Understanding hypoglycemia in hospitalized patients

Glucometrics utilisation in an urban teaching hospital in ireland: current practice and future aims

BACKGROUND

Dysglycaemia in hospitalised patients is associated with poorer clinical outcomes, including cardiovascular events, longer hospital stays, and increased risk of mortality. Therefore, glucose monitoring is necessary to achieve best outcomes.

AIMS

This audit assesses use of point-of-care (POC) blood glucose (BG) testing in Tallaght University Hospital (TUH) over an 8-day period. It evaluates compliance with international and TUH glucose monitoring protocols and determines frequency of diabetes team consultations for inpatient adults.

METHODS

Data from an 8-day period (12/03/2023-19/03/2023) were extracted from the TUH COBAS-IT system and analysed. Invalid tests were excluded. Hyperglycaemia was defined as ≥ 10 mmol/L and hypoglycaemia as ≤ 3.9 mmol/L. Persistent hyperglycaemia was defined as two BG results of ≥ 10 mmol/L. A chart review was conducted on adult patients with persistent hyperglycaemia to assess for HbA1C results, diabetes diagnosis, and diabetes consult.

RESULTS

3,530 valid tests were included and analysed. 674 individual patients had tests done. 1,165 tests (33.00%) were hyperglycaemic and 75 (2.12%) were hypoglycaemic. 68.25% of adults with persistent hyperglycaemia had an HbA1C test performed or documented within three months. 42.71% of inpatient adults with persistent hyperglycaemia and a known diabetes diagnosis received a consult from the diabetes team.

CONCLUSION

Increased adherence to hospital protocols for testing HbA1C in adults with persistent hyperglycaemia could improve treatment and clinical outcomes. Increased diabetes team consultation could facilitate appropriate treatment and improve patient outcomes in persistently hyperglycaemic adult patient populations.

Medications that Regulate Gastrointestinal Transit Influence Inpatient Blood Glucose

Objective

A multitude of factors affect a hospitalized individual's blood glucose (BG), making BG difficult to predict and manage. Beyond medications well established to alter BG, such as beta-blockers, there are likely many medications with undiscovered effects on BG variability. Identification of these medications and the strength and timing of these relationships has potential to improve glycemic management and patient safety.

Materials and Methods

EHR data from 103,871 inpatient encounters over 8 years within a large, urban health system was used to extract over 500 medications, laboratory measurements, and clinical predictors of BG. Feature selection was performed using an optimized Lasso model with repeated 5-fold cross-validation on the 80% training set, followed by a linear mixed regression model to evaluate statistical significance. Significant medication predictors were then evaluated for novelty against a comprehensive adverse drug event database.

Results

We found 29 statistically significant features associated with BG; 24 were medications including 10 medications not previously documented to alter BG. The remaining five factors were Black/African American race, history of type 2 diabetes mellitus, prior BG (mean and last) and creatinine.

Discussion

The unexpected medications, including several agents involved in gastrointestinal motility, found to affect BG were supported by available studies. This study may bring to light medications to use with caution in individuals with hyper- or hypoglycemia. Further investigation of these potential candidates is needed to enhance clinical utility of these findings.

Conclusion

This study uniquely identifies medications involved in gastrointestinal transit to be predictors of BG that may not well established and recognized in clinical practice.

Risk factors for hypoglycaemia in non-critical hospitalised diabetic patients

OBJECTIVE

To determine the risk factors for hypoglycaemia in patients with diabetes on general hospital wards based on a systematic review of the literature since 2013 and meta-analysis.

METHODS

Systematic review of the literature focused on the conceptual and methodological aspects of the PRISMA Declaration. The search carried out in Pub Med, Web of Science, Medline, Scielo, Lilacs, OVID, grey literature and Google Academic focused on risk factors for hypoglycaemia in patients with diabetes on general hospital wards. The CASPe (Critical Appraisal Skills Programme Spanish) tool was applied for quality control.

RESULTS

From 805 references, 70 potentially eligible articles were identified for review of abstracts and full text. Finally, according to inclusion and exclusion criteria, seven studies with 554,601 patients of Asian, European and North American ethnicity were selected. A meta-analysis performed using the random effects model found an association between the presence of hypoglycaemia and: the use of insulin (OR 2.89 [95% CI: 1.8-4.5]); the use of long-acting insulin (OR 2.27 [95% CI: 1.8-2.8]) or fast-acting insulin (OR 1.4 [95% CI: 1.18-1.85]); nasogastric tube feeding (OR 1.75 [95% CI: 1.33-2.3]); chronic kidney disease (OR 1.65 [95% CI: 1.14-2.38]); congestive heart failure (OR 1.36 [95% CI: 1.10-1.68]); and elevated levels of glycosylated haemoglobin (OR 1.59 [95% CI: 1.32-1.91]).

CONCLUSION

The factors associated with the risk of hypoglycaemia in non-critically ill hospitalised patients with type 2 diabetes were: use of any insulin; nasogastric tube feeding; elevated glycosylated haemoglobin levels; history of congestive heart failure; and chronic kidney disease.

Evaluation of Nurse-Driven Management of Hypoglycemia In Critically Ill Patients

BACKGROUND

Intensive care unit (ICU) patients experience hypoglycemia at nearly 4 times the rate seen in non-ICU counterparts. Although inpatient hypoglycemia management relies on nurse-driven protocols, protocol adherence varies between institutions and units.

OBJECTIVE

To compare hypoglycemia management between ICU and non-ICU patients in an institution with high adherence to a hypoglycemia protocol.

METHODS

This secondary analysis used retrospective medical record data. Cases were ICU patients aged 18 years or older with at least 1 hypoglycemic event (blood glucose level < 70 mg/dL); non-ICU controls were matched by age within 10 years, sex, and comorbidities. Time from initial hypoglycemic blood glucose level to subsequent blood glucose recheck, number of interventions, time to normoglycemia, and number of spontaneous hypoglycemic events were compared between groups.

RESULTS

The sample included 140 ICU patients and 280 non-ICU controls. Median time to blood glucose recheck did not differ significantly between groups (19 minutes for both groups). Difference in mean number of interventions before normoglycemia was statistically but not clinically significant (ICU, 1.12; non-ICU, 1.35; P < .001). Eighty-four percent of ICU patients and 86% of non-ICU patients returned to normoglycemia within 1 hour. Median time to normoglycemia was lower in ICU patients than non-ICU patients (21.5 vs 26 minutes; P = .01). About 25% of patients in both groups experienced a spontaneous hypoglycemic event.

CONCLUSION

Adherence to nurse-driven hypoglycemia protocols can be equally effective in ICU and non-ICU patients. Further research is needed to determine protocol adherence barriers and patient characteristics that influence response to hypoglycemia interventions.

The Canadian Hypoglycemia During Hospitalization Score Is Externally Valid in the Australian Diabetes IN-hospital: Glucose & Outcomes (DINGO) Cohort

OBJECTIVES

The Hypoglycemia During Hospitalization (HyDHo) score predicts hypoglycemia in a population of Canadian inpatients by assigning various weightings to 5 key clinical criteria known at the time of admission, in particular age, recent presentation to an emergency department, insulin use, use of oral hypoglycemic agents, and chronic kidney disease. Our aim in this study was to externally validate the HyDHo score by applying this risk calculator to an Australian population of inpatients with diabetes.

METHODS

This study was a retrospective data analysis of a subset of the Diabetes IN-hospital: Glucose & Outcomes (DINGO) cohort. The HyDHo score was applied based on clinical information known at the time of admission to stratify risk of inpatient hypoglycemia.

RESULTS

The HyDHo score was applied to 1,015 patients, generating a receiver-operating characteristic c-statistic of 0.607. A threshold of ≥9, as per the original study, generated a sensitivity of 83% and specificity of 20%. A threshold of ≥10, to better suit this Australian population, generated a sensitivity of 90% and specificity of 34%. The HyDHo score has been externally valid in a geographically different population; in fact, it outperformed the original study after accounting for local hypoglycemia rates.

CONCLUSIONS

Our findings support the external validity of the HyDHo score in a geographically different population. Application of this simple and accessible tool can serve as an adjunct to predict an inpatient's risk of hypoglycemia and guide more appropriate glucose monitoring and diabetes management.

