Dexcom G6 Continuous Glucose Monitoring Research Articles

Feasibility of continuous glucose monitoring in patients with type 1 diabetes at two district hospitals in Neno, Malawi: a randomised controlled trial

ObjectivesTo assess the feasibility and change in clinical outcomes associated with continuous glucose monitoring (CGM) use among a rural population in Malawi living with type 1 diabetes.DesignA 2:1 open randomised...

Continuous Glucose Monitoring Profiles in Pregnancies With and Without Gestational Diabetes Mellitus.

To determine whether continuous glucose monitoring (CGM)-derived glycemic patterns can characterize pregnancies with gestational diabetes mellitus (GDM) as diagnosed by standard oral glucose tolerance test at 24-28 weeks' gestation compared with those without GDM. The analysis includes 768 individuals enrolled from two sites prior to 17 weeks' gestation between June 2020 and December 2021 in a prospective observational study. Participants wore blinded Dexcom G6 CGMs throughout gestation. Main outcome of interest was a diagnosis of GDM by oral glucose tolerance test (OGTT). Glycemic levels in participants with GDM versus without GDM were characterized using CGM-measured glycemic metrics. Participants with GDM (n = 58 [8%]) had higher mean glucose (109 ± 13 vs. 100 ± 8 mg/dL [6.0 ± 0.7 vs. 5.6 ± 0.4 mmol/L], P < 0.001), greater glucose SD (23 ± 4 vs. 19 ± 3 mg/dL [1.3 ± 0.2 vs. 1.1 ± 0.2 mmol/L], P < 0.001), less time in range 63-120 mg/dL (3.5-6.7 mmol/L) (70% ± 17% vs. 84% ± 8%, P < 0.001), greater percent time >120 mg/dL (>6.7 mmol/L) (median 23% vs. 12%, P < 0.001), and greater percent time >140 mg/dL (>7.8 mmol/L) (median 7.4% vs. 2.7%, P < 0.001) than those without GDM throughout gestation prior to OGTT. Median percent time >120 mg/dL (>6.7 mmol/L) and time >140 mg/dL (>7.8 mmol/L) were higher as early as 13-14 weeks of gestation (32% vs. 14%, P < 0.001, and 5.2% vs. 2.0%, P < 0.001, respectively) and persisted during the entire study period prior to OGTT. Prior to OGTT at 24-34 weeks' gestation, pregnant individuals who develop GDM have higher CGM-measured glucose levels and more hyperglycemia compared with those who do not develop GDM.

Glucose levels measured with continuous glucose monitoring in uncomplicated pregnancies

IntroductionTo characterize glucose levels during uncomplicated pregnancies, defined as pregnancy with a hemoglobin A1c <5.7% (<39 mmol/mol) in early pregnancy, and without a large-for-gestational-age birth, hypertensive disorders of pregnancy, or...

Continuous glucose monitoring for glycaemic control and cardiovascular risk reduction in patients with type 2 diabetes not on insulin therapy: A clinical trial.

To evaluate the impact of the Dexcom G6 continuous glucose monitoring (CGM) device on glycaemic control and cardiometabolic risk in patients with type 2 diabetes mellitus (T2DM) at high cardiovascular risk who are not on insulin therapy. Adults with T2DM with glycated haemoglobin (HbA1c) >7% and body mass index (BMI) ≥30 kg/m2 not using insulin were enrolled in a two-phase cross-over study. In phase 1, CGM data were blinded, and participants performed standard glucose self-monitoring. In phase 2, the CGM data were unblinded, and CGM, demographic and cardiovascular risk factor data were collected through 90 days of follow-up and compared using paired tests. Forty-seven participants were included (44% women; 34% Black; mean age 63 years; BMI 37 kg/m2; HbA1c 8.4%; 10-year predicted atherosclerotic cardiovascular disease risk 24.0%). CGM use was associated with a reduction in average glucose (184.0 to 147.2 mg/dl, p < .001), an increase in time in range (57.8 to 82.8%, p < .001) and a trend towards lower glucose variability (26.2 to 23.8%). There were significant reductions in HbA1c, BMI, triglycerides, blood pressure, total cholesterol, diabetes distress and 10-year predicted risk for atherosclerotic cardiovascular disease (p < .05 for all) and an increase in prescriptions for sodium-glucose cotransporter 2 inhibitors (36.2 to 83.0%) and glucagon-like peptide-1 receptor agonists (42.5 to 87.2%, p < .001 for both). Dexcom G6 CGM was associated with improved glycaemic control and cardiometabolic risk in patients with T2DM who were not on insulin. CGM can be a safe and effective tool to improve diabetes management in patients at high risk for adverse cardiovascular outcomes.

Concordance of Blood Glucose and CGM During a Pilot Trial of Automated Insulin Delivery in Type 1 Diabetes Pregnancies.

