greater T<70, and the ratio generally is higher with blinded than unblinded CGM data, with the latter appearing to be similar to AID system data. The finding of greater T<54 for a given T<70 with blinded CGM data is presumed to be due to an action being taken by the unblinded CGM user and/or by the AID system to minimize hypoglycemia which will have the effect of reducing the amount of T<54.

AimsThe use of automated insulin delivery (AID) systems is associated with improved glycaemic control in individuals with type 1 diabetes (T1DM). However, AID systems are more expensive than other treatment modalities for T1DM. The aim of this study was to evaluate the long‐term cost‐effectiveness of AID compared to continuous subcutaneous insulin infusion (CSII) combined with continuous glucose monitoring (CGM) in individuals with T1DM at Kuopio University Hospital, Finland.Materials and MethodsThe study included 336 individuals (mean age: 26.7 years, SD: 15.9 years), with a mean duration of diabetes of 16.6 years. Outcomes were projected in the base case of 50 years using the IQVIA CORE Diabetes Model (v10.0). Clinical data were sourced from electronic health records (EHRs), including changes in glycated haemoglobin (HbA1c) and events of hypoglycaemia and ketoacidosis. Costs were expressed in 2023 Euros (EUR).ResultsThe AID system was associated with an improvement in quality‐adjusted life expectancy of 2.3 quality‐adjusted life‐years (QALYs) compared to CSII plus CGM. These benefits came from the delayed and reduced incidence of diabetes‐related complications. The mean HbA1c improvement was 12.1 ± 11.7 mmol/mol (3.3% ± 3.2%) in the AID group. Direct costs were estimated to be 26 076 EUR higher for AID than for CSII plus CGM, and AID was associated with an incremental cost‐effectiveness ratio (ICER) of 11 184 EUR per QALY gained.ConclusionsBased on the results of this first cost‐effectiveness study conducted in Finland, a willingness‐to‐pay (WTP) threshold of 50 000 EUR per QALY gained suggests that AID is more cost‐effective than CSII plus CGM for the treatment of T1DM in a real‐world setting.

Foster et al. provide a superb and timely analysis of the current state of treatment of people with type 1 diabetes (T1D) in the United States in the years 2016-2018 using extensive data from the T1D Exchange Registry. 1This study is a follow-up to an analysis by Miller et al., utilizing a similar source of data for 2010-2012. 2 This analysis of a rich data set for 22,697 individuals from 81 pediatric and adult endocrinology clinics and practices in the United States provides an update on progress and obstacles facing the entire diabetes community. 1ome of the major findings include:

BackgroundArtificial pancreas systems aim to reduce the burden of type 1 diabetes by automating insulin dosing. These systems link a continuous glucose monitor (CGM) and insulin pump with a control algorithm, but require users to announce meals, without which the system can only react to the rise in blood glucose.ObjectiveWe investigate whether CGM data can be used to automatically infer meals in daily life even in the presence of physical activity, which can raise or lower blood glucose.Materials and MethodsWe propose a novel meal detection algorithm that combines simulations with CGM, insulin pump, and heart rate monitor data. When observed and predicted glucose differ, our algorithm uses simulations to test whether a meal may explain this difference. We evaluated our method on simulated data and real-world data from individuals with type 1 diabetes.ResultsIn simulated data, we detected meals earlier and with higher accuracy than was found in prior work (25.7 minutes, 1.2 g error; compared with 48.3 minutes, 17.2 g error). In real-world data, we discovered a larger number of plausible meals than was found in prior work (30 meals, 76.7% accepted; compared with 33 meals, 39.4% accepted).DiscussionPrior research attempted meal detection from CGM, but had delays and lower accuracy in real data or did not allow for physical activity. Our approach can be used to improve insulin dosing in an artificial pancreas and trigger reminders for missed meal boluses.ConclusionsWe demonstrate that meal information can be robustly inferred from CGM and body-worn sensor data, even in challenging environments of daily life.