Solitary pleural fibroma causing IGF-2-mediated hypoglycaemia in a non-diabetic patient

A patient without a diagnosis of diabetes mellitus presented to the hospital due to a fall and hypoglycaemia on admission. The patient was found to have recurrent nocturnal fasting hypoglycaemia. CT revealed a large lung mass consistent with a solitary pleural fibroma, a rare tumour associated with insulin-like growth factor 2 (IGF-2) production. This case is an important reminder that potential causes of hypoglycaemia should be considered in non-diabetic patients.

Glycemic Management in Insulin-Naive Patients in the Inpatient Setting

Objective

The ideal inpatient insulin regimen efficiently attains the target blood glucose range, effectively treats hyperglycemia, and minimizes the risk of hypoglycemia. The objective of this study was to compare glycemic targets achieved by using correctional monotherapy (CM) and basal-bolus therapy (BBT) in insulin-naive patients in the inpatient setting to determine optimal blood glucose management for these patients.

Design

This was a retrospective observational cohort study of 792 patients with diabetes not on home insulin therapy who were admitted to an academic hospital over a 5.5-month period. The percentages of hyperglycemic and hypoglycemic values in each group were compared.

Results

Among the 3,112 measured blood glucose values obtained from 792 patients within the first 24 hours of insulin administration, 28.5% were hyperglycemic in the BBT group compared with 23.5% in the CM group. When adjusted for covariates, there was a 23% decrease in hyperglycemia in the BBT group (incidence rate ratio = 0.77, 95% CI 0.64-0.95, P = 0.006). Increases in A1C and admission blood glucose, as well as decreases in admission creatinine and inpatient steroid use, were independently associated with higher rates of hyperglycemia, adjusted for all other covariates. There was no significant difference between the groups in the rate of hypoglycemia in the first 24 hours, which was 1.9% in the BBT group and 1.4% in the CM group (P = 0.301).

Conclusion

Utilizing BBT in insulin-naive patients admitted to the hospital within the first 24 hours of insulin administration results in lower rates of hyperglycemia without higher rates of hypoglycemia when compared with CM.

Glycemic variability and mortality in oncologic intensive care units

OBJECTIVE

This study aimed to investigate the association between glycemic variability and mortality in patients admitted to oncologic intensive care units.

METHODS

A retrospective cohort study was conducted using a convenience sample of 30 medical records of patients over 18 years of age of both sexes. Glycemic variability was measured using the standard deviation and general amplitude. Statistical analysis was performed using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The significance level (α) was set at 5% with a confidence interval (CI) of 95%.

RESULTS

The study included 14 male patients (46.67%) with a mean age of 60±15 years. A total of 1503 blood glucose samples were collected. The AUC analysis for the standard deviation did not show a statistically significant result (p=.966; 95% CI=[0.283, 0.726]). In contrast, the general amplitude had a statistically significant association with mortality (p=.049; 95% CI=[0.514, 0.916]).

CONCLUSIONS

This study found that glycemic variability measured by the general amplitude was significantly associated with patient mortality in oncologic intensive care units. These findings suggest that monitoring glycemic variability may be an important factor in the management of critically ill patients in oncologic intensive care units.

Quantile regression for nonignorable missing data with its application of analyzing electronic medical records

Over the past decade, there has been growing enthusiasm for using electronic medical records (EMRs) for biomedical research. Quantile regression estimates distributional associations, providing unique insights into the intricacies and heterogeneity of the EMR data. However, the widespread nonignorable missing observations in EMR often obscure the true associations and challenge its potential for robust biomedical discoveries. We propose a novel method to estimate the covariate effects in the presence of nonignorable missing responses under quantile regression. This method imposes no parametric specifications on response distributions, which subtly uses implicit distributions induced by the corresponding quantile regression models. We show that the proposed estimator is consistent and asymptotically normal. We also provide an efficient algorithm to obtain the proposed estimate and a randomly weighted bootstrap approach for statistical inferences. Numerical studies, including an empirical analysis of real-world EMR data, are used to assess the proposed method's finite-sample performance compared to existing literature.

Safety of Antithyroid Drugs in Avoiding Hyperglycemia or Hypoglycemia in Patients With Graves' Disease and Type 2 Diabetes Mellitus: A Literature Review

Graves' disease (GD) may increase the difficulty of glucose control in patients with type 2 diabetes mellitus (T2DM). Therefore, selecting a drug with limited blood glucose side effects is an important issue in patients with T2DM and GD. Antithyroid drugs (ATDs) including propylthiouracil (PTU), methimazole, and carbimazole are commonly prescribed for the treatment of GD. Here, we review and summarize the literature from the last 10 years and discuss the effects of current ATDs used for GD for blood glucose control in patients with T2DM. A search of the literature published between January 1, 2012 and December 1, 2022 was conducted using three major medical databases: Google Scholar, Ovid Medline, and Scopus. An initial search was conducted on PubMed using the MeSH terms "propylthiouracil," "methimazole," "carbimazole," and "hyperglycemia" or "hypoglycemia" in academic databases. All articles included "Graves' disease" and "type 2 diabetes mellitus" in the title. Based on the results of previous studies, the hyperglycemic and hypoglycemic side effects of ATDs can be explained by several possible mechanisms. The most widely accepted hypothesis is that sulfhydryl group drugs (e.g., methimazole and carbimazole) cleave the disulfide bond of insulin and enhance its immunogenicity, resulting in hypoglycemia. Moreover, some reports have indicated that methimazole is associated with hypoglycemia; therefore, if the patient has a history of autoimmune diseases, it is necessary to consider whether to change drugs or actively track the production of autoimmune antibodies. In non-diabetic and diabetic patients with GD, the hyperglycemic and hypoglycemic side effects of PTU (on glycemic variation) were less than that of thiamazole. However, as relatively few reports have investigated the side effects of blood sugar changes, further research is necessary to confirm these effects. In addition to autoimmune diseases, drug side effects may need to be considered. These findings provide considerations for clinicians to select more appropriate ATDs for patients with GD and T2DM, and implement improved care guidelines.

Effectiveness of an online course for medicine residents on glycaemic management of hospitalized patients with diabetes

Background

Hyperglycaemia and hypoglycaemia in hospitalized patients are associated with poor clinical outcomes. We assessed whether administration of an online educational course for medicine residents improves the glycaemic management of hospitalized patients with diabetes.

Methods

We conducted this quasi-experimental, pre-post study from January 2019 to April 2019. The contents of the course were in compliance with the American Diabetes Association Guidelines 2018. After participating in the online course, the change in the knowledge of residents was assessed by pre- and post-test questionnaire and changes in the inpatient diabetes care were assessed by case record review of the hospitalized patients with diabetes managed by them before and after the participation in the course.

Results

Fifty-six residents participated and completed the online course. Their average post-test scores increased by 11.6%. One hundred and eighteen patients managed by the residents in the same wards before and after the participation in the course were studied. After attending the course, glycaemic targets were predetermined in 75.4% of patients compared to 32.2% before and adequacy of glucose monitoring improved. The total hypoglycaemic event rate reduced significantly by 45.8% and this was accompanied by a trend towards improved glycaemic control. At the time of discharge, the patient awareness on insulin injection technique, hypoglycaemic symptoms and its home remedies were significantly increased.

Conclusion

The administration of an online course increases the knowledge level of residents, improves patient safety and may improve glycaemic control in hospitalized patients with diabetes.

Management of Individuals With Diabetes at High Risk for Hypoglycemia: An Endocrine Society Clinical Practice Guideline

CONTEXT

Hypoglycemia in people with diabetes is common, especially in those taking medications such as insulin and sulfonylureas (SU) that place them at higher risk. Hypoglycemia is associated with distress in those with diabetes and their families, medication nonadherence, and disruption of life and work, and it leads to costly emergency department visits and hospitalizations, morbidity, and mortality.

OBJECTIVE

To review and update the diabetes-specific parts of the 2009 Evaluation and Management of Adult Hypoglycemic Disorders: Endocrine Society Clinical Practice Guideline and to address developing issues surrounding hypoglycemia in both adults and children living with diabetes. The overriding objectives are to reduce and prevent hypoglycemia.

METHODS

A multidisciplinary panel of clinician experts, together with a patient representative, and methodologists with expertise in evidence synthesis and guideline development, identified and prioritized 10 clinical questions related to hypoglycemia in people living with diabetes. Systematic reviews were conducted to address all the questions. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was used to assess the certainty of evidence and make recommendations.

