Risk and factors determining diabetes after mild, nonnecrotizing acute pancreatitis.

PURPOSE OF REVIEW: Diabetes mellitus (DM) is relatively common following acute pancreatitis (AP), even after mild acute pancreatitis (MAP), the most frequent AP presentation, in which there is no overt beta cell injury. Post-AP related diabetes is widely misdiagnosed, resulting in potentially inappropriate treatment and worse outcomes than type 2 diabetes (T2D). Thus, it is important to understand risk across the spectrum of AP severity. RECENT FINDINGS: Biological mechanisms are unclear and may include local and systemic inflammation leading to beta cell dysfunction and insulin resistance, altered gut barrier and/or gut peptides and possibly islet autoimmunity, though no studies have specifically focused on MAP. While studies examining clinical risk factors on MAP exclusively are lacking, there are studies which include MAP. These studies vary in scientific rigor, approaches to rule out preexisting diabetes, variable AP severity, diagnostic testing methods, and duration of follow-up. Overall, disease related factors, including AP severity, as well as established T2D risk factors are reported to contribute to the risk for DM following AP. SUMMARY: Though numerous studies have explored risk factors for DM after AP, few studies specifically focused on MAP, highlighting a key knowledge gap that is relevant to the majority of patients with AP.

Comparative Effectiveness of Hybrid Closed-Loop Automated Insulin Delivery Systems Among Patients with Type 1 Diabetes.

BACKGROUND: Clinical trials have demonstrated the efficacy and safety of hybrid closed-loop (HCL) systems, yet few studies have compared outcomes in the real-world setting. METHOD: This retrospective study analyzed patients from an academic endocrinology practice between January 1, 2018, and November 18, 2022. The inclusion criteria were diagnosis code for type I diabetes (T1D), >18 years of age, new to any HCL system [Medtronic 670G/770G (MT), Tandem Control IQ (CIQ), or Omnipod 5 (OP5)], and availability of a pump download within three months. The outcomes included %time in range (TIR) of 70 to 180 mg/dL, %time below range (TBR) <70 mg/dL at 90 days, and HbA1c for 91 to 180 days. RESULT: Of the 176 participants, 47 were MT, 74 CIQ, and 55 OP5. Median (25%, 75%) change in HbA1c was -0.1 (-0.8, 0.3), -0.6 (-1.1, -0.15), and -0.55 (-0.98, 0)% for MT, CIQ, and OP5, respectively, (P = .04). TIR was 70 (57, 76), 67 (59, 75), and 68 (60, 76)% (P = .95) at 90 days while TBR was 2 (1, 3), 1 (0, 2), and 1 (0, 1)%, respectively, (P = .002). The %time in automated delivery was associated with TIR and change in HbA1c. After controlling other factors including %time in automated delivery, HCL type was not an independent predictor of change in HbA1c nor TIR but remained a significant predictor of TBR. CONCLUSION: There were significant reductions in HbA1c in CIQ and OP5. TIR was similar across pumps, but TBR was highest with MT. The %time in automated delivery likely explains differences in change in HbA1c but not TBR between HCL systems.

A Medicaid-Funded Statewide Diabetes Quality Improvement Collaborative: Ohio 2020‒2022.

We used a collective impact model to form a statewide diabetes quality improvement collaborative to improve diabetes outcomes and advance diabetes health equity. Between 2020 and 2022, in collaboration with the Ohio Department of Medicaid, Medicaid Managed Care Plans, and Ohio's seven medical schools, we recruited 20 primary care practices across the state. The percentage of patients with hemoglobin A1c greater than 9% improved from 25% to 20% over two years. Applying our model more broadly could accelerate improvement in diabetes outcomes. (Am J Public Health. 2023;113(12):1254-1257. https://doi.org/10.2105/AJPH.2023.307410).

A Consensus Statement for Continuous Glucose Monitoring Metrics for Inpatient Clinical Trials.