Customized and standard automated insulin delivery (AID) systems for use in pregnancies of women with preexisting type 1 diabetes (T1D) are being developed and tested to achieve pregnancy appropriate continuous glucose monitoring (CGM) targets. Guidance on the use of CGM for treatment decisions during pregnancy in the United States is limited. Ten pregnant women with preexisting T1D participated in a trial evaluating at-home use of a pregnancy-specific AID system. Seven-point self-monitoring of blood glucose (SMBG) was compared to the closest sensor glucose (Dexcom G6 CGM) value biweekly to assess safety and reliability based on the 20%/20 mg/dL criteria. All participants completed the study with 7 participants satisfying the safety and reliability criteria with a mean absolute relative difference of 10.3%. Three participants did not fulfill the criteria, mainly because the frequency of SMBG did not meet the requirements. Dexcom G6 CGM is safe and accurate in the real-world setting for use in pregnant women with preexisting T1D with reduced SMBG testing as part of a pregnancy-specific AID system.

Feasibility and Performance of Continuous Glucose Monitoring to Guide Computerized Insulin Infusion Therapy in Cardiovascular Intensive Care Unit.

We evaluated the feasibility of real-time continuous glucose monitoring (CGM) for titrating continuous intravenous insulin infusion (CII) to manage hyperglycemia in postoperative individuals in the cardiovascular intensive care unit and assessed their accuracy, nursing acceptance, and postoperative individual satisfaction. Dexcom G6 CGM devices were applied to 59 postsurgical patients with hyperglycemia receiving CII. A hybrid approach combining CGM with periodic point-of-care blood glucose (POC-BG) tests with two phases (initial-ongoing) of validation was used to determine CGM accuracy. Mean and median absolute relative differences and Clarke Error Grid were plotted to evaluate the CGM accuracy. Surveys of nurses and patients on the use of CGMs experience were conducted and results were analyzed. In this cohort (mean age 64, 32% female, 32% with diabetes) with 864 paired POC-BG and CGM values analyzed, mean and median absolute relative difference between POC-BG and CGM values were 13.2% and 9.8%, respectively. 99.7% of paired CGM and POC-BG were in Zones A and B of the Clarke Error Grid. Responses from nurses reported CGMs being very or quite convenient (n = 28; 93%) and it was favored over POC-BG testing (n = 28; 93%). Majority of patients (n = 42; 93%) reported their care process using CGM as being good or very good. This pilot study demonstrates the feasibility, accuracy, and nursing convenience of adopting CGM via a hybrid approach for insulin titration in postoperative settings. These findings provide robust rationale for larger confirmatory studies to evaluate the benefit of CGM in postoperative care to improve workflow, enhance health outcomes, and cost-effectiveness.

Discrete glucose profiles identified using continuous glucose monitoring data and their association with adverse pregnancy outcomes

Continuous glucose monitoring has facilitated the evaluation of dynamic changes in glucose throughout the day and their effect on fetal growth abnormalities in pregnancy. However, studies of multiple continuous glucose monitoring metrics combined and their association with other adverse pregnancy outcomes are limited. This study aimed to (1) use machine learning techniques to identify discrete glucose profiles based on weekly continuous glucose monitoring metrics in pregnant individuals with pregestational diabetes mellitus and (2) investigate their association with adverse pregnancy outcomes. This study analyzed data from a retrospective cohort study of pregnant patients with type 1 or 2 diabetes mellitus who used Dexcom G6 continuous glucose monitoring and delivered a nonanomalous, singleton pregnancy at a tertiary center between 2019 and 2023. Continuous glucose monitoring data were collapsed into 39 weekly glycemic measures related to centrality, spread, excursions, and circadian cycle patterns. Principal component analysis and k-means clustering were used to identify 4 discrete groups, and patients were assigned to the group that best represented their continuous glucose monitoring patterns during pregnancy. Finally, the association between glucose profile groups and outcomes (preterm birth, cesarean delivery, preeclampsia, large-for-gestational-age neonate, neonatal hypoglycemia, and neonatal intensive care unit admission) was estimated using multivariate logistic regression adjusted for diabetes mellitus type, maternal age, insurance, continuous glucose monitoring use before pregnancy, and parity. Of 177 included patients, 90 (50.8%) had type 1 diabetes mellitus, and 85 (48.3%) had type 2 diabetes mellitus. This study identified 4 glucose profiles: (1) well controlled; (2) suboptimally controlled with high variability, fasting hypoglycemia, and daytime hyperglycemia; (3) suboptimally controlled with minimal circadian variation; and (4) poorly controlled with peak hyperglycemia overnight. Compared with the well-controlled profile, the suboptimally controlled profile with high variability had higher odds of a large-for-gestational-age neonate (adjusted odds ratio, 3.34; 95% confidence interval, 1.15-9.89). The suboptimally controlled with minimal circadian variation profile had higher odds of preterm birth (adjusted odds ratio, 2.59; 95% confidence interval, 1.10-6.24), cesarean delivery (adjusted odds ratio, 2.76; 95% confidence interval, 1.09-7.46), and neonatal intensive care unit admission (adjusted odds ratio, 4.08; 95% confidence interval, 1.58-11.40). The poorly controlled profile with peak hyperglycemia overnight had higher odds of preeclampsia (adjusted odds ratio, 2.54; 95% confidence interval, 1.02-6.52), large-for-gestational-age neonate (adjusted odds ratio, 3.72; 95% confidence interval, 1.37-10.4), neonatal hypoglycemia (adjusted odds ratio, 3.53; 95% confidence interval, 1.37-9.71), and neonatal intensive care unit admission (adjusted odds ratio, 3.15; 95% confidence interval, 1.20-9.09). Discrete glucose profiles of pregnant individuals with pregestational diabetes mellitus were identified through joint consideration of multiple continuous glucose monitoring metrics. Prolonged exposure to maternal hyperglycemia may be associated with a higher risk of adverse pregnancy outcomes than suboptimal glycemic control characterized by high glucose variability and intermittent hyperglycemia.

