ABSTRACT Background Optimal inpatient diabetes management requires accurate blood glucose (BG) and ketone (BK) documentation. In 2019, Royal Melbourne Hospital implemented hospital‐wide networked blood glucose monitoring (NBGM) (NovaBiomed StatStrip), which automatically uploads BG/BK values to point‐of‐care device software (Bioconnect). Until electronic health record (EHR) implementation in 2020, nurses manually recorded BG/BK data into paper‐records, enabling blinded gold‐standard accuracy assessment by digital software. This study evaluated the accuracy and potential clinical significance of inaccurate manual POC BG/BK values compared with NBGM records. Methods DINGO POC, a sub‐analysis of the Diabetes IN‐hospital: Glucose and Outcomes (DINGO) study, audited paper‐based glucose charts against NBGM‐uploaded BG/BK values and BG time‐stamps to identify manual transcription inaccuracies. Discrepancy rates, magnitude and potential clinical significance were analysed. Results 4391 BG and 378 BK NBGM measures from 250 admissions over a two‐month period were assessed. Of BG measures, 325 (7.4%) were not recorded in patient charts and 558 (13%) were inaccurate. 302 (54%) inaccurate BGs had potentially clinically significant discrepancies (≥ 0.4 mmol/L). 1570 (36%) BG time‐stamps were recorded inaccurately, and 329 (7.5%) were not recorded. 524 (33%) inaccurate BG time‐stamps had discrepancies > 15 min. Of BK measures, 153 (41%) were not recorded and 18 (8%) were transcribed inaccurately. Inaccuracy rates were similar across wards and patient groups. Conclusion Manual transcription of POC BG/BK values and time‐stamps, evaluated against blinded concurrent gold‐standard digital software, was often inaccurate with potential for clinical harm. Implementation of hospital‐wide NBGM systems with automated POC BG/BK upload to EHRs may mitigate manual transcription errors.

Background: Suboptimal inpatient glycemia is associated with adverse outcomes, including infection, length of stay, and hospitalization costs. Interventions to improve inpatient glycemia may benefit from standardization of in-hospital glycemic measurement and reporting. “Glucometrics,” as coined by Goldberg et al (2006), proposes models and metrics that allow quantitative inpatient glycemic data analysis. This systematic review investigates the actual use of “glucometric” terminology and its derivations since conception. Methods: Original research articles on “glucometrics” and its derivations in inpatient contexts, published between 2006 and 2023, were searched in five databases. Studies were screened and extracted through PRISMA-compliant review software (Covidence®) and systematically reviewed. Results: Of 767 studies identified, 44 were included for final review. Study settings included non-critical care wards (n=19), critical care (n=6), and both (n=13). Of the Goldberg models, “patient-day” was most used (n=33). Most studies (n=30) referred to “glucometrics” per the original description. An increase in the introduction of new metrics (e.g., time-weighted averages, adverse glycemic days, and glucose excursions) was seen over the study period, as well as an increase in the use of “glucometric” to refer to glycemic measurement/reporting in general. Significant variation in thresholds defining hyperglycemia/hypoglycemia existed between studies, where hyperglycemia ranged between 140 and 432 mg/dL (most commonly 300 mg/dL), while the hypoglycemia ranged between 40 and 70 mg/dL (most commonly 70 mg/dL). Conclusion: This systematic review provides insights into contemporary use of glucometric terminology, highlighting the lack of consensus on a standardized approach toward analyzing inpatient glycemia, and the need for glucometric harmonization to improve inpatient glycemia and diabetes care.

Continuous glucose monitoring (CGM) technology is transforming community diabetes management. Interest in the utility of CGM during hospitalisation is increasing. This multicentre retrospective observational study found that, among adult inpatients with type 1 diabetes, the proportion with inpatient CGM glucose data in hospital-linked CGM software accounts increased from 3.2% in 2021 to 20.5% in 2023. This study highlights the need for hospital-based clinicians to familiarise themselves with CGM technology.

Introduction: Continuous glucose monitoring (CGM) use in people with type 1 diabetes (T1D) is revolutionizing management. Use of CGM in hospital is poised to transform care, however routine use is not currently recommended due to lack of accuracy validation in acute care, including in people with T1D. We aimed to determine real-world CGM accuracy in hospitalized adults with T1D. Materials and Methods: In this multicenter retrospective observational study, we compared CGM interstitial fluid glucose with reference blood glucose (capillary/whole-blood point-of-care [POC], blood gas [GAS]) in adults with T1D requiring multiday admissions during 2020-2023 across three health services in Australia. Patients requiring dialysis or admitted under pediatric/obstetric/palliative care/psychiatry units were excluded. CGM accuracy was assessed by comparison with time-matched (±5 min) reference glucose measures, utilizing median absolute relative difference (ARD), mean ARD (MARD), and consensus error grid (CEG) analysis. Results: In total, 2,199 CGM-reference glucose pairs from 214 admissions (146 patients) were assessed. Overall, mean (SD) ARD was 12.8% (13.1) and median (IQR) ARD was 9.4% (3.7-17.7). MARD for CGM-POC pairs was 12.3%; MARD for CGM-GAS pairs was 14.3%. In CEG analysis, 99.3% of glucose pairs were within zones A/B. Accuracy was lower in critical care compared with noncritical care wards (MARD 16.1% vs. 12.0%, P < 0.001). Conclusions: In this real-world multicenter study, CGM glucose agreed well with reference blood glucose, suggesting modern CGM devices could be safely and effectively used in hospitalized adults with T1D. Further prospective studies of CGM accuracy with newer generation devices across different scenarios will further elucidate inpatient CGM accuracy and safety.

