A Systematic Approach for the Prevention and Reduction of Hypoglycemia in Hospitalized Patients

[Point-of-Care Blood Glucose Testing: Post-Market Performance Assessment of the Accu-Chek Inform II Hospital-Use Glucose Meter]

BACKGROUND

A point-of-care glucose testing (POCT) is an essential component of care in patients with hyperglycemia and hypoglycemia in inpatient and outpatient settings. In Russian medical facilities (MFs), conventional glucose meters designed for self-monitoring by patients with diabetes are commonly used for POCT. These home-use meters have two serious disadvantages: the first is large measurement bias and the second - they can't be integrated into laboratory information systems, so measurement data have to be recorded into patient charts manually. Both factors may lead to medical errors. It is reasonable to use in the MFs specialized POCT glucose meters, as they are superior to conventional ones in accuracy and may be easily connected to laboratory information systems. With this in mind, physicians at the Russian Children's Clinical Hospital decided to substitute conventional meters with the Accu-Chek Inform II POCT meter, however, after preliminary performance assessment of the model.

AIM

To test the Accu-Chek Inform II performance characteristics: accuracy, linearity, repeatability, and mean absolute relative difference (MARD).

MATERIALS AND METHODS

Performance of the Accu-Chek Inform II was tested by comparing the results of parallel CGL measurements with the meter and reference laboratory analyzer in capillary blood samples. Overall, 99 parallel CGL measurements were made in 45 samples. Accuracy was evaluated according to the ISO 15197-2013 and POCT12-A3 criteria.

RESULTS

The Accu-Chek Inform II meter met the requirements of ISO 15197-2013 and POCT12-A3 and demonstrated high linearity (correlation coefficient, r=1,0), good repeatability (mean coefficient of variation, CV=1,38%) and acceptable MARD (4,9%).

CONCLUSION

The Accu-Chek Inform II POCT glucose meter may be efficiently and safely used in inpatient and outpatient MFs and particularly in pediatric clinics.

Hypoglycemia Management Using a Bundled Care Approach: A Quality Improvement Project

BACKGROUND

Hypoglycemia is a leading cause of preventable hospitalization, and can increase morbidity, mortality, and length of hospital stay. Up to 35% of diabetic patients experience severe hypoglycemia during hospitalization; this concerns veterans, as 25% have been diagnosed with diabetes.

LOCAL PROBLEM

A medical-surgical unit in a Veterans Affairs facility saw increased hypoglycemic episodes, with 26.8 episodes per 1000 patient days. Staff noted knowledge deficits with how to manage hypoglycemia episodes.

METHODS

A pre-/post-implementation quality improvement project was conducted over 8 weeks.

INTERVENTIONS

An implementation bundle was used to improve hypoglycemic episodes, including patient and staff education, coordination between meal delivery and insulin coverage, and developing a hypoglycemia protocol.

RESULTS

Hypoglycemia rates significantly decreased to 10.27 per 1000 patient days ( P = .001), and occasions where insulin was given with food increased significantly to 76.2% ( P < .001).

CONCLUSIONS

A bundled approach was effective in decreasing hypoglycemia episodes and improved consistent management of hypoglycemia.

Nutritional intervention for diabetes mellitus with Alzheimer's disease

The combined disease burden of diabetes mellitus (DM) and Alzheimer's disease (AD) is increasing, and the two diseases share some common pathological changes. However, the pharmacotherapeutic approach to this clinical complexity is limited to symptomatic rather than disease-arresting, with the possible exception of metformin. Whether nutritional intervention might extend or synergize with these effects of metformin is of interest. In particular, dietary patterns with an emphasis on dietary diversity shown to affect cognitive function are of growing interest in a range of food cultural settings. This paper presents the association between diabetes and AD. In addition, the cross-cultural nutritional intervention programs with the potential to mitigate both insulin resistance (IR) and hyperglycemia, together with cognitive impairment are also reviewed. Both dietary patterns and nutritional supplementation showed the effects of improving glycemic control and reducing cognitive decline in diabetes associated with AD, but the intervention specificity remained controversial. Multi-nutrient supplements combined with diverse diets may have preventive and therapeutic potential for DM combined with AD, at least as related to the B vitamin group and folate-dependent homocysteine (Hcy). The nutritional intervention has promise in the prevention and management of DM and AD comorbidities, and more clinical studies would be of nutritional scientific merit.

Machine Learning Models for Inpatient Glucose Prediction

PURPOSE OF REVIEW

Glucose management in the hospital is difficult due to non-static factors such as antihyperglycemic and steroid doses, renal function, infection, surgical status, and diet. Given these complex and dynamic factors, machine learning approaches can be leveraged for prediction of glucose trends in the hospital to mitigate and prevent suboptimal hypoglycemic and hyperglycemic outcomes. Our aim was to review the clinical evidence for the role of machine learning-based models in predicting hospitalized patients' glucose trajectory.