Accuracy and Feasibility of Real-time Continuous Glucose Monitoring in Critically Ill Patients After Abdominal Surgery and Solid Organ Transplantation.

Glycemia management in critical care is posing a challenge in frequent measuring and adequate insulin dose adjustment. In recent years, continuous glucose measurement has gained accuracy and reliability in outpatient and inpatient settings. The aim of this study was to assess the feasibility and accuracy of real-time continuous glucose monitoring (CGM) in ICU patients after major abdominal surgery. We included patients undergoing pancreatic surgery and solid organ transplantation (liver, pancreas, islets of Langerhans, kidney) requiring an ICU stay after surgery. We used a Dexcom G6 sensor, placed in the infraclavicular region, for real-time CGM. Arterial blood glucose measured by the amperometric principle (ABL 800; Radiometer, Copenhagen, Denmark) served as a reference value and for calibration. Blood glucose was also routinely monitored by a StatStrip bedside glucose meter. Sensor accuracy was assessed by mean absolute relative difference (MARD), bias, modified Bland-Altman plot, and surveillance error grid for paired samples of glucose values from CGM and acid-base analyzer (ABL). We analyzed data from 61 patients and obtained 1,546 paired glucose values from CGM and ABL. Active sensor use was 95.1%. MARD was 9.4%, relative bias was 1.4%, and 92.8% of values fell in zone A, 6.1% fell in zone B, and 1.2% fell in zone C of the surveillance error grid. Median time in range was 78%, with minimum (<1%) time spent in hypoglycemia. StatStrip glucose meter MARD compared with ABL was 5.8%. Our study shows clinically applicable accuracy and reliability of Dexcom G6 CGM in postoperative ICU patients and a feasible alternative sensor placement site.

Assessment of Glycemic Control by Continuous Glucose Monitoring, Hemoglobin A1c, Fructosamine, and Glycated Albumin in Patients With End-Stage Kidney Disease and Burnt-Out Diabetes.

Patients with diabetes and end-stage kidney disease (ESKD) may experience "burnt-out diabetes," defined as having an HbA1c value <6.5% without antidiabetic therapy for >6 months. We aim to assess glycemic control by continuous glucose monitoring (Dexcom G6 CGM) metrics and glycemic markers in ESKD patients on hemodialysis with burnt-out diabetes. In this pilot prospective study, glycemic control was assessed by continuous glucose monitoring (CGM), HbA1c measures, and glycated albumin and fructosamine measurements in patients with burnt-out diabetes (n = 20) and without a history of diabetes (n = 20). Patients with burnt-out diabetes had higher CGM-measured daily glucose levels, lower percent time in the range 70-180 mg/dL, higher percent time above range (>250 mg/dL), and longer duration of hyperglycemia >180 mg/dL (hours/day) compared with patients without diabetes (all P < 0.01). HbA1c and fructosamine levels were similar; however, patients with burnt-out diabetes had higher levels of glycated albumin than did patients without diabetes. The use of CGM demonstrated that patients with burnt-out diabetes have significant undiagnosed hyperglycemia. CGM and glycated albumin provide better assessment of glycemic control than do values of HbA1c and fructosamine in patients with ESKD.

