The risk factors of inpatient hypoglycemia: A systematic review

Development and Validation of the Scale of Hypoglycemia Self-Care Behavior in Type 2 Diabetes

BACKGROUND

Inappropriate dietary, exercise, and medication self-care behaviors among persons with diabetes can easily trigger hypoglycemia. Clinically, it is necessary to quickly identify high-risk groups for hypoglycemic events to provide targeted hypoglycemia education. However, there is currently a lack of precise tools to assess self-care behaviors related to hypoglycemia.

OBJECTIVES

The aim of the study was to develop and validate a hypoglycemia self-care behavior scale for evaluating the behaviors of persons with diabetes in handling and preventing hypoglycemic events.

METHODS

A cross-sectional study with purposive sampling was conducted to recruit 300 persons with Type 2 diabetes who had experienced hypoglycemic events from a medical center and a primary care clinic in southern Taiwan. Data were collected using a structured questionnaire from December 2021 to September 2023.

RESULTS

The original Hypoglycemia Self-Care Behavior Scale, comprising 26 items, was reduced to 17 items, measuring six factors after exploratory factor analysis. Subsequently, the scale was further refined to 10 items covering four factors through structural equation modeling. Validity and reliability were assessed during this process, and the developed scale was subsequently verified for both. Factor loadings ranged from .50-.97, explaining 76% of the total variance. The four factors included "hypoglycemia recognition and carbohydrate supplementation," "prevention of hypoglycemia during exercise," "carbohydrate assessment," and "seeking medical assistance." The Hypoglycemia Self-Care Behavior Scale-developed and validated through structural equation modeling-demonstrates satisfactory model fit, convergent validity, and discriminant validity. Internal consistency was within the range of .73-.94, indicating strong reliability.

DISCUSSION

The Hypoglycemia Self-Care Behavior Scale not only functions as a tool for the rapid assessment of self-care behaviors during hypoglycemic events by clinical healthcare professionals but can also serve as reference for hypoglycemia-related health education.

Time at High Risk of Hypoglycemia: Validation of a Consensus-Based Continuous Glucose Monitoring-Metric for Hospitalized Patients

BACKGROUND

Time at high risk of hypoglycemia (THRH), 3.9 to 5.6 mmol/L, is a continuous glucose monitoring (CGM)-based metric recommended for reporting in hospitalized patients. This study aims to validate THRH as a predictor of hypoglycemia.

METHODS

The CGM data from 166 non-intensive care unit (non-ICU) inpatients with type 2 diabetes from the DIATEC trial were analyzed. All participants received basal-bolus insulin therapy. Of these, 82 were monitored with point-of-care glucose testing and blinded CGM, while 84 had open CGM. Linear and negative binomial regression analyses assessed the relationship between THRH and time below range (TBR) (<3.0 mmol/L, 3.0-3.9 mmol/L, and <3.9 mmol/L) and hypoglycemic events. Analyses were conducted for day (07:00-23:00), night (23:01-06:59), and 24-hour periods.

RESULTS

For CGM-monitored patients, every 10%-point increase in THRH was associated with a 0.13%-point increase in TBR (<3.0 mmol/L) (95% confidence interval [CI] = 0.06-0.21), 0.66%-point increase in TBR (3.0-3.9 mmol/L) (95% CI = 0.47-0.86), and 0.74%-point increase in TBR (<3.9 mmol/L) (95% CI = 0.51-0.97), all P < .001. A THRH threshold below 50% was linked to a TBR <3.9 mmol/L of less than 4%, as recommended. Similar results were observed during both day and night analyses and for point-of-care monitored patients, also for hypoglycemic events.

CONCLUSIONS

The THRH is strongly associated with hypoglycemia in non-ICU hospitalized patients with type 2 diabetes on basal-bolus insulin. Aiming for THRH below 50% aligns with the recommended TBR target of <3.9 mmol/L below 4%, supporting THRH's role in guiding hypoglycemia prevention strategies.

In-Hospital Management of Hyperglycemia: The Role of Insulin Degludec

INTRODUCTION

Hyperglycemia is a common and challenging condition in hospitalized patients both with and without a history of diabetes. Managing hyperglycemia effectively is critical in reducing complications, mortality, and the length of hospital stays. Insulin degludec (IDeg), an ultralong-acting basal insulin, has a well-established efficacy and safety profile in terms of managing hyperglycemia in outpatients; it has demonstrated benefits in clinical practice across various patient populations. This review aims to assess the evidence on its clinical suitability, as well as efficacy and safety, for managing hyperglycemia across different inpatient populations. The review specifically focuses on outcomes such as glycemic control, glycemic variability, safety (particularly hypoglycemia risk), dosing flexibility, ease of titration, and use in special populations.