Diabetes Technology Society organized an expert consensus panel to develop metrics for research in the use of continuous glucose monitors (CGMs) in a hospital setting. The experts met virtually in small groups both before and after an April 13, 2023 virtual meeting of the entire panel. The goal of the panel was to develop consensus definitions in anticipation of greater use of CGMs in hospital settings in the future. Establishment of consensus definitions of inpatient analytical metrics will be easier to compare outcomes between studies. Panelists defined terms related to 10 dimensions of measurements related to the use of CGMs including (1) hospital hypoglycemia, (2) hospital hyperglycemia, (3) hospital time in range, (4) hospital glycemic variability, (5) hospital glycemia risk index, (6) accuracy of CGM devices and reference methods for CGMs in the hospital, (7) meaningful time blocks for hospital glycemic goals, (8) hospital CGM data sufficiency, (9) using CGM data for insulin dosing, and (10) miscellaneous factors. The panelists voted on 51 proposed recommendations. Based on the panel vote, 51 recommendations were classified as either strong (43) or mild (8). Additional research is needed on CGM performance in the hospital. This consensus report is intended to support that type of research intended to improve outcomes for hospitalized people with diabetes.

Greater Combined Reductions of HbA1c ≥ 1.0% and Body Weight Loss ≥ 5.0% or ≥ 10.0% with Orally Administered Semaglutide Versus Comparators.

INTRODUCTION: A post hoc analysis of the PIONEER 1-5 and 8 trials assessed the clinically relevant composite endpoints of HbA1c (glycated haemoglobin) reduction ≥ 1% and body weight loss of ≥ 5% or ≥ 10% with orally administered semaglutide versus comparators. METHODS: In the PIONEER trials, people with type 2 diabetes were randomised to orally administered semaglutide versus placebo (PIONEER 1, 4, 5 and 8), empagliflozin (PIONEER 2), sitagliptin (PIONEER 3) and liraglutide (PIONEER 4) for 26-78 weeks. This analysis assessed the proportion of people achieving an HbA1c reduction of ≥ 1% and body weight loss of ≥ 5% at week 26 and at end of treatment, and the proportion of people achieving an HbA1c reduction of ≥ 1% and body weight loss of ≥ 10% at end of treatment. RESULTS: Overall, 3506 people in PIONEER 1-5 and 8 were included. At week 26 and at end of treatment, odds of achieving the composite endpoint of an HbA1c reduction of ≥ 1% and body weight loss of ≥ 5% were significantly greater with orally administered semaglutide 14 mg than with placebo (PIONEER 1, 4, 5 and 8; all p < 0.0001), empagliflozin 25 mg (PIONEER 2, p < 0.0001), sitagliptin 100 mg (PIONEER 3, p < 0.0001) and liraglutide 1.8 mg (PIONEER 4, p < 0.0001). Odds of achieving the composite endpoint of HbA1c reduction of ≥ 1% and body weight loss of ≥ 10% at end of treatment were also significantly greater with orally administered semaglutide versus comparators. CONCLUSION: In PIONEER 1-5 and 8, odds of achieving clinically relevant reductions in both HbA1c and body weight were significantly greater with orally administered semaglutide versus comparators.

Nursing Perspectives on the Use of Continuous Glucose Monitoring in the Intensive Care Unit.

BACKGROUND: The COVID-19 pandemic necessitated rapid implementation of continuous glucose monitoring (CGM) in the intensive care unit (ICU). Although rarely reported, perceptions from nursing staff who used the systems are critical for successful implementation and future expanded use of CGM in the inpatient setting. METHODS: A 22-item survey focused on CGM use was distributed to ICU nurses at two large academic medical centers in the United States in 2022. Both institutions initiated inpatient CGM in the spring of 2020 using the same CGM+point of care (POC) hybrid protocol. The survey employed a 1- to 5-point Likert scale regarding CGM sensor insertion, accuracy, acceptability, usability, training, and perceptions on workload. RESULTS: Of the 71 surveys completed, 68 (96%) nurses reported they cared for an ICU patient on CGM and 53% reported they had independently performed CGM sensor insertion. The ICU nurses overwhelmingly reported that CGM was accurate, reduced their workload, provided safer patient care, and was preferred over POC glucose testing alone. Interestingly, nearly half of nurses (49%) reported that they considered trend arrows in dosing decisions although trends were not included in the CGM+POC hybrid protocol. Nurses received training through multiple modalities, with the majority (80%) of nurses reporting that CGM training was sufficient and prepared them for its use. CONCLUSION: These results confirm nursing acceptance and preference for CGM use within a hybrid glucose monitoring protocol in the ICU setting. These data lay a blueprint for successful implementation and training strategies for future widespread use.