4 Associations Between Glycemia and Cognitive Performance in Adults with Type 1 Diabetes (T1D) using Continuous Glucose Monitoring (CGM) and Ecological Momentary Assessment (EMA)

Objective:Despite associations between hypoglycemia and cognitive performance using cross-sectional and experimental methods (e.g., Insulin clamp studies), few studies have evaluated this relationship in a naturalistic setting. This pilot study utilizes an EMA study design in adults with T1D to examine the impact of hypoglycemia and hyperglycemia, measured using CGM, on cognitive performance, measured via ambulatory assessment.Participants and Methods:Twenty adults with T1D (mean age 38.9 years, range 26-67; 55% female; 55% bachelor’s degree or higher; mean HbA1c = 8.3%, range 5.4% - 12.5%), were recruited from the Joslin Diabetes Center at SUNY Upstate Medical University. A blinded Dexcom G6 CGM was worn during everyday activities while completing 3-6 daily EMAs using personal smartphones. EMAs were delivered between 9 am and 9 pm, for 15 days. EMAs included 3 brief cognitive tests developed by testmybrain.org and validated for brief mobile administration (Gradual Onset CPT d-prime, Digit Symbol Matching median reaction time, Multiple Object Tracking percent accuracy) and self-reported momentary negative affect. Day-level average scores were calculated for the cognitive and negative affect measures. Hypoglycemia and hyperglycemia were defined as the percentage of time spent with a sensor glucose value &lt;70 mg/dL or &gt; 180 mg/dL, respectively. Daytime (8 am to 9 pm) and nighttime (9 pm to 8 am) glycemic excursions were calculated separately. Multilevel models estimated the between- and within-person association between the night prior to, or the same day, time spent in hypoglycemia or hyperglycemia and cognitive performance (each cognitive test was modeled separately). To evaluate the effect of between-person differences, person-level variables were calculated as the mean across the study and grand-mean centered. To evaluate the effect of within-person fluctuations, day-level variables were calculated as deviations from these person-level means.Results:Within-person fluctuations in nighttime hypoglycemia were associated with daytime processing speed. Specifically, participants who spent a higher percentage of time in hypoglycemia than their average percentage the night prior to assessment performed slower than their average performance on the processing speed test (Digit Symbol Matching median reaction time, b = 94.16, p = 0.042), while same day variation in hypoglycemia was not associated with variation in Digit Symbol Matching performance. This association remained significant (b = 97.46, p = 0.037) after controlling for within-person and between-person effects of negative affect. There were no significant within-person associations between time spent in hyperglycemia and Digit Symbol Matching, nor day/night hypoglycemia or hyperglycemia and Gradual Onset CPT or Multiple Object Tracking.Conclusions:Our findings from this EMA study suggest that when individuals with T1D experience more time in hypoglycemia at night (compared to their average), they have slower processing speed the following day, while same day hypoglycemia and hyperglycemia does not similarly impact processing speed performance. These results showcase the power of intensive longitudinal designs using ambulatory cognitive assessment to uncover novel determinants of cognitive variation in real world settings that have direct clinical applications for optimizing cognitive performance. Future research with larger samples is needed to replicate these findings.

SAT106 Use Of Data Sharing Feature Of Dexcom G6 Sensor Is Associated With Improved Glycemic Control: A Comparative Analysis

Abstract Disclosure: B. Sadek: None. Introduction: The use of Continuous Glucose Monitoring (CGM) sensors has significantly increased in part due to the seamless integration with smartphones. The Dexcom G6 CGM automatically sends real-time glucose readings to a compatible smart device. Moreover, the Dexcom Clarity app provides analytic tools and allows users to continuously share glucose data with their healthcare professionals, enabling remote monitoring of patients' glycemic control. Although multiple studies have shown better glucose control in patients who use CGM systems, what remains unexplored is if glucose metrics are further improved in those who share CGM data with healthcare providers. In this study, we compared glycemic outcomes among patients who use the sharing feature of the Dexcom G6 sensor to share their data with their healthcare providers to those who do not. Method: We conducted a single-center cross-sectional study in a community hospital with patients who use the Dexcom G6 CGM (n=201). Data was obtained from Dexcom Clarity for Healthcare Professionals, an online platform for clinicians to review patients' glucose patterns and statistics through interactive reports. The following data was collected for each patient: date of the last upload, data sharing status, average glucose, standard deviation, glucose management indicator (GMI) (%), glucose ranges (categorized as very high, high, in range, low, very low), sensor usage, and coefficient of variation (%). Two-tailed unpaired Student's t-tests were used to analyze the CGM metrics based on data sharing status. Significance was defined as a p-value less than 0.05. Results: 123 (61.19%) of the study participants shared data with healthcare providers. Participants with sharing data 'on' were significantly younger (49±15 years, p-value = 0.0007) and had increased sensor usage (90±21%, p-value = 0.0012), compared to those with sharing off. Participants who shared data spent more time in range (57±24 %, p-value = 0.0435), indicating a higher percentage of readings with glucose levels in a healthy range (70-180 mg/dL). Lastly, average glucose was lower in participants who shared data, although not statistically significant (179 ±44 mg/dL, p=0.08). Conclusion: The data-sharing feature on Dexcom G6 was utilized more frequently by younger people and was associated with a higher percentage of sensor usage. In addition, using the data-sharing feature of the Dexcom G6 could lead to a clinically meaningful improvement in glycemic control. Presentation: Saturday, June 17, 2023