METHODS

A comprehensive literature search was conducted using PubMed to identify studies published between 2014 and 2024. Eligible studies included randomized controlled trials, real-world evidence, and case series that examined the use of IDeg for hyperglycemia management in hospitalized patients.

RESULTS

The reviewed studies consistently demonstrated that IDeg provides stable and predictable glycemic control with low glycemic variability. The ultralong duration of action, ability to be titrated daily, and flexibility in dosing make IDeg suitable for noncritical care settings with difficult-to-maintain rigid insulin schedules. Furthermore, the risk of hypoglycemia, particularly nocturnal hypoglycemia, is low with IDeg. These attributes are beneficial across diverse inpatient populations. Practical advantages, such as ease of administration with a specialized delivery device, further support its use in hospital settings.

CONCLUSIONS

Unique pharmacokinetic and pharmacodynamic properties of IDeg, reduced glycemic variability, low hypoglycemia risk, ease of daily titration, and dosing flexibility make it appropriate for managing hyperglycemia in hospitalized patients.

Flash Glucose Monitoring for Predicting Cardiogenic Shock Occurrence in Critically Ill Patients: A Retrospective Pilot Study

Background/Objectives: Continuous and flash glucose monitoring (CGM and FGM) may enhance glucose management by providing real-time glucose data. Furthermore, growing evidence is linking altered blood glucose concentrations and worse short-term outcomes in critically ill patients. While hyperglycemia is more common in these patients and is associated with an increased risk of adverse events, hypoglycemia is particularly concerning and significantly raises the risk of fatal outcomes. This exploratory study investigated the link between FGM variables and cardiogenic shock in critically ill Coronary Care Unit (CCU) patients. Methods: Twenty-eight CCU patients (1 May 2021-31 January 2022) were monitored using a Libre FreeStyle system. Analyzed data included patient demographic and laboratory data, left ventricular ejection fraction, standard glucose monitoring, APACHE IV scores, and cardiogenic shock occurrence. Analysis was performed using the χ2 test, Mann-Whitney U test, and logistic regression. Results: Among the patients, 13 (46.43%) developed cardiogenic shock. FGM detected hypoglycemia in 18 (64.29%) patients, while standard methods in 6 (21.43%) patients. FGM-detected hypoglycemia was more frequent in patients who developed cardiogenic shock (p = 0.0129, χ2 test) with a significantly higher time below range reading (p = 0.0093, Mann Withney U test), despite no differences in mean glucose values. In addition, hypoglycemia detected by FGM was an independent predictor of shock (p = 0.0390, logistic regression). Conclusions: FGM identified more hypoglycemic events compared to standard glucose monitoring in the CCU. Frequent FGM-detected hypoglycemic events were associated with cardiogenic shock, regardless of a history of diabetes. Due to a limited sample size, these results should be interpreted cautiously and further research in this area is justified.

Hypoglycemia in Hospitalized Patients with Diabetes

Approximately 38% of the patient population in community hospitals throughout the United States has diabetes mellitus. Hypoglycemia is defined as less than 70 mg/dL. Patients with hypoglycemia are at more risk for clinically adverse outcomes including death. Critical care nurses must recognize the signs and symptoms of hypoglycemia and initiate prompt intervention to reduce the risk of mortality.

Investigation of Severe Hypoglycemia Risk Among Patients with Diabetes Treated with Ultra-Rapid Lispro in Japan

INTRODUCTION

There is no information on the incidence of severe hypoglycemia in real-world patients with diabetes receiving ultra-rapid lispro (URLi). This post-marketing, observational, safety study assessed the incidence proportion and incidence rate of the first severe hypoglycemia event requiring a hospital visit in URLi-treated patients. It also compared the risk of severe hypoglycemia between patients treated with URLi or other rapid-acting insulin analogs (RAIAs).

METHODS

Claims data were obtained from a nationwide hospital-based administrative database in Japan (Medical Data Vision). Adults with diabetes who initiated URLi or other RAIA on/after June 01, 2020, were followed up through May 31, 2023. Severe hypoglycemia was identified using a validated algorithm. Incidence proportion and incidence rate of the first severe hypoglycemia event requiring a hospital visit was described in URLi-treated patients (descriptive analysis). These outcomes were also compared against propensity score (PS)-matched other RAIA-treated patients (comparator; comparative analysis). Hazard ratio (HR) and 95% confidence interval (CI) was estimated with a Cox proportional hazards model.