RESULTS

The panel agreed on 10 questions specific to hypoglycemia risk and prevention in people with diabetes for which 10 recommendations were made. The guideline includes conditional recommendations for use of real-time continuous glucose monitoring (CGM) and algorithm-driven insulin pumps in people with type 1 diabetes (T1D), use of CGM for outpatients with type 2 diabetes at high risk for hypoglycemia, use of long-acting and rapid-acting insulin analogs, and initiation of and continuation of CGM for select inpatient populations at high risk for hypoglycemia. Strong recommendations were made for structured diabetes education programs for those at high risk for hypoglycemia, use of glucagon preparations that do not require reconstitution vs those that do for managing severe outpatient hypoglycemia for adults and children, use of real-time CGM for individuals with T1D receiving multiple daily injections, and the use of inpatient glycemic management programs leveraging electronic health record data to reduce the risk of hypoglycemia.

CONCLUSION

The recommendations are based on the consideration of critical outcomes as well as implementation factors such as feasibility and values and preferences of people with diabetes. These recommendations can be used to inform clinical practice and health care system improvement for this important complication for people living with diabetes.

Insulin murder and the case of Colin Norris

Although insulin is an essential medicine and a life-saving drug, it has also been incriminated in many poisoning deaths; accidental, suicidal and some with malicious intent. Overdosing with insulin precipitates a life-threatening state of hypoglycemia and if untreated leads to coma, irreversible brain damage and death. Normally, the pancreatic β-cells secrete equimolar amounts of insulin and C-peptide into the portal venous blood, although under physiological conditions the plasma concentration ratio (insulin/C-peptide) is less than unity, because insulin is more susceptible to hepatic first-pass metabolism. A high ratio of insulin/C-peptide in plasma from a poisoned patient is compelling evidence that pharmaceutical insulin was administered, which does not contain C-peptide. The analysis of insulin and C-peptide was traditionally done by immunoassay methods (RIA and/or ELISA), although high resolution LC-MS/MS is more suitable for forensic purposes and permits the identification of insulin analogues. Use of insulin as a murder weapon is exemplified by the case of Colin Norris, a male nurse found guilty of murdering four elderly patients and the attempted murder of a fifth by injecting them with insulin. However, the prosecution evidence against Norris was mainly circumstantial and hearsay. Toxicological evidence against Norris consisted of a high insulin/C-peptide concentration ratio in plasma from one of the victims. This analysis was done by an immunoassay method at a clinical laboratory and not a forensic laboratory. Analytical procedures, including chain-of-custody routines, are more stringent at forensic laboratories. Since his conviction, some of the medical evidence against Norris has been called into question, especially the prevalence of spontaneous attacks of hypoglycemia in elderly and frail patients with co-morbidities.

Predicting hypoglycemia in critically Ill patients using machine learning and electronic health records

Hypoglycemia is a common occurrence in critically ill patients and is associated with significant mortality and morbidity. We developed a machine learning model to predict hypoglycemia by using a multicenter intensive care unit (ICU) electronic health record dataset. Machine learning algorithms were trained and tested on patient data from the publicly available eICU Collaborative Research Database. Forty-four features including patient demographics, laboratory test results, medications, and vitals sign recordings were considered. The outcome of interest was the occurrence of a hypoglycemic event (blood glucose < 72 mg/dL) during a patient's ICU stay. Machine learning models used data prior to the second hour of the ICU stay to predict hypoglycemic outcome. Data from 61,575 patients who underwent 82,479 admissions at 199 hospitals were considered in the study. The best-performing predictive model was the eXtreme gradient boosting model (XGBoost), which achieved an area under the received operating curve (AUROC) of 0.85, a sensitivity of 0.76, and a specificity of 0.76. The machine learning model developed has strong discrimination and calibration for the prediction of hypoglycemia in ICU patients. Prospective trials of these models are required to evaluate their clinical utility in averting hypoglycemia within critically ill patient populations.

Predicting Risk of Hypoglycemia in Patients With Type 2 Diabetes by Electronic Health Record-Based Machine Learning: Development and Validation

BACKGROUND

Hypoglycemia is a common adverse event in the treatment of diabetes. To efficiently cope with hypoglycemia, effective hypoglycemia prediction models need to be developed.

OBJECTIVE

The aim of this study was to develop and validate machine learning models to predict the risk of hypoglycemia in adult patients with type 2 diabetes.

METHODS

We used the electronic health records of all adult patients with type 2 diabetes admitted to West China Hospital between November 2019 and December 2021. The prediction model was developed based on XGBoost and natural language processing. F1 score, area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA) were used as the main criteria to evaluate model performance.

RESULTS

We included 29,843 patients with type 2 diabetes, of whom 2804 patients (9.4%) developed hypoglycemia. In this study, the embedding machine learning model (XGBoost3) showed the best performance among all the models. The AUC and the accuracy of XGBoost are 0.82 and 0.93, respectively. The XGboost3 was also superior to other models in DCA.

CONCLUSIONS

The Paragraph Vector-Distributed Memory model can effectively extract features and improve the performance of the XGBoost model, which can then effectively predict hypoglycemia in patients with type 2 diabetes.

A Pharmacist-Driven Glycemic Control Protocol to Reduce the Rate of Severe Hypoglycemia in High-Risk Patients

Purpose: Hospital pharmacists contribute to patient safety and quality initiatives by overseeing the prescribing of antidiabetic medications. A pharmacist-driven glycemic control protocol was developed to reduce the rate of severe hypoglycemia events (SHE) in high-risk hospitalized patients. Methods: We retrospectively analyzed the rates of SHE (defined as blood glucose ≤40 mg/dL), before and after instituting a pharmacist-driven glycemic control protocol over a 4-year period. A hospital glucose management team that included a lead Certified Diabetes Educator Pharmacist (CDEP), 5 pharmacists trained in diabetes, a lead hospitalist, critical care and hospital providers established a process to first identify patients at risk for severe hypoglycemia and then implement our protocol. Criteria from the American Diabetes Association and the American Association of Clinical Endocrinologists was utilized to identify and treat patients at risk for SHE. We analyzed and compared the rate of SHE and physician acceptance rates before and after protocol initiation. Results: From January 2015 to March 2019, 18 297 patients met criteria for this study; 139 patients experienced a SHE and approximately 80% were considered high risk diabetes patients. Physician acceptance rates for the new protocol ranged from 77% to 81% from the year of initiation (2016) through 2018. The absolute risk reduction of SHE was 9 events per 1000 hospitalized diabetic patients and the relative risk reduction was 74% SHE from the start to the end of the protocol implementation. Linear regression analysis demonstrated that SHE decreased by 1.5 events per 1000 hospitalized diabetic patients (95% confidence interval, -1.54 to -1.48, P < .001) during the 2 years following the introduction of the protocol. This represents a 15% relative reduction of SHE per year. Conclusion: The pharmacist-driven glycemic control protocol was well accepted by our hospitalists and led to a significant reduction in SHE in high-risk diabetes patient groups at our hospital. It was cost effective and strengthened our physician-pharmacist relationship while improving diabetes care.

Using machine learning to predict severe hypoglycaemia in hospital

AIM

To predict the risk of hypoglycaemia using machine-learning techniques in hospitalized patients.

METHODS

We conducted a retrospective cohort study of patients hospitalized under general internal medicine (GIM) and cardiovascular surgery (CV) at a tertiary care teaching hospital in Toronto, Ontario. Three models were generated using supervised machine learning: least absolute shrinkage and selection operator (LASSO) logistic regression; gradient-boosted trees; and a recurrent neural network. Each model included baseline patient data and time-varying data. Natural-language processing was used to incorporate text data from physician and nursing notes.

RESULTS

We included 8492 GIM admissions and 8044 CV admissions. Hypoglycaemia occurred in 16% of GIM admissions and 13% of CV admissions. The area under the curve for the models in the held-out validation set was approximately 0.80 on the GIM ward and 0.82 on the CV ward. When the threshold for hypoglycaemia was lowered to 2.9 mmol/L (52 mg/dL), similar results were observed. Among the patients at the highest decile of risk, the positive predictive value was approximately 50% and the sensitivity was 99%.

CONCLUSION

Machine-learning approaches can accurately identify patients at high risk of hypoglycaemia in hospital. Future work will involve evaluating whether implementing this model with targeted clinical interventions can improve clinical outcomes.