Implementation of Continuous Glucose Monitoring in Critical Care: A Scoping Review.

PURPOSE OF REVIEW: The aim of this review is to identify the implementation approaches, strategies, and outcomes for continuous glucose monitoring (CGM) in the intensive care unit (ICU). Medline and Web of Science databases were searched to report relevant literature published between September 12, 2016 and September 12, 2021. Implementation outcomes and strategies, defined by the Expert Recommendations for Implementing Change (ERIC) project, were extracted. RECENT FINDINGS: Of the 324 titles reviewed, 16 articles were included in the review. While no studies were identified as implementation research, 14 of 16 identified implementation strategies that aligned with ERIC definitions. Included studies described a multi-disciplinary approach. Clinical outcomes included Mean Absolute Relative Difference (MARD), ranging from 7.5 to 15.3%, and 33-71% reduction in frequency of point-of-care (POC) blood glucose monitoring (BGM) using hybrid protocols. This scoping review provides valuable insight into the process of CGM implementation in the ICU. Continued research should include implementation outcomes to inform widespread utilization.

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings.

BACKGROUND: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. METHODS: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. RESULTS: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. CONCLUSION: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Supporting the Use of a Person's Own Diabetes Technology in the Inpatient Setting.

The use of diabetes technology, including insulin pumps, continuous glucose monitoring devices, and automated insulin delivery systems, has increased significantly in recent years. As more people with diabetes adopt technology in the outpatient setting, we are seeing these devices more frequently in the inpatient setting. This review offers best-practice guidelines for the continuation of personal diabetes technology use in the inpatient setting. It describes policy and guideline stipulations, roles and responsibilities, and device- and brand-specific considerations. Although these devices are not approved for inpatient use by the U.S. Food and Drug Administration, there is general expert consensus that the continuation of personal diabetes devices during hospitalization is appropriate for patients who have sufficient knowledge, are not critically ill, and retain sufficient mental capacity during an acute illness. Health care systems and inpatient providers need to understand the benefits and limitations of personal diabetes technology use during hospitalization.

Rationale and Design for the Diabetes RElated to Acute Pancreatitis and Its Mechanisms Study: A Prospective Cohort Study From the Type 1 Diabetes in Acute Pancreatitis Consortium.

ABSTRACT: Acute pancreatitis (AP) is a disease characterized by an acute inflammatory phase followed by a convalescent phase. Diabetes mellitus (DM) was historically felt to be a transient phenomenon related to acute inflammation; however, it is increasingly recognized as an important late and chronic complication. There are several challenges that have prevented precisely determining the incidence rate of DM after AP and understanding the underlying mechanisms. The DREAM (Diabetes RElated to Acute Pancreatitis and its Mechanisms) Study is a prospective cohort study designed to address these and other knowledge gaps to provide the evidence needed to screen for, prevent, and treat DM after AP. In the following article, we summarize literature regarding the epidemiology of DM after AP and provide the rationale and an overview of the DREAM study.

Assessing the Pathophysiology of Hyperglycemia in the Diabetes RElated to Acute Pancreatitis and Its Mechanisms Study: From the Type 1 Diabetes in Acute Pancreatitis Consortium.

OBJECTIVES: The metabolic abnormalities that lead to diabetes mellitus (DM) after an episode of acute pancreatitis (AP) have not been extensively studied. This article describes the objectives, hypotheses, and methods of mechanistic studies of glucose metabolism that comprise secondary outcomes of the DREAM (Diabetes RElated to Acute pancreatitis and its Mechanisms) Study. METHODS: Three months after an index episode of AP, participants without preexisting DM will undergo baseline testing with an oral glucose tolerance test. Participants will be followed longitudinally in three subcohorts with distinct metabolic tests. In the first and largest subcohort, oral glucose tolerance tests will be repeated 12 months after AP and annually to assess changes in ß-cell function, insulin secretion, and insulin sensitivity. In the second, mixed meal tolerance tests will be performed at 3 and 12 months, then annually, and following incident DM to assess incretin and pancreatic polypeptide responses. In the third, frequently sampled intravenous glucose tolerance tests will be performed at 3 months and 12 months to assess the first-phase insulin response and more precisely measure ß-cell function and insulin sensitivity. CONCLUSIONS: The DREAM study will comprehensively assess the metabolic and endocrine changes that precede and lead to the development of DM after AP.