RESULTS

The descriptive analysis' URLi-treated cohort included 17,838 patients [mean (standard deviation, SD) age 65.9 (15.7) years; 58.3% male]. The majority had type 2 diabetes (75.7%). The incidence proportion of the first severe hypoglycemia event requiring a hospital visit was 0.6% (95% CI 0.5, 0.8) and the incidence rate was 1.7 per 100 person-years (95% CI 0.7, 4.3). The comparative analysis included 10,592 URLi-treated and 52,917 comparator-treated patients. The incidence rate of severe hypoglycemia did not significantly differ between these cohorts (HR 0.8; 95% CI 0.5, 1.1; p = 0.132;.

CONCLUSION

This study did not show a statistically significant increase in the incidence and risk of the first severe hypoglycemia event requiring a hospital visit in real-world URLi-treated patients in Japan, compared with a PS-matched cohort of other RAIA-treated patients.

A Six-Month Prospective Audit of Hypoglycemia in Non-Critically Ill Inpatients at a Tertiary Care Hospital in North India: Prevalence, Presentation, and Prevention

OBJECTIVE

 This research aimed to assess the prevalence, presentation, and risk factors associated with hypoglycemia in non-critically ill vs. critically ill inpatients at a tertiary care hospital in North India, focusing on identifying differences in clinical parameters and outcomes between these two patient populations over six months.

METHODOLOGY

 This six-month prospective study, conducted at a tertiary care hospital in North India, evaluated the frequency, presentation, and prevention of hypoglycemia in 200 hospitalized patients, evenly divided between non-critically ill and critically ill groups. Data collection involved recording baseline parameters and daily blood glucose levels and documenting hypoglycemic episodes and their severity. Preventive strategies, including glucose monitoring, medication adjustments, and dietary interventions, were also tracked. The study used chi-square and t test analysis to determine the prevalence of hypoglycemia, recurrent episodes, and the effectiveness of preventive measures, focusing on differences between the two patient groups and the impact of management strategies.

RESULTS

 The study found that critically ill patients were older (65 ± 15 years) than non-critically ill patients (60 ± 12 years, p = 0.036) with a similar proportion of females in both groups (50% vs. 45%, p = 0.527). Hypoglycemia was more common in critically ill patients (45% vs. 25%, p = 0.005), as were cardiovascular disease (50% vs. 30%, p = 0.004) and chronic kidney disease (35% vs. 20%, p = 0.023). Nutrition consultations were more frequent in non-critically ill patients (30% vs. 15%, p = 0.025), while medication adjustments were more common in critically ill patients (40% vs. 20%, p = 0.004). Non-recurrent hypoglycemia was higher in non-critically ill patients (68% vs. 44.4%, p = 0.038), whereas recurrence was higher in critically ill patients (55.6% vs. 32%, p = 0.038).

CONCLUSION

 The study highlights the significance of addressing hypoglycemia in non-critically ill inpatients, a group that is often overlooked compared to critically ill patients. Although non-critically ill patients had fewer comorbidities and a lower incidence of previous hypoglycemia, the occurrence of hypoglycemia in this group remains a concern. The findings indicate that, even in non-critically ill patients, careful management of factors such as insulin therapy and underlying conditions like type 2 diabetes is essential to prevent hypoglycemic episodes. These results emphasize the need for targeted interventions in non-critical care settings to mitigate the risk of hypoglycemia and enhance patient safety and outcomes.

Impact of Lowered Inpatient Correctional Bedtime Insulin Dosing on Glycemic Outcomes of Veterans

Purpose: This study evaluated glycemic outcomes for hospitalized patients after reduction in bedtime correctional insulin dosing. Methods: This was a retrospective, single-center analysis of a protocol change that reduced bedtime correctional insulin scale. Comparable cohorts pre- and post-protocol change were created which included patients who were ordered correctional insulin with at least 1 blood glucose (BG) reading. The primary outcome was number of nocturnal hypoglycemia readings. Secondary outcomes included, but were not limited to, mean fasting BG, BG within various ranges, and length of stay. Results: 3 percent of patients in the post-protocol change group (N = 100) experienced nocturnal hypoglycemia compared to 6% of patients in the pre-change group (N = 100) (P = .507). There were no significant differences in BG ranges <110 mg/dL, <140 mg/dL, 140 to 180 mg/dL, and >180 mg/dL. However, 19% of patients in the post-protocol change group had BG of >250 mg/dL as compared to 9% in the pre-change group (P = .033). Mean fasting BG was higher in the post-protocol change group compared to the pre-change group (156.5 mg/dL vs 139.3 mg/dL [P = .002]), as was hospital length of stay (5.17 vs 4.6 days, [P = .024]). Conclusions: A decreased bedtime correctional insulin scale had mixed results with more patients achieving goal fasting BG but also more patients experiencing BG > 250 mg/dL and longer length of stay. Larger prospective studies are required to evaluate the safety and efficacy of this type of intervention and its long-term impact.

