USE OF A COMPUTER-GUIDED GLUCOSE MANAGEMENT SYSTEM TO IMPROVE GLYCEMIC CONTROL AND ADDRESS NATIONAL QUALITY MEASURES: A 7-YEAR, RETROSPECTIVE OBSERVATIONAL STUDY AT A TERTIARY CARE TEACHING HOSPITAL

Evaluation of the effectiveness regarding the participation of pharmacists in perioperative blood glucose management via the iGMS: a pilot RCT

BACKGROUND

Excellent blood glucose management is a key guarantee for successful progress of surgery. However, the impact of clinical pharmacists on blood glucose management of perioperative patients needs to be further investigated. To investigate the effectiveness regarding the participation of pharmacists in blood glucose management via the informatized glucose management system (iGMS) on perioperative patients with type 2 diabetes mellitus (T2DM).

METHODS

The working mode of clinical pharmacists participating in blood glucose management of perioperative patients with diabetes was constructed. A total of 300 patients with T2DM who underwent elective surgery were recruited and divided into a clinical pharmacist management group (intervention group) of 150 patients (94 men and 56 women; mean age: 44.38 ± 14.03 years) and a control group of 150 patients (101 men and 49 women; mean age: 47.85 ± 12.26 years) between September 2019 to April 2020. The outcomes of perioperative blood glucose management, and healthcare indicators such as preoperative waiting time, total hospitalization time, postoperative infection rate and other indicators were analyzed statistically between the two groups.

RESULT

In the blood glucose management team of the whole hospital, the physicians, clinical pharmacists and nurses of blood glucose management in endocrinology department were the core members, and were responsible for perioperative blood glucose management of the participants in the intervention group. All subjects had lower blood glucose after 3 days of management compared to the time of admission, and blood glucose was significantly lower in the intervention group compared to the control group (P < 0.05). As compared with the control group, subjects in intervention group demonstrated significant differences in outcome measures. The relevant parameters included preoperative blood glucose compliance rate (60.67% vs. 35.33%, P<0.05), preoperative waiting time [(5.27 ± 3.34) vs. (7.45 ± 4.38), P<0.05], length of hospitalization [(11.11 ± 4.56) vs. (14.87 ± 5.39), P<0.05], incidence of hypoglycemia (8.67% vs. 18.00%, P<0.05), incidence of hyperglycemia (32.00% vs. 62.67%, P<0.05) and postoperative infection rate (18.00% vs. 24.67%, P > 0.05).

CONCLUSION

The involvement of clinical pharmacists in blood glucose management utilizing the iGMS can control the blood glucose level of patients with T2DM in the perioperative period more stably and effectively, thereby leading to an improvement in the quality of healthcare.

Euglycemic diabetic ketoacidosis: The paradox of delayed correction of acidosis

OBJECTIVE

The effectiveness of standard treatment for diabetic ketoacidosis (DKA) in "euglycemic DKA" (EuDKA, blood glucose (BG) ≤ 250 mg/dL) was evaluated with respect to the time to correction of BG ≤ 200 mg/dL, anion gap (AG)≤12 mmol/L, and serum bicarbonate [HCO3] ≥18 mmol/L.

METHODS

Data were retrieved from an electronic health record (EPIC) for "diabetic ketoacidosis." Patients were categorized by initial BG as EuDKA, middle range DKA (MrDKA, >250 < 600 mg/dL) and hyperosmolar DKA (HyperDKA ≥600 mg/dL).

RESULTS

There were 56 patients (27men, 29women; age 45.8 ± 15.6 (SD) years. The initial 8-h insulin infusion rate (0.05 ± 0.02, 0.09 ± 0.03, 0.14 ± 0.05units/kg/h, p < 0.001) and the time to correction of BG (3.4 ± 1.9, 6.1 ± 2.9 and 9.6 ± 3.9 h, p < 0.001), differed for EuDKA, MrDKA and HyperDKA. There were no differences in the time to correction of AG or [HCO3]. The earlier time to correction of BG in EuDKA resulted in paradoxical longer lag times for correction of [HCO3] (p = 0.003) and AG (p = 0.048). Changes in BG, AG and [HCO3] correlated with insulin infusion rates of 0.08-0.1units/kg/h whereas in EuDKA the insulin infusion rate was 0.05 ± 0.02 units/kg/h.

CONCLUSION

In EuDKA, correlation analyses suggest that higher glucose and insulin infusion rates than what would be projected for the level of blood glucose are required to reverse ketoacidosis. Prospective trials are required to optimize the levels of glucose and insulin infusions in EuDKA.

