Continuous Glucose Monitoring Values Research Articles

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.

Glucose values from the same continuous glucose monitoring sensor significantly differ among readers with different generations of algorithm

Continuous glucose monitoring (CGM) values obtained from CGM systems using the same sensor but with different internal algorithms (the first- and third-generation FreeStyle Libre (1st-gen-libre and 3rd-gen-libre, respectively)) were compared. We used 19,819 paired and simultaneously measured CGM values of 13 patients with diabetes. The average CGM value was significantly higher (P < 0.0001) and the time below range (CGM value < 70 mg/dL) was significantly lower (P < 0.0001) with the 3rd-gen-libre than with the 1st-gen-libre. There was a significant correlation (P < 0.0001) between the CGM values of the 3rd-gen-libre (y-axis, mg/dL) and 1st-gen-libre (x-axis, mg/dL) using the following formula: y = 0.9728x + 10.024. On assessing the association between glycated hemoglobin (HbA1c (%), y-axis) and the average CGM values (x-axis, mg/dL) by applying the obtained equation to previously reported 1st-gen-libre data and converting it to 3rd-gen-libre data, we obtained the equation y = 0.02628x + 3.233, indicating that the glucose management indicator reported in the West may be underestimated compared with the laboratory-measured HbA1c in the Japanese population. Glucose values from the same sensor were found to be significantly different between readers with different algorithms, and the calculation of CGM-related indices may need to be individualized for each device.

Accuracy and Safety of the 15-Day CareSens Air Continuous Glucose Monitoring System.

Background: We evaluated the accuracy and safety of the CareSens Air, a novel real-time continuous glucose monitoring system (CGMS), during 15 days of use in adults with diabetes. Methods: Adults with either type 1 diabetes or type 2 diabetes requiring intensive insulin therapy participated at four sites in South Korea. All participants wore the sensor for 15 days. Participants were scheduled for four 8-h clinic sessions on Day 1, 5 ± 1, 10 ± 1, and 15. Accuracy was evaluated based on the proportion of continuous glucose monitoring (CGM) values within 15% of YSI values ≥100 mg/dL or within 15 mg/dL of YSI values <100 mg/dL (%15/15), along with the %20/20, %30/30, and %40/40 agreement rates. The mean absolute relative difference (MARD) between the CGM and YSI values was calculated. Results: Data from 83 participants (83 sensors, 10,029 CGM-YSI matched pairs) were analyzed. The overall MARD was 10.42%, and the overall %15/15, %20/20, %30/30, and %40/40 accuracy were 78.55%, 89.04%, 96.47%, and 98.87%, respectively. The consensus error grid analysis showed that 99.92% of CGM values fell into Zone A or B (Zone A: 89.83%, Zone B: 10.09%). The %20/20 accuracy of CGMS was 88.11% on Day 1, 90.11% on Day 3-5, 92.09% on Day 8-10, and 85.63% on Day 15. No serious adverse events were reported. Conclusions: The CareSens Air demonstrated accurate performance across the wide glycemic range and was well tolerated during the 15-day sensor use period.

Performance of the Dexcom G7 Continuous Glucose Monitoring System in Pregnant Women with Diabetes.