Hypoglycemia in Critically Ill Patients: A Concise Clinical Review

Hypoglycemia in critical care is a well-documented phenomenon, linking both physiological and clinical evidence to harmful outcomes and an increased risk of mortality. Its implications span medical and non-medical consequences, such as cardiovascular and cerebrovascular complications, and escalated health care expenses and hospitalization duration. Mitigation measures for modifiable risk factors and education for both patients and health care providers on hypoglycemia can effectively prevent the onset of inpatient hypoglycemia. This concise clinical review offers a brief overview of hypoglycemia in critically ill patients, encompassing its pathophysiology, etiology, diagnosis, management, and prevention.

Predicting hypoglycemia in ICU patients: a machine learning approach

BACKGROUND

The current study sets out to develop and validate a robust machine-learning model utilizing electronic health records (EHR) to forecast the risk of hypoglycemia among ICU patients in Jordan.

RESEARCH DESIGN AND METHODS

The present study drew upon a substantial cohort of 13,567 patients admitted 26,248 times to the intensive care unit (ICU) over 10 years from July 2012 to July 2022. The primary outcome of interest was the occurrence of any hypoglycemic episode during the patient's ICU stay. Developing and testing predictor models was conducted using Python machine-learning libraries.

RESULTS

A total of 1,896 were eligible to participate in the study, 206 experienced at least one hypoglycemic episode. Eight machine-learning models were trained to predict hypoglycemia. All models showed predicting power with a range of 74.53-99.69 for AUROC. Except for Naive Bayes, the six remaining models performed distinctly better than the basic logistic regression usually used for prediction in epidemiological studies. CatBoost model was consistently the best performer with the highest AUROC (0.99), accuracy and precision, sensitivity and specificity, and recall.

CONCLUSIONS

We used machine learning to anticipate the likelihood of hypoglycemia, which can significantly decrease hypoglycemia incidents and enhance patient outcomes.

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 at admission of in-hospital dysglycemia, mortality, and readmissions in patients with type 2 diabetes and pneumonia

AIMS

In-hospital dysglycemia is associated with adverse outcomes. Identifying patients at risk of in-hospital dysglycemia early on admission may improve patient outcomes.

METHODS

We analysed 117 inpatients admitted with pneumonia and type 2 diabetes monitored by continuous glucose monitoring. We assessed potential risk factors for in-hospital dysglycemia and adverse clinical outcomes.

RESULTS

Time in range (3.9-10.0 mmol/l) decreased by 2.9 %-points [95 % CI 0.7-5.0] per 5 mmol/mol [2.6 %] increase in admission haemoglobin A1c, 16.2 %-points if admission diabetes therapy included insulin therapy [95 % CI 2.9-29.5], and 2.4 %-points [95 % CI 0.3-4.6] per increase in the Charlson Comorbidity Index (CCI) (integer, as a measure of severity and amount of comorbidities). Thirty-day readmission rate increased with an IRR of 1.24 [95 % CI 1.06-1.45] per increase in CCI. In-hospital mortality risk increased with an OR of 1.41 [95 % CI 1.07-1.87] per increase in Early Warning Score (EWS) (integer, as a measure of acute illness) at admission.

CONCLUSIONS

Dysglycemia among hospitalised patients with pneumonia and type 2 diabetes was associated with high haemoglobin A1c, insulin treatment before admission, and the amount and severity of comorbidities (i.e., CCI). Thirty-day readmission rate increased with high CCI. The risk of in-hospital mortality increased with the degree of acute illness (i.e., high EWS) at admission. Clinical outcomes were independent of chronic glycemic status, i.e. HbA1c, and in-hospital glycemic status.

Efficacy and safety of sitagliptin with basal-plus insulin regimen versus insulin alone in non-critically ill hospitalized patients with type 2 diabetes: SITA-PLUS hospital trial

AIMS

To compare the efficacy and safety of basal-plus (BP) insulin regimen with or without sitagliptin in non-critically ill patients with type 2 diabetes (T2D).