Hypoglycemia symptoms are reduced in hospitalized patients with diabetes

AIMS

Hospitalized patients with diabetes are have an impaired ability to detect hypoglycemia events. The purpose of this study was to compare hypoglycemia symptom scores (HSS) in hospitalized patients with diabetes after a documented blood glucose (BG) <70mg/dl with recalled HSS with outpatient hypoglycemia events.

METHODS

Non-critically ill hospitalized patients with diabetes grouped as symptomatic (n=23) or asymptomatic (n=32) at time of index hypoglycemia completed a standardized HSS-Questionnaires (HSS-Q) related to the inpatient event and to recall of symptoms with outpatient hypoglycemia.

RESULTS

After controlling for BG at time of index hypoglycemia (49.8±11.4 vs. 57.4±6.8mg/dl, p=0.02), symptomatic patients reported higher HSS than asymptomatic patients with the inpatient event (11.6±7.3 vs. 1.5±3.4, p<0.001) and in the outpatient setting (13.9±8.6 vs. 10.1±10.6, p<0.01). Recurrent hypoglycemia was more frequent in asymptomatic patients (13% vs. 44%, p=0.015) during the hospitalization.

CONCLUSIONS

Compared to symptomatic patients, asymptomatic patients had lower inpatient and outpatient HSS and more frequent recurrent hypoglycemia events. These results suggest modification of glycemic management strategies in high risk patients to reduce risk for hypoglycemia events.

Evaluation of a Broad-Spectrum Partially Automated Adverse Event Surveillance System: A Potential Tool for Patient Safety Improvement in Hospitals With Limited Resources

OBJECTIVE

The aim of the study was to evaluate the sensitivity and resource efficiency of a partially automated adverse event (AE) surveillance system for routine patient safety efforts in hospitals with limited resources.

METHODS

Twenty-eight automated triggers from the hospital information system's clinical and administrative databases identified cases that were then filtered by exclusion criteria per trigger and then reviewed by an interdisciplinary team. The system, developed and implemented using in-house resources, was applied for 45 days of surveillance, for all hospital inpatient admissions (N = 1107). Each trigger was evaluated for its positive predictive value (PPV). Furthermore, the sensitivity of the surveillance system (overall and by AE category) was estimated relative to incidence ranges in the literature.

RESULTS

The surveillance system identified a total of 123 AEs among 283 reviewed medical records, yielding an overall PPV of 52%. The tool showed variable levels of sensitivity across and within AE categories when compared with the literature, with a relatively low overall sensitivity estimated between 21% and 44%. Adverse events were detected in 23 of the 36 AE categories defined by an established harm classification system. Furthermore, none of the detected AEs were voluntarily reported.

CONCLUSIONS

The surveillance system showed variable sensitivity levels across a broad range of AE categories with an acceptable PPV, overcoming certain limitations associated with other harm detection methods. The number of cases captured was substantial, and none had been previously detected or voluntarily reported. For hospitals with limited resources, this methodology provides valuable safety information from which interventions for quality improvement can be formulated.

Birkenfeld A, Sponholz C, A. Müller U, von Loeffelholz C. Blood Sugar Targets in Surgical Intensive Care—Management and Special Considerations in Patients With Diabetes

BACKGROUND

30-80% of patients being treated in intensive care units in the perioperative period develop hyperglycemia. This stress hyperglycemia is induced and maintained by inflammatory-endocrine and iatrogenic stimuli and generally requires treatment. There is uncertainty regarding the optimal blood glucose targets for patients with diabetes mellitus.

METHODS

This review is based on pertinent publications retrieved by a selective search in PubMed and Google Scholar.

RESULTS

Patients in intensive care with pre-existing diabetes do not benefit from blood sugar reduction to the same extent as metabolically healthy individuals, but they, too, are exposed to a clinically relevant risk of hypoglycemia. A therapeutic range from 4.4 to 6.1 mmol/L (79-110 mg/dL) cannot be justified for patients with diabetes mellitus. The primary therapeutic strategy in the perioperative setting should be to strictly avoid hypoglycemia. Neurotoxic effects and the promotion of wound-healing disturbances are among the adverse consequences of hyperglycemia. Meta-analyses have shown that an upper blood sugar limit of 10 mmol/L (180 mg/dL) is associated with better outcomes for diabetic patients than an upper limit of less than this value. The target range of 7.8-10 mmol/L (140-180 mg/dL) proposed by specialty societies for hospitalized patients with diabetes seems to be the best compromise at present for optimizing clinical outcomes while avoiding hypoglycemia. The method of choice for achieving this goal in intensive care medicine is the continuous intravenous administration of insulin, requirng standardized, high-quality monitoring conditions.

CONCLUSION

Optimal blood sugar control for diabetic patients in intensive care meets the dual objectives of avoiding hypoglycemia while keeping the blood glucose concentration under 10 mmol/L (180 mg/dL). Nutrition therapy in accordance with the relevant guidelines is an indispensable pre - requisite.

Nutritional management of type 2 diabetes in subjects with obesity: an international guideline for clinical practice

Type 2 diabetes mellitus (T2DM) and obesity represent a global public health problem. Current nutritional recommendations focused on weight loss and overall dietary quality. However, there is no consensus on the optimal macronutrient composition of the diet, particularly for the long-term management of T2DM in subjects with obesity. An international panel of experts reviewed and critically appraised the updated literature published on the topic. This review primarily examines the evidence for areas of consensus and uncertainty about nutritional therapy in patients with T2DM and obesity. The aim of this article is to provide nutritional advice to manage these patients in clinical practice.

Outcomes of "Real-World" Insulin Strategies in the Management of Hospital Hyperglycemia

Context

Guidelines recommend scheduled long-acting basal and short-acting bolus insulin several times daily to manage inpatient hyperglycemia. In the “real world,” insulin therapy is complicated, with limited data on the comparative effectiveness of different insulin strategies.

Objective

This work aimed to evaluate the association of different insulin strategies with glucose control and hospital outcomes after adjustment for patient and physician factors that influence choice of therapy.

Methods

This retrospective, observational study took place at an academic hospital. Participants included noncritically ill hospitalized medical/surgical patients (n = 4558) receiving subcutaneous insulin for 75% or longer during admission. Insulin therapy was grouped into 3 strategies within the first 48 hours: basal bolus (BB: scheduled long and short/rapid n = 2358), sliding scale (SS: short/rapid acting n = 1855), or basal only (BO: long only: n = 345). Main outcome measures included glucose control: hypoglycemic days, hyperglycemic days, euglycemic days, mean glucose; and hospitalization: in-hospital mortality, length of stay (LOS), and readmissions.

Results

Initial therapy with BB was associated with more hypoglycemic (2.40; CI, 2.04 to 2.82) (P < .001) and fewer euglycemic days (0.90; CI, 0.85 to 0.97) (P = .003) than SS, whereas BO was associated with fewer hyperglycemic days (0.70; CI, 0.62 to 0.79) (P < .001), lower mean glucose (–18.03; CI, –22.46 to –12.61) (P < .001), and more euglycemic days (1.22; CI, 1.09 to 1.37) (P < .001) compared to SS. No difference in mortality, LOS, and readmissions was found. However, decreased LOS was observed in the BB subgroup with a medical diagnostic related group (0.93; CI, 0.89 to 0.97) (P < .001).

Conclusion

BO had a more favorable hyperglycemia profile than SS. BB, on the other hand, showed worse glycemic control as compared to SS. In the real-world hospital, BO may be a simpler and more effective insulin strategy.

Hypoglycemia in a Surgical Intensive Care Unit

BACKGROUND

Glycemic control is an important aspect of critical care because derangements are associated with morbidity and mortality. Patients at highest risk for hypoglycemia in the surgical intensive care unit (SICU) are incompletely described by existing literature. Our objective was to delineate this high-risk patient population in our SICU.

STUDY DESIGN

In this single-center, retrospective, observational study, SICU patients admitted from June 1, 2019 to July 31, 2020 with ≥1 episode of hypoglycemia (blood glucose <60 mg/dL) were included.

RESULTS

There were 41 hypoglycemic events in 27 patients, comprising an incidence of 1.5% among SICU patients. The most common admission diagnoses were cirrhosis (n = 13, 48%), polytrauma (n = 12, 44%), multisystem organ failure (n = 11, 41%), diabetes mellitus (n = 9, 33%), and soft tissue infection (n = 8, 30%). Four high-risk populations were identified: patients in multisystem organ failure (MSOF) (n = 11, 41%); those who were nil per os (NPO) (n = 10, 37%); patients receiving long acting subcutaneous insulin, for example, Lantus (n = 3, 11%); and those on continuous intravenous insulin infusions (n = 3, 11%). After multi-disciplinary peer review, most hypoglycemic events (n = 16, 59%) were deemed iatrogenic.