Background: We evaluated accuracy and safety of a seventh-generation real-time continuous glucose monitoring (CGM) system during pregnancy. Materials and Methods: Evaluable data for accuracy analysis were obtained from 96 G7 sensors (Dexcom, Inc.) worn by 96 of 105 enrolled pregnant women with type 1 (n = 59), type 2 (n = 21), or gestational diabetes (n = 25). CGM values were compared with arterialized venous glucose values from the YSI comparator instrument during 6-h clinic sessions at different time points throughout the sensors' 10-day wear period. The primary endpoint was the proportion of CGM values in the 70-180 mg/dL range within 15% of comparator glucose values. Secondary endpoints included the proportion of CGM values within 20% or 20 mg/dL of comparator values ≥ or <100 mg/dL, respectively (the %20/20 agreement rate). Results: Of the 1739 pairs with CGM in the 70-180 mg/dL range, 83.2% were within 15% of comparator values. The lower bound of the 95% confidence interval was 79.8%. Of the 2102 pairs with CGM values in the 40-400 mg/dL range, the %20/20 agreement rate was 92.5%. Of the 1659 pairs with comparator values in the 63-140 mg/dL range, the %20/20 agreement rate was 92.3%. The %20/20 agreement rates on days 1, 4 and 7, and 10 were 78.6%, 96.3%, and 97.3%, respectively. Consensus error grid analysis showed 99.8% of pairs in the clinically acceptable A and B zones. There were no serious adverse events. The sensors' 10-day survival rate was 90.3%. Conclusion: The G7 system is accurate and safe during pregnancies complicated by diabetes and does not require confirmatory fingerstick testing. Clinical Trial Registration: clinicaltrials.gov NCT04905628.

Prediction of Hypoglycemia From Continuous Glucose Monitoring in Insulin-Treated Patients With Type 2 Diabetes Using Transfer Learning on Type 1 Diabetes Data: A Deep Transfer Learning Approach.

Hypoglycemia is common in insulin-treated type 2 diabetes (T2D) patients, which can lead to decreased quality of life or premature death. Deep learning models offer promise of accurate predictions, but data scarcity poses a challenge. This study aims to develop a deep learning model utilizing transfer learning to predict hypoglycemia. Continuous glucose monitoring (CGM) data from 226 patients with type 1 diabetes (T1D) and 180 patients with T2D were utilized. Data were structured into one-hour samples and labeled as hypoglycemia or not depending on whether three consecutive CGM values were below 3.9 [mmol/L] (70 mg/dL) one hour after the sample. A convolutional neural network (CNN) was pre-trained with the T1D data set and subsequently fitted using a T2D data set, all while being optimized toward maximizing the area under the receiver operating characteristics curve (AUC) value, and it was externally validated on a separate T2D data set. The developed model was externally validated with 334 711 one-hour CGM samples, of which 15 695 (4.69%) were labeled as hypoglycemic. The model achieved an AUC of 0.941 and a positive predictive value of 40.49% at a specificity of 95% and a sensitivity of 69.16%. The transfer learned CNN model showed promising performance in predicting hypoglycemic episodes and with slightly better results than a non-transfer learned CNN model.

HPB P28 Continuous glucose monitoring improves the diagnosis of insulinoma and surveillance after endoscopic ablation therapy

