In 2023, a 67-year-old man with long-standing type 1 diabetes experienced a severe hypoglycemic episode and drove into an outdoor dining area in Victoria, Australia, killing 5 people and injuring several others. Although the charges against the driver were dismissed, future cases involving new continuous glucose monitors enhanced with artificial intelligence that allow users to anticipate impairment before it occurs may have different outcomes. These biopredictive technologies transform hypoglycemia from a background risk into a more foreseeable event. This generates new conditional obligations to act on predictive warnings and, in some cases, obligations on those with higher risks to acquire such technologies. Furthermore, these individual responsibilities can generate corresponding state obligations to subsidize access and update licensing frameworks, ensuring safety without exacerbating inequity or stigma.
BACKGROUND:Parental fear of hypoglycemia (FoH) may influence diabetes management, family well-being, and perceptions of safety in young children with type 1 diabetes (T1D). The aim was to describe FoH among parents of young children with T1D using a validated version of the Children's Hypoglycemia Index (CHI) and to examine parent-level differences, interparent agreement, and associations with clinical parameters, treatment modality, and glycemic outcomes of their children. METHODS:Cross-sectional cohort linking parent-reported questionnaire data with clinical and device-derived data. Analyses were conducted at the individual parent level and dyadic (parent-pair) level. Parental agreement was assessed using intraclass correlation coefficients (ICCs) and Bland-Altman analyses to evaluate mean differences and variability. Associations with HbA1c and continuous glucose monitoring metrics were analyzed using mixed-effect models adjusted for age, sex, and automated insulin delivery (AID) use. RESULTS:Among 135 children (mean age 6.8 ± 2.1 years; HbA1c 6.8% ± 0.7%; 89.6% using AID), mothers reported higher CHI scores than fathers (β = .35- .58, P < .001-.003), except for behavioral fear. The ICC indicated low parental agreements (0.00-0.38), reflecting substantial within-family variability. The Bland-Altman analyses showed higher mean scores among mothers, but wide limits of agreement, indicating inconsistent differences within dyads. No associations were detected between CHI and HbA1c, continuous glucose monitoring (CGM) metrics (time in range [TIR], time below range [TBR]), treatment modality, or child characteristics. CONCLUSIONS:Although mothers reported higher FoH at the group level, low agreement indicates that FoH varies within families. No association with glycemic outcomes was observed, although interpretations are limited by high AID use and low HbA1c variability. Individual assessment of FoH in both parents may be clinically relevant.
Background: Background spectral variance (BSV) is a critical parameter that strongly impacts calibration models for noninvasive glucose measurements. To date, the ability to characterize such variances is limited. A protocol is described for collecting skin spectra under fasting conditions and using these spectra to characterize how the BSV impacts measurement accuracy. Methods: Transmission noninvasive skin spectra were collected over the combination region of the near infrared spectrum. These spectra were collected continuously over a period of 270 minutes while maintaining fasting conditions. A glucose transient was created by linearly adding a set of concentration-scaled pure component glucose spectra to each fasting spectrum. Results: Quality of the noninvasive fasting spectra was evaluated by root mean square noise analysis of 100% lines, resulting in a noise level of 26 micro absorbance units (µAU). Evaluation of the BSV was done by (1) creating a set of net analyte signal calibration models for glucose with different subsets of the fasting spectra and (2) using each calibration model to predict the concentration of glucose from a set of synthesized skin spectra that represent the transient glucose concentration profile. Results reveal that the glucose transient can be predicted when the BSV is captured within the calibration dataset. Specific time points were identifiable, however, when the calibration dataset no longer fully represented the BSV in the prediction dataset. Conclusions: This preliminary report proposes a fasting protocol suitable for characterizing the BSV for noninvasive near infrared spectra. Net analyte signal calibration models are used to illustrate how unaccounted for BSV negatively impacts analytical accuracy. This protocol is general and can be applied to other approaches for noninvasive glucose sensing.
