Objective: To evaluate how continuous glucose monitoring (CGM) glucose thresholds, sampling duration, and statistical modeling influence associations between CGM-derived range metrics and HbA1c, and to compare empirically derived glucose ranges with time in range (TIR) and time in tight range (TITR). Research Design and Methods: Out of 8160 HbA1c-matched CGM datasets from 1584 adults, CGM data from the 14, 30, and 90 days preceding each HbA1c measurement were evaluated. Associations between HbA1c and 7790 CGM glucose ranges were assessed across varying lower and upper glucose thresholds. Linear and log-linear mixed-effects models adjusted for age and race with participant-level random intercepts were compared using root mean square error and marginal R 2 . Participant-level derivation (80%) and validation (20%) cohorts were used for cross-validated threshold selection and discrimination analyses for HbA1c ≤7.0%. Results: Ninety-day CGM sampling showed the strongest associations with HbA1c. Log-linear models frequently provided comparable or improved fit, particularly for glucose ranges with upper thresholds below 180 mg/dL. Stronger HbA1c associations clustered around upper glucose thresholds of 155–175 mg/dL. In validation analyses, empirically derived ranges demonstrated strong discrimination of HbA1c ≤7.0% (area under the receiver operating characteristic curve [AUC] 0.909–0.915), compared with 0.903 for TIR and 0.905 for TITR, although absolute differences were small. Mean glucose demonstrated the strongest explanatory performance for continuous HbA1c (marginal R 2 = 0.74) and similarly strong discrimination (AUC 0.913). Conclusions: CGM–HbA1c relationships are influenced by glucose range, sampling duration, and statistical modeling. Our findings reinforce TIR and TITR as robust CGM metrics with strong associations with HbA1c, while also showing that selected empirically derived glucose ranges can provide modestly stronger statistical alignment. These results support the continued use of TIR and TITR while highlighting how glucose range definition may refine interpretation of CGM-based endpoints in future research.
Objective: Current commercially available automated insulin delivery systems require meal and exercise announcements for optimal function, placing them in the hybrid closed loop category. Ongoing advancements aim to establish fully closed loop (FCL) systems as the next-generation standard for type 1 diabetes (T1D) management, eliminating the need for such inputs. We sought to develop a shared definition of FCL so that emerging technologies, including those using artificial intelligence, multi-hormone delivery, multi-analyte sensing, and/or adjunctive therapies, are held to uniform performance standards. Methods: A group of invited experts, including clinicians, researchers, and people with lived experience convened to propose an outcome-driven definition of FCL systems. Results: FCL systems are defined by three core domains centered on meeting intensive glycemic targets while reducing self-management burden and improving lived experience: Time in Tight Range (TITR) 70–140 mg/dL (3.9–7.8 mmol/L), Time Below Range (TBR) <54 mg/dL (<3.0 mmol/L), and person-reported outcomes (PROs) measured with validated T1D-specific assessments. This definition rests on a three-tiered classification in which all FCL systems improve lived experience and attain >50% TITR and <1% TBR without mandatory meal or activity announcements. Class 3 systems must attain >50% TITR; Class 2, >70%; and Class 1, >90%—while simultaneously demonstrating improved quality of life and reduced treatment burden per validated PROs. Given the elevated hypoglycemia risk from insulin-on-board during exercise, Class 1a requires >90% TITR with demonstrable hypoglycemia prevention during standardized exercise testing (0% TBR). Conclusion: This framework provides a uniform benchmark for evaluating future FCL technologies. Taken together, this person-centered, outcome-driven framework holds that FCL systems should (1) autonomously attain near-normoglycemia, (2) meaningfully improve lived experience across the lifespan and clinical contexts, and (3) prevent exercise-related hypoglycemia.
BACKGROUND AND AIMS:Automated Insulin Delivery (AID) systems are the recommended standard of care for Type 1 Diabetes (T1D) management in children and adolescents, according to latest international guidelines. This study aims to assess the cost-effectiveness of AID systems for pediatric T1D care in Australia, Canada, the United Kingdom (UK), and the United States (USA). MATERIALS AND METHODS:The IQVIA Core Diabetes Model was used to compare AID systems with Multiple Daily Injections and Continuous Glucose Monitoring (MDI+CGM). Data were sourced from the National Health System (NHS) England real-world AID pilot initiative, a prospective follow-up study of 251 children and adolescents with T1D that showed a 0.63% reduction in HbA1c following the first year of system use. Clinical and economic outcomes were analyzed over a 55-year time horizon from a health care system perspective, applying country-specific discount rates and direct costs to estimate cost-effectiveness ratios (ICERs). RESULTS:AID systems were associated with Quality-Adjusted Life Years (QALYs) gains ranging from 1.35 to 2.72 across all countries. AID use resulted in significant relative risk reduction for long-term complications, such as proliferative diabetic retinopathy (51.3%), end-stage renal disease (39.5%), and severe vision loss (38.8%). Total cost savings ranged from USD-9340 in Australia to USD-28,798 in Canada. AID use was associated with an ICER of 14,590 in Australia, 25,798 in Canada, 32,459 in the UK, and 35,648 in USA per QALY gained (all values in USD). CONCLUSIONS:AID therapy was predicted to reduce mid- and long-term diabetes-related complications and improve quality of life while concomitantly reducing the economic burden on health care systems. AID was projected to be cost-effective versus MDI+CGM for pediatrics and adolescents with T1D in all analyzed countries. These findings highlight the need to accelerate the adoption of AID systems and to secure universal access and funding during this vulnerable period of life.
