Subcutaneous Insulin Dosing Calculators for Inpatient Glucose Control

Glycemic Management of Patients with Hospital Hyperglycemia: A Retrospective Cohort Study on Adults Admitted in the Non-ICU Wards

Purpose

To identify the key populations for Hospital Hyperglycemia (HH) management and to assess recent trends in the management of HH.

Patients and Methods

This retrospective study analyzed 1,136,092 point-of-care blood glucose (POC-BG) measurements from 40,758 patients with HH in non-intensive care unit (non-ICU) wards at Ningbo No.2 hospital from January 2020 to December 2022. We compared glucose monitoring and management across varying years, age groups, and hospital departments.

Results

The overall incidence of HH was 16.87%. From 2020 to 2022, the number of patients with HH increased from 9,893 to 15,639, accompanied by a marginal improvement in average BG levels (slope difference, -8.137E-09 [CI, -8.742E-09 to -7.531E-09]; p <0.001). In the ≥80 years group, the median BG was 9.4 mmol/L, significantly higher than in other age groups (p<0.001). Hypoglycemia in this group was most frequently detected during nighttime and bedtime, with an incidence of 2.67%, significantly higher than at other times of the day (p<0.001). The daily POC-BG testing rate was significantly higher in the medical ward group than it in the surgical ward group (57.9% vs 51.7%, p<0.05). Proportions of glycemic targets days were 35.66% and 39.90% in the medical wards on day 1 and day 7, respectively (Day 7 39.90% vs Day 1 35.66%, p>0.05), and 46.16% and 45.07% in the surgical wards (Day 7 45.07% vs Day 1 46.16%, p>0.05), showing no significant improvements in glycemic control. Endocrinology consultations occurred at rates of 14.2% in the medical wards and 14.9% in the surgical wards (p>0.05).

Conclusion

Although the prevalence of HH is consistently high and the number of affected patients continues to rise, modest improvements in glycemic management have been observed. However, control among the elderly remains poor, with a notably high risk of hypoglycemia during nighttime and bedtime periods.

Results From a Randomized Trial of Intensive Glucose Management Using CGM Versus Usual Care in Hospitalized Adults With Type 2 Diabetes: The TIGHT Study

OBJECTIVE

To evaluate whether continuous glucose monitoring (CGM) could assist providers in intensifying glycemic management in hospitalized patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

At six academic hospitals, adults with type 2 diabetes hospitalized in a non-intensive care setting were randomly assigned to either standard therapy with glucose target 140-180 mg/dL (standard group) or intensive therapy with glucose target 90-130 mg/dL guided by CGM (intensive group). The primary outcome was mean glucose measured with CGM (blinded in standard group), and the key secondary outcome was CGM glucose <54 mg/dL.

RESULTS

For the 110 participants included in the primary analysis, mean ± SD age was 61 ± 12 years and mean HbA1c was 8.9 ± 2.3% (73.8 ± 1.6 mmol/mol). During the study, CGM-measured mean glucose was 170 mg/dL for the intensive group (n = 60) vs. 175 mg/dL for the standard group (n = 50; risk-adjusted difference -7 mg/dL, 95% CI -19 to 5; P = 0.25). Only 7% of the intensive group achieved the mean glucose target range of 90-130 mg/dL. CGM readings <54 mg/dL were infrequent (0.2% for intensive and 0.4% for standard; adjusted treatment group difference -0.1%, 95% CI -0.6 to 0.3). One severe hypoglycemia event occurred in the standard group.

CONCLUSIONS

The study's glucose management approach using CGM did not improve glucose levels compared with standard glucose management in the non-intensive care unit hospital setting. A glucose target of 90-130 mg/dL may not be realistic in the current environment of insulin management in the hospital.

Development and Validation of Inpatient Hypoglycemia Models Centered Around the Insulin Ordering Process

BACKGROUND

The insulin ordering process is an opportunity to provide clinicians with hypoglycemia risk predictions, but few hypoglycemia models centered around the insulin ordering process exist.

METHODS

We used data on adult patients, admitted in 2019 to non-ICU floors of a large teaching hospital, who had orders for subcutaneous insulin. Our outcome was hypoglycemia, defined as a blood glucose (BG) <70 mg/dL within 24 hours after ordering insulin. We trained and evaluated models to predict hypoglycemia at the time of placing an insulin order, using logistic regression, random forest, and extreme gradient boosting (XGBoost). We compared performance using area under the receiver operating characteristic curve (AUCs) and precision-recall curves. We determined recall at our goal precision of 0.30.

RESULTS

Of 21 052 included insulin orders, 1839 (9%) were followed by a hypoglycemic event within 24 hours. Logistic regression, random forest, and XGBoost models had AUCs of 0.81, 0.80, and 0.79, and recall of 0.44, 0.49, and 0.32, respectively. The most significant predictor was the lowest BG value in the 24 hours preceding the order. Predictors related to the insulin order being placed at the time of the prediction were useful to the model but less important than the patient's history of BG values over time.

CONCLUSIONS

Hypoglycemia within the next 24 hours can be predicted at the time an insulin order is placed, providing an opportunity to integrate decision support into the medication ordering process to make insulin therapy safer.

Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review

BACKGROUND

Providing comprehensive and individualized diabetes care remains a significant challenge in the face of the increasing complexity of diabetes management and a lack of specialized endocrinologists to support diabetes care. Clinical decision support systems (CDSSs) are progressively being used to improve diabetes care, while many health care providers lack awareness and knowledge about CDSSs in diabetes care. A comprehensive analysis of the applications of CDSSs in diabetes care is still lacking.

OBJECTIVE

This review aimed to summarize the research landscape, clinical applications, and impact on both patients and physicians of CDSSs in diabetes care.

METHODS

We conducted a scoping review following the Arksey and O'Malley framework. A search was conducted in 7 electronic databases to identify the clinical applications of CDSSs in diabetes care up to June 30, 2022. Additional searches were conducted for conference abstracts from the period of 2021-2022. Two researchers independently performed the screening and data charting processes.

RESULTS

Of 11,569 retrieved studies, 85 (0.7%) were included for analysis. Research interest is growing in this field, with 45 (53%) of the 85 studies published in the past 5 years. Among the 58 (68%) out of 85 studies disclosing the underlying decision-making mechanism, most CDSSs (44/58, 76%) were knowledge based, while the number of non-knowledge-based systems has been increasing in recent years. Among the 81 (95%) out of 85 studies disclosing application scenarios, the majority of CDSSs were used for treatment recommendation (63/81, 78%). Among the 39 (46%) out of 85 studies disclosing physician user types, primary care physicians (20/39, 51%) were the most common, followed by endocrinologists (15/39, 39%) and nonendocrinology specialists (8/39, 21%). CDSSs significantly improved patients' blood glucose, blood pressure, and lipid profiles in 71% (45/63), 67% (12/18), and 38% (8/21) of the studies, respectively, with no increase in the risk of hypoglycemia.

CONCLUSIONS

CDSSs are both effective and safe in improving diabetes care, implying that they could be a potentially reliable assistant in diabetes care, especially for physicians with limited experience and patients with limited access to medical resources.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.37766/inplasy2022.9.0061.

Smart Insulin Pens: Advancing Digital Transformation and a Connected Diabetes Care Ecosystem

With the first commercially available smart insulin pens, the predominant insulin delivery device for millions of people living with diabetes is now coming into the digital age. Smart insulin pens (SIPs) have the potential to reshape a connected diabetes care ecosystem for patients, providers, and health systems. Existing SIPs are enhanced with real-time wireless connectivity, digital dose capture, and integration with personalized dosing decision support. Automatic dose capture can promote effective retrospective review of insulin dose data, particularly when paired with glucose data. Patients, providers, and diabetes care teams will be able to make increasingly data-driven decisions and recommendations, in real time, during scheduled visits, and in a more continuous, asynchronous care model. As SIPs continue to progress along the path of digital transformation, we can expect additional benefits: iteratively improving software, machine learning, and advanced decision support. Both these technological advances, and future care delivery models with asynchronous interactions, will depend on easy, open, and continuous data exchange between the growing number of diabetes devices. SIPs have a key role in modernizing diabetes care for a large population of people living with diabetes.

Pathophysiologic Signature of Impending ICU Hypoglycemia in Bedside Monitoring and Electronic Health Record Data: Model Development and External Validation

OBJECTIVES

We tested the hypothesis that routine monitoring data could describe a detailed and distinct pathophysiologic phenotype of impending hypoglycemia in adult ICU patients.

DESIGN

Retrospective analysis leading to model development and validation.

SETTING

All ICU admissions wherein patients received insulin therapy during a 4-year period at the University of Virginia Medical Center. Each ICU was equipped with continuous physiologic monitoring systems whose signals were archived in an electronic data warehouse along with the entire medical record.

PATIENTS

Eleven thousand eight hundred forty-seven ICU patient admissions.

INTERVENTIONS

The primary outcome was hypoglycemia, defined as any episode of blood glucose less than 70 mg/dL where 50% dextrose injection was administered within 1 hour. We used 61 physiologic markers (including vital signs, laboratory values, demographics, and continuous cardiorespiratory monitoring variables) to inform the model.

MEASUREMENTS AND MAIN RESULTS

Our dataset consisted of 11,847 ICU patient admissions, 721 (6.1%) of which had one or more hypoglycemic episodes. Multivariable logistic regression analysis revealed a pathophysiologic signature of 41 independent variables that best characterized ICU hypoglycemia. The final model had a cross-validated area under the receiver operating characteristic curve of 0.83 (95% CI, 0.78-0.87) for prediction of impending ICU hypoglycemia. We externally validated the model in the Medical Information Mart for Intensive Care III critical care dataset, where it also demonstrated good performance with an area under the receiver operating characteristic curve of 0.79 (95% CI, 0.77-0.81).

CONCLUSIONS

We used data from a large number of critically ill inpatients to develop and externally validate a predictive model of impending ICU hypoglycemia. Future steps include incorporating this model into a clinical decision support system and testing its effects in a multicenter randomized controlled clinical trial.