SAT134 CGM Use During Pregnancy Complicated By CF-Induced Diabetes

Abstract Disclosure: A.Y. Mazori: None. E.S. Markovic: None. C.J. Levy: Advisory Board Member; Self; Dexcom, Eli Lilly &amp; Company. Research Investigator; Self; Abbott Laboratories, Insulet Corporation, Tandem Diabetes Care, Dexcom. Introduction: Glycemic control remains challenging in pregnancies complicated by diabetes mellitus. Strict glycemic targets are required to optimize maternal and neonatal outcomes, but euglycemia is hindered by fluctuating insulin sensitivity and the need for frequent blood-glucose monitoring (BGM). Consensus guidelines recommend therapeutic continuous glucose monitoring (CGM) for pregnant and nonpregnant individuals with type 1 and type 2 diabetes, but current data support therapeutic CGM use in cystic fibrosis (CF)-related diabetes (CFRD) only outside of pregnancy. We report the case of a patient with CFRD who achieved recommended glycemic targets with a therapeutic CGM during a singleton pregnancy. Methods: A case report and literature review are presented. Results: A 32-year-old nulliparous woman with CF became pregnant via frozen-embryo transfer. Prior CF complications included CFRD, pancreatic insufficiency, and recurrent pancreatitis. A therapeutic Dexcom G6 CGM and intermittent BGM were used to monitor glycemic control before and during pregnancy; sensor use exceeded 96%. Pressure-induced sensor attenuations yielded sporadic hypoglycemic values discordant with simultaneous BGM. CGM analysis for the 90 days preceding conception revealed: time in range of 63-140 mg/dL (TIR) was 90.8%, time above range (TAR) 6.3%, and time below range (TBR) 2.9%. Throughout pregnancy, TIR was 92.9%, TAR 3.4%, and TBR 3.6%; no severe hypoglycemia occurred. Daytime (6AM to midnight) and nighttime hypoglycemia were 4.6% and 2.2%, respectively. In the first trimester, TIR was 90.8%, TAR 3.7%, and TBR 5.5%. During the second trimester, the patient achieved TIR 94.2%, TAR 2.6%, and TBR 3.2%. In the third trimester, TIR was 95.7%, TAR 0.9%, and TBR 3.4%. The pregestational CFRD regimen consisted of insulin glargine 12 units and lispro 3-8 units. The basal-insulin dose was 10 units at 13 weeks’ gestation, 9 units at 27 weeks’ gestation, and 8 units at 38 weeks’ gestation. The prandial-insulin doses varied between 1.5-5 units at 13 weeks’ gestation, 0-7 units at 27 weeks’ gestation, and 0-5 units at 38 weeks’ gestation. After an uncomplicated vaginal delivery at 38 weeks and six days’ gestation, maternal fasting hypoglycemia occurred after one dose of glargine 3 units. Euglycemia was obtained without basal, prandial, or correctional insulin. The neonate experienced mild hypoglycemia that resolved with oral glucose gel and feeding. The neonate was neither macrosomic nor large for gestational age (birth weight, 3.54 kg, 71st percentile) and did not require intensive-care unit management. Both the patient and neonate were discharged on postpartum day two. Conclusion: Therapeutic CGM use in a pregnancy complicated by CFRD was effective for maternal and neonatal outcomes. Further study is warranted to examine CGM use and efficacy in this population. Presentation: Saturday, June 17, 2023

Feasibility and acceptability of continuous glucose monitoring in pregnancy for the diagnosis of gestational diabetes: A single-centre prospective mixed methods study.

Undiagnosed diabetes in pregnancy is associated with stillbirth and perinatal complications, but standard testing for gestational diabetes using the oral glucose tolerance test (OGTT) is impractical and exacerbates healthcare inequalities. There is an urgent need to improve the accuracy, acceptability and accessibility of glucose testing in pregnancy. We qualitatively assessed the feasibility and acceptability of two alternative home-based methods of glucose testing in pregnant women, using continuous glucose monitoring (CGM), with or without a home-based OGTT. We recruited women with a singleton pregnancy at 28 weeks' gestation with ≥1 risk factor for gestational diabetes attending antenatal glucose testing. A Dexcom G6 CGM device was sited and women were asked to take a 75g OGTT solution (Rapilose) on day 4 after an overnight fast. Qualitative interviews were performed with 20 participants using video conferencing according to a semi-structured interview schedule and thematically analysed using NVIVO software. 92 women were recruited; 73 also underwent a home OGTT. Women had an average of 6.9 days of glucose monitoring and found the CGM painless, easy to use with few or no adverse events. During the qualitative study, the main themes identified were reassurance and convenience. All women interviewed would recommend CGM and a home OGTT for diagnosis of gestational diabetes. CGM with or without a home OGTT is feasible and acceptable to pregnant women for diagnosis of gestational diabetes and offered advantages of convenience and reassurance. Further work is needed to clarify diagnostic thresholds for gestational diabetes using CGM metrics.

