Journal of Diabetes Science and Technology最新文献

Background: In-hospital hyperglycemia poses significant risks for patients with diabetes mellitus undergoing coronary artery bypass graft (CABG) surgery. Electronic glycemic management systems (eGMSs) like InsulinAPP offer promise in standardizing and improving glycemic control (GC) in these settings. This study evaluated the efficacy of the InsulinAPP protocol in optimizing GC and reducing adverse outcomes post-CABG.

Methods: This prospective, randomized, open-label study was conducted with 100 adult type 2 diabetes mellitus (T2DM) patients post-CABG surgery, who were randomized into two groups: conventional care (gCONV) and eGMS protocol (gAPP). The gAPP used InsulinAPP for insulin therapy management, whereas the gCONV received standard clinical care. The primary outcome was a composite of hospital-acquired infections, renal function deterioration, and symptomatic atrial arrhythmia. Secondary outcomes included GC, hypoglycemia incidence, hospital stay length, and costs.

Results: The gAPP achieved lower mean glucose levels (167.2 ± 42.5 mg/dL vs 188.7 ± 54.4 mg/dL; P = .040) and fewer patients-day with BG above 180 mg/dL (51.3% vs 74.8%, P = .011). The gAPP received an insulin regimen that included more prandial bolus and correction insulin (either bolus-correction or basal-bolus regimens) than the gCONV (90.3% vs 16.7%). The primary composite outcome occurred in 16% of gAPP patients compared with 58% in gCONV (P < .010). Hypoglycemia incidence was lower in the gAPP (4% vs 16%, P = .046). The gAPP protocol also resulted in shorter hospital stays and reduced costs.

Conclusions: The InsulinAPP protocol effectively optimizes GC and reduces adverse outcomes in T2DM patients' post-CABG surgery, offering a cost-effective solution for inpatient diabetes management.

Background: Despite established guidelines and increasing national hospital quality metrics, achieving consistent inpatient glycemic control remains challenging. A system-wide glucose data monitoring dashboard can help consolidate and visualize key metrics to support quality improvement (QI) and standardize care.

Methods: A web-based diabetes dashboard was implemented across 7 hospitals within a large health care system to monitor monthly data from the electronic health record. Metrics included patient-days with hypoglycemia (<70 mg/dL), hyperglycemia (mean >180 mg/dL), in-hospital mortality, hospital length of stay (LOS), and 30-day readmissions to the emergency department (ED) or inpatient/observation (IP/OBS). A total of 455 303 admissions were analyzed between January 2018 and March 2025, comparing pre-implementation (2018-2022) to post-implementation (2023-2025). Statistical analyses included t tests or Wilcoxon rank-sum tests. Given differences between the large academic site and 6 community hospitals, a difference-in-differences analysis was performed to evaluate impact by hospital type.

Results: After implementation of the dashboard, patient-days with hypoglycemia decreased from 4.81% to 4.15%, hyperglycemia from 25.30% to 23.46%, mortality from 2.69% to 2.13%, and LOS from 7.56 to 7.29 days (all P < .01). Emergency department and IP/OBS readmissions increased slightly (P < .01 and P = .01, respectively). Comparing the community hospitals to the academic, statistically significant reductions were observed in hypoglycemia, hyperglycemia, and mortality but with increased ED readmissions. There were no differences in LOS or IP/OBS readmission.

Conclusions: Implementation of a system-wide electronic dashboard was associated with improved glycemic control, mortality, and LOS. Dashboards can effectively support multidisciplinary collaboration and QI in diverse hospital settings.

Background: The study aimed to determine the relationship between basal insulin adherence and glycemic control evaluated by time in range (TIR) in people with insulin-treated type 2 diabetes (T2D), using data from both continuous glucose monitors (CGM) and connected insulin pens. Furthermore, the study aimed to determine the best basal insulin adherence metric.

Methods: CGM data and basal insulin data were collected from 106 insulin-treated people (aged ≥18 years) with T2D. Three different adherence metrics were employed (dose deviation, dose deviation ≤20%, and a traditional metric) and a three-step methodology was used to measure insulin adherence level. The coefficient of determination (R²), based on a univariate linear regression analysis, was used to determine the relationship between each adherence metric and TIR.

Results: A statistically significant relationship was observed between TIR and adherence quantified as the dose deviation ≤20% metric (R² = 0.67, P = .006). Neither the relationship between the dose deviation metric and TIR (R² = 0.43, P = .08) nor the relationship between the traditional metric and TIR (R² = 0.35, P =.23) was found to be statistically significant.

Conclusions: Our study indicates a relationship between basal insulin adherence and TIR in people with insulin-treated T2D. This seems to underscore the role of basal insulin adherence for optimal glycemic outcomes and utilizing TIR as a clinical marker. Furthermore, the results suggest that the magnitude of deviation from the recommended basal insulin dose impacts glycemic control, indicating dose deviation ≤20% as a more accurate metric for quantifying adherence.

Background: A diagnosis of type 1 diabetes in a young person can create vulnerability for sleep. Historically it has been rare for young people to be offered a closed-loop system soon after diagnosis meaning that studies examining sleep under these circumstances in comparison with standard treatment have not been possible. In this study, we examine sleep in young people (and their parents) who were provided with hybrid closed-loop therapy at diagnosis of type 1 diabetes versus those who receive standard treatment over a 2-year period.

Methods: The sample comprised 97 participants (mean age = 12.0 years; SD = 1.7) from a multicenter, open-label, randomized, parallel trial, where young people were randomized to either hybrid closed-loop insulin delivery or standard care at diagnosis. Sleep was measured using actigraphy and the Pittsburgh Sleep Quality Index (PSQI) in the young people, and using the PSQI in parents.

Results: Sleep in young people using hybrid closed-loop insulin delivery did not differ significantly compared with those receiving standard care (although there were nonsignificant trends for better sleep in the closed-loop group for 4 of the 5 sleep actigraphy measures and PSQI). Similarly, there were nonsignificant differences for sleep between the groups at 24 months (with mixed direction of effects).

Conclusions: This study assessed for the first time sleep in young people using a closed-loop system soon after diagnosis. Although sleep was not significantly different for young people using closed-loop insulin delivery as compared with those receiving standard care, the direction of effects of the nonsignificant results indicates a possible tendency for better sleep quality in the hybrid closed-loop insulin delivery group at the beginning of the treatment.

Background: Diabetes mellitus and dementia are common chronic diseases affecting older people in the community and in hospitals. Even though both diseases have been independently well-characterized, comorbid diabetes and dementia/cognitive impairment are much less understood. In particular, cognitive impairment can make glucose monitoring much more challenging and can more readily lead to diabetes-related emergencies such as hypoglycemia, hyperosmolar hyperglycemic state, or diabetic ketoacidosis. Based on this, improving diabetes management in the community and in the hospital settings via glucose monitoring is essential in older people with T2DM and particularly those with comorbid diabetes and dementia.

Aim: The use of continuous glucose monitoring (CGM) holds promise for greater glycemic management in older patients with diabetes and those at high risk for dementia. In this brief review, we will review the few existing studies for CGM use in the community and the hospital in this population, as well as the link between hospital admissions.

Results: Existing studies show high feasibility and good adherence with using CGM among older people. In addition, diabetes technologies can improve risk factors associated with hospitalization, leading to decreased hospitalization rates. We illustrate how the current studies highlight the need for studies in the hospital in this frail population, who potentially will benefit most from CGM systems.

Conclusion: Although existing feasibility studies show high promise in this frail population, more data are needed on CGM for older people living with diabetes and memory problems in the hospital setting.