CONCLUSIONS

Hypoglycemia is rare in surgical critical care. When it does occur, patients are typically in MSOF, NPO, on long acting subcutaneous insulin or continuous insulin infusions, have soft tissue infections, or have acute or chronic liver failure. Increased vigilance with frequent blood glucose monitoring in these high-risk patients may reduce the risk of hypoglycemia in the SICU.

The Impact of a Nursing Strike on Glycemic Control in Hospitalized Patients with Diabetes

Introduction Hyperglycemia and hypoglycemia have been found to increase morbidity and mortality among hospitalized patients with diabetes. In July of 2018, our academic medical center experienced a 48-hour nursing strike, during which time 600 replacement nurses were employed. This cohort study evaluated the impact of the nursing strike on glycemic control among hospitalized patients with diabetes. Methods Point-of-care fingerstick blood glucose (POC BG) values among hospitalized patients with diabetes were compared between the 48-hour nursing strike period and two 48-hour periods when the nursing strike did not occur. We evaluated the percentage of POC BG values that were hyperglycemic (POC BG 181-250 mg/dL), severely hyperglycemic (POC BG >250 mg/dL), and hypoglycemic (POC BG <70 mg/dL). Additionally, we assessed the proportion of patients who experienced one or more days of hypoglycemia, hyperglycemia, or severe hyperglycemia. Results We found a significant association between the distributions of POC BG test results during the nursing strike; test results more frequently showed hyperglycemia, severe hyperglycemia, or hypoglycemia during the nursing strike than during the control period (p=0.006). There was a significant difference in the days of hypoglycemia, with 7.7% of patients experiencing one or more days of hypoglycemia during the strike period compared with 1.4% of patients during the control period (p=0.03). Conclusion Nursing strikes have been employed as a last resort in contract negotiations with hospitals, but they have the potential to significantly affect patient care and safety. Further studies are needed to evaluate these impacts to prepare for future workforce disruptions.

Hypoglycemic episodes in hospitalized people with diabetes in Portugal: the HIPOS-WARD study

BACKGROUND

We intended to estimate the proportion hypoglycemic/hyperglycemic emergency episodes in treated diabetes mellitus (DM) patients admitted to a hospital ward, and calculate the prevalence of risk factors for hypoglycemia and diabetic complications.

METHODS

In this cross-sectional, multicentered study, the observational data was collected by physicians from patient's hospitalization to discharge/death. Statistical tests were 2-tailed considering 5% significance level.

RESULTS

There were 646 ward admissions due to hyperglycemic emergencies and 176 hypoglycemic episodes with a ratio hypoglycemia/hyperglycemia 0.27 for all DM patients. In T2DM patients the ratio was 0.38. These were mainly female (55.1%), functionally dependent (61.4%) and retired/disabled (73.1%). Median age was 75 years and median duration of disease 11 years. Half the patients were on insulin-based therapy and 30.1% on secretagogue-based therapy. Approximately 57% of patients needed occasional/full assistance to manage the disease. The most frequent risk factor for hypoglycemia was polypharmacy (85.0%). Hypoglycemia in the 12 months before admission was higher in insulin-based therapy patients (66.1%; p = 0.001).

CONCLUSIONS

Hyperglycemic emergencies are the most frequent cause of hospitalization in Portugal, although severe hypoglycemic events represent a health and social problem in elderly/frail patients. There is still the need to optimize therapy in terms of the potential for hypoglycemia in this patient group and a review of anti-hyperglycemic agents to add on to insulin.

Development and Validation of a Machine Learning Model to Predict Near-Term Risk of Iatrogenic Hypoglycemia in Hospitalized Patients

Importance

Accurate clinical decision support tools are needed to identify patients at risk for iatrogenic hypoglycemia, a potentially serious adverse event, throughout hospitalization.

Objective

To predict the risk of iatrogenic hypoglycemia within 24 hours after each blood glucose (BG) measurement during hospitalization using a machine learning model.

Design, Setting, and Participants

This retrospective cohort study, conducted at 5 hospitals within the Johns Hopkins Health System, included 54 978 admissions of 35 147 inpatients who had at least 4 BG measurements and received at least 1 U of insulin during hospitalization between December 1, 2014, and July 31, 2018. Data from the largest hospital were split into a 70% training set and 30% test set. A stochastic gradient boosting machine learning model was developed using the training set and validated on internal and external validation.

Exposures

A total of 43 clinical predictors of iatrogenic hypoglycemia were extracted from the electronic medical record, including demographic characteristics, diagnoses, procedures, laboratory data, medications, orders, anthropomorphometric data, and vital signs.

Main Outcomes and Measures

Iatrogenic hypoglycemia was defined as a BG measurement less than or equal to 70 mg/dL occurring within the pharmacologic duration of action of administered insulin, sulfonylurea, or meglitinide.

Results

This cohort study included 54 978 admissions (35 147 inpatients; median [interquartile range] age, 66.0 [56.0-75.0] years; 27 781 [50.5%] male; 30 429 [55.3%] White) from 5 hospitals. Of 1 612 425 index BG measurements, 50 354 (3.1%) were followed by iatrogenic hypoglycemia in the subsequent 24 hours. On internal validation, the model achieved a C statistic of 0.90 (95% CI, 0.89-0.90), a positive predictive value of 0.09 (95% CI, 0.08-0.09), a positive likelihood ratio of 4.67 (95% CI, 4.59-4.74), a negative predictive value of 1.00 (95% CI, 1.00-1.00), and a negative likelihood ratio of 0.22 (95% CI, 0.21-0.23). On external validation, the model achieved C statistics ranging from 0.86 to 0.88, positive predictive values ranging from 0.12 to 0.13, negative predictive values of 0.99, positive likelihood ratios ranging from 3.09 to 3.89, and negative likelihood ratios ranging from 0.23 to 0.25. Basal insulin dose, coefficient of variation of BG, and previous hypoglycemic episodes were the strongest predictors.

Conclusions and Relevance

These findings suggest that iatrogenic hypoglycemia can be predicted in a short-term prediction horizon after each BG measurement during hospitalization. Further studies are needed to translate this model into a real-time informatics alert and evaluate its effectiveness in reducing the incidence of inpatient iatrogenic hypoglycemia.

A prospective six-month audit of inpatient hypoglycemia in step-down general medical and geriatric wards

This study aimed to assess the incidence and associates of hypoglycemia in patients transferred after stabilization on an Acute Medical Unit to two general medical or two geriatric wards at an urban Australian hospital. In a six-month audit representing 20,284 patient-days of observation, 59 inpatients experienced hypoglycaemia (blood glucose ≤3.9 mmol/L) during 65 hospitalizations. Inpatients experiencing hypoglycemia accounted for 7.2% of all inpatient bed-days, a figure that was greater for general medical (9.2% of bed-days) compared with geriatric (6.0% of bed-days) wards (P<0.001). Inpatient hypoglycemia often had no precipitant such as a missed/delayed meal, occurred disproportionately at night (41% of episodes), was severe (blood glucose ≤3.0 mmol/L) in one-third of cases, and appeared more frequent in patients with psychiatric/cognitive issues. These data highlight the ongoing issue of hypoglycemia in relatively stable inpatients in an era of blood glucose-lowering therapies associated with a low rate of this acute metabolic complication.

Considerations for Insulin-Treated Type 2 Diabetes Patients During Hospitalization: A Narrative Review of What We Need to Know in the Age of Second-Generation Basal Insulin Analogs

With the availability of second-generation basal insulin analogs, insulin degludec (100 and 200 units/ml [degludec]) and insulin glargine 300 units/ml (glargine U300), clinicians now have long-acting, efficacious treatment options with stable pharmacokinetic profiles and associated low risks of hypoglycemia that may be desirable for many patients with type 2 diabetes. In this narrative review, we summarize the current evidence on glycemic control in hospitalized patients and review the pharmacokinetic properties of degludec and glargine U300 in relation to the challenges these may pose during the hospitalization of patients with type 2 diabetes who are receiving outpatient regimens involving these newer insulins. Their increased use in clinical practice requires that hospital healthcare professionals (HCPs) have appropriate protocols to transfer patients from these second-generation insulins to formulary insulin on admission, and ensure the safe discharge of patients and transition back to degludec or glargine U300. However, there is no guidance available on this. Based on the authors' clinical experience, we identify key issues to consider when arranging hospital care of such patients. We also summarize the limited available evidence on the potential utility of these second-generation basal insulin analogs in the non-critical inpatient setting and identify avenues for future research. To address current knowledge gaps, it is important that HCPs are educated about the differences between standard formulary insulins and second-generation insulins, and the importance of clear communication during patient transitions.