Abstract Background Insulinomas are the most common functioning pancreatic neuroendocrine tumours (PNET) and are classically diagnosed according to Whipple’s triad. The gold-standard diagnostic test (72 hour fasting glucose/C-peptide) is difficult for patients, and risks mis-diagnosing patients who only experience post-prandial symptoms. Moreover, the advent of endoscopic ablative techniques means that confirming successful treatment can be challenging when compared with surgical resection. This study aimed to investigate the value of continuous glucose monitoring (CGM) to: a) confirm the diagnosis of insulinoma; b) guide symptom management whilst awaiting definitive treatment; and c) as a surveillance tool in patients electing for non-surgical interventions. Methods A 61-year-old healthy male presented with post-prandial sweating, vacant episodes and reduced exercise tolerance which resolved upon consuming glucose. Computed Tomography demonstrated a 2cm exophytic hypervascular lesion in the pancreatic body, consistent with a NET, and correlated with an Octreotide avid lesion upon SPECT/CT. Overnight fasting glucose/c-peptide was not confirmatory. The target glucose range was defined as 4.0-9.9 mmol/L and critical hypoglycaemia as ≤2.9mmol/L. Data from CGM were analysed (Time in range and critical hypoglycaemia [Chi2]; glycaemic control and glycaemic variability [T-test]) during diagnosis, in the first week post-ablation, and the recovery phase beyond 3 weeks. Results Data from 22,008 glucose levels were analysed over a 90 day period. Time in range during diagnosis was 67.25%, 82.8% (p&amp;lt;0.0001) post-ablation, and 98.9% (P&amp;lt;0.0001) during recovery. Critical hypoglycaemia occurred in 5.2% of measurements during diagnosis, 1.9% (p&amp;lt;0.0001) post-ablation, and 0.1% (p&amp;lt;0.0001) during recovery. Glucose levels (mean [SD] mmol/L) were 4.4 (0.47) during diagnosis, 4.9 (0.29) (p=0.0001) post-ablation, and 5.7 (0.42) (p&amp;lt;0.0001) during recovery. Glycaemic variability (mean [SD] %CV) was 19.4 (5.09) during diagnosis, 20.17 (5.60) post-ablation (p=0.630), and 11.3 (5.53) (p&amp;lt;0.0001) during recovery. Temporal changes identifying post-prandial hypoglycaemia during diagnosis, and resolution during recovery, are illustrated (Figure 1). Conclusions CGM has utility to accurately diagnose insulinoma when standard tests are negative, and to improve quality of life by alerting patients to life-threatening critical hypoglycaemia. As parenchymal preserving non-operative therapies increase in popularity, especially for patients with small PNETs, sensitive, reliable, and acceptable surveillance techniques are required in order to confirm treatment success and monitor for recurrence. Proactive engagement with commissioning and regulatory bodies is necessary in order to harness the potential from emerging technologies, so that PNET patients can benefit in a similar manner as those with type 1 Diabetes Mellitus.

Factors that determine glucose variability, defined by the coefficient of variation in continuous glucose monitoring values, in a Chinese population with type 2 diabetes.

To elucidate the clinical determinants of the coefficient of variation (CV) of glucose by analysing the pancreatic β-cell function of subjects with type 2 diabetes mellitus (T2DM). A total of 716 Chinese subjects with T2DM were included. Continuous glucose monitoring (CGM) was used to assess blood glucose, and the CV was calculated. C-peptide concentration at 0, 0.5, 1, 2 and 3 hours (Cp0h, Cp0.5h, Cp1h, Cp2h and Cp3h, respectively) was measured after a standard 100-g steamed bun meal test to assess pancreatic β-cell function. The determinants of glucose variability defined by the CV of CGM values were explored from two perspectives: the CV of qualitative variables and the CV of quantitative variables. Our data revealed that C-peptide concentration (Cp0h, Cp0.5h, Cp1h, Cp2h, Cp3h), area under the curve for C-peptide concentration at 0.5 and 3 hours (AUC-Cp0.5h and AUC-Cp3h) decreased with increasing CV quartile (P < 0.05). The CV was negatively correlated with homeostatic model assessment of β-cell function index, C-peptide concentration at all timepoints, and AUC-Cp0.5h and AUC-Cp3h (P < 0.001). Quantile regression analysis showed that AUC-Cp0.5h had an overall negative effect on the CV in the 0.05 to 0.95 quartiles, and AUC-Cp3h tended to have a negative effect on the CV in the 0.2 to 0.65 quartiles. After adjusting for confounders, multinomial logistic regression showed that each 1-unit increase in AUC-Cp0.5h was associated with a 31.7% reduction in the risk of unstable glucose homeostasis (CV > 36%; P = 0.036; odds ratio 0.683; 95% confidence interval 0.478-0.976). We also identified the AUC-Cp0.5h (0.735 ng/mL) and AUC-Cp3h (13.355 ng/mL) cut-off values for predicting unstable glucose homeostasis (CV >36%) in T2DM subjects. Our study suggests that impaired pancreatic β-cell function may be a clinical determining factor of CV of glucose in people with T2DM.

Accuracy and impact on quality of life of real-time continuous glucose monitoring in children with hyperinsulinaemic hypoglycaemia.