Influence of Literacy, Self-Efficacy, and Social Support on Diabetes-Related Outcomes Following Hospital Discharge.

Objective: To evaluate the relationship between health literacy, social support, and self-efficacy as predictors of change in A1c and readmission among hospitalized patients with type 2 diabetes (T2D). Methods: This is a secondary analysis of patients with T2D (A1c >8.5%) enrolled in a randomized trial in which health literacy (Newest Vital Sign), social support (Multidimensional Scale of Perceived Social Support), and empowerment (Diabetes Empowerment Scale-Short Form) was assessed at baseline. Multivariable models evaluated whether these concepts were associated with A1c reduction at 12 weeks (absolute change, % with >1% reduction, % reaching individualized target) and readmission (14 and 30 days). Results: A1c (N=108) decreased >1% in 60%, while individualized A1c target was achieved in 31%. After adjustment for baseline A1c and potential confounders, health literacy was associated with significant reduction in A1c (Estimate -0.21, 95% CI -0.40, -0.01, p=0.041) and >1% decrease in A1c (OR 1.37, 95% CI 1.08, 1.73, p=0.009). However, higher social support was associated with greater adjusted odds of reaching the individualized A1c target (OR 1.63, 95% CI 1.04, 2.55, p=0.32). Both higher empowerment (OR 0.23, 95% CI 0.08, 0.64, p=0.005) and social support (OR 0.57, 95% CI 0.36, 0.91, p=0.018) were associated with fewer readmissions by 14 days, but not 30 days. Conclusion: The study indicates that health literacy and social support may be important predictors of A1c reduction post-discharge among hospitalized patients with T2D. Social support and diabetes self-management skills should be addressed and early follow-up may be critical for avoiding readmissions. Clinical Trial: NCT03455985.

From Consultant to Co-investigator: One Diabetes Research Team's Journey of Patient Engagement.

Patients with diabetes are experts in the lived experience of self-management, making patient engagement beyond the role of research "subject" imperative for the creation of health care solutions that meaningfully address the problems they identify. We discuss our research team's relationship with our university's College Diabetes Network (CDN), an advocacy and support group for emerging adults with diabetes. Our collaborative research relationship has spanned three years, and multiple research studies with members serving as co-designers, consultants, and co-investigators. We discuss the CDN's role in two particular studies in which CDN members made substantive contributions to study design, instrument adaptation, and interpretation of findings. Key CDN members played a larger role in research activities and facilitated sustained engagement with the larger university CDN chapter. Barriers encountered included navigating research regulatory requirements while engaging CDN members in research and facilitating sustained engagement as CDN membership changes.

Individualizing Glycemic Control in the Critically Ill.

Hyperglycemia is a common phenomenon in critically ill patients, even in those without diabetes. Two landmark studies established the benefits of tight glucose control (blood glucose target 80-110 mg/dL) in surgical and medical patients. Since then, literature has consistently demonstrated that both hyperglycemia and hypoglycemia are independently associated with increased morbidity and mortality in a variety of critically ill patients. However, tight glycemic control has subsequently come into question due to risks of hypoglycemia and increased mortality. More recently, strategies targeting euglycemia (blood glucose ≤180 mg/dL) have been associated with improved outcomes, although the risk of hypoglycemia remains. More complex targets (ie, glycemic variability and time within target glucose range) and the impact of individual patient characteristics (ie, diabetic status and prehospital glucose control) have more recently been shown to influence the relationship between glycemic control and outcomes in critically ill patients. Although our understanding has increased, the optimal glycemic target is still unclear and glucose management strategies may require adjustment for individual patient characteristics. As glucose management increases in complexity, we realize that traditional means of using meters and strips and paper insulin titration algorithms are potential limitations to our success. To achieve these complex goals for glycemic control, the use of continuous or near-continuous glucose monitoring combined with computerized insulin titration algorithms may be required. The purpose of this review is to discuss the evidence surrounding the various domains of glycemic control and the emerging data supporting the need for individualized glucose targets in critically ill patients.