Electronic Measurement of a Clinical Quality Measure for Inpatient Hypoglycemic Events: A Multicenter Validation Study

BACKGROUND

Hypoglycemia related to antidiabetic drugs (ADDs) is important iatrogenic harm in hospitalized patients. Electronic identification of ADD-related hypoglycemia may be an efficient, reliable method to inform quality improvement.

OBJECTIVE

Develop electronic queries of electronic health records for facility-wide and unit-specific inpatient hypoglycemia event rates and validate query findings with manual chart review.

METHODS

Electronic queries were created to associate blood glucose (BG) values with ADD administration and inpatient location in 3 tertiary care hospitals with Patient-Centered Outcomes Research Network (PCORnet) databases. Queries were based on National Quality Forum criteria with hypoglycemia thresholds <40 and <54 mg/dL, and validated using a stratified random sample of 321 BG events. Sensitivity and specificity were calculated with manual chart review as the reference standard.

RESULTS

The sensitivity and specificity of queries for hypoglycemia events were 97.3% [95% confidence interval (CI), 90.5%-99.7%] and 100.0% (95% CI, 92.6%-100.0%), respectively for BG <40 mg/dL, and 97.7% (95% CI, 93.3%-99.5%) and 100.0% (95% CI, 95.3%-100.0%), respectively for <54 mg/dL. The sensitivity and specificity of the query for identifying ADD days were 91.8% (95% CI, 89.2%-94.0%) and 99.0% (95% CI, 97.5%-99.7%). Of 48 events missed by the queries, 37 (77.1%) were due to incomplete identification of insulin administered by infusion. Facility-wide hypoglycemia rates were 0.4%-0.8% (BG <40 mg/dL) and 1.9%-3.0% (BG <54 mg/dL); rates varied by patient care unit.

CONCLUSIONS

Electronic queries can accurately identify inpatient hypoglycemia. Implementation in non-PCORnet-participating facilities should be assessed, with particular attention to patient location and insulin infusions.

The risk factors of inpatient hypoglycemia: A systematic review

Hypoglycemia is an important and harmful complication of Diabetes Mellitus (DM) that often occurs in inpatient or outpatient settings. Hypoglycemia can be divided into two types, i.e. primary hypoglycemia when hypoglycemia is the main diagnosis for admission, whereas secondary hypoglycemia if hypoglycemia occurs during hospitalization. Hypoglycemia during hospitalization or secondary hypoglycemia may arise from various risk factors, such as advanced age, comorbid diseases, type of diabetes, previous history of hypoglycemia, body mass index, hyperglycemia therapy given, as well as other risk factors such as inadequate glucose monitoring, unclear or unreadable physician instructions, limited health personnel, limited facilities, prolonged fasting and incompatibility of nutritional intake and therapy administered. Hypoglycemia can lead to medical and non-medical impacts, such as increased mortality, cardiovascular disorders, cerebrovascular disorders, and increased health care costs and length of stay. The incidence of inpatient hypoglycemia can actually be prevented by controlling modifiable risk factors and also giving education about hypoglycemia to patients and health workers. We performed a literature research in Pubmed, EBSCOhost, and Scopus to review the possible risk factors for inpatient hypoglycemia. Eleven studies were retrieved. We presented the result of these studies as well as a brief overview of the epidemiology, pathophysiology, impact and preventive strategy.

Influence of Ethnicity on Outcomes of Diabetes Inpatient Hypoglycemia: an Australian Perspective

Aims

To evaluate outcomes of diabetic inpatient hypoglycemia among Aboriginal and Torres Strait Islander (ATSI) compared with Australian Caucasian patients.

Methods

A retrospective audit of diabetic patients aged &gt; 18 years admitted at a regional hospital general ward between April 1, 2015, and March 31, 2016, was analyzed. The database contains clinical information at the time of admission and initial discharge and readmission within 4 weeks thereafter.

Results

A total of 1618 (of 6027) patients were admitted with diabetes representing 23.7% of the total ward admissions, of which 484 (29.9%) had inpatient hypoglycemia. Of the 91 patients with available data analyzed, ATSI origin with inpatient hypoglycemia was associated with longer length of stay (LOS) (hazard ratio [HR], 2.1, 95% confidence interval [CI], 1.2-3.5), whereas severe hypoglycemia (≤ 2.2 mmol/L) in both ATSI and non-ATSI was significantly associated with longer LOS (HR, 2.3; 95% CI, 1.2-4.2). No significant differences in LOS were found for gender, age, and Carlson comorbidity index (CCI). The adjusted model for likelihood of readmission, gender, indigenous status, and CCI were not significant risk factors for readmission to the hospital. Readmitted patients were older (50-59 years vs &lt; 50 years, P = 0.001; 60-69 years vs &lt; 50 years, P = 0.032; 70+ years vs &lt; 50 years, P = 0.031).

Conclusion

We reported high rate of inpatient hypoglycemia in our study population. Indigenous Australian diabetic patients with inpatient hypoglycemia had significantly longer LOS compared with non-Indigenous Caucasian counterparts. Further prospective studies on a larger population are needed to confirm our findings.

Increasing Individual Target Glucose Levels to Prevent Hypoglycemia in Patients with Diabetes

Hypoglycemia is one of the severe complications of diabetes. To prevent hypoglycemia, an emphasis is placed on maintaining an appropriate balance between nutrition, activity, and treatment, which can be achieved by the repetition of self-trials based on self-monitoring. Clinicians routinely focus on patients’ contribution, including timely intake of an adequate amount of carbohydrates, physical activity, antidiabetic medication, and abstinence from alcohol. Recently, many guidelines have highlighted the importance of clinicians’ factors and recommend individualized treatments according to lifestyle patterns and specific needs following the de-intensification of treatment. The optimal value of hemoglobin A1c (HbA1c) levels for blood glucose level regulation remains controversial among countries, but it generally does not exceed 8.0%. In populations that are at a risk of hypoglycemia, such as the older adults, it is advisable to adjust the target blood glucose level to less than 8.0%. Meanwhile, a blood glucose level of 7.0%–7.5% is generally recommended for healthy older adults. If the expected lifetime is shorter than 10 years or in patients with chronic kidney disease and severe cardiovascular disease, the HbA1c level target can be increased to 7.5%–8.0%. For even shorter lifetime expectancy, the target can be adjusted up to 8.0%–9.0%. To prevent hypoglycemia, the target blood glucose level needs to be adjusted, particularly in older adult patients. Ultimately, it is important to identify the maximum blood glucose levels that do not cause hypoglycemia and the minimum blood glucose levels that do not cause hyperglycemia-associated complications.

Hypoglycaemia

In health hypoglycaemia is rare and occurs only in circumstances like extreme sports. Hypoglycaemia in type 1 Diabetes (T1D) and advanced type 2 Diabetes (T2D) are the result of interplay between absolute or relative insulin access and defective glucose counterregulation. The basic mechanism is, failure of decreasing insulin and failure of the compensatory increasing counterregulatory hormones at the background of falling blood glucose. Any person with Diabetes on anti-diabetic medication who behaves oddly in any way whatsoever is hypoglycaemic until proven otherwise. Hypoglycaemia can be a terrifying experience for a patient with Diabetes. By definition, hypoglycaemic symptoms are subjective and vary from person to person and even episode to episode in same person. Fear of iatrogenic hypoglycaemia is a major barrier in achieving optimum glycaemic control and quality of life which limits the reduction of diabetic complications. Diabetes patients with comorbidities especially with chronic renal failure, hepatic dysfunction, major limb amputation, terminal illness, cognitive dysfunction etc. are more vulnerable to hypoglycaemia. In most cases, prompt glucose intake reverts hypoglycaemia. Exogenous insulin in T1D and insulin treated advanced T2D have no control by pancreatic regulation. Moreover, failure of increase of glucagon and attenuated secretion in epinephrine causes the defective glucose counterregulation. In this comprehensive review, I will try to touch all related topics for better understanding of hypoglycaemia.