Continuous glucose monitoring (CGM) is the standard of care for glucose monitoring in children with diabetes, however there are limited data reporting their use in hyperinsulinaemic hypoglycaemia (HH). Here, we evaluate CGM accuracy and its impact on quality of life in children with HH. Real-time CGM (Dexcom G5 and G6) was used in children with HH aged 0-16years. Data from self-monitoring capillary blood glucose (CBG) and CGM were collected over a period of up to 28days and analysed. Quality of life was assessed by the PedsQL4.0 general module and PedsQL2.0 family impact module, completed by children and their parents/carers before and after CGM insertion. Analysis of accuracy metrics included mean absolute relative difference (MARD) and proportion of CGM values within 15, 20, and 30% or 15, 20, and 30 mg/dL of reference glucose values >100 mg/dL or ≤100 mg/dL, respectively (% 15/15, % 20/20, % 30/30). Clinical reliability was assessed with Clarke error grid (CEG) analyses. Prospective longitudinal study with data analysed from 40 children. The overall MARD between reference glucose and paired CGM values (n=4,928) was 13.0% (Dexcom G5 12.8%, Dexcom G6 13.1%). The proportion of readings meeting %15/15 and %20/20 were 77.3% and 86.4%, respectively, with CEG analysis demonstrating 97.4% of all values in zones A and B. Within the hypoglycaemia range (<70 mg/dL), the median ARD was 11.4% with a sensitivity and specificity of 64.2% and 91.3%, respectively. Overall PedsQL child report at baseline and endpoint were 57.6 (50.5 - 75.8) and 87.0 (82.9 - 91.2), and for parents were 60.3 (44.8 - 66.0) and 85.3 (83.7 - 91.3), respectively (both p<0.001). Use of CGM for children with HH is feasible, with clinically acceptable accuracy, particularly in the hypoglycaemic range. Quality of life measures demonstrate significant improvement after CGM use. These data are important to explore use of CGM in disease indications, including neonatal and paediatric diabetes, cystic fibrosis and glycogen storage disorders.

Using Continuous Glucose Monitoring Values for Bolus Size Calculation in Smart Multiple Daily Injection Systems: No Negative Impact on Post-bolus Glycemic Outcomes Found in Real-World Data.

Recent evidence shows that it may be safe to estimate bolus sizes based on continuous glucose monitoring (CGM) rather than blood glucose (BG) values using glycemic trend-adjusted bolus calculators. Users may already be doing this in the real world, though it is unclear whether this is safe or effective for calculators not employing trend adjustment. We assessed real-world data from a smart multiple daily injections (MDIs) device users with a CGM system, hypothesizing that four-hour post-bolus outcomes using CGM values are not inferior to those using BG values. Our data set included 184 users and spanned 18 months with 79 000 bolus observations. We tested differences using logistic regression predicting CGM or BG value usage based on outcomes and confirmed initial results using a mixed model regression accounting for within-subject correlations. Comparing four-hour outcomes for bolus events using CGM and BG values revealed no differences using our initial approach (P > .183). This finding was confirmed by our mixed model regression approach in all cases (P > .199), except for times below range outcomes. Higher times below range were predictive of lower odds of CGM-based bolus calculations (OR = 0.987, P < .0001 and OR = 0.987, P = .0276, for time below 70 and 54 mg/dL, respectively). We found no differences in four-hour post-bolus glycemic outcomes when using CGM or BG except for time below range, which showed evidence of being lower for CGM. Though preliminary, our results confirm prior findings showing non-inferiority of using CGM values for bolus calculation compared with BG usage in the real world.

OGTT Metrics Surpass Continuous Glucose Monitoring Data for T1D Prediction in Multiple-Autoantibody-Positive Individuals.