METHODS

This open-label, randomized clinical trial included inpatients with a previous diagnosis of T2D and blood glucose (BG) between 180 and 400 mg/dL. Participants received basal and correctional insulin doses (BP regimen) either with or without sitagliptin. The primary outcome was the difference in the mean daily BG among the groups.

RESULTS

Seventy-six patients (mean age 60 years, 64 % men) were randomized. Compared with BP insulin therapy alone, the sitagliptin-BP combination led to a lower mean daily BG (158.8 vs 175.0 mg/dL, P = 0.014), a higher percentage of readings within a BG range of 70-180 mg/dL (75.9 % vs 64.7 %, P < 0.001), and a lower number of BG readings >180 mg/dL (P < 0.001). Sitagliptin-BP resulted in fewer basal and supplementary insulin doses (P = 0.024 and P = 0.017, respectively) and lower daily insulin injections (P = 0.023) than those with insulin alone. The proportion of patients with hypoglycemia was similar in the two groups.

CONCLUSIONS

For inpatients with T2D and hyperglycemia, the sitagliptin and BP regimen combination is safe and more effective than insulin therapy alone.

CLINICALTRIALS

gov identifier: NCT05579119.

Factors Influencing Hypoglycemia in Type 2 Diabetes Mellitus Outpatients with State Health Insurance at Regional General Hospitals in Jakarta, Indonesia

BACKGROUND

Hypoglycemia is an acute episode that can lead to death in patients with diabetes mellitus (DM). This condition is preventable with patient education, and identifying factors influencing their occurrence is essential to creating effective and efficient education. It also leads to prevention and control by re-organizing the service system and diabetes policies. This study aimed to determine factors contributing to hypoglycemic episodes in type 2 DM outpatients covered by the state-provided Jaminan Kesehatan Nasional (JKN) health insurance.

METHODS

The study used a cross-sectional design and collected data from five regional general hospitals in Jakarta, Indonesia. The outpatients were sampled consecutively from two hospitals in September-November 2021, one in January-March 2022, and two others in April-June 2023. Interviews produced primary data related to experienced hypoglycemic episodes, and medical records provided secondary data on patients' clinical characteristics and treatments. Binary logistic regression analysis was employed to process the contributing factors statistically.

RESULTS

From 501 patients who met the inclusion and exclusion criteria, it was found that the prevalence of hypoglycemia was 53.3%. Factors that significantly increased hypoglycemic risk (p < 0.05) were high HbA1c levels (OR 1.9; 95% CI 1.2-2.9), comorbidities (OR 1.6; 95% CI 1.1-2.4), insulin/sulfonylurea therapy (OR 2; 95% CI 1-4), non-smoking habit (OR 2.2; 95% CI 1.3-3.6) and physically active lifestyle (OR 1.8; 95% CI 1.2-2.6).

CONCLUSION

The prevalence of hypoglycemia in type 2 diabetes mellitus (DM) outpatients with the state-provided health insurance Jaminan Kesehatan Nasional (JKN) at general hospitals in Jakarta is high. The diabetes self-management education (DSME) services provided by health professionals for these outpatients must be further improved.

Risk factors of severe hypoglycemia among patients with type 2 diabetes mellitus in outpatient clinic of tertiary hospital in Indonesia

This study aimed to describe risk factors of severe hypoglycemia in type 2 diabetes mellitus (T2DM) patients in a tertiary care hospital in Indonesia. This study was a retrospective cohort study in the Endocrinology Outpatient Clinic of Dr. Cipto Mangunkusumo National General Hospital, Jakarta, Indonesia. All subjects more than 18 years old who had been visiting the clinic for at least a year were included. Subjects were interviewed whether they had any severe hypoglycemia events within the past year, while data on risk factor variables of severe hypoglycemia was taken from medical records one year before data collection. We recruited 291 subjects, among whom 25.4% suffered at least one episode of severe hypoglycemia within one year. History of severe hypoglycemia (OR 5.864, p ≤ 0.001), eGFR less than 60 mL/min/1.73m2 (OR 1.976, p = 0.028), and insulin use (OR 2.257, p = 0.021) were associated with increased risk of severe hypoglycemia. In conclusion, history of previous severe hypoglycemia, eGFR less than 60 mL/min/1.73m2, and insulin use were associated with severe hypoglycemia.