Cardiovascular risk and the implications for clinical practice of cardiovascular outcome trials in type 2 diabetes.

Cardiovascular disease (CVD) is the primary cause of morbidity and mortality in patients with type 2 diabetes (T2D). This review examines the impact of cardiovascular outcome trials (CVOTs) on clinical practice. To date, all CVOTs have shown non-inferiority versus placebo (both added to standard of care) against a primary endpoint of 3- or 4-point major adverse cardiovascular event (MACE), confirming CV safety of these treatments. Additionally, some CVOTs have shown superiority to placebo against the same MACE endpoint, suggesting a cardioprotective action for these treatments. This is reflected in guideline updates, which primary care physicians should consider when personalizing treatments.

REAL-WORLD IMPLICATIONS OF HYBRID CLOSE LOOP (HCL) INSULIN DELIVERY SYSTEM.

Objective: Clinical trial data demonstrates improved glycemic control with hybrid close loop (HCL) insulin delivery systems, yet limited real-world data exists. Data from the inaugural cohort of patients initiating a HCL system (Medtronic MiniMed™ 670G, Medtronic Canada, Brampton, ON) at a university medical center was used to examine real-world utilization and glycemic control following a standardized implementation process. Methods: Data from 34 adult patients with type 1 diabetes were obtained from pump downloads at 4 time points: (1) previous insulin pump, (2) HCL in manual-mode, (3) 2 weeks after HCL auto-mode transition, and (4) 6 to 12 weeks after initiation of HCL. In-person training by certified diabetes educators was performed across 3 sessions with phone and electronic messaging following auto-mode start. Results: Mean self-monitored blood glucose (SMBG) per day increased from 5.15 baseline to 6.49 at 6 to 12 weeks (P<.05) with 3.26 sensor calibrations per day. Time-in-auto-mode was 79.3% at 2 weeks and 72.3% at final follow-up, with 82% of patients spending >50% of time in auto-mode. There were 8.2 auto-mode exits over the final 14-day download. Time-in-target was 67.3% in manual-mode, 73.4% at 2 weeks (P = .09), and 71.7% by 6 to 12 weeks (P = .06). Hemoglobin A1c (HbA1c) decreased by 0.51% (P = .02), while total daily dose and % basal did not change. Patients with HbA1c <7.0% (53 mmol/mol) at baseline spent more time-in-target than those with HbA1c ≥7.0% (53 mmol/mol; 78.0% versus 67.5%) despite spending less time-in-auto-mode (66.5% versus 74.8%). Conclusion: These data illustrate real-world implementation of HCL technology using a structured education program within a major medical center. Overall benefit may vary based on baseline characteristics such as HbA1c. Abbreviations: CDE = certified diabetes educator; HbA1c = hemoglobin A1c; HCL = hybrid closed loop; SMBG = self-monitored blood glucose.

THE EFFICACY AND SAFETY OF CO-ADMINISTRATION OF SITAGLIPTIN WITH METFORMIN IN PATIENTS WITH TYPE 2 DIABETES AT HOSPITAL DISCHARGE.