The value of continuous glucose monitoring (CGM) for monitoring autoantibody (AAB)-positive individuals in clinical trials for progression of type 1 diabetes (T1D) is unknown. Compare CGM with oral glucose tolerance test (OGTT)-based metrics in prediction of T1D. At academic centers, OGTT and CGM data from multiple-AAB relatives were evaluated for associations with T1D diagnosis. Participants were multiple-AAB-positive individuals in a TrialNet Pathway to Prevention (TN01) CGM ancillary study (n = 93). The intervention was CGM for 1 week at baseline, 6 months, and 12 months. Receiver operating characteristic (ROC) curves of CGM and OGTT metrics for prediction of T1D were analyzed. Five of 7 OGTT metrics and 29/48 CGM metrics but not HbA1c differed between those who subsequently did or did not develop T1D. ROC area under the curve (AUC) of individual CGM values ranged from 50% to 69% and increased when adjusted for age and AABs. However, the highest-ranking metrics were derived from OGTT: 4/7 with AUC ∼80%. Compared with adjusted multivariable models using CGM data, OGTT-derived variables, Index60 and DPTRS (Diabetes Prevention Trial-Type 1 Risk Score), had higher discriminative ability (higher ROC AUC and positive predictive value with similar negative predictive value). Every 6-month CGM measures in multiple-AAB-positive individuals are predictive of subsequent T1D, but less so than OGTT-derived variables. CGM may have feasibility advantages and be useful in some settings. However, our data suggest there is insufficient evidence to replace OGTT measures with CGM in the context of clinical trials.

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.

Continuous Glucose Monitoring by Insulin-Treated Pilots Flying Commercial Aircraft Within the ARA.MED.330 Diabetes Protocol: A Preliminary Feasibility Study.

Background and Aims: A preliminary study compared the use of continuous glucose monitoring (CGM) with the use of self-monitored blood glucose (SMBG) by aircraft pilots with insulin-treated diabetes in the United Kingdom, Ireland, and Austria, certified to fly commercial aircraft within the European Aviation Safety Agency ARA.MED.330 protocol. Methods: SMBG and simultaneous interstitial glucose measurements using CGM (Dexcom G6®) were recorded during pre- and in-flight periods. Results: Eight male pilots (seven with type 1 diabetes and one with type 3c diabetes), median age of 48.5 years and median diabetes duration of 11.5 years, participated. The correlation coefficient (R) between 874 contemporaneously recorded SMBG and CGM values was 0.843, P < 0.001. The mean glucose concentration was 8.78 mmol/L (standard deviation [SD] 0.67) using SMBG compared with 8.71 mmol/L (SD 0.85) recorded using CGM. The mean absolute relative difference was 9.39% (SD 3.12). Conclusions: CGM using Dexcom G6 systems is a credible alternative to SMBG for monitoring glucose levels when insulin-treated pilots fly commercial aircraft. The study was registered with Clinical Trials.gov NCT04395378.

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.

The Type 1 Diabetes and EXercise Initiative: Predicting Hypoglycemia Risk During Exercise for Participants with Type 1 Diabetes Using Repeated Measures Random Forest.

Objective: Exercise is known to increase the risk for hypoglycemia in type 1 diabetes (T1D) but predicting when it may occur remains a major challenge. The objective of this study was to develop a hypoglycemia prediction model based on a large real-world study of exercise in T1D. Research Design and Methods: Structured study-specified exercise (aerobic, interval, and resistance training videos) and free-living exercise sessions from the T1D Exercise Initiative study were used to build a model for predicting hypoglycemia, a continuous glucose monitoring value <70 mg/dL, during exercise. Repeated measures random forest (RMRF) and repeated measures logistic regression (RMLR) models were constructed to predict hypoglycemia using predictors at the start of exercise and baseline characteristics. Models were evaluated with area under the receiver operating characteristic curve (AUC) and balanced accuracy. Results: RMRF and RMLR had similar AUC (0.833 vs. 0.825, respectively) and both models had a balanced accuracy of 77%. The probability of hypoglycemia was higher for exercise sessions with lower pre-exercise glucose levels, negative pre-exercise glucose rates of change, greater percent time <70 mg/dL in the 24 h before exercise, and greater pre-exercise bolus insulin-on-board (IOB). Free-living aerobic exercises, walking/hiking, and physical labor had the highest probability of hypoglycemia, while structured exercises had the lowest probability of hypoglycemia. Conclusions: RMRF and RMLR accurately predict hypoglycemia during exercise and identify factors that increase the risk of hypoglycemia. Lower glucose, decreasing levels of glucose before exercise, and greater pre-exercise IOB largely predict hypoglycemia risk in adults with T1D.