A Supportive Role of Mesenchymal Stem Cells on Insulin-Producing Langerhans Islets with a Specific Emphasis on The Secretome

Type 1 Diabetes (T1D) is a chronic autoimmune disease characterized by a gradual destruction of insulin-producing β-cells in the endocrine pancreas due to innate and specific immune responses, leading to impaired glucose homeostasis. T1D patients usually require regular insulin injections after meals to maintain normal serum glucose levels. In severe cases, pancreas or Langerhans islet transplantation can assist in reaching a sufficient β-mass to normalize glucose homeostasis. The latter procedure is limited because of low donor availability, high islet loss, and immune rejection. There is still a need to develop new technologies to improve islet survival and implantation and to keep the islets functional. Mesenchymal stem cells (MSCs) are multipotent non-hematopoietic progenitor cells with high plasticity that can support human pancreatic islet function both in vitro and in vivo and islet co-transplantation with MSCs is more effective than islet transplantation alone in attenuating diabetes progression. The beneficial effect of MSCs on islet function is due to a combined effect on angiogenesis, suppression of immune responses, and secretion of growth factors essential for islet survival and function. In this review, various aspects of MSCs related to islet function and diabetes are described.

Recurrent hypoglycemia dampens functional regulation mediated via Neurexin-1, Neuroligin-2 and Mint-1 docking proteins: Intensified complications during diabetes

Glycemic regulation is important for maintaining critical physiological functions. Extreme variation in levels of circulating glucose are known to affect insulin secretion. Elevated insulin levels result in lowering of circulating glycemic levels culminating into hypoglycemia. Recurrence of hypoglycemia are often noted owing to fasting conditions, untimely meals, irregular dietary consumption, or as a side-effect of disease pathophysiology. Such events of hypoglycemia threaten to hamper the patterns of insulin secretion in diabetic condition. Insulin vesicle docking is a prerequisite phase which ensures anchoring of the vesicles to the β-cell membrane in order to expel the insulin cargo. Neurexin and Neuroligin are the marker docking proteins which assists in the tethering of the insulin granules to the secretory membrane. However, these cell adhesion molecules indirectly affect the glycemic levels by regulating insulin secretion. The effect of extreme levels of glycemic fluctuations on these molecules, and how it affects the docking machinery remains obscure. Our current study demonstrates down-regulated expression of Neurexin-1, Neuroligin-2 and Mint-1 molecules during hyperglycemia, hypoglycemia and diabetic hypoglycemia in rodents as well as for an in-vitro system using MIN6 cell-line. Studies with fluorescently labelled insulin revealed presence of lessened functional insulin secretory granules, concomitant with the alterations in morphology and as a result of hypoglycemia in control and diabetic condition which was found to be further deteriorating. Our studies indicate towards a feeble vesicular anchorage, which may partly be responsible for dwindled insulin secretion during diabetes. However, hypoglycemia poses as a potent diabetic complication in further deteriorating the docking machinery. To the best of our knowledge this is the first report which demonstrates the effect of hypoglycemic events in affecting insulin secretion by weakening insulin vesicular anchorage in normal and diabetic individuals.

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

BACKGROUND

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

LOCAL PROBLEM

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

METHODS

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

INTERVENTIONS

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

RESULTS

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

CONCLUSIONS

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

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.

Effectiveness and safety of new oral and injectable agents for in-hospital management of type 2 diabetes in general wards: Systematic review and meta-analysis

BACKGROUND

Current guidelines recommend insulin alone for in-hospital management of diabetes, but growing information suggests that new oral or injectable agents may be as effective and safe.

METHODS

Systematic review and meta-analysis with evidence from randomized (RCT) and non-randomized (NRS) studies in PubMed, EMBASE and LILACS databases up to February 10, 2022, for studies including hospitalized type 2 diabetes patients, comparing dipeptidyl peptidase 4 inhibitors (DPP4i), sodium glucose co-transporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonist (GLP1Ra) with insulin alone for glycemic control and safety outcomes.

FINDINGS

7 RCT and 3 NRTs were included. There were no differences in mean blood glucose, measurements within range or rate of hypoglycemia between DPP4i and insulin. We found a lower mean glucose for GLP1Ra plus insulin subgroup (-16.36 mg/dL, 95 % CI -27.31, -5.41; I² = 0 %) with lower incidence of hypoglycemia < 70 mg/dL with GLP1Ra (RR 0.31, CI 95 % 0.14-0.70, I² = 0 %). SGLT2i data was limited. Adverse events rates were similar between treatments.

CONCLUSION

Our review suggests that inpatient management in the general ward with DPP4i and GLP1Ra is as effective and safe as management with insulin. More randomized studies are required to support these findings before they could be recommended as usual practice.

