How Low Can You Go? Reducing Rates of Hypoglycemia in the Non-critical Care Hospital Setting

Towards a Region-Wide Glycaemic Management System: Strategies and Applications for Glycaemic Management of Patients with Diabetes During Hospitalisation

Objective

This study proposes a region-wide blood glucose management system to solve the problem of blood glucose management in patients with diabetes.

Methods

A professional team of doctors, nurses and dietitians jointly developed a region-wide blood glucose management system. The system operates through a collaborative approach where each team member utilises their specialised role, such as data monitoring, algorithm development or patient support, to contribute to a comprehensive blood glucose management network. This integration ensures accurate glucose tracking, personalised feedback and timely adjustments to treatment plans. The system allows the patient to have a good treatment plan, giving comprehensive medical guidance, and the physician team is responsible for the patient's health status.

Results

The region-wide blood glucose management system increased the overall blood glucose monitoring rate of patients and reduced the hospitalisation time (from 11.27 days to 9.52 days) and hospitalisation costs (from 12,173.8 yuan to 9502.4 yuan). At the same time, the system effectively counted the incidence and occurrence time of hyperglycaemia and hypoglycaemia adverse events, which can provide a reference for clinical prevention of adverse events.

Conclusion

A region-wide blood glucose management system can improve medical efficiency, save medical resources and provide a strong guarantee for the health of patients with diabetes. Compared with the traditional diabetes management mode, the region-wide blood glucose management system is more systematic and standardised, meaning it can better meet the needs of patients with diabetes.

Society for Ambulatory Anesthesia Updated Consensus Statement on Perioperative Blood Glucose Management in Adult Patients With Diabetes Mellitus Undergoing Ambulatory Surgery

This consensus statement is a comprehensive update of the 2010 Society for Ambulatory Anesthesia (SAMBA) Consensus Statement on perioperative blood glucose management in patients with diabetes mellitus (DM) undergoing ambulatory surgery. Since the original consensus guidelines in 2010, several novel therapeutic interventions have been introduced to treat DM, including new hypoglycemic agents and increasing prevalence of insulin pumps and continuous glucose monitors. The updated recommendations were developed by an expert task force under the provision of SAMBA and are based on a comprehensive review of the literature from 1980 to 2022. The task force included SAMBA members with expertise on this topic and those contributing to the primary literature regarding the management of DM in the perioperative period. The recommendations encompass preoperative evaluation of patients with DM presenting for ambulatory surgery, management of preoperative oral hypoglycemic agents and home insulins, intraoperative testing and treatment modalities, and blood glucose management in the postanesthesia care unit and transition to home after surgery. High-quality evidence pertaining to perioperative blood glucose management in patients with DM undergoing ambulatory surgery remains sparse. Recommendations are therefore based on recent guidelines and available literature, including general glucose management in patients with DM, data from inpatient surgical populations, drug pharmacology, and emerging treatment data. Areas in need of further research are also identified. Importantly, the benefits and risks of interventions and clinical practice information were considered to ensure that the recommendations maintain patient safety and are clinically valid and useful in the ambulatory setting. What Other Guidelines Are Available on This Topic? Since the publication of the SAMBA Consensus Statement for perioperative blood glucose management in the ambulatory setting in 2010, several recent guidelines have been issued by the American Diabetes Association (ADA), the American Association of Clinical Endocrinologists (AACE), the Endocrine Society, the Centre for Perioperative Care (CPOC), and the Association of Anaesthetists of Great Britain and Ireland (AAGBI) on DM care in hospitalized patients; however, none are specific to ambulatory surgery. How Does This Guideline Differ From the Previous Guidelines? Previously posed clinical questions that were outdated were revised to reflect current clinical practice. Additional questions were developed relating to the perioperative management of patients with DM to include the newer therapeutic interventions.

Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes

Artificial intelligence can use real-world data to create models capable of making predictions and medical diagnosis for diabetes and its complications. The aim of this commentary article is to provide a general perspective and present recent advances on how artificial intelligence can be applied to improve the prediction and diagnosis of six significant complications of diabetes including (1) gestational diabetes, (2) hypoglycemia in the hospital, (3) diabetic retinopathy, (4) diabetic foot ulcers, (5) diabetic peripheral neuropathy, and (6) diabetic nephropathy.

Electronic Health Record-Based Decision-Making Support in Inpatient Diabetes Management

PURPOSE OF REVIEW

This review discusses ways in which the electronic health record (EHR) can offer clinical decision support (CDS) tools for management of inpatient diabetes and hyperglycemia.

RECENT FINDINGS

The use of electronic order sets can help providers order comprehensive basal bolus insulin regimens that are consistent with current guidelines. Order sets have been shown to reduce insulin errors and hypoglycemia rates. They can also help set glycemic targets, give hemoglobin A1C reminders, guide weight-based dosing, and match insulin regimen to nutritional profile. Glycemic management dashboards allow multiple variables affecting blood glucose to be shown in a single view, which allows for efficient evaluation of glucose trends and adjustment of insulin regimen. With the use glycemic management dashboards, active surveillance and remote management also become feasible. Hypoglycemia prevention and management are another part of inpatient diabetes management that is enhanced by EHR CDS tools. Furthermore, diagnosis and management of diabetic ketoacidosis and hyperglycemia hyperosmolar state are improved with the aid of EHR CDS tools. The use of EHR CDS tools helps improve the care of patients with diabetes and hyperglycemia in the inpatient hospital setting.

The Role of the Diabetes Care and Education Specialist in the Hospital Setting

It is the position of Association of Diabetes Care & Education Specialists that all inpatient interdisciplinary teams include a diabetes care and education specialist to lead or support quality improvement initiatives that affect persons hospitalized with diabetes and/or hyperglycemia. This encompasses not only patient, family, and caregiver education but also education of interdisciplinary team members and achievement of diabetes-related organizational quality metrics and performance outcomes.