Assessing Time in Range with Postprandial Glucose-Focused Titration of Ultra Rapid Lispro (URLi) in People with Type1 Diabetes.

To assess time in range (TIR) (70-180mg/dL) with postprandial glucose (PPG)-focused titration of ultra rapid lispro (URLi; Lyumjev®) in combination with insulin degludec in people with type1 diabetes (T1D). This phase2, single-group, open-label, exploratory study was conducted in 31 participants with T1D on multiple daily injection therapy. Participants were treated with insulin degludec and Lispro for an 11-day lead-in and then URLi for a 46-day treatment period consisting of 35-day titration and 11-day endpoint maintenance period. Glucose targets for the titration period were PPG < 140mg/dL or < 20% increase from premeal, fasting glucose 80-110mg/dL, and overnight excursion ± 30mg/dL or less. Participants used the InPen™ bolus calculator and Dexcom G6 continuous glucose monitoring (CGM). Primary endpoint mean TIR (70-180mg/dL) with URLi during the maintenance period was 70.2%. TIR (70-180mg/dL) and times below/above range were not significantly different with URLi (maintenance) versus lispro (lead-in). HbA1c decreased from 7.1% at screening to 6.8% at endpoint (least squares mean [LSM] change from baseline, - 0.36%; P < 0.001). Fructosamine and 1,5-anhydroglucitol improved (P < 0.001). Mean hourly glucose using CGM was reduced from 8:00AM to 4:00PM with URLi. Overall highest PPG excursion across meals was significantly reduced at URLi endpoint compared with lispro lead-in (mean 56.5 vs 72.4mg/dL; P < 0.001). Insulin-to-carbohydrate ratio (U/Xg) was reduced (more insulin given) at breakfast at URLi endpoint vs lead-in (LSM 9.0 vs 9.7g; P = 0.002) and numerically decreased at other meals. Total daily insulin dose (TDD) was higher at URLi endpoint compared with lispro lead-in (mean 50.2 vs 47.0U; P = 0.046) with similar prandial/TDD ratio (mean 52.1% vs 51.2%). There were no severe hypoglycemia events during the study. URLi in a basal-bolus regimen focusing on PPG targets demonstrated improved overall glycemic control and reduced PPG excursions without increased hypoglycemia in participants with T1D. ClinicalTrial.gov, NCT04585776.

Real-world Accuracy of CGM in Inpatient Critical and Noncritical Care Settings at a Safety-Net Hospital.

We sought to determine real-world accuracy of inpatient continuous glucose monitoring (CGM) at multiple levels of acuity in a large safety-net hospital. We analyzed records from hospitalized patients on Dexcom G6 CGM, including clinical, point of care (POC), and laboratory (Lab) glucose, and CGM data. POC/Lab values were matched to the closest timed CGM value. Encounters were divided into not critically ill (NCI) versus critically ill (CI). CGM accuracy was evaluated. Paired readings (2,744 POC-CGM; 3,705 Lab-CGM) were analyzed for 233 patients with 239 encounters (83 NCI, 156 CI). POC-CGM aggregated and average mean absolute relative differences (MARD) were 15.1% and 17.1%. Lab-CGM aggregated and average MARDs were 11.4% and 12.2%. Accuracy for POC-CGM and Lab-CGM was 96.5% and 99.1% in Clarke Error Grid zones A/B. Real-world accuracy of inpatient CGM is acceptable for NCI and CI patients. Further exploration of conditions associated with lower CGM accuracy in real-world settings is warranted.

1816-PUB: Training and Support for Hospital Staff in the Use of Continuous Glucose Monitoring as Standard of Care in a Real-World Hospital Setting

Use of continuous glucose monitoring (CGM) in the hospital is limited and staff familiarity with CGM technology is low. During the COVID-19 pandemic and following the non-objection of the FDA to use of CGM in the hospital, the CGM as “standard of care” (CGM as SOC) program was implemented at Scripps Mercy Hospital in San Diego, CA using the Dexcom G6 CGM system. The implementation process of CGM as SOC included generating daily eligible patient lists, deploying specialized teams for CGM placement and connecting to nursing station iPads and centralized monitoring dashboards, and training of hospital nursing staff for CGM readings. A remote Advanced Practice Nurse (APN) served as an on-site trainer and expert consultant for troubleshooting. A Learning Management System (LMS) module was created for staff training and surveys assessed the training process. From May 2020-December 2022, CGM devices were placed on over 1200 adult hospitalized patients with diabetes requiring insulin. The APN conducted 30 live trainings for all units and shifts over 6 months. N=838 RNs completed the LMS module and met the required post-test passing rate of ≥ 80%. A total of N=38 self-report surveys were completed by nursing staff. Survey responses confirmed that most RNs felt confident in using the CGM nursing guide (97%) and had access to experts for troubleshooting (89%). Most common questions were related to technology troubleshooting (71%) compared to clinical issues (29%). Daily bedtime validation was transitioned from the APN to the bedside nursing staff using a validation calculator within EPIC. Despite limited baseline knowledge of CGM by hospital staff, deployment of specialized teams, APN on-site training and delivery of LMS modules supported the rapid implementation of CGM as SOC in a large community-based hospital. Standard training tools and processes can allow replication in health systems for use as part of the current provisional FDA allowance. Disclosure S.Lohnes: Consultant; Dexcom, Inc. R.Acuna: None. S.M.Saenz: None. T.Juarez: None. A.Philis-tsimikas: Advisory Panel; Dexcom, Inc., Novo Nordisk A/S, Sanofi, Other Relationship; Medtronic, Research Support; Novo Nordisk A/S, Lilly, Viking Therapeutics, NIH - National Institutes of Health. A.Bastian: None. A.L.Fortmann: Employee; Dexcom, Inc. L.Talavera: None. R.Belasco: None. H.Sandoval: None. M.Chichmarenko: None. S.R.Spierling bagsic: None. E.Soriano: None.

