Predisposing Factors for Hypoglycemia and Its Relation With Mortality in Critically Ill Patients Undergoing Insulin Therapy in an Intensive Care Unit

Nomogram for Predicting Hypoglycemia in Type 2 Diabetes Mellitus Patients Treated with Insulin Pump During Enteral Nutrition

Purpose

To develop a prediction model for hypoglycemia in type 2 diabetes mellitus (T2DM) patients treated with an insulin pump during enteral nutrition.

Methods

This retrospective study included T2DM patients treated with an insulin pump during enteral nutrition at the First Affiliated Hospital of Jinan University, Guangzhou Red Cross Hospital, Foshan First People's Hospital, and Guangdong Provincial Hospital of Traditional Chinese Medicine between January 2016 and December 2023. The patients were randomized 3:1 to the training and validation sets. The risk factors for hypoglycemia were analyzed. A prediction model was developed.

Results

This study included 122 patients, and 57 patients had at least one hypoglycemic event during their hospitalization (46.72%). The multivariable logistic regression analysis showed that the time to reach the glycemic targets (odds ratio (OR)=1.408, 95% confidence interval (CI)=1.084-1.825, P=0.006), average glycemia (OR=0.387, 95% CI=0.233-0.643, P=0.010), coronary heart disease (OR=0.089, 95% CI=0.016-0.497, P<0.001), and the administration of hormone therapy (OR=6.807, 95% CI=1.128-41.081, P=0.037) were independently associated with hypoglycemia. A nomogram was built. The receiver operating characteristics analysis showed that the area under the curve of the model was 0.872 (95% CI=0.0.803-0.940) for the training set and 0.839 (95% CI=0.688-0.991) in the validation set.

Conclusion

A nomogram was successfully built to predict hypoglycemia in T2DM patients treated with an insulin pump during enteral nutrition, based on the time to reach the glycemic targets, average glycemia, coronary heart disease, and the administration of hormone therapy.

Relationship between time-weighted average glucose and mortality in critically ill patients: a retrospective analysis of the MIMIC-IV database

Blood glucose management in intensive care units (ICU) remains a controversial topic. We assessed the association between time-weighted average glucose (TWAG) levels and ICU mortality in critically ill patients in a real-world study. This retrospective study included critically ill patients from the Medical Information Mart for Intensive Care IV database. Glycemic distance is the difference between TWAG in the ICU and preadmission usual glycemia assessed with glycated hemoglobin at ICU admission. The TWAG and glycemic distance were divided into 4 groups and 3 groups, and their associations with ICU mortality risk were evaluated using multivariate logistic regression. Restricted cubic splines were used to explore the non-linear relationship. A total of 4737 adult patients were included. After adjusting for covariates, compared with TWAG ≤ 110 mg/dL, the odds ratios (ORs) of the TWAG > 110 mg/dL groups were 1.62 (95% CI 0.97-2.84, p = 0.075), 3.41 (95% CI 1.97-6.15, p < 0.05), and 6.62 (95% CI 3.6-12.6, p < 0.05). Compared with glycemic distance at - 15.1-20.1 mg/dL, the ORs of lower or higher groups were 0.78 (95% CI 0.50-1.21, p = 0.3) and 2.84 (95% CI 2.12-3.82, p < 0.05). The effect of hyperglycemia on ICU mortality was more pronounced in non-diabetic and non-septic patients. TWAG showed a U-shaped relationship with ICU mortality risk, and the mortality risk was minimal at 111 mg/dL. Maintaining glycemic distance ≤ 20.1 mg/dL may be beneficial. In different subgroups, the impact of hyperglycemia varied.

Non-linear relationship between baseline fasting blood glucose and mortality in peritoneal dialysis patients, a retrospective cohort study

Background

The relationship between baseline fasting blood glucose (bFBG) and mortality in peritoneal dialysis (PD) patients has been the subject of debate, with limited exploration of the non-linear relationship between bFBG and death in these patients.

Methods

This retrospective study categorized patients into four groups based on their bFBG using quartiles. Baseline clinical data at the initiation of dialysis were compared. Survival curves were plotted, and subgroup analyses were stratified by relevant covariates. To address the non-linear relationship, curve fitting and a threshold effect analysis were performed.

Results

The study included 379 PD patients with a median follow-up of 41.8 (22.6, 60.1) months. The COX proportional hazards model showed an association between bFBG and the risk of death after adjusting for confounding factors [hazard ratio (HR): 1.22, 95% CI: 1.05-1.41, P = 0.009]. Stratified analyses indicated a stable correlation between bFBG and mortality. The Kaplan-Meier curve analysis revealed significant differences in survival rates among different groups based on bFBG levels (P < 0.01). The curve fitting analysis revealed a U-shaped relationship between bFBG and mortality, with an inflection point at approximately 5.1 mmol/L.