BACKGROUND:In people with type 1 diabetes (T1D) admitted to hospital, adverse glycemic events (AGE), both hypoglycemia and hyperglycemia, bestow risk for adverse outcomes. Continuous glucose monitoring (CGM) use is increasingly common amongst people with T1D. We investigated AGE frequency in hospital, based on CGM versus point-of-care (POC) blood glucose measures. METHODS:In this multi-center retrospective analysis of non-critically ill hospitalized adults with T1D who continued wearing their unmasked CGM (FreeStyle Libre 1/2, Dexcom G5/G6, Medtronic Guardian 3) during admission and received standard ward-based POC testing, we compared CGM- and POC-based AGE detection of hypoglycemia (<70 mg/dL) and hyperglycemia (>180 mg/dL). RESULTS:In 253 admissions, 127 837 CGM and 5508 POC glucose measures were analyzed, yielding 1391 CGM-detected hyperglycemia AGE and 317 CGM-detected hypoglycemia AGE. For CGM-detected AGE with a concurrent POC AGE evident, CGM detected hyperglycemia a median [interquartile range, IQR] of 70 minutes [22, 166] before POC and at lower glucose concentrations (187 vs 223 mg/dL, P < .0001) and detected hypoglycemia a median [IQR] of 38 minutes [14, 65] before POC and at higher glucose concentrations (67 vs 56 mg/dL, P < .0001). A quarter of CGM-detected AGE were not detected by POC. Only 3% of POC-detected AGE were not detected by CGM. CONCLUSIONS:Almost all AGE in hospital were detected by CGM, with few detected by POC alone. Compared to POC, CGM detected AGE earlier, with a lesser glycemic extreme, although unmasked CGM use may have influenced these results. Detecting AGE in hospital appears superior with CGM compared to POC glucose alone in people with T1D.

AIMS:Continuous glucose monitoring (CGM) during intravenous insulin infusions (IVII) could reduce blood glucose (BG) testing burden in hospital, however CGM accuracy concerns exist. We aimed to assess CGM accuracy during IVII. METHODS:This multi-centre observational study included adults with type 1 diabetes (T1D) who required IVII treatment during hospital admission whilst wearing their own CGM devices (Abbott FreeStyle Libre 2, Medtronic Guardian 3, Dexcom G6). IVII dose adjustments were performed based upon standard of care BG measures. Accuracy was assessed according to mean absolute relative difference (MARD) and Consensus error grid (CEG) analysis, using time-matched (±5 minutes) pairs of CGM glucose and reference BG (point-of-care [POC], blood gas [GAS]) obtained during IVII. RESULTS:In total, 736 time-matched glucose pairs were obtained from 56 hospital admissions (52% with diabetic ketoacidosis; 32% requiring intensive care). Median IVII duration was 16 hours (IQR 7.2-28). Overall MARD was 12.5% (11.9% for CGM-POC pairs; 14.1% for CGM-GAS pairs). In CEG analysis, 99.0% of glucose pairs were within zones A/B. Based on local hospital IVII dose titration protocols for non-intensive care wards, if CGM measures had been used instead of POC, dose adjustments would have been the same in 77% of instances. CONCLUSIONS:This real-world study of adults with T1D demonstrated high concordance of CGM measures with BG during IVII. The accuracy of CGM during IVII might enable its greater clinical utility when treating inpatients receiving IVII. More inpatient studies are required to validate the use of CGM during IVII.

We thank Barmanray and colleagues for their positive comments on our publication which reported improved perioperative outcomes for people with diabetes following implementation of the IP3D programme across multiple hospital sites across England [1].

Regional centres have smaller workforces in acute diabetes care compared to their metropolitan counterparts. A cross-sectional audit performed at Albury Hospital identified a high prevalence (34%) of diabetes for inpatients compared with metropolitan centres. The high prevalence highlights the need for all healthcare services to consider appropriate resources for the management of diabetes in people admitted to hospital.

Use of medical device technologies for diabetes mellitus, including continuous glucose monitoring devices, is becoming more frequently encountered in end-of-life care. Good communication is paramount to determine patient and carer preferences for if, when, and how blood glucose monitoring should occur in the end-of-life setting. We present two differing cases of how continuous glucose monitoring in an Australian setting impacted end-of-life care for the patients and their carers.