Standardized Glycemic Management versus Conventional Glycemic Management and Postoperative Outcomes in Type 2 Diabetes Patients Undergoing Elective Surgery

PURPOSE

Optimized postoperative blood glucose control can minimize postoperative complications. Conventional perioperative glycemic control protocol (CG), which has been routinely used in our institution, lacks detailed perioperative glycemic management. A new standardized glycemic control protocol (SG) was designed which employs frequent postoperative monitoring of blood glucose, more tightly targeted blood glucose control, and adjustment of insulin dosage prior to surgery. This study compared the efficacy of postoperative glycemic control and complications with the two protocols, CG and SG.

PATIENTS AND METHODS

Three hundred and eighty type 2 diabetes patients who underwent elective surgeries were included in the study. Of those, 182 patients with CG were identified retrospectively as a historical control cohort. Additional 198 patients with SG were prospectively enrolled. Covariate imbalance was controlled using propensity score matching. Outcomes were evaluated using regression analysis clustered by type of surgery.

RESULTS

The SG group had lower mean levels of postoperative 24-hr blood glucose than the CG group (β =-8.6 mg/dL; 95% CI (-16.5 to -7.9), p=0.042). In SG group, the incidence of ICU admission and of acute kidney injury after surgery was lower than in the CG group (OR 0.36; 95% CI (0.18-0.74), p=0.005 and OR=0.59; 95% CI (0.41-0.85), p=0.005, respectively). There was no significant difference in postoperative hypoglycemia, infection, cardiovascular complications, stroke, or mortality rate between the two groups.

CONCLUSION

For type 2 diabetes patients undergoing elective surgery, the SG protocol is more effective in controlling blood glucose. The protocol can also reduce the incidence of some postoperative complications compared to CG with no increased risk of hypoglycemia.

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.

The effect of hypoglycaemia during hospital admission on health-related outcomes for people with diabetes: a systematic review and meta-analysis

AIM

To assess the health-related outcomes of hypoglycaemia for people with diabetes admitted to hospital; specifically, hospital length of stay and mortality.

METHODS

We conducted a systematic review and meta-analysis of studies relating to hypoglycaemia (< 4 mmol/l) for hospitalized adults (≥ 16 years) with diabetes reporting the primary outcomes of interest, hospital length of stay or mortality. Final papers for inclusion were reviewed in duplicate and the adjusted results of each were pooled, using a random effects model then undergoing further prespecified subgroup analysis.

RESULTS

In total, 15 studies were included in the meta-analysis. The pooled mean difference in length of stay for ward-based inpatients exposed to hypoglycaemia was 4.1 days longer [95% confidence interval (CI) 2.36 to 5.79; I² = 99%] compared with those without hypoglycaemia. This association remained robust across the pre-specified subgroup analyses. The pooled relative risk (RR) of in-hospital mortality was greater for those exposed to hypoglycaemia (RR 2.09, 95% CI 1.64 to 2.67; I² = 94%, n = 7 studies) but not in intensive care unit mortality (RR 0.75, 95% CI 0.49 to 1.16; I² =0%, n = 2 studies).

CONCLUSION

There is an association between inpatient hypoglycaemia and longer length of stay and greater in-hospital mortality. Studies examining this association were heterogenous in terms of both clinical populations and effect size, but the overall direction of the association was consistent. Therefore, glucose concentration should be considered a potential tool to aid the identification of inpatients at risk of poor health-related outcomes.

Association of Inpatient Glucose Measurements With Amputations in Patients Hospitalized With Acute Diabetic Foot

CONTEXT

The association of inpatient glucose measurements with amputations in patients admitted with acute diabetic foot has not been described.

OBJECTIVE

To evaluate the relationship of hyperglycemia, hypoglycemia, and glucose variability during hospitalization with amputations in patients hospitalized with acute diabetic foot.

DESIGN

Retrospective cohort study.

SETTING

Academic tertiary hospital.

PATIENTS

We reviewed demographic, clinical, laboratory, and point-of-care glucose data in patients hospitalized with acute diabetic foot in the Diabetic Foot Unit during 2015 through 2017.

MAIN OUTCOME MEASURES

The primary outcomes were any or major amputations during hospitalization. Secondary outcomes included length of hospitalization and in-hospital mortality.

RESULTS

During the study period, 418 patients were hospitalized in the Diabetic Foot Unit and 45,496 glucose measurements were taken. Patients experiencing any hyperglycemia and any or severe hypoglycemia were more likely to undergo any or major amputations during hospitalization. High glycemic variability was associated with major amputations. Peripheral vascular disease (PVD), high Wagner score, and hypoglycemia were independent predictors of amputations. Older age, PVD, previous amputation, elevated white blood cell level, high Wagner score, and hypoglycemia were independent predictors of major amputations.

CONCLUSIONS

In-patient hypoglycemia emerged as an independent risk factor for any and major amputations. Although it is unclear whether hypoglycemia directly contributes to adverse outcomes or is simply a biomarker of disease severity, efforts to minimize in-hospital hypoglycemic events are warranted.

Postoperative Management in Patients with Pheochromocytoma and Paraganglioma

Pheochromocytomas and paragangliomas (PPGLs) are rare catecholamine-secreting neuroendocrine tumors of the adrenal medulla and sympathetic/parasympathetic ganglion cells, respectively. Excessive release of catecholamines leads to episodic symptoms and signs of PPGL, which include hypertension, headache, palpitations, and diaphoresis. Intraoperatively, large amounts of catecholamines are released into the bloodstream through handling and manipulation of the tumor(s). In contrast, there could also be an abrupt decline in catecholamine levels after tumor resection. Because of such binary manifestations of PPGL, patients may develop perplexing and substantially devastating cardiovascular complications during the perioperative period. These complications include hypertension, hypotension, arrhythmias, myocardial infarction, heart failure, and cerebrovascular accident. Other complications seen in the postoperative period include fever, hypoglycemia, cortisol deficiency, urinary retention, etc. In the interest of safe patient care, such emergencies require precise diagnosis and treatment. Surgeons, anesthesiologists, and intensivists must be aware of the clinical manifestations and complications associated with a sudden increase or decrease in catecholamine levels and should work closely together to be able to provide appropriate management to minimize morbidity and mortality associated with PPGLs.

Common inpatient hypoglycemia phenotypes identified from an automated electronic health record-based prediction model

PURPOSE

Common inpatient hypoglycemia risk factor patterns (phenotypes) from an electronic health record (EHR)-based prediction model and preventive strategies were identified.

METHODS

Patients admitted to 2 large academic medical centers who were in the top fifth percentile of a previously developed hypoglycemia risk score and developed hypoglycemia (blood glucose [BG] of <50mg/dL) were included in the study. Frequencies of all combinations of ≥4 risk factors contributing to the risk score among these patients were determined to identify common risk patterns. Clinical pharmacists developed clinical vignettes for each common pattern and formulated medication therapy management recommendations for hypoglycemia prevention.

RESULTS

A total of 401 admissions with a hypoglycemic event were identified among 1,875 admissions whose hypoglycemic risk was in the top fifth percentile among all admissions that received antihyperglycemic drugs and evaluated. Five distinct phenotypes emerged: (1) frail patients with history of hypoglycemia receiving insulin on hospital day 1, (2) a rapid downward trend in BG values in patients receiving an insulin infusion or with a history of hypoglycemia, (3) administration of insulin in the presence of an active nothing by mouth order in frail patients, (4) repeated low BG level in frail patients, and (5) inadequate night-time BG monitoring for patients on long-acting insulin. The 5 themes jointly described 53.0% of high-risk patients who experienced hypoglycemia.

CONCLUSION

Five distinct phenotypes that are prevalent in patients at greatest risk for inpatient hypoglycemia were identified.

What Can We Learn From Point-of-Care Blood Glucose Values Deleted and Repeated by Nurses?

BACKGROUND

Hospitals rely on point-of-care (POC) blood glucose (BG) values to guide important decisions related to insulin administration and glycemic control. Evaluation of POC BG in hospitalized patients is associated with measurement and operator errors. Based on a previous quality improvement (QI) project we introduced an option for operators to delete and repeat POC BG values suspected as erroneous. The current project evaluated our experience with deleted POC BG values over a 2-year period.

METHOD

A retrospective QI project included all patients hospitalized at two regional academic medical centers in the Pacific Northwest during 2014 and 2015. Laboratory Medicine POC BG data were reviewed to evaluate all inpatient episodes of deleted and repeated POC BG.