Conclusion

Our study has demonstrated a non-linear relationship between bFBG and mortality in PD patients. Additionally, we have found that the optimal bFBG value associated with the lowest risk of mortality is approximately 5.1 mmol/L.

Preoperative prognostic predictors and treatment strategies for surgical procedure focused on the sequential organ failure assessment score in nonocclusive mesenteric ischemia: a multicenter retrospective cohort study

BACKGROUND

Several large-scale studies have assessed endovascular and surgical treatment methods for nonocclusive mesenteric ischemia (NOMI); however, the prognostic factors for NOMI remain unclear. Therefore, this study aimed to evaluate risk factors for in-hospital mortality among patients with NOMI who underwent laparotomy and to examine therapeutic strategies that may improve the prognosis.

MATERIALS AND METHODS

In this multicenter retrospective study, the authors reviewed the electronic medical records retrieved from the inpatient database of patients with NOMI at eight district general hospitals between January 2011 and January 2021. A total of 88 patients who underwent laparotomies were divided into survivor and nonsurvivor groups, and statistical analysis was performed to determine clinical and physiological factors.

RESULTS

Exploratory laparotomy based on second-look surgery was the first treatment choice. The overall mortality rate was 48.8%, with a male-to-female ratio of 1.1:1. The median Sequential Organ Failure Assessment (SOFA) score was 8 [interquartile range: 3.75-14.2], and the median SOFA scores were 5 [3-7] in the survivor group and 13 [9-17.5] in the nonsurvivor group. Univariate analysis revealed a significant difference in BMI ( P <0.001), hypoglycemia ( P =0.0012), previous cardiovascular surgery ( P =0.0019), catecholamine use ( P <0.001), SOFA score ( P <0.001), platelet count ( P =0.0023), and lactate level ( P <0.001). Logistic regression analysis using the factors with significant differences revealed that SOFA score ≥10 (odds ratio 23.3; 95% CI: 1.94-280.00; P =0.013) was an independent prognostic factor. In addition, catecholamine use was suggested as a factor with a SOFA score greater than or equal to 10.

CONCLUSION

This study confirmed that a SOFA score of greater than or equal to 10 may be associated with increased mortality. While closely monitoring low blood pressure and renal dysfunction, survival rates may be improved if surgical intervention is performed before the SOFA score reaches greater than or equal to 10.

Acute kidney injury: a strong risk factor for hypoglycaemia in hospitalized patients with type 2 diabetes

AIMS

Acute kidney injury (AKI) is highly prevalent during hospitalization of patients with type 2 diabetes (T2D). We aimed to assess the impact of AKI and its severity and duration on the risk of hypoglycaemia in hospitalized patients with T2D.

METHODS

Retrospective cohort analysis of patients with T2D, admitted at a University Hospital in 2018-2019. AKI was defined as an increase in serum creatinine by ≥ 0.3 mg/dl (48 h) or ≥ 1.5 times baseline (7 days), and hypoglycaemia as blood glucose concentration < 70 mg/dl. Patients with chronic kidney disease stage ≥ 4 were excluded. We registered 239 hospitalizations with AKI and randomly selected 239 without AKI (control). Multiple logistic regression was used to adjust for confounding factors and ROC curve analysis to determine a cutoff for AKI duration.

RESULTS

The risk of hypoglycaemia was higher in the AKI group (crude OR 3.6, 95%CI 1.8-9.6), even after adjusting for covariates (OR 4.2, 95%CI 1.8-9.6). Each day of AKI duration was associated with a 14% increase in the risk of hypoglycaemia (95%CI 1.1-1.2), and a cutoff of 5.5 days of AKI duration was obtained for increased risk of hypoglycaemia and mortality. AKI severity was also associated with mortality, but showed no significant association with hypoglycaemia. Patients with hypoglycaemia had 4.4 times greater risk of mortality (95%CI 2.4-8.2).

CONCLUSIONS

AKI increased the risk of hypoglycaemia during hospitalization of patients with T2D, and its duration was the main risk factor. These results highlight the need for specific protocols to avoid hypoglycaemia and its burden in patients with AKI.