American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan-2022 Update

OBJECTIVE

The objective of this clinical practice guideline is to provide updated and new evidence-based recommendations for the comprehensive care of persons with diabetes mellitus to clinicians, diabetes-care teams, other health care professionals and stakeholders, and individuals with diabetes and their caregivers.

METHODS

The American Association of Clinical Endocrinology selected a task force of medical experts and staff who updated and assessed clinical questions and recommendations from the prior 2015 version of this guideline and conducted literature searches for relevant scientific papers published from January 1, 2015, through May 15, 2022. Selected studies from results of literature searches composed the evidence base to update 2015 recommendations as well as to develop new recommendations based on review of clinical evidence, current practice, expertise, and consensus, according to established American Association of Clinical Endocrinology protocol for guideline development.

RESULTS

This guideline includes 170 updated and new evidence-based clinical practice recommendations for the comprehensive care of persons with diabetes. Recommendations are divided into four sections: (1) screening, diagnosis, glycemic targets, and glycemic monitoring; (2) comorbidities and complications, including obesity and management with lifestyle, nutrition, and bariatric surgery, hypertension, dyslipidemia, retinopathy, neuropathy, diabetic kidney disease, and cardiovascular disease; (3) management of prediabetes, type 2 diabetes with antihyperglycemic pharmacotherapy and glycemic targets, type 1 diabetes with insulin therapy, hypoglycemia, hospitalized persons, and women with diabetes in pregnancy; (4) education and new topics regarding diabetes and infertility, nutritional supplements, secondary diabetes, social determinants of health, and virtual care, as well as updated recommendations on cancer risk, nonpharmacologic components of pediatric care plans, depression, education and team approach, occupational risk, role of sleep medicine, and vaccinations in persons with diabetes.

CONCLUSIONS

This updated clinical practice guideline provides evidence-based recommendations to assist with person-centered, team-based clinical decision-making to improve the care of persons with diabetes mellitus.

A continuous intravenous insulin infusion protocol to manage high-dose methylprednisolone-induced hyperglycemia in patients with severe COVID-19

Background

Many patients with severe COVID-19 have impaired glucose tolerance, and steroid therapy is a standard treatment. Thus, good glycemic control is important and correlates with better patient outcomes. We began using a continuous intravenous insulin infusion protocol for glycemic control whose infusion rate changes based on the currently measured value and previous value. This study aimed to evaluate this protocol for COVID-19 patients requiring mechanical ventilation.

Methods

This single-center, retrospective, case control study was conducted on all adult patients who required mechanical ventilation for severe COVID-19 pneumonia admitted to our critical care center from April 1, 2020 through June 20, 2021. Blood glucose levels were measured in all patients every 4 h after admission. We started using the insulin infusion protocol from August 1, 2020. Patients before starting the protocol comprised the non-protocol group and those after starting the protocol comprised the protocol group. Blood glucose levels and hypo- or hyperglycemia events were compared between groups. We also surveyed ICU nurses about their experience using the protocol.

Results

During the study period, 173 patients with COVID-19 were admitted. After 15 patients were excluded for several reasons, the study included 158 patients: non-protocol group (n = 14) and protocol group (n = 144). In the initial phase (days 1–2), blood glucose levels of the protocol group were higher compared with the non-protocol group, and as the number of measurements increased, blood glucose levels were gradually brought under control within the target range in the protocol group. Almost no hypoglycemic events (blood glucose < 80 mg/dL) were detected in either group. The rate of hyperglycemia (blood glucose > 300 mg/dL) was about 5–10% in the initial phase in the protocol group and about 10–15% in the early phase (days 3–4) in the non-protocol group. The questionnaire survey revealed that 80% of ICU nurses responded favorably.

Conclusions

This insulin protocol gradually brought the blood glucose level within target levels in severe COVID-19 patients treated with high-dose steroid. Some hyperglycemia events were detected despite patients being under the protocol in the initial phase, and thus, minor modifications of the protocol might be required in the initial phase.

Digital interventions to improve safety and quality of inpatient diabetes management: A systematic review

IMPORTANCE

Diabetes is common amongst hospitalised patients and contributes to increased length of stay and poorer outcomes. Digital transformation, particularly the implementation of electronic medical records (EMRs), is rapidly occurring across the healthcare sector and provides an opportunity to improve the safety and quality of inpatient diabetes care. Alongside this revolution has been a considerable and ongoing evolution of digital interventions to optimise care of inpatients with diabetes including optimisation of EMRs, digital clinical decision support systems (CDSS) and solutions utilising data visibility to allow targeted patient review.