Rate of Inpatient Hypoglycemia Following a 1:1 Dose Interchange Between Concentrated Insulin Glargine to Insulin Detemir

BACKGROUND

Insulin remains a mainstay of treating hyperglycemia in an acute setting. Insulin glargine 300 units/mL (Toujeo, iGlar300) has a different pharmacokinetic profile than 100 units/mL basal insulins, such as insulin detemir (iDet100) and iGlar100. While conversion from iGlar300 to iGlar100 requires a 20% dose decrease, there is currently no recommended interchange from iGlar300 to iDet100.

OBJECTIVE

Compare the incidence of hypoglycemia in patients who received a 1:1 unit interchange from home iGlar300 or iGlar100 to iDet100 while admitted.

METHODS

A retrospective study was conducted to evaluate adults within a multi-site network admitted between May and December 2019. Patients were included if they received at least one dose of iDet100 following interchange from home iGlar300 or iGlar100. The primary endpoint was the incidence of hypoglycemic events following a 1:1 interchange of iGlar300 vs. iGlar100 to inpatient iDet100. Secondary outcomes include overall hypoglycemic events, time to hypoglycemia, and doses given before hypoglycemia.

RESULTS

Of 615 patients, 394 received a 1:1 unit interchange to iDet100 (52 from iGlar300 and 342 from iGlar100). Incidence of hypoglycemic events was significantly higher in those with a 1:1 interchange from iGlar300 versus iGlar100 (36.5% vs. 18.7%, p = 0.007). Significant differences were observed in overall hypoglycemic events, time to hypoglycemia, and number of doses given before hypoglycemic event.

CONCLUSION AND RELEVANCE

A 1:1 unit interchange from iGlar300 to iDet100 led to a higher incidence of hypoglycemic events compared to those interchanged from iGlar100. Dose reduction should be considered when transitioning from home iGlar300 to iDet100 in the inpatient setting.

Machine Learning Models for Nocturnal Hypoglycemia Prediction in Hospitalized Patients with Type 1 Diabetes

Nocturnal hypoglycemia (NH) is a dangerous complication of insulin therapy that often goes undetected. In this study, we aimed to generate machine learning (ML)-based models for short-term NH prediction in hospitalized patients with type 1 diabetes (T1D). The models were trained on continuous glucose monitoring (CGM) data obtained from 406 adult patients admitted to a tertiary referral hospital. Eight CGM-derived metrics of glycemic control and glucose variability were included in the models. Combinations of CGM and clinical data (23 parameters) were also assessed. Random Forest (RF), Logistic Linear Regression with Lasso regularization, and Artificial Neuron Networks algorithms were applied. In our models, RF provided the best prediction accuracy with 15 min and 30 min prediction horizons. The addition of clinical parameters slightly improved the prediction accuracy of most models, whereas oversampling and undersampling procedures did not have significant effects. The areas under the curve of the best models based on CGM and clinical data with 15 min and 30 min prediction horizons were 0.97 and 0.942, respectively. Basal insulin dose, diabetes duration, proteinuria, and HbA1c were the most important clinical predictors of NH assessed by RF. In conclusion, ML is a promising approach to personalized prediction of NH in hospitalized patients with T1D.

Machine-learning Prediction of Hypo- and Hyperglycemia from Electronic Health Records: Algorithm Development and Validation

Background

Acute blood glucose (BG) decompensations (hypoglycemia and hyperglycemia) represent a frequent and significant risk for inpatients and adversely affect patient outcomes and safety. The increasing need for BG management in inpatients poses a high demand on clinical staff and health care systems in addition.

Objective

This study aimed to generate a broadly applicable multiclass classification model for predicting BG decompensation events from patients’ electronic health records to indicate where adjustments in patient monitoring and therapeutic interventions are required. This should allow for taking proactive measures before BG levels are derailed.

Methods

A retrospective cohort study was conducted on patients who were hospitalized at a tertiary hospital in Bern, Switzerland. Using patient details and routine data from electronic health records, a multiclass prediction model for BG decompensation events (<3.9 mmol/L [hypoglycemia] or >10, >13.9, or >16.7 mmol/L [representing different degrees of hyperglycemia]) was generated based on a second-level ensemble of gradient-boosted binary trees.

Results

A total of 63,579 hospital admissions of 38,250 patients were included in this study. The multiclass prediction model reached specificities of 93.7%, 98.9%, and 93.9% and sensitivities of 67.1%, 59%, and 63.6% for the main categories of interest, which were nondecompensated cases, hypoglycemia, or hyperglycemia, respectively. The median prediction horizon was 7 hours and 4 hours for hypoglycemia and hyperglycemia, respectively.