What, why and how to monitor blood glucose in critically ill patients

Critically ill patients are prone to high glycemic variations irrespective of their diabetes status. This mandates frequent blood glucose (BG) monitoring and regulation of insulin therapy. Even though the most commonly employed capillary BG monitoring is convenient and rapid, it is inaccurate and prone to high bias, overestimating BG levels in critically ill patients. The targets for BG levels have also varied in the past few years ranging from tight glucose control to a more liberal approach. Each of these has its own fallacies, while tight control increases risk of hypoglycemia, liberal BG targets make the patients prone to hyperglycemia. Moreover, the recent evidence suggests that BG indices, such as glycemic variability and time in target range, may also affect patient outcomes. In this review, we highlight the nuances associated with BG monitoring, including the various indices required to be monitored, BG targets and recent advances in BG monitoring in critically ill patients.

Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost

Objective

The purpose of this study was to develop and validate a predictive model based on a machine learning (ML) approach to identify patients with DKA at increased risk of AKI within 1 week of hospitalization in the intensive care unit (ICU).

Methods

Patients diagnosed with DKA from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database according to the International Classification of Diseases (ICD)-9/10 code were included. The patient's medical history is extracted, along with data on their demographics, vital signs, clinical characteristics, laboratory results, and therapeutic measures. The best-performing model is chosen by contrasting the 8 Ml models. The area under the receiver operating characteristic curve (AUC), sensitivity, accuracy, and specificity were calculated to select the best-performing ML model.

Results

The final study enrolled 1,322 patients with DKA in total, randomly split into training (1,124, 85%) and validation sets (198, 15%). 497 (37.5%) of them experienced AKI within a week of being admitted to the ICU. The eXtreme Gradient Boosting (XGBoost) model performed best of the 8 Ml models, and the AUC of the training and validation sets were 0.835 and 0.800, respectively. According to the result of feature importance, the top 5 main features contributing to the XGBoost model were blood urea nitrogen (BUN), urine output, weight, age, and platelet count (PLT).

Conclusion

An ML-based individual prediction model for DKA-associated AKI (DKA-AKI) was developed and validated. The model performs robustly, identifies high-risk patients early, can assist in clinical decision-making, and can improve the prognosis of DKA patients to some extent.

Association of Blood Glucose Level and Glycemic Variability With Mortality in Sepsis Patients During ICU Hospitalization

Background

There was considerable debate regarding the effect of mean blood glucose (MBG) and glycemic variability (GV) on the mortality of septic patients. This retrospective cohort study aimed to assess the association between MBG and GV with ICU mortality of sepsis patients and to explore the optimal MBG range.

Methods

Sepsis patients were enrolled from the Medical Information Mart for Intensive Care IV database (MIMIC-IV). MBG and glycemic coefficient of variation (GluCV) were, respectively, calculated to represent the overall glycemic status and GV during ICU stay. The associations between MBG, GluCV, and ICU mortality of the septic patients were assessed by using multivariate logistic regression in different subgroups and the severity of sepsis. Restricted cubic splines evaluated the optimal MBG target.

Results

A total of 7,104 adult sepsis patients were included. The multivariate logistic regression results showed that increased MBG and GluCV were significantly correlated with ICU mortality. The adjusted odds ratios were 1.14 (95% CI 1.09-1.20) and 1.05 (95% CI 1.00-1.12). However, there was no association between hyperglycemia and ICU mortality among diabetes, liver disease, immunosuppression, and hypoglycemia patients. And the impact of high GluCV on ICU mortality was not observed in those with diabetes, immunosuppression, liver disease, and non-septic shock. The ICU mortality risk of severe hyperglycemia (≧200 mg/dl) and high GluCV (>31.429%), respectively, elevated 2.30, 3.15, 3.06, and 2.37, 2.79, 3.14-folds in mild (SOFA ≦ 3), middle (SOFA 3-7), and severe group (SOFA ≧ 7). The MBG level was associated with the lowest risk of ICU mortality and hypoglycemia between 120 and 140 mg/dl in the subgroup without diabetes. For the diabetic subset, the incidence of hypoglycemia was significantly reduced when the MBG was 140-190 mg/dl, but a glycemic control target effectively reducing ICU mortality was not observed.

Conclusion

MBG and GluCV during the ICU stay were associated with all-cause ICU mortality in sepsis patients; however, their harms are not apparent in some particular subgroups. The impact of hyperglycemia and high GV on death increased with the severity of sepsis. The risk of ICU mortality and hypoglycemia in those with no pre-existing diabetes was lower when maintaining the MBG in the range of 120-140 mg/dl.