OBJECTIVE

To systematically appraise the recent literature to determine which digitally-enabled interventions including EMR, CDSS and data visibility solutions improve the safety and quality of non-critical care inpatient diabetes management.

METHODS

Pubmed, Embase and Cochrane databases were searched for suitable articles. Selected articles underwent quality assessment and analysis with results grouped by intervention type.

RESULTS

1202 articles were identified with 42 meeting inclusion criteria. Four key interventions were identified; computerised physician order entry (n = 4), clinician decision support systems (n = 21), EMR driven active case finding (data visibility solutions) and targeted patient review (n = 10) and multicomponent system interventions (n = 7). Studies reported on glucometric outcomes, evidence-based medication ordering including medication errors, and patient and user outcomes. An improvement in glucometric measures particularly mean blood glucose and proportion of target range blood glucose levels and rates of evidence-based insulin prescribing were consistently demonstrated.

CONCLUSION

Digitally-enabled interventions utilised to improve quality and safety of inpatient diabetes care were heterogenous in design. The majority of studies across all intervention types reported positive effects for evidence-based prescribing and glucometric outcomes. There was less evidence for digital interventions reducing diabetes medication administration errors or impacting patient outcomes (length of stay).

Insulin Infusion Computer Calculator Programmed Directly Into Electronic Health Record Medication Administration Record

BACKGROUND

Computerized insulin infusion protocols have demonstrated higher staff satisfaction, better compliance with protocols, and increased time with glucose in range compared to paper protocols. At University of California San Diego Health (UCSDH), we implemented an insulin infusion computer calculator (IICC) and transitioned it from a web-based platform directly into the electronic medication administration record (eMAR) of our primary electronic health record (EHR).

METHODS

This is a retrospective analysis of 6306 adult patients at UCSDH receiving intravenous (IV) insulin infusion from March 7, 2013 to May 30, 2019. We created three periods of the study-(1) the pre-eMAR integration period; (2) the eMAR integration period; and (3) the post-eMAR integration period-and looked at the percentage of readings within goal range (90-150 mg/dL for intensive care unit [ICU], 90-180 mg/dL for non-ICU) in patients with and without hyperglycemic emergencies. As our safety endpoints, we elected to look at incidence of blood glucose (BG) readings <70 mg/dL, <54 mg/dL, and <40 mg/dL.

RESULTS

Pre-eMAR 69.8% of readings were in the 90-150 mg/dL range compared to 70.2% post-eMAR (P = .03) and 82.7% of readings were in the 90-180 mg/dL range pre-eMAR versus 82.9% (P = .09) post-eMAR in patients without hyperglycemic emergencies. Rates of hypoglycemia with BG <70 mg/dL were 0.43%, <54 mg/dL were 0.07%, and <40 mg/dL were 0.01% of readings pre- and post-eMAR.

CONCLUSIONS

At UCSDH, our IICC has shown to be safe and effective in a wide variety of clinical situations and we were able to successfully transition it from a web-based platform directly into the eMAR of our primary EHR.

Improving Hospital Glucometrics, Workflow, and Outcomes with a Computerized Intravenous Insulin Dose Calculator Built into the Electronic Health Record

OBJECTIVE

To adjust for dynamic insulin requirements in critically ill patients, intravenous (IV) insulin infusions allow for titration of the dose according to a prespecified algorithm. Despite the adaptability of IV insulin protocols, human involvement in dose calculation allows for error. We integrated a previously validated IV insulin calculator into our electronic health record (Epic) and instituted it in the cardiovascular intensive care unit (CVICU). We aim to describe the design of the calculator, the implementation process, and evaluate the calculator's impact.

METHOD

Employing an aggressive training program and user acceptance testing prior to significant elbow support at the time of institution, we successfully integrated the insulin calculator in our CVICU. We evaluated the glucometrics before and after implementation as well as nursing satisfaction following calculator implementation.

RESULTS

Overall, our implementation led to increased frequency of blood sugar at various glycemic targets, a trend toward less hypoglycemia or hyperglycemia. For severe hypoglycemia, our preintervention cohort had 0.02% of blood sugars less than 40 mg/dL but no blood sugars less than 40 mg/dL were identified in our patient's postintervention. For the CVICU target blood glucose of 70-180 mg/dL, 87.97% blood sugars at baseline met goal compared to 91.39% at one month, 91.24% at three months, and 90.87% at six months postintervention.