RECENT FINDINGS

The published literature on machine learning algorithms has varied in terms of population studied, outcomes of interest, and validation methods. There have been tools developed that utilize data from both continuous glucose monitors and large electronic health records (EHRs). With increasing sample sizes, inclusion of a greater number of predictor variables, and use of more advanced machine learning algorithms, there has been a trend in recent years towards increasing predictive accuracy for glycemic outcomes in the hospital setting. While current models predicting glucose trajectory offer promising results, they have not been tested prospectively in the clinical setting. Accurate machine learning algorithms have been developed and validated for prediction of hypoglycemia and hyperglycemia in the hospital. Further work is needed in implementation/integration of machine learning models into EHR systems, with prospective studies to evaluate effectiveness and safety of such clinical decision support on glycemic and other clinical outcomes.

Stakeholder Perspectives on an Inpatient Hypoglycemia Informatics Alert: Mixed Methods Study

Background

Iatrogenic hypoglycemia is a common occurrence among hospitalized patients and is associated with poor clinical outcomes and increased mortality. Clinical decision support systems can be used to reduce the incidence of this potentially avoidable adverse event.

Objective

This study aims to determine the desired features and functionality of a real-time informatics alert to prevent iatrogenic hypoglycemia in a hospital setting.

Methods

Using the Agency for Healthcare Research and Quality Five Rights of Effective Clinical Decision Support Framework, we conducted a mixed methods study using an electronic survey and focus group sessions of hospital-based providers. The goal was to elicit stakeholder input to inform the future development of a real-time informatics alert to target iatrogenic hypoglycemia. In addition to perceptions about the importance of the problem and existing barriers, we sought input regarding the content, format, channel, timing, and recipient for the alert (ie, the Five Rights). Thematic analysis of focus group sessions was conducted using deductive and inductive approaches.

Results

A 21-item electronic survey was completed by 102 inpatient-based providers, followed by 2 focus group sessions (6 providers per session). Respondents universally agreed or strongly agreed that inpatient iatrogenic hypoglycemia is an important problem that can be addressed with an informatics alert. Stakeholders expressed a preference for an alert that is nonintrusive, accurate, communicated in near real time to the ordering provider, and provides actionable treatment recommendations. Several electronic medical record tools, including alert indicators in the patient header, glucose management report, and laboratory results section, were deemed acceptable formats for consideration. Concerns regarding alert fatigue were prevalent among both survey respondents and focus group participants.

Conclusions

The design preferences identified in this study will provide the framework needed for an informatics team to develop a prototype alert for pilot testing and evaluation. This alert will help meet the needs of hospital-based clinicians caring for patients with diabetes who are at a high risk of treatment-related hypoglycemia.

Analysis of inpatient and high-risk medicine pharmacist interventions associated with insulin prescribing for hospital inpatients with diabetes

Background Insulin is a high-risk medicine, associated with hospital medication errors. Pharmacists play an important role in the monitoring of patients on insulin.Objective To analyse interventions made by hospital pharmacists that were associated with insulin prescribing for inpatients with diabetes.Method Retrospective audit of pharmacist interventions for adult inpatients for an 8-month period, 1 June 2019-31 January 2020. Pharmacist interventions recorded in the electronic medication management system by inpatient unit and dedicated high-risk medicine pharmacists were extracted, screened, and analysed.Results Of 3975 pharmacist interventions 3356 (84.43%) were recorded by high-risk medicine pharmacists and 619 (15.57%) by inpatient unit pharmacists. July and August 2019 had the highest numbers of interventions with 628 and 643 (15.80 and 16.18%) respectively. Most of the interventions, namely 3410 (85.79%) were classified as medicine optimisation interventions and 565 (14.21%) as prescribing errors. In the medicine optimisation intervention category, 2985 (75.09%) were due to insulin not charted for ongoing administration.Conclusion This study provides insights into pharmacist interventions for inpatients on insulin, showing that high-risk medicine pharmacists recorded most interventions. The classification of the insulin interventions into medicine optimisation and prescribing errors provides useful information for the training of prescribers in insulin management.

Evaluating the Impact of Inadequate Meal Consumption on Insulin-Related Hypoglycemia in Hospitalized Patients

OBJECTIVE

Meal intake is sometimes reduced in hospitalized patients. Meal-time insulin administration can cause hypoglycemia when a meal is not consumed. Inpatient providers may avoid ordering meal-time insulin due to hypoglycemia concerns, which can result in hyperglycemia. The frequency of reduced meal intake in hospitalized patients remains inadequately determined. This quality improvement project evaluates the percentage of meals consumed by hospitalized patients with insulin orders and the resulting risk of postmeal hypoglycemia (blood glucose [BG] <70 mg/dL, <3.9 mmol/L).