949-P: Hypoglycemia Detection during Real-Time Continuous Glucose Monitoring in a Real-World Hospital Setting

Continuous glucose monitoring (CGM) in the hospital can detect hypoglycemia that may go unnoticed by point-of-care testing (POCT). Following the non-objection of the FDA to the use of CGM in the hospital, using the Dexcom G6 CGM system, the “CGM as Standard of Care” (CGM as SOC) program was implemented at Scripps Mercy Hospital San Diego with a 24/7 remote-monitoring team (RMT) using a Digital Dashboard (DD). The DD alerts for glucose ≤80mg/dL and prioritizes patients by clinical risk. Using these alerts, the RMT notifies on-site bedside nurses (RN). Hypoglycemic events, defined as &amp;gt;15 minutes of consecutive glucose readings &amp;lt;70mg/dL, identified in retrospective evaluation of CGM data were analyzed to 1) evaluate appropriateness of response to events and 2) assess the accuracy of the events. 86/745 (12%) unique admissions enrolled in the CGM as SOC program between January 2021-June 2022 experienced a hypoglycemic event during the RMT period. These patients averaged 60.2 years of age, 62% male, 48% Hispanic and 87% with a type 2 diabetes diagnosis. Median number of events per admission = 1.90, for a total of 163 events. All alerts were acknowledged by the RMT, documentation that bedside RN was alerted was found on 134/163 (82%) events. It is possible that the RMT neglected to document RN outreach in the remaining 29 (18%). 90/163 events (55%) were treated as appropriate clinically, with insulin dosing changes as needed. In the remaining 69/163 (42%) events, patients were determined to not be (or no longer be) in an actionable hypoglycemia range that warranted treatment when evaluated by the RN. A small portion (10/69, 14%) were caused by documented compression on device resulting in artificially low CGM values. There were 4 (3%) events with insufficient documentation. These findings provide preliminary support of the value of an RMT in hypoglycemia surveillance in a real-world hospital setting. Additional data and recommendations for process refinements will be presented. Disclosure R.Belasco: None. L.Talavera: 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.L.Fortmann: Employee; Dexcom, Inc. S.R.Spierling bagsic: None. A.Bastian: None. S.Lohnes: Consultant; Dexcom, Inc. A.Ritko: None. H.Sandoval: None. M.Chichmarenko: None. E.C.Soriano: None.

Advancing Diabetes Quality Measurement in the Era of Continuous Glucose Monitoring.

The purpose of this research is to develop a set of continuous glucose monitoring (CGM)-related measure concepts to be tested in a health care system. Existing measures assessing the quality of diabetes care do not include modern approaches to diabetes management, such as CGM. Continuous glucose monitors rival traditional methods of measuring diabetes management by providing real-time, longitudinal data and demonstrating glucose variability over time. The Improving Diabetes Quality Initiative seeks to address this gap in diabetes quality measurement. A Technical Expert Panel (TEP) was convened to curate a diabetes quality measures portfolio and conceptualize three new CGM-related quality measures within the portfolio. From the additional measure concepts identified in the portfolio, the TEP prioritized three for conceptualization. High-level measure concept specifications were made available during a public comment period. The measure concepts prioritized by the TEP included a shared decision-making measure to assess the value of initiating CGM for disease management, a utilization measure to address disparities in access and use of CGM, and a patient-provider review of CGM data to promote routine consideration of these assessments in treatment and ongoing management. Clinical literature, public comments, and TEP feedback informed full measure specifications. The evolution of diabetes technology reflects the need to shift diabetes quality of care. The measure concepts will be tested in a flexible pilot setting to understand the future of diabetes care and communicate the value of CGM to people with diabetes, providers, and payers.