RESULTS

Inpatient operators choose to delete and repeat only 0.8% of all POC BG tests. Hypoglycemic and extreme hyperglycemic BG values are more likely to be deleted and repeated. Of initial values <40 mg/dL, 58% of deleted values (18% of all values) are errors. Of values >400 mg/dL, 40% of deleted values (5% of all values) are errors. Not all repeated POC BG values are first deleted. Optimal use of the option to delete and repeat POC BG values <40 mg/dL could decrease reported rates of severe hypoglycemia by as much as 40%.

CONCLUSIONS

This project demonstrates that operators are frequently able to identify POC BG values that are measurement/operator errors. Eliminating these errors significantly reduces documented rates of severe hypoglycemia and hyperglycemia, and has the potential to improve patient safety.

Continuous glucose monitoring system can improve the quality of glucose control and glucose variability compared with point-of-care measurement in critically ill patients: A randomized controlled trial

BACKGROUND

The purpose of this study was to determine whether subcutaneous continuous glucose monitoring systems (CGMS) could improve glucose management in critically ill patients compared with frequent and conventional point-of-care (POC) glucose measurements.

METHODS

A total of 144 patients with an expected length of stay in the ICU of at least 72 hours and with an admission glucose or two random glucose values of >10.0 mmol/L within 24 hours after admission, were randomly assigned to the CGMS group (n = 74) or the conventional group (C group, n = 70). Both groups used the same insulin algorithm to reach the same glucose target range (8.0-10.0 mmol/L).

RESULTS

Time in range (TIR, 8.0-10.0 mmol/L), which is our primary outcome measure, was higher in the CGMS group than in the C group (51.5% vs. 29.0%, P < .001). Glucose variability (coefficient of variation, CV; standard deviation, SD; glucose lability index, and GLI) was improved by CGMS (all P < .05). Mean glucose level (MGL) (9.6 vs. 10.3 mmol/L, P = .156) and the proportion of patients with hypoglycemia did not differ between CGMS (5.4%) and C (5.7%) (P = 1.000). However, duration of hypoglycemia was reduced in the CGMS group (15 vs. 28 minutes, P = .032). Clinical outcomes were similar between groups except for the fewer usage of CRRT and lower peak plasma urea nitrogen level in the CGMS group.

CONCLUSION

The use of CGMS, compared with POC glucose measurement, could improve the TIR, GV and duration of hypoglycemia.

A Retrospective Cohort Study of Patients with Type 2 Diabetes in China: Associations of Hypoglycemia with Health Care Resource Utilization and Associated Costs

INTRODUCTION

This study aimed to examine the associations of hypoglycemia with health care resource utilization (HCRU) and health care costs among patients with type 2 diabetes mellitus (T2DM) in China.

METHODS

This retrospective cohort study was conducted with 23,680 T2DM patients >18 years old who visited the Second Affiliated Hospital of Nanchang University between 1 January 2011 and 31 December 2015. Univariate descriptive statistics were used to relate the HCRU and associated costs to patient characteristics, and regression analysis was used to examine the association between hypoglycemia and HCRU, controlling for other confounding factors.

RESULTS

In the T2DM patients with or without insulin treatment, when compared with nonhypoglycemic patients, hypoglycemia was associated with more medical visits (all T2DM patients 19.48 vs. 10.46, insulin users 23.45 vs. 14.12) and higher diabetes-related medical costs (all T2DM patients ¥5187.54 vs. ¥3525.00, insulin users ¥6948.84 vs. ¥3401.15) and medication costs (T2DM patients ¥1349.40 vs. ¥641.92, insulin users: ¥1363.87 vs. ¥853.96). Controlling for age, gender, and Charlson comorbidity index (CCI) score, hypoglycemia and insulin intake were associated with greater health care resource utilization. As compared to nonhypoglycemic patients, hypoglycemic T2DM patients and those on insulin therapy performed more outpatient visits (proportions of hypoglycemic vs nonhypoglycemic T2DM patients performing 3+ visits: 72.69% vs. 65.49%; proportions of hypoglycemic vs nonhypoglycemic patients on insulin therapy performing 3+ visits: 78.26% vs. 71.73%) and were hospitalized more often (proportions of hypoglycemic vs nonhypoglycemic T2DM patients with 3+ admissions 75.90% vs. 50.24%; proportions of hypoglycemic vs nonhypoglycemic patients on insulin therapy with 3+ admissions: 83.19% vs. 58.51%).

CONCLUSION

Hypoglycemia in diabetes patients was associated with increased healthcare resource utilization and health-related expenditure, especially for patients on insulin treatment. Insulin treatment regimens should be more individualized and account for hypoglycemia risk.

Insulin Pump and Continuous Glucose Monitor Initiation in Hospitalized Patients with Type 2 Diabetes Mellitus

BACKGROUND

Insulin pumps and continuous glucose monitoring (CGM) are commonly used by patients with diabetes mellitus in the outpatient setting. The efficacy and safety of initiating inpatient insulin pumps and CGM in the nonintensive care unit setting is unknown.

MATERIALS AND METHODS

In a prospective pilot study, inpatients with type 2 diabetes were randomized to receive standard subcutaneous basal-bolus insulin and blinded CGM (group 1, n = 5), insulin pump and blinded CGM (group 2, n = 6), or insulin pump and nonblinded CGM (group 3, n = 5). Feasibility, glycemic control, and patient satisfaction were evaluated among groups.

RESULTS

Group 1 had lower mean capillary glucose levels, 144.5 ± 19.5 mg/dL, compared with groups 2 and 3, 191.5 ± 52.3 and 182.7 ± 59.9 mg/dL (P1 vs. 2+3 = 0.05). CGM detected 19 hypoglycemic episodes (glucose <70 mg/dL) among all treatment groups, compared with 12 episodes detected by capillary testing, although not statistically significant. No significant differences were found for the total daily dose of insulin or percentage of time spent below target glucose range (<90 mg/dL), in target glucose range (90-180 mg/dL), or above target glucose range (>180 mg/dL). On the Diabetes Treatment Satisfaction Questionnaire-Change, group 3 reported increased hyperglycemia and decreased hypoglycemia frequency compared with the other two groups, although the differences did not reach statistical significance.

CONCLUSIONS

Insulin pump and CGM initiation are feasible during hospitalization, although they are labor intensive. Although insulin pump initiation may not lead to improved glycemic control, there is a trend toward CGM detecting a greater number of hypoglycemic episodes. Larger studies are needed to determine whether use of this technology can lower inpatient morbidity and mortality.

Comparative Simulation Study of Glucose Control Methods Designed for Use in the Intensive Care Unit Setting via a Novel Controller Scoring Metric

BACKGROUND

Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates and thereby decrease health care expenditures. To evaluate what constitutes effective glucose control, typically several metrics are reported, including time in range, time in mild and severe hypoglycemia, coefficient of variation, and others. To date, there is no one metric that combines all of these individual metrics to give a number indicative of overall performance. We proposed a composite metric that combines 5 commonly reported metrics, and we used this composite metric to compare 6 glucose controllers.

METHODS

We evaluated the following controllers: Ideal Medical Technologies (IMT) artificial-intelligence-based controller, Yale protocol, Glucommander, Wintergerst et al PID controller, GRIP, and NICE-SUGAR. We evaluated each controller across 80 simulated patients, 4 clinically relevant exogenous dextrose infusions, and one nonclinical infusion as a test of the controller's ability to handle difficult situations. This gave a total of 2400 5-day simulations, and 585 604 individual glucose values for analysis. We used a random walk sensor error model that gave a 10% MARD. For each controller, we calculated severe hypoglycemia (<40 mg/dL), mild hypoglycemia (40-69 mg/dL), normoglycemia (70-140 mg/dL), hyperglycemia (>140 mg/dL), and coefficient of variation (CV), as well as our novel controller metric.

RESULTS

For the controllers tested, we achieved the following median values for our novel controller scoring metric: IMT: 88.1, YALE: 46.7, GLUC: 47.2, PID: 50, GRIP: 48.2, NICE: 46.4.

CONCLUSION

The novel scoring metric employed in this study shows promise as a means for evaluating new and existing ICU-based glucose controllers, and it could be used in the future to compare results of glucose control studies in critical care. The IMT AI-based glucose controller demonstrated the most consistent performance results based on this new metric.