BACKGROUND:Automated insulin delivery (AID) systems are increasingly used by individuals living with diabetes in the outpatient setting, but data on their safety and effectiveness during hospitalizations remains limited. The objective is to evaluate the use and efficiency of AID systems in hospitalized adults with diabetes. METHODS:We performed a retrospective cohort study based on electronic health record data from hospital admissions of 316 patients with diabetes using insulin pumps. Automated insulin delivery and continuous glucose monitor use, insulin pump continuation, length of stay (LOS), and glycemic outcomes among the cohort were analyzed. The primary aims of the study were to describe rates of insulin pump continuation and reasons associated with removal, and rates of pump reinitiation. Secondary outcomes included effects of insulin pump removal and prompt endocrinology consultation on inpatient glycemic outcomes and LOS. RESULTS:Insulin pumps were continued in 64.2% of all admissions. The most common reason for pump discontinuation was surgery (30.1%). Length of stay was longer when pumps were removed (8.3 vs 4.7 days, P < .001) and when endocrinology consultation occurred more than 24 hours after admission (P < .001). Insulin pump continuation was associated with lower hyperglycemia (glucose >180 mg/dL) rates without an increase in hypoglycemia (glucose <70 mg/dL) in both critical and noncritical care settings. CONCLUSIONS:Continuation of insulin pumps was associated with improvement in hyperglycemia without increased hypoglycemia risk and with shorter LOS in the hospital. Standardized institutional policies including prompt glycemic management team consultation may help expand safe continuation of AID during hospitalizations.
Background: We evaluated whether the frequency of algorithm-generated coaching events, a decision support system (DSS) integrated with a smart insulin pen (SIP), differed according to glycemic outcomes, thereby establishing the algorithmic foundation. Methods: This retrospective analysis evaluated continuous glucose monitoring (CGM) and SIP data from a 12-week randomized controlled trial (NCT06406439) involving individuals with diabetes managed with multiple daily injections (MDI). A total of 338 biweekly CGM profiles from 58 participants were analyzed. For each 14-day period, the coaching event frequency was calculated across 9 predefined coaching categories. Results: Frequencies of hyperglycemia-related coaching (time in range [TIR]: 18.8 vs 34.8; time above range [TAR > 180 mg/dL]: 18.3 vs 33.8) and missed bolus events (TIR: 7.6 vs 11.2; TAR: 7.0 vs 11.1) were significantly higher in profiles not meeting hyperglycemia-related CGM targets (all P < .001). Both coaching categories demonstrated good discrimination for TIR and TAR target achievement (all area under the curve [AUC] > 0.7). Frequency of missed basal detection also showed significant associations with achievement of TIR and TAR targets (all P < .05). The hypoglycemia coaching group effectively discriminated the absence of nocturnal time below range <54 mg/dL (AUC = 0.740). Basal insulin titration coaching frequencies were associated with nocturnal mean glucose changes (all P < .005). Real-time insulin and correction factor reduction recommendations were independently associated with deterioration in TIR and TAR metrics (all P < .05). Conclusions: The frequency of algorithm-based coaching events served as a robust indicator of glycemic control. These findings validate Stage 5 SIP-integrated DSS and suggest its potential utility in identifying unresolved glycemic issues in individuals treated with MDI.
BACKGROUND:Continuous glucose monitoring (CGM) is increasingly used in patients with cancer, a population in whom HbA1c may be unreliable. The Glycemia Risk Index (GRI) is a CGM-derived measure integrating hypoglycemia and hyperglycemia risk. We evaluated trends in GRI and other CGM metrics among adults followed in a remote CGM (RCGM) clinic. METHODS:Retrospective chart review of adults (≥18 years) with diabetes and cancer seen at a comprehensive cancer center in 2024. Eligible patients had ≥2 CGM assessments (≥1 RCGM) and a baseline glucose management indicator (GMI) or HbA1c ≥8.0%. Generalized linear mixed models examined the effects of visit sequence (time) and RCGM use on GRI, GMI, average glucose, time in range (TIR), time above range (TAR), and time below range (TBR). Pre-specified conservative and liberal noninferiority margins were applied to the remote-use effect. RESULTS:Forty-two patients (mean age 60.2 ± 10.6 years; 95% type 2 diabetes) were included. At baseline, mean GRI was 70 ± 27, GMI 8.5% ± 1.1%, average glucose 214 ± 46 mg/dL, TIR 38% ± 21%, TAR 61% ± 21%, and TBR 1% ± 1%. Across sequential assessments, GRI, GMI, average glucose, and TAR decreased, whereas TIR increased (all P < .05). Remote CGM did not predict any glycemic outcome. Under liberal margins, RCGM was noninferior to nonremote review for GRI, GMI, TIR, and TAR, and noninferior for TBR under both conservative and liberal margins. CONCLUSIONS:Sequential CGM review was associated with improved glycemic quality in adults with diabetes and cancer. Remote CGM achieved glycemic outcomes comparable with nonremote review, supporting its use as a viable adjunct to in-person visits in the oncology setting.