CONCLUSION

By utilizing an aggressive education campaign championing superusers and making adjustments to the calculator based on early problems that were encountered, we were able to improve glycemic control and limit glucose variability at our institution.

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.

Assessment of Glycemic Control Protocol (STAR) Through Compliance Analysis Amongst Malaysian ICU Patients

Purpose

This paper presents an assessment of an automated and personalized stochastic targeted (STAR) glycemic control protocol compliance in Malaysian intensive care unit (ICU) patients to ensure an optimized usage.

Patients and Methods

STAR proposes 1–3 hours treatment based on individual insulin sensitivity variation and history of blood glucose, insulin, and nutrition. A total of 136 patients recorded data from STAR pilot trial in Malaysia (2017–quarter of 2019*) were used in the study to identify the gap between chosen administered insulin and nutrition intervention as recommended by STAR, and the real intervention performed.

Results

The results show the percentage of insulin compliance increased from 2017 to first quarter of 2019* and fluctuated in feed administrations. Overall compliance amounted to 98.8% and 97.7% for administered insulin and feed, respectively. There was higher average of 17 blood glucose measurements per day than in other centres that have been using STAR, but longer intervals were selected when recommended. Control safety and performance were similar for all periods showing no obvious correlation to compliance.

Conclusion

The results indicate that STAR, an automated model-based protocol is positively accepted among the Malaysian ICU clinicians to automate glycemic control and the usage can be extended to other hospitals already. Performance could be improved with several propositions.

Diabetes Technology in the Inpatient Setting for Management of Hyperglycemia

In past decades, a rapid evolution of diabetes technology led to increased popularity and use of continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) in the ambulatory setting for diabetes management, and recently, the artificial pancreas became available. Efforts to translate this technology to the hospital setting have shown accuracy and reliability of CGM, safety of CSII in appropriate populations, improvement of inpatient glycemic control with computerized glycemic management systems, and feasibility of inpatient CGM-CSII closed-loop systems. Several ongoing studies are focusing on continued translation of this technology to improve glycemic control and outcomes in hospitalized patients.

Subcutaneous Insulin Dosing Calculators for Inpatient Glucose Control

PURPOSE OF REVIEW

The goal of this review is to summarize information about insulin dosing software and calculators used as computerized decision support systems or electronic glucose management systems (eGMS). These are used for hospitalized, insulin-treated patients with diabetes. We describe the advantages and disadvantages and the rationale for their use.

RECENT FINDINGS

We compared commercially available insulin dosing software, namely, Glucommander™, EndoTool®, GlucoStabilizer®, and GlucoTab®, in addition to computerized order entry systems that are available in electronic health records. The common feature among these eGMS is their ability to limit occurrences of hypoglycemia while achieving and maintaining patients at target blood glucose level. More research needs to be done examining the efficacy of eGMS in disease-specific states and their benefits and utility in preventing adverse outcomes. Their long-term benefits to health care systems are beginning to emerge in cost-saving benefits and prevention of readmissions.

Glucose Management Technologies for the Critically Ill

Hyperglycemia is common in the intensive care unit (ICU) both in patients with and without a previous diagnosis of diabetes. The optimal glucose range in the ICU population is still a matter of debate. Given the risk of hypoglycemia associated with intensive insulin therapy, current recommendations include treating hyperglycemia after two consecutive glucose >180 mg/dL with target levels of 140-180 mg/dL for most patients. The optimal method of sampling glucose and delivery of insulin in critically ill patients remains elusive. While point of care glucose meters are not consistently accurate and have to be used with caution, continuous glucose monitoring (CGM) is not standard of care, nor is it generally recommended for inpatient use. Intravenous insulin therapy using paper or electronic protocols remains the preferred approach for critically ill patients. The advent of new technologies, such as electronic glucose management, CGM, and closed-loop systems, promises to improve inpatient glycemic control in the critically ill with lower rates of hypoglycemia.