120-LB: Sharing Dexcom G6 Data and Glycemic Outcome—A Comparative Analysis

Introduction: In recent years the use of Continuous Glucose Monitoring (CGM) has significantly increased. The Dexcom G6 CGM sends real-time glucose readings automatically to a smart device. The Dexcom Clarity app allows users to share up-to-date glucose data with providers, enabling remote monitoring of patients' glycemic control. Although studies have shown better glucose control in patients who use CGM, it is unexplored if glucose metrics are further improved in those who share CGM data with providers. In this study, we compared glycemic outcomes in patients who share their data with their providers via the Dexcom G6 sensor to those who do not. Method: We conducted a single-center cross-sectional study in a community hospital with patients who use Dexcom G6 CGM. Data was obtained from Dexcom Clarity for Healthcare Professionals, an online platform for clinicians to review patient trends. The following data was collected for each patient: date of last upload, data sharing status, average glucose, standard deviation, glucose management indicator, glucose range, sensor usage, and coefficient of variation. Two-tailed unpaired Student's t-tests were used to analyze CGM metrics based on data sharing status. Significance was defined as a p-value &amp;lt; 0.05. Results: 123 (61.19%) out of 201 participants shared data with their provider. Participants with sharing data ‘on’ were significantly younger (49±15 years,) and had increased sensor usage (90±21%) compared to those with sharing off. Participants who shared data also spent more time in range (57±24 %), indicating a higher percentage of readings with glucose levels in a healthy range (70-180 mg/dL). Lastly, average glucose was lower in participants who shared data, although not statistically significant. Conclusion: The data-sharing feature on Dexcom G6 was utilized more by younger people and was associated with a higher percentage of time in range. Using the data-sharing feature of the Dexcom G6 could lead to clinically meaningful improvement in glycemic control. Disclosure B. Sadek: None. M. Hashem: None. M. Seetha: None. R. Raj: None.

1200-P: A1C and Neonatal Outcomes Improved with Real-Time Continuous Glucose Monitoring in Pregnant Women with Preexisting Type 2 Diabetes

Preexisting type 2 diabetes (T2D) in pregnancy is increasing and confers significant health risks to the mother and fetus. Continuous glucose monitoring (CGM) systems provide real-time feedback for patients and allow for remote monitoring and therapeutic adjustments. CGM is ideal for pregnancy where insulin requirements and glucose levels are rapidly changing throughout the pregnancy and postpartum. Previous studies have demonstrated improved maternal and neonatal outcomes with the use of CGM in the context of T1D. This study used the Dexcom G6 CGM in pregnant women with preexisting T2D to determine if there is a reduction of A1C and neonatal hypoglycemia compared to preexisting T2D in women using self-monitored blood glucose (SMBG) to manage their diabetes. Women aged 18-40 years with preexisting T2D, using multiple daily insulin injections, and &amp;lt;14 weeks pregnant were enrolled in the study. Participants were randomly assigned to use G6 or SMBG during pregnancy. Mann Whitney U Test, Wilcoxon Signed Rank Test, and Chi-square tests were used to analyze gestational age (GA), A1C at baseline and at 34 weeks, and occurrence of neonatal hypoglycemia. Fourteen participants completed the study. In the G6 group (n=7, median age 28 years, GA 37 weeks), A1c decreased from 7.0% to 6.1% (p=0.045). In the SMBG group (n=7, median age 36 years, GA 38 weeks), A1c decreased from 6.3% to 6.1% (p=NS). There was no significant difference in the GA between the groups. None of the 7 infants in the G6 group had perinatal hypoglycemia compared to 4 out of 7 infants in the SMBG group (p=0.005). This study suggests that use of the DexcomG6 CGM in preexisting T2D during pregnancy can lower A1C and reduce neonatal hypoglycemia. Disclosure G.Nunlee-bland: Research Support; Dexcom, Inc. P.Cofie: None. Funding Dexcom, Inc. (11S-2019-037)