BACKGROUND:Continuous glucose monitoring (CGM) improves glycemic outcomes but inpatient use in the United States is not FDA approved, despite the potential need for frequent glycemic monitoring during hospitalization. We sought to assess CGM accuracy in the pediatric hospital setting and to evaluate the impact of potential modifiers of accuracy, including sensor wear day, hospitalization day, and glucose changes. METHODS:In this retrospective cross-sectional accuracy study among youth and young adults with diabetes hospitalized at two pediatric centers, reference point-of-care (POC) capillary glucose values were compared with Dexcom G7 CGM glucose readings (paired within 5 min). Accuracy was assessed using standard metrics, including mean absolute relative difference (MARD) and Parkes error grids, overall, and stratified by sensor wear day, hospital day, and CGM glucose rate of change. RESULTS:We analyzed 544 POC-CGM glucose pairs from 68 participants with diabetes aged 2-22 years (mean 12.4 ± 5.8 years old, 58.8% female, 66.2% White, 8.8% Hispanic) during 80 hospital encounters. The overall MARD was 12.5%. Accuracy was comparable on sensor wear day 1 and days 2-6 (MARD 12.3% vs 13.6%, respectively) and was best on wear days 7-10 (MARD 10.6%). Accuracy improved from hospital day 1 (MARD 13.7%) to hospital days 2-4 and 5-30 (MARD 10.4% and 12.5%, respectively). Overall and on wear day 1, almost all glucose pairs (99%) fell within low-risk Parkes error grid zones A and B. Accuracy was best with a flat [-1 to 1 mg/(dL·min)] rate of change (MARD 11.5%-11.9%) and was reduced with rapidly rising or falling [beyond -2 or 2 mg/(dL·min)] CGM values (MARD 16.6%-18.4%). CONCLUSIONS:In this pediatric hospital CGM accuracy study, MARD was within a favorable range of <14% overall and throughout sensor wear and hospitalization. Reduced accuracy with rapidly changing glucose highlights the need to incorporate trend arrows into hospital CGM protocols.
BACKGROUND:The Eversense 365 Continuous Glucose Monitoring (CGM) System is the first implantable, 1-year CGM approved in the United States and Europe. In February 2026, Eversense 365 became available in the United States with the twiist automated insulin delivery (AID) pump and twiist loop algorithm. While clinical trial performance has been reported, this article describes the real-world experience with the Eversense 365 CGM system using open-loop therapies and with the twiist AID system. METHODS:Deidentified sensor glucose (SG) data from the Eversense Data Management System were analyzed for the first 12,360 sensors used with open-loop therapies. Analysis included mean SG, glucose management indicator (GMI), percent in glycemic ranges and achieving hypoglycemia targets, and median transmitter wear time. Performance across the full 365-day duration and between the first and second 180-day periods was evaluated. For the twiist AID system, analysis included 153 sensors with >30 days following twiist pairing. RESULTS:For the 365 CGM system in open-loop, the mean age was 57 years. A mean of 43,225 (±32,394) CGM readings per sensor were available. Mean SG was 161 mg/dL, and GMI was 7.16%. Mean percent time < 54 mg/dL was 0.6%, <70 mg/dL 2.7%, 70-180 mg/dL (time in range, TIR) 66.2%, >180 mg/dL 31.2%, and >250 mg/dL 9.8%. Median transmitter wear time was 93%. Glycemic outcomes remained stable across the first and second 180-day periods. For users of the twiist + Eversense 365 AID system, mean SG was 145 mg/dL, GMI 6.78%, and TIR 76.1%, with 2.3% <70 mg/dL and 0.5% <54 mg/dL, and 99% median transmitter wear time. CONCLUSION:In this large real-world dataset, the 365-day CGM system demonstrated high adherence and favorable glucometrics sustained over extended wear. Early data from the twiist + Eversense 365 system support integration of the implantable CGM within an AID system to support consistent and reliable insulin delivery.