Assessing non-adjunctive CGM safety at home and in new markets (ANSHIN).

Continuous glucose monitoring (CGM) can guide treatment for people with type 1 (T1D) and type 2 diabetes (T2D). The ANSHIN study assessed the impact of non-adjunctive CGM use in adults with diabetes using intensive insulin therapy (IIT). This single-arm, prospective, interventional study enrolled adults with T1D or T2D who had not used CGM in the prior 6 months. Participants wore blinded CGMs (Dexcom G6) during a 20-day run-in phase, with treatment based on fingerstick glucose values, followed by a 16-week intervention phase and then a randomized 12-week extension phase with treatment based on CGM values. The primary outcome was change in HbA1c. Secondary outcomes were CGM metrics. Safety endpoints were the number of severe hypoglycaemic (SH) and diabetic ketoacidosis (DKA) events. Of the 77 adults enrolled, 63 completed the study. Those enrolled had mean (SD) baseline HbA1c of 9.8% (1.9%), 36% had T1D, and 44% were ≥65 years old. Mean HbA1c decreased by 1.3, 1.0 and 1.0 percentage points for participants with T1D, T2D or age ≥65, respectively (p < .001 for each). CGM-based metrics including time in range also improved significantly. SH events decreased from the run-in period (67.3 per 100 person-years) to the intervention period (17.0 per 100 person-years). Three DKA events unrelated to CGM use occurred during the total intervention period. Non-adjunctive use of the Dexcom G6 CGM system improved glycaemic control and was safe for adults using IIT.

Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data.

To develop a noninvasive hypoglycemia detection approach using smartwatch data. We prospectively collected data from two wrist-worn wearables (Garminvivoactive 4S, Empatica E4) and continuous glucose monitoring values in adults with diabetes on insulin treatment. Using these data, we developed a machine learning (ML) approach to detect hypoglycemia (<3.9 mmol/L) noninvasively in unseen individuals and solely based on wearable data. Twenty-two individuals were included in the final analysis (age 54.5 ± 15.2 years, HbA1c 6.9 ± 0.6%, 16 males). Hypoglycemia was detected with an area under the receiver operating characteristic curve of 0.76 ± 0.07 solely based on wearable data. Feature analysis revealed that the ML model associated increased heart rate, decreased heart rate variability, and increased tonic electrodermal activity with hypoglycemia. Our approach may allow for noninvasive hypoglycemia detection using wearables in people with diabetes and thus complement existing methods for hypoglycemia detection and warning.

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

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

Safety and accuracy of neonatal continuous glucose monitoring.

Hypoglycemia is a significant problem for all neonates and requires minimally invasive and reliable monitoring. The primary objective of this study was to verify the safety and accuracy of the continuous glucose monitoring (CGM) of full-term neonates using Freestyle Libre, a flash glucose monitoring (FGM) device. The study was conducted on 20 neonates. Shortly after birth, we placed the FGM sensor on the outside of the neonates' thighs. We scanned the CGM values at 60, 120, 180, and 360 min after birth and simultaneously obtained blood glucose values with plantar capillaries by heel puncture. The neonates wore the sensors for up to 6 h and then they were removed. Of the 75 data points to be measured, 65 points (86.7%) were obtained by scan. There was no change in the sensor attachment site in 12 of 18 completed cases in this study but we observed slight induration in four cases (22.2%) and slight redness in one case (5.5%) at the sensor puncture site. A moderate correlation was observed between the CGM and blood glucose values. The CGM values tended to be low at 120, 180, and 360 min after birth, and tended to be high only at 60 min after birth. The CGM device was safe to wear on the neonate and the CGM data correlated well with blood glucose levels. There was dissociation between CGM data and blood glucose levels in the acute period soon after birth when the blood glucose levels changed rapidly.