METHODS

This was a retrospective quality improvement project evaluating patients with any subcutaneous insulin orders hospitalized at a regional academic medical center between 2015 and 2017. BG, laboratory values, point of care, insulin administration, diet orders, and percentage of meal consumed documented by registered nurses were abstracted from electronic health records.

RESULTS

Meal consumption ≥50% was observed for 85% of meals with insulin orders, and bedside registered nurses were accurate at estimating this percentage. Age ≥65 years was a risk factor for reduced meal consumption (21% of meals 0%-49% consumed, P < .05 vs age < 65 years [12%]). Receiving meal-time insulin and then consuming only 0% to 49% of a meal (defined here as a mismatch) was not rare (6% of meals) and increased postmeal hypoglycemia risk. However, the attributable risk of postmeal hypoglycemia due to this mismatch was low (4 events per 1000) in patients with premeal BG between 70 and 180 mg/dL.

CONCLUSION

This project demonstrates that hospitalized patients treated with subcutaneous insulin have a low attributable risk of postmeal hypoglycemia related to inadequate meal intake.

Continuous Glucose Monitors and Automated Insulin Dosing Systems in the Hospital Consensus Guideline

This article is the work product of the Continuous Glucose Monitor and Automated Insulin Dosing Systems in the Hospital Consensus Guideline Panel, which was organized by Diabetes Technology Society and met virtually on April 23, 2020. The guideline panel consisted of 24 international experts in the use of continuous glucose monitors (CGMs) and automated insulin dosing (AID) systems representing adult endocrinology, pediatric endocrinology, obstetrics and gynecology, advanced practice nursing, diabetes care and education, clinical chemistry, bioengineering, and product liability law. The panelists reviewed the medical literature pertaining to five topics: (1) continuation of home CGMs after hospitalization, (2) initiation of CGMs in the hospital, (3) continuation of AID systems in the hospital, (4) logistics and hands-on care of hospitalized patients using CGMs and AID systems, and (5) data management of CGMs and AID systems in the hospital. The panelists then developed three types of recommendations for each topic, including clinical practice (to use the technology optimally), research (to improve the safety and effectiveness of the technology), and hospital policies (to build an environment for facilitating use of these devices) for each of the five topics. The panelists voted on 78 proposed recommendations. Based on the panel vote, 77 recommendations were classified as either strong or mild. One recommendation failed to reach consensus. Additional research is needed on CGMs and AID systems in the hospital setting regarding device accuracy, practices for deployment, data management, and achievable outcomes. This guideline is intended to support these technologies for the management of hospitalized patients with diabetes.

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.

Effect of a Standardized Treatment Panel on Hypoglycemic Events in Hospitalized Acute Hyperkalemic Patients Treated With Intravenous Regular Insulin

Purpose: Regular insulin is a commonly utilized treatment option for acute hyperkalemia. Despite its benefit, hypoglycemia and associated morbidity/mortality remain important concerns. This institution recently created a treatment panel to standardize regular insulin dosing (0.1 unit/kg) and blood glucose (BG) monitoring in patients with acute hyperkalemia. The purpose of this study is to investigate whether the order panel reduces hypoglycemic events in adults treated with intravenous (IV) regular insulin for hyperkalemia and to determine the effect the treatment panel has on regular insulin dosing, serum potassium, BG monitoring, and dextrose supplementation. Methods: This retrospective study was performed at a single academic medical center. Adults receiving IV regular insulin for acute hyperkalemia were included if BG was assessed prior to and following regular insulin administration. Primary outcome was hypoglycemia within 4 hours of regular insulin administration. Secondary outcomes were the change from baseline serum potassium, frequency of severe hypoglycemia, BG checks within 30 minutes prior to and within 4 hours following insulin administration, regular insulin dosing, and administration of dextrose 50% in water (D50W) following regular insulin administration. Hypoglycemia and severe hypoglycemia were defined as a BG concentration of <70 mg/dL and <50 mg/dL, respectively. Results: One hundred sixty-five patients were included; 75 using the treatment panel and 90 not. Patients using the treatment panel received a lower median (interquartile range [IQR]) regular insulin dose (.10 [0.09-0.10 unit/kg] vs 0.11 [0.09-0.14 unit/kg], P = .004) and had more frequent BG checks during the 4 hours following regular insulin administration (median [IQR]: 4 [3-5] vs 2 [1-3], P < .001). Hypoglycemia (13.3% vs 27.8%, P = .024) and severe hypoglycemia (2.7% vs 11.1%, P = .038) occurred less frequently with the treatment panel. Similar decreases in serum potassium were noted following IV regular insulin administration. Conclusions: Acute hyperkalemic patients utilizing a standardized treatment panel for the dosing and monitoring of IV regular insulin experienced fewer hypoglycemic and severe hypoglycemic episodes and had similar potassium lower effects. The treatment panel decreased regular insulin dosing and increased BG monitoring prior to and following regular insulin administration.