Management of diabetes and hyperglycaemia in the hospital

Hyperglycaemia in people with and without diabetes admitted to the hospital is associated with a substantial increase in morbidity, mortality, and health-care costs. Professional societies have recommended insulin therapy as the cornerstone of inpatient pharmacological management. Intravenous insulin therapy is the treatment of choice in the critical care setting. In non-intensive care settings, several insulin protocols have been proposed to manage patients with hyperglycaemia; however, meta-analyses comparing different treatment regimens have not clearly endorsed the benefits of any particular strategy. Clinical guidelines recommend stopping oral antidiabetes drugs during hospitalisation; however, in some countries continuation of oral antidiabetes drugs is commonplace in some patients with type 2 diabetes admitted to hospital, and findings from clinical trials have suggested that non-insulin drugs, alone or in combination with basal insulin, can be used to achieve appropriate glycaemic control in selected populations. Advances in diabetes technology are revolutionising day-to-day diabetes care and work is ongoing to implement these technologies (ie, continuous glucose monitoring, automated insulin delivery) for inpatient care. Additionally, transformations in care have occurred during the COVID-19 pandemic, including the use of remote inpatient diabetes management-research is needed to assess the effects of such adaptations.

Impact of Initial eGlycemic Management System Dosing Strategy on Time to Target Blood Glucose Range

BACKGROUND

Glucommander™ (GM), an electronic glycemic management system, was implemented across a multi-hospital health system as the standard of care for glycemic control. GM provides insulin dosing recommendations based on patient-specific blood glucose (BG) trends after providers select either a custom dose or weight-based multiplier as the initial dosing strategy for the first 24 hours. This study evaluated the impact of initial subcutaneous (SC) GM insulin dosing strategies on glycemic management.

METHODS

Non-intensive care unit patients treated with SC GM using either initial custom (based on provider discretion) or weight-based multiplier settings (0.3, 0.5, or 0.7 units/kg/day) were evaluated in this retrospective chart review. The primary endpoint was time to target BG range defined as time to first two consecutive in range point of care BG. Secondary endpoints included percentage of BG values in target range, percentage of orders following institutional recommendations, length of stay (LOS), average BG, and incidence of hypoglycemia and hyperglycemia.

RESULTS

A review of 348 patients showed time to target BG was not significantly different between custom and multiplier groups (55 vs 64 hours, P = .07). Target BG was achieved in less than half of patients in both groups (47% vs 44%, respectively). There were no differences in hospital LOS, proportion of BG in target range, rates of hypo/hyperglycemia, and average BG.

CONCLUSIONS

Custom initial SC GM insulin dosing settings showed a nonsignificant decrease in time to target BG range compared to pre-defined multiplier settings. Future studies evaluating the impact of compliance with institutional recommendations on BG control are warranted.

Inpatient and Outpatient Technologies to Assist in the Management of Insulin Dosing

Several new technologies use computer algorithms to analyze a person's blood glucose response to insulin treatment, calculate the person's next recommended insulin dose, advise the person regarding when to check blood glucose next, and provide alerts regarding glucose control for the individual patient or across a hospital system. This article reviews U.S. Food and Drug Administration (FDA)-approved products designed to help manage insulin dosing for inpatients, as well as those available to provide people with insulin-requiring diabetes support in making adjustments to their basal and/or mealtime insulin doses. Many of these products have a provider interface that allows for remote monitoring of patients' glucose readings and insulin doses. By alleviating some of the burdens of insulin initiation and dose adjustment, these products may facilitate improved glycemic management and patient outcomes.

Review of Intravenous and Subcutaneous Electronic Glucose Management Systems for Inpatient Glycemic Control

PURPOSE OF REVIEW

The goal of this review is to summarize current literature on electronic glucose management systems (eGMS) and discuss their benefits and disadvantages in the inpatient setting.

RECENT FINDINGS

We review different versions of commercially available eGMS: Glucommander™ (Glytec, Greenville, SC), EndoTool^R (MD Scientific LLC, Charlotte, NC), GlucoStabilizer™ (Medical Decision Network, Charlottesville, VA), GlucoCare™ (Pronia Medical Systems, KY), and discuss advantages such as reducing rates of hypoglycemia, hyperglycemia, and glycemic variability. In addition, eCGMs offer a uniform standard of care and may improve workflows across institutions as well reduce barriers. Despite ample literature on intravenous (IV) versions of eGMS, there is little published research on subcutaneous (SQ) insulin guidance. Although use of eGMS requires extensive training and institution-wide adoption, time spent on diabetes management is better facilitated by their use.

Inpatient Hypoglycemia: The Challenge Remains

Hypoglycemia in inpatients with diabetes remains the most common complication of diabetes therapies. Hypoglycemia is independently associated with increased morbidity and mortality, increased length of stay, increased readmission rate, and increased cost. This review describes the importance of reporting and addressing inpatient hypoglycemia; it further summarizes eight strategies that aid clinicians in the prevention of inpatient hypoglycemia: auditing the electronic medical record, formulary restrictions and dose-limiting strategies, hyperkalemia order sets, electronic glucose management systems, prediction tools, diabetes self-management, remote surveillance, and noninsulin medications.