Improving glycemic control in critically ill patients: personalized care to mimic the endocrine pancreas

There is considerable physiological and clinical evidence of harm and increased risk of death associated with dysglycemia in critical care. However, glycemic control (GC) currently leads to increased hypoglycemia, independently associated with a greater risk of death. Indeed, recent evidence suggests GC is difficult to safely and effectively achieve for all patients. In this review, leading experts in the field discuss this evidence and relevant data in diabetology, including the artificial pancreas, and suggest how safe, effective GC can be achieved in critically ill patients in ways seeking to mimic normal islet cell function. The review is structured around the specific clinical hurdles of: understanding the patient’s metabolic state; designing GC to fit clinical practice, safety, efficacy, and workload; and the need for standardized metrics. These aspects are addressed by reviewing relevant recent advances in science and technology. Finally, we provide a set of concise recommendations to advance the safety, quality, consistency, and clinical uptake of GC in critical care. This review thus presents a roadmap toward better, more personalized metabolic care and improved patient outcomes.

Hypoglycemia Prevention by Algorithm Design During Intravenous Insulin Infusion

PURPOSE OF REVIEW

This review examines algorithm design features that may reduce risk for hypoglycemia while preserving glycemic control during intravenous insulin infusion. We focus principally upon algorithms in which the assignment of the insulin infusion rate (IR) depends upon maintenance rate of insulin infusion (MR) or a multiplier.

RECENT FINDINGS

Design features that may mitigate risk for hypoglycemia include use of a mid-protocol bolus feature and establishment of a low BG threshold for temporary interruption of infusion. Computer-guided dosing may improve target attainment without exacerbating risk for hypoglycemia. Column assignment (MR) within a tabular user-interpreted algorithm or multiplier may be specified initially according to patient characteristics and medical condition with revision during treatment based on patient response. We hypothesize that a strictly increasing sigmoidal relationship between MR-dependent IR and BG may reduce risk for hypoglycemia, in comparison to a linear relationship between multiplier-dependent IR and BG. Guidelines are needed that curb excessive up-titration of MR and recommend periodic pre-emptive trials of MR reduction. Future research should foster development of recommendations for "protocol maxima" of IR appropriate to patient condition.

Risk of Hypoglycemia During Insulin Infusion Directed by Paper Protocol Versus Electronic Glycemic Management System in Critically Ill Patients at a Large Academic Medical Center

BACKGROUND

Insulin infusions are commonly utilized to control hyperglycemia in critically ill patients and decrease hyperglycemia associated complications. Safety concerns have been raised in trials evaluating methods of glycemic control regarding the incidence of hypoglycemia and its relationship to increased mortality. Electronic glycemic management systems (eGMS) may result in less variable blood glucose (BG) control and less hypoglycemia. This study aimed to compare BG control, time in target BG range, and the rate of hypoglycemia when critically ill patients were managed with an insulin infusion guided by paper-based protocol (PBP) versus eGMS.

METHODS

This retrospective review compared critically ill patients ≥ 18 years old that received insulin infusion from March to May 2015 (PBP group) and October to January 2017 (eGMS group). The primary outcome was the incidence of hypoglycemia. Secondary outcomes included frequency and severity of hypoglycemia, duration in glycemic target, length of insulin therapy, as well as ICU and hospital length of stay.

RESULTS

Fifty-four patients were evaluated, 27 in each group. Percentage of days with BG <70 mg/dL was significantly reduced after eGMS implementation (21.5% v 1.3%, P < .0001) including the frequency of severe hypoglycemia (BG < 40 mg/dL) (5.4% v 0.01%, P < .0001). Patients in the eGMS group spent a greater amount of time in target BG range (31.5% v 63.7%, P < .0001).

CONCLUSIONS

An eGMS has the potential to address many of the unmet needs of an optimal glycemic control strategy, minimizing hypoglycemia, and glycemic variability in a heterogeneous critically ill population.

Management of Type 1 Diabetes in the Hospital Setting

PURPOSE OF REVIEW

The purpose of this article was to review recent guideline recommendations on glycemic target, glucose monitoring, and therapeutic strategies, while providing practical recommendations for the management of medical and surgical patients with type 1 diabetes (T1D) admitted to critical and non-critical care settings.

RECENT FINDINGS

Studies evaluating safety and efficacy of insulin pump therapy, continuous glucose monitoring, electronic glucose management systems, and closed loop systems for the inpatient management of hyperglycemia are described. Due to the increased prevalence and life expectancy of patients with type 1 diabetes, a growing number of these patients require hospitalization every year. Inpatient diabetes management is complex and is best provided by a multidisciplinary diabetes team. In the absence of such resource, providers and health care staff must become familiar with the features of this condition to avoid complications such as severe hyperglycemia, ketoacidosis, hypoglycemia, or glycemic variability. We reviewed most recent guidelines and relevant literature in the topic to provide practical recommendations for the inpatient management of patients with T1D.