1040-P: Feasibility and Performance of Continuous Glucose Monitoring (CGM) to Guide Computerized Insulin Infusion Therapy in Cardiovascular Intensive Care Unit (CV-ICU)

We evaluated the utility of real-time CGM for titrating intravenous (IV) insulin via a validated institutional computerized insulin infusion (CII) algorithm in the CV-ICU. We used a hybrid approach of combining CGM with periodic point-of-care blood glucose (POC-BG) tests to validate the continued accuracy of CGM. We also surveyed nurses on this care change. Dexcom G6 CGMs were applied to 61 post-surgical patients with hyperglycemia (34% with diabetes) receiving IV insulin. CGM values were validated with POC-BG every(Q) 1-2 h per the CII protocol. Once validated (i.e., within 20% of POC-BG values if BG ≥100 mg/dL or within 20 mg/dL if BG &amp;lt;100 mg/dl), sensor values were then used to titrate IV insulin doses per CII algorithm. POC-BG checks were then reduced to Q6h validation. Among 857 paired POC-BG and CGM values analyzed, the mean and median average relative difference between POC-BG and CGM values were 13.2% and 9.8%, respectively. 99.6% of paired CGM and POC-BG were in Zone A and B of the Clarke Error Grid (Figure). Thirty nurse respondents found CGM very or quite convenient (n=28; 93%) and favored it over POC-BG testing (n=28; 93%). This pilot study demonstrates that using CGM via a hybrid approach for CII titration protocol is feasible, has high accuracy, and higher nursing convenience. Disclosure L.Ang: None. Y.Qu: None. R.Freeman: None. N.H.Esfandiari: None. R.Busui: Board Member; American Diabetes Association, Consultant; Averitas Pharma, Inc., Lexicon Pharmaceuticals, Inc., Nevro Corp., Novo Nordisk, Roche Diagnostics, Procter &amp; Gamble, Research Support; Novo Nordisk, Medtronic, National Institutes of Health. R.Gianchandani: None. F.Akanbi: None. L.F.Schroeder: None. Y.Lin: None. C.A.Degeorge: None. P.Arnold: None. S.Knotts: None. E.Dubois: None. N.Desbrough: None.

228-OR: Accuracy and Reliability of Real-Time Continuous Glucose Monitoring in the Intensive Care Unit after Major Abdominal Surgery

Introduction: Blood glucose control in patients requiring intensive care remains a therapeutic challenge; however, recent advancements in glucose sensing technologies have sparked interest in the potential use of real-time continuous glucose monitoring in the subcutaneous compartment (rtCGM) also in the intensive care unit (ICU) setting. We performed a clinical trial assessing the accuracy and reliability of rtCGM in postoperative ICU patients after major abdominal surgery. Methods: Patients undergoing pancreatic surgery, liver transplantation, pancreas (or pancreatic islets) and kidney transplantation were enrolled in the trial. Dexcom G6 (Dexcom, Inc., San Diego, USA) was used for rtCGM. Arterial blood glucose measured by the amperometric principle (ABL 800, Radiometer, Copenhagen, Denmark) served as reference and to calibrate the Dexcom G6 (every 6 hours on day 1, once daily day 2 and 3). Sensor accuracy was assessed by computing mean absolute relative difference (MARD), bias, modified Bland-Altman plot and surveillance error grid for paired samples of glucose values from CGM and ABL. Results: Sixty-one patients after major abdominal surgery staying in ICU post-operatively were included into this analysis. Median monitoring length was 7 days with a 97.1 ± 6.5% proportion of active CGM use. Overall, 1566 paired glucose values measured with CGM and ABL were obtained. MARD for CGM compared with ABL-measured glucose was 9.7% with a bias of 0.9% and coefficient of variation of 14%. In the surveillance error grid analysis, 90.9% of pairs were in the 0 risk zone and 7.9% in the risk zone 1 (out of 7). Throughout the study 3 sensors needed to be replaced due to technical problems. Conclusion: Our results show a clinically applicable accuracy and reliability of the Dexcom G6 continuous glucose monitoring system in postoperative ICU. This opens up new possibilities in intensifying ICU glucose control, lowering hypoglycaemia risk and reducing nursing staff workload. Disclosure B.Hagerf (voglová): None. M.Protus: None. L.Nemetova: None. M.Mraz: None. M.Haluzik: Advisory Panel; Novo Nordisk, Lilly Diabetes, Boehringer-Ingelheim, Research Support; Sanofi, Speaker's Bureau; Abbott, AstraZeneca. P.Girman: None. J.Franekova: None. V.Svirlochova: None. A.Jabor: None. Funding Charles University; Czech Ministry of Health; Institute for Clinical and Experimental Medicine (IKEM, IN00023001); National Institute for Research of Metabolic and Cardiovascular Diseases (EXCELES, LX22NPO5104)