BACKGROUND:The glycemic ratio (GR), defined as the ratio of mean intensive care unit (ICU) blood glucose (BG) to estimated preadmission BG (EPBG), may provide superior prognostic insight compared with the single admission snapshot represented by the stress hyperglycemia ratio (SHR, the ratio of ICU admission BG to EPAG). METHODS:This retrospective study included 4148 patients treated in a university-affiliated medical-surgical ICU from 2019 to 2023 who had >4 ICU BG measurements and a glycated hemoglobin (HbA1c) measured at admission. We compared SHR and GR prognostic ability for mortality, analyzed across prespecified GR and SHR bands, and calculated observed:expected mortality ratios (OEMRs). RESULTS:We observed a more sharply defined J-shaped relationship between GR and mortality compared with that generated by SHR. Mortality in the reference band of 0.8 to <1.0 for GR and SHR mortality was 7.5% vs 10.9%, respectively (P = .0087), and for the strata ≥1.4, mortality was 25.6% vs 20.5% (P = .0376). Compared with the reference band, GR < 0.8 and GR > 1.0 had higher OEMR (P < .0001 for each), but the OEMR for SHR <0.8 and >1.0 compared with the reference band was not significantly different. CONCLUSIONS:Glycemic ratio was superior to SHR as a predictor of mortality. This study demonstrates that a mean ICU BG level representing 80% to 100% of the patient's EPBG was associated with the lowest mortality rate in a heterogeneous cohort of critically ill patients. These results may inform current BG management strategies and should be considered when designing future interventional trials in the critically ill.
BACKGROUND:Diabetic foot wounds disproportionately affect patients from ethnic minorities and lower-socioeconomic status, many of whom face barriers to accessing diabetes technology. To evaluate whether short-term virtual glucose monitoring (VGM) has the potential to improve clinical outcomes in this high-risk population, we implemented a pilot VGM program within a safety-net health system. METHOD:We enrolled 40 hospitalized patients with diabetic foot wounds into a 3-month postdischarge VGM program that included 2 clinic visits and remote glucose monitoring every 1 to 2 weeks. Clinical outcomes were compared with a retrospective preintervention cohort of 78 similar patients. RESULTS:Although both groups had similar HgbA1c at diagnosis (VGM 10.6 ± 1.8% vs preintervention 11.1 ± 2.0%, P = .20), the HgbA1c at 3 to 6 months was lower in the VGM cohort (7.6 ± 1.1% vs 8.5 ± 2.0%, P < .01). Wound healing occurred more frequently in the VGM participants, with 68% achieving wound closure by 3-4 months versus only 47% in the preintervention cohort (P = .04). Nonsignificant reductions in emergency department visits and hospital readmissions for wound complications or hypoglycemia were observed in the VGM versus the preintervention cohort. The VGM program allowed for more timely and frequent opportunities to adjust diabetes medications and address social barriers to care. CONCLUSIONS:A short-term postdischarge VGM program has the potential to not only increase access to diabetes technology but also meaningfully improve clinical outcomes among patients with diabetic foot wounds in a safety-net health setting. Such program may offer a scalable strategy to reduce rates of complications and lower healthcare costs in a safety-net health system.