Conclusions

Electronic health records have the potential to reliably predict all types of BG decompensation. Readily available patient details and routine laboratory data can support the decisions for proactive interventions and thus help to reduce the detrimental health effects of hypoglycemia and hyperglycemia.

Incretin-based therapy for glycemic control of hospitalized patients with type 2 diabetes: a systematic review

Incretin-based therapy leads to glycemic control in a glucose-dependent manner with a low risk of hypoglycemia, making it appealing for use in the hospital. The aim of this systematic review was to assess the benefits of incretin-based therapy in patients with type 2 diabetes hospitalized outside of the intensive care unit. We searched for studies published up to August 2021 in the PubMed and Scopus databases. Clinical trials comparing incretin-based therapy (alone or in combination with insulin) versus an insulin regimen were selected. The results of the included studies showed that incretin-based therapy showed mean blood glucose values, a percentage of records within the therapeutic target, and a percentage of treatment failure similar to insulin management, particularly in patients with mild to moderate hyperglycemia. Furthermore, incretin-based treatment was associated with a lower total insulin dose and a lower incidence of hypoglycemia. In conclusion, incretin-based therapy achieved glycemic control similar to insulin treatment in patients with type 2 diabetes hospitalized outside the intensive care unit and has the advantages of reducing the insulin requirement and a lower risk of hypoglycemia.

Fasting biochemical hypoglycemia and related-factors in non-diabetic population: Kanagawa Investigation of Total Check-up Data from National Database-8

BACKGROUND

In healthy people, the lowest daily blood glucose concentration is usually observed in the early morning, after overnight fasting. However, the clinical relevance and the prevalence of fasting biochemical hypoglycemia (FBH) are poorly understood in people who do not have diabetes, although the clinical implications of such hypoglycemia have been extensively studied in patients with diabetes. FBH can be influenced by many factors, including age, sex, body mass, smoking, alcohol drinking, exercise levels, medications, and eating behaviors, such as breakfast skipping and late-night eating.

AIM

To determine the prevalence of FBH and investigated its association with potential risk factors in a population without diabetes.

METHODS

Clinical parameters and lifestyle-related factors were assessed in a cross-sectional study of 695613 people aged 40-74 years who had undergone a health check-up (390282 men and 305331 women). FBH was defined as fasting plasma glucose < 70 mg/dL (3.9 mmol/L) after overnight fasting, regardless of any symptoms. The absence of diabetes was defined as HbA1c < 6.5%, fasting plasma glucose < 126 mg/dL (7.0 mmol/L), and no pharmacotherapy for diabetes. Multivariate logistic regression analysis, with adjustment for confounding factors, was used to identify associations.

RESULTS

FBH was present in 1842 participants (0.26%). There were significantly more women in the FBH group (59.1%) than in the non-FBH group (43.9%). Values of most of the clinical parameters, but not age, were significantly lower in the FBH group than in the non-FBH group. Logistic regression analysis showed that a body mass index of ≤ 20.9 kg/m2 (reference: 21-22.9 kg/m2) and current smoking were significantly associated with FBH, and this was not altered by adjustment for age, sex, and pharmacotherapy for hypertension or dyslipidemia. Female sex was associated with FBH. When the data were analyzed according to sex, men in their 60s or 70s appeared more likely to experience FBH compared with their 40s, whereas men in their 50s and women aged ≥ 50 years appeared less likely to experience FBH. The relationships of FBH with other factors including alcohol drinking and pharmacotherapies for hypertension and dyslipidemia also differed between men and women.

CONCLUSION

FBH occurs even in non-diabetic people, albeit at a very low frequency. FBH is robustly associated with low body mass and smoking, and its relationship with lifestyle factors varies according to sex.

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.

Single prognostic cut-off value for admission glycemia in acute myocardial infarction has been used although high-risk stems from hyperglycemia as well as from hypoglycemia (a narrative review)

All original articles and meta-analysis use the single cut-off value to distinguish high-risk hyperglycemic from other acute myocardial infarction (AMI) patients. The mortality rate is 3.9 times higher in non-diabetic AMI patients with admission glycemia ≥6.1mmol compared to normoglycemic non-diabetic AMI patients. On the other hand, admission hypoglycemia in AMI is an important predictor of mortality. Because both admission hypo- and hyperglycemia correspond to higher in-hospital mortality, this graph is recognized as "J or U shaped curve". The review suggests two cut-off values for admission glycemia for risk assessment in AMI instead of single one because hypoglycemia as well as hyperglycemia represents a high-risk factor.