Development and Implementation of a Subcutaneous Insulin Clinical Decision Support Tool for Hospitalized Patients

BACKGROUND

Insulin is one of the highest risk medications used in hospitalized patients. Multiple complex factors must be considered in determining a safe and effective insulin regimen. We sought to develop a computerized clinical decision support (CDS) tool to assist hospital-based clinicians in insulin management.

METHODS

Adapting existing clinical practice guidelines for inpatient glucose management, a design team selected, configured, and implemented a CDS tool to guide subcutaneous insulin dosing in non-critically ill hospitalized patients at two academic medical centers that use the EpicCare® electronic medical record (EMR). The Agency for Healthcare Research and Quality (AHRQ) best practices in CDS design and implementation were followed.

RESULTS

A CDS tool was developed in the form of an EpicCare SmartForm, which generates an insulin regimen by integrating information about the patient's body weight, diabetes type, home and hospital insulin requirements, and nutritional status. Total daily recommended insulin doses are distributed into respective basal and nutritional doses with a tailored correctional insulin scale. Preimplementation, several approaches were used to communicate this new tool to clinicians, including emails, lectures, and videos. Postimplementation, a support team was available to address user technical issues. Feedback from stakeholders has been used to continuously refine the tool. Inclusion of the programming in the EMR vendor's community library has allowed dissemination of the tool outside our institution.

CONCLUSIONS

We have developed an EMR-based tool to guide SQ insulin dosing in non-critically ill hospitalized patients. Further studies are needed to evaluate adoption and clinical effectiveness of this intervention.

Development and validation of a prediction model for insulin-associated hypoglycemia in non-critically ill hospitalized adults

OBJECTIVE

To develop and validate a multivariable prediction model for insulin-associated hypoglycemia in non-critically ill hospitalized adults.

RESEARCH DESIGN AND METHODS

We collected pharmacologic, demographic, laboratory, and diagnostic data from 128 657 inpatient days in which at least 1 unit of subcutaneous insulin was administered in the absence of intravenous insulin, total parenteral nutrition, or insulin pump use (index days). These data were used to develop multivariable prediction models for biochemical and clinically significant hypoglycemia (blood glucose (BG) of ≤70 mg/dL and <54 mg/dL, respectively) occurring within 24 hours of the index day. Split-sample internal validation was performed, with 70% and 30% of index days used for model development and validation, respectively.

RESULTS

Using predictors of age, weight, admitting service, insulin doses, mean BG, nadir BG, BG coefficient of variation (CVBG), diet status, type 1 diabetes, type 2 diabetes, acute kidney injury, chronic kidney disease (CKD), liver disease, and digestive disease, our model achieved a c-statistic of 0.77 (95% CI 0.75 to 0.78), positive likelihood ratio (+LR) of 3.5 (95% CI 3.4 to 3.6) and negative likelihood ratio (-LR) of 0.32 (95% CI 0.30 to 0.35) for prediction of biochemical hypoglycemia. Using predictors of sex, weight, insulin doses, mean BG, nadir BG, CVBG, diet status, type 1 diabetes, type 2 diabetes, CKD stage, and steroid use, our model achieved a c-statistic of 0.80 (95% CI 0.78 to 0.82), +LR of 3.8 (95% CI 3.7 to 4.0) and -LR of 0.2 (95% CI 0.2 to 0.3) for prediction of clinically significant hypoglycemia.

CONCLUSIONS

Hospitalized patients at risk of insulin-associated hypoglycemia can be identified using validated prediction models, which may support the development of real-time preventive interventions.

Clinical characteristics and outcomes of symptomatic and asymptomatic hypoglycemia in hospitalized patients with diabetes

IMPORTANCE

The frequency and impact of asymptomatic hypoglycemia in hospitalized patients with diabetes is not known.

OBJECTIVE

We determined the clinical characteristics and hospital outcomes of general medicine and surgery patients with symptomatic and asymptomatic hypoglycemia.

RESEARCH DESIGN AND METHODS

Prospective observational study in adult patients with diabetes and blood glucose (BG) <70 mg/dL. Participants were interviewed about signs and symptoms of hypoglycemia using a standardized questionnaire. Precipitating causes, demographics, insulin regimen, and complications data during admission was collected.

RESULTS

Among 250 patients with hypoglycemia, 112 (44.8%) patients were asymptomatic and 138 (55.2%) had symptomatic hypoglycemia. Patients with asymptomatic hypoglycemia were older (59±11 years vs 54.8±13 years, p=0.003), predominantly males (63% vs 48%, p=0.014), and had lower admission glycosylated hemoglobin (8.2%±2.6 % vs 9.1±2.9%, p=0.006) compared with symptomatic patients. Compared with symptomatic patients, those with asymptomatic hypoglycemia had higher mean BG during the episode (60.0±8 mg/dL vs 53.8±11 mg/dL, p<0.001). In multivariate analysis, male gender (OR 2.08, 95% CI 1.13 to 3.83, p=0.02) and age >65 years (OR 4.01, 95% CI 1.62 to 9.92, p=0.02) were independent predictors of asymptomatic hypoglycemia. There were no differences in clinical outcome, composite of hospital complications (27% vs 22%, p=0.41) or in-hospital length of stay (8 days (IQR 4-14) vs 7 days (IQR 5-15), p=0.92)) between groups.

CONCLUSIONS

Asymptomatic hypoglycemia was common among insulin-treated patients with diabetes but was not associated with worse clinical outcome compared with patients with symptomatic hypoglycemia. Older age and male gender were independent risk factors for asymptomatic hypoglycemia.