OBJECTIVE:To report and analyze the long-term performance of Glucopilot, a clinical decision support system for intravenous (IV) insulin therapy. METHODS:This retrospective cohort study included adult patients across 9 hospitals who received IV insulin guided by Glucopilot between August 18, 2020 and May 30, 2025. Admissions with fewer than 10 hours of IV insulin or insufficient glucose monitoring were excluded. Using point-of-care glucose data from the electronic health record, we evaluated time to euglycemia, time in glycemic ranges, and severe dysglycemic events, with subgroup analyses (eg, diabetes type, diabetic ketoacidosis, sepsis, and steroid use) and multivariable models to assess glycemic performance and factors associated with sustained euglycemia (ie, 100% time in euglycemia after initial attainment) and severe hyperglycemia (ie, ≥300 mg/dL). RESULTS:There were 1239 admissions included. All patients presenting with hyperglycemia achieved euglycemia, with a median time to euglycemia of 174 minutes. Thereafter, median time in euglycemia was 96%, with 4% median time in hyperglycemia, and 0% median time in hypoglycemia. Sustained euglycemia was observed in 43.4% of admissions, and steroid use was not associated with reduced likelihood of achieving sustained euglycemia. Baseline glucose was strongly associated with time to euglycemia but not with subsequent time in euglycemia after attainment. Glycemic control was consistent across subgroups. CONCLUSIONS:Use of Glucopilot, an electronic medical record-integrated adaptive insulin dosing software, was associated with timely attainment of euglycemia and sustained glycemic control with a median of 0% time in hypoglycemia in real-world practice.
BACKGROUND:Glucose management indicator (GMI) value is often used without a paired hemoglobin A1c (A1c) during telehealth encounters. There are known discrepancies between GMI and A1c that have the potential to influence decision-making regarding weaning off insulin in the post-total pancreatectomy with islet auto transplantation (TPIAT) population. Our objective was to compare point-of-care (POC) A1c with 14-day GMI levels, 1-year post-TPIAT, and to consider its impact on insulin management. METHODS:We reviewed the medical records of 59 patients who underwent TPIAT surgery at Cincinnati Children's Hospital 1-year post-TPIAT. Patients who had a POC A1c result and continuous glucose monitoring (CGM) data 12 months post-TPIAT were included in this analysis and were stratified into 2 groups based on POC A1c above or below 6.5%. RESULTS:A total of 33 patients, 1-year post-TPIAT, were included in this study. Median POC A1c was 6% (interquartile range [IQR] = 5.7 to 6.8), while median 14-day GMI was significantly higher at 6.5% (IQR = 6.2 to 6.8) (P < .0001). The GMI median was 0.5% (IQR = 0.3 to 0.7) higher in the POC A1c <6.5% group, compared to 0.0% (IQR: -0.2 to 0.1) in the POC A1c ≥6.5% group (P < .0001). CONCLUSION:Our data demonstrate a significant discordance between 14-day GMI and POC A1c values, particularly when A1c falls below 6.5%, in patients 1-year post-TPIAT. This threshold is critical for patients post-TPIAT, as an A1c of 6.5% or lower is commonly used to wean insulin therapy. Discordance in GMI at this level, if used independently, may therefore directly impact clinical decision-making regarding insulin management in this population.
BACKGROUND:A proprietary continuous glucose monitoring (CGM) system that uses third-generation sensor technology has previously been shown to exhibit stable, single-digit mean absolute relative difference (MARD) and 15-day wear life in adults with diabetes. METHOD:This was a prospective, single-arm study of pediatric participants aged 2 to 17 years that were enrolled at 3 clinical centers. Participants wore 2 sensors, one on each side of the abdomen, and were randomized to assessment of device performance on days 1 and 2, days 7 to 9 or days 15 and 16. Device performance across a range of metrics was assessed by comparison with venous blood glucose values obtained using a laboratory reference device. Per-protocol results are reported for the sensor with the inferior MARD. RESULTS:The per-protocol set comprised 75 participants, of whom 16 were aged 2 to 5 years. The overall MARD was 8.89%, and MARDs at days 1 and 2, 7 to 9 and 15 and 16 were 8.65%, 8.70% and 9.30%, respectively. DTS error grid analyses showed that 100.0% of data pairs fell in clinically acceptable zones A+B. True alarm rates for hypoglycemia and hyperglycemia were high, at 98.6% and 98.8%, respectively. Mean sensor wear life was 14.4 days, and participant/guardian assessments of sensor usability revealed high satisfaction with respect to system assembly, sensor insertion and overall comfort. No device-related or skin-related adverse events were reported. CONCLUSIONS:In a pediatric population, the novel CGM system demonstrated accurate and stable performance across its 15-day wear life and showed high usability.