OBJECTIVE: Few randomized controlled trials have focused on the optimal management of patients with type 2 diabetes (T2D) during the transition from the inpatient to outpatient setting. This multicenter open-label study explored a discharge strategy based on admission hemoglobin A1c (HbA1c) to guide therapy in general medicine and surgery patients with T2D. METHODS: Patients with HbA1c ≤7% (53 mmol/mol) were discharged on sitagliptin and metformin; patients with HbA1c between 7 and 9% (53-75 mmol/mol) and those >9% (75 mmol/mol) were discharged on sitagliptinmetformin with glargine U-100 at 50% or 80% of the hospital daily dose. The primary outcome was change in HbA1c at 3 and 6 months after discharge. RESULTS: Mean HbA1c on admission for the entire cohort (N = 253) was 8.70 ± 2.3% and decreased to 7.30 ± 1.5% and 7.30 ± 1.7% at 3 and 6 months ( P<.001). Patients with HbA1c <7% went from 6.3 ± 0.5% to 6.3 ± 0.80% and 6.2 ± 1.0% at 3 and 6 months. Patients with HbA1c between 7 and 9% had a reduction from 8.0 ± 0.6% to 7.3 ± 1.1% and 7.3 ± 1.3%, and those with HbA1c >9% from 11.3 ± 1.7% to 8.0 ± 1.8% and 8.0 ± 2.0% at 3 and 6 months after discharge (both P<.001). Clinically significant hypoglycemia (<54 mg/dL) was observed in 4%, 4%, and 7% among patients with a HbA1c <7%, 7 to 9%, and >9%, while a glucose <40 mg/dL was reported in <1% in all groups. CONCLUSION: The proposed HbA1c-based hospital discharge algorithm using a combination of sitagliptin-metformin was safe and significantly improved glycemic control after hospital discharge in general medicine and surgery patients with T2D. ABBREVIATIONS: BG = blood glucose; DPP-4 = dipeptidyl peptidase-4; eGFR = estimated glomerular filtration rate; HbA1c = hemoglobin A1c; T2D = type 2 diabetes.

Hypoglycaemia in hospitalized patients with or without heart failure.

Hospitalized patients with heart failure (HF) undergoing bedside glucose monitoring with subcutaneous insulin orders were retrospectively identified over 2 years. Hypoglycaemia was defined as any glucose value <3.9 mmol/L (70 mg/dL) within 24 hours of admission (Hypo1day ) or throughout the hospitalization (HypoT ) or any glucose value <2.2 mmol/L (40 mg/dL) throughout the hospitalization (HypoSevere ). A total of 13 424 patients were included, of whom 2484 had HF. Patients with HF were more likely to have Hypo1day (9.1% vs 7.0%, P = .0003), HypoT (28% vs 18.5%, P < .0001), or Hypo Severe (3.4% vs 2.1%, P = .0001). After controlling for other variables, the odds of Hypo1day were similar between the HF and non-HF groups (odds ratio [OR] 1.14, 95% CI 0.94-1.39, P = .18, fully adjusted model), slightly lower for HypoT (OR 0.85, 95% CI 0.73-0.99, P = .03, fully adjusted model), and similar for HypoSevere (OR 1.25, 95% CI 0.91-1.70, P = .17). Hypo1day , HypoT and HypoSevere were all associated with increased mortality; there was no evidence of an interaction by HF status. Hypoglycaemia occurred at a similar or lower frequency in hospitalized patients with HF compared to those without HF. Hypoglycaemia was associated with increased hospital mortality, regardless of HF status.

Tiered protocol implementation improves treatment of hypoglycaemia in a neurosciences critical care and surgical intensive care unit.

BACKGROUND: Although studies demonstrate techniques to limit hypoglycaemia in critically ill patients, there are limited data supporting methods to improve management of existing hypoglycaemia. OBJECTIVE: Assess the impact and sustainability of a computerised, three tiered, nurse driven protocol for hypoglycaemia treatment. DESIGN: Retrospective pre and post protocol study. SETTING: Neurosciences and surgical intensive care units at a tertiary academic medical centre. MEASUREMENTS: Patients with a hypoglycaemic episode were included during a pre-protocol or post-protocol implementation period. An additional six-month cohort was evaluated to assess sustainability. RESULTS: Fifty-four patients were included for evaluation (35 pre- and 19 post-protocol); 122 patients were included in the sustainability cohort. Hypoglycaemia treatment significantly improved in the post-protocol cohort (20% vs. 52.6%, p=0.014); with additional improvement to 79.5% in the sustainability cohort. Time to follow-up blood glucose was decreased after treatment from 122 [Q1-Q3: 46-242] minutes pre-protocol to 25 [Q1-Q3: 9-48] minutes post protocol (p<0.0001). This reduction was maintained in the sustainability cohort [median of 29min (Q1-Q3: 20-51)]. CONCLUSION: Implementation of a nurse-driven, three-tiered protocol for treatment of hypoglyacemia significantly improved treatment rates, as well as reduced time to recheck blood glucose measurement. These benefits were sustained during a six-month period after protocol implementation.