INTRODUCTION:Computerized intravenous (IV) insulin infusion calculators may offer a safe and efficacious means of achieving glycemic control. Historically, our health system utilized various paper-based IV insulin infusion protocols but transitioned to a computerized insulin infusion calculator protocol based on the insulin sensitivity coefficient (ISC) in 2024. The objective of this study was to compare the safety and efficacy of the computerized ISC-based insulin infusion calculator protocol with previous protocols. METHODS:This was a pre-post intervention analysis of critically ill adult patients initiated on a paper-based insulin infusion across 13 institutions from March 2023 to May 2023 for pre-analysis and patients on a computerized, ISC-based insulin infusion from November 2024 to December 2024 for post-analysis. The primary outcome was the percentage of blood glucose levels within target range (80-180 mg/dL). Secondary outcomes included rates of hyperglycemia (>200 mg/dL), severe hyperglycemia (>250 mg/dL), hypoglycemia (<70 mg/dL), and severe hypoglycemia (<54 mg/dL). RESULTS:In total, 207 and 237 patients were included in the pre- and post-intervention groups, respectively. For the primary outcome, 4610 of 6313 (73%) and 6163 of 7870 (78.3%) of blood glucose values were in range in the pre- and post-intervention groups, respectively (P < .01). There was no difference in severe hypoglycemia (0.2% vs 0.1%, P = .24), but rates of hyperglycemia (16.6% vs 12.6%, P < .01), severe hyperglycemia (5.7% vs 4.0%, P < .01), and hypoglycemia (0.7% vs 0.2%, P < .01) were higher in the pre-intervention group. CONCLUSIONS:A computerized ISC-based insulin infusion protocol resulted in more blood glucose values in target range and lower rates of hypoglycemia compared with paper-based protocols.
PURPOSE:This review synthesizes evidence on objectively measured sleep and circadian parameters in adolescents with type 1 diabetes mellitus (T1DM) and associations with glycaemic outcomes. METHODS:A systematic review was conducted per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines and registered with PROSPERO (CRD420251164605). Five electronic databases were searched for studies reporting objective sleep or circadian measures in adolescents aged 10 to 19 years with T1DM. Eligible designs included cross-sectional, cohort, longitudinal, and randomized controlled trials. Risk of bias was assessed using the JBI checklist, Newcastle-Ottawa Scale, and Cochrane RoB 2. Narrative synthesis was conducted per Synthesis Without Meta-analysis guidelines. RESULTS:Ten studies met inclusion criteria. Adolescents with T1DM consistently demonstrated suboptimal sleep duration (mean total sleep time 6.8-7.6 h/night), high night-to-night variability, impaired continuity, and circadian misalignment. Associations between sleep duration and HbA1c were inconsistent; however, sleep efficiency, fragmentation, variability, and circadian timing showed more consistent associations with higher HbA1c and increased glucose variability. Only 1 study included a healthy control group; most were single-cohort observational designs, limiting causal inference. Risk of bias was moderate in cross-sectional and low to moderate in cohort studies. Randomized evidence comprised 2 trials; neither demonstrated sustained improvements in objective sleep outcomes or glycaemic control with closed-loop therapy versus standard care. CONCLUSIONS:In adolescents with T1DM, disturbed sleep involves insufficient duration, fragmentation, variability, and circadian misalignment. Sleep efficiency and regularity showed more consistent associations with glycaemic outcomes than duration alone, positioning sleep assessment as a potential research target in adolescent diabetes care. Future research should integrate standardized actigraphy with continuous glucose monitoring to establish causal pathways.