The Role of the Diabetes Care and Education Specialist in the Hospital Setting

It is the position of Association of Diabetes Care & Education Specialists that all inpatient interdisciplinary teams include a diabetes care and education specialist to lead or support quality improvement initiatives that affect persons hospitalized with diabetes and/or hyperglycemia. This encompasses not only patient, family, and caregiver education but also education of interdisciplinary team members and achievement of diabetes-related organizational quality metrics and performance outcomes.

Relative Hypoglycemia is Associated with Delirium in Critically Ill Patients with Diabetes: A Cohort Study

PURPOSE

Critically ill patients with premorbid diabetes can suffer from relative hypoglycemia (RHG), falling below the normal blood glucose (BG) target. However, these events have not been well defined or studied. In the present study, we aimed to explore the incidence and clinical significance of RHG events in critically ill patients with diabetes.

PATIENTS AND METHODS

Patients with a history of diabetes who stayed in the intensive care unit (ICU) for more than three days with at least 12 BG recordings were retrospectively included in the study. A BG level > 30% below the estimated average according to patient hemoglobin A1c measured at admission was defined as a single RHG event. Outcomes were compared between patients with and those without RHG events.

RESULTS

In total, 113 patients were included in the final analysis. RHG was detected in 73 patients (64.6%). Those who experienced RHG events had a significantly higher incidence of ICU delirium. They also had a higher risk of 28-day mortality, but this was not statistically significant. However, patients with a higher frequency of RHG events did have a significantly higher risk of overall mortality (57.1% for more than four events vs 15.4% for three to four events, P=0.006 and 15.1% for one to two events, P=0.003).

CONCLUSION

In conclusion, RHG is a common finding in critically ill patients with diabetes and is associated with mortality and the occurrence of delirium.

Association Between Intra-Hospital Uncontrolled Glycemia and Health Outcomes in Patients with Diabetes: A Systematic Review of Observational Studies

BACKGROUND

Many people are still getting affected by uncontrolled glycemic events during hospital admission, which encompasses hypoglycemia, hyperglycemia, and high glycemic variability.

INTRODUCTION

Primary studies have shown an association of glycemic dysregulation with increased length of hospital stay and mortality among overall patients, however, there is no systematic review of current evidence on the association between uncontrolled in-hospital glycemia in patients with diabetes and health outcomes. This study aimed to systematically review the current evidence on the association between uncontrolled in-hospital glycemia in patients with diabetes and health outcomes.

METHODS

The association between glycemic dysregulation and health outcomes for inpatients with diabetes was systematically reviewed. PubMed, Embase, and LILACS databases were searched. Two independent reviewers were involved in each of the following steps: screening titles, abstracts, and fulltexts; assessing the methodological quality; and extracting data from included reviews. Descriptive analysis method was used.

RESULTS

Seven cohort studies were included, and only two had a prospective design, consisting of 7,174 hospitalized patients with diabetes. In-hospital occurrence of hypoglycemia, hyperglycemia, and glycemic variability were assessed, and outcomes were mortality, infections, renal complications, and adverse events. Among the exposure and outcomes, an association was observed between severe hypoglycemia and mortality, hyperglycemia and infection, and hyperglycemia and adverse events.

CONCLUSION

In-hospital uncontrolled glycemia in patients with diabetes is associated with poor health outcomes. More studies should be conducted for proper investigation because diabetes is a complex condition. Effects of glycemic dysregulation should be investigated on the basis of overall health of a patient instead from only organ-target perspective, which makes the investigation difficult.

Risk factors for kidney disorders in patients with type 2 diabetes at high cardiovascular risk: An exploratory analysis (DEVOTE 12)

AIM

To investigate risk factors associated with kidney disorders in patients with type 2 diabetes (T2D) at high cardiovascular (CV) risk.

METHODS

In DEVOTE, a cardiovascular outcomes trial, 7637 patients were randomised to insulin degludec (degludec) or insulin glargine 100 units/mL (glargine U100), with standard of care. In these exploratory post hoc analyses, serious adverse event reports were searched using Standardised MedDRA^® Queries related to chronic kidney disease (CKD) or acute kidney injury (AKI). Baseline predictors of CKD, AKI and change in estimated glomerular filtration rate (eGFR) were identified using stepwise selection and Cox or linear regression.

RESULTS

Over 2 years, eGFR (mL/min/1.73 m²) decline was small and similar between treatments (degludec: 2.70; glargine U100: 2.92). Overall, 97 and 208 patients experienced CKD and AKI events, respectively. A history of heart failure was a risk factor for CKD (hazard ratio [HR] 1.97 [95% confidence interval [CI] 1.41; 2.75]) and AKI (HR 2.28 [95% CI 1.64; 3.17]). A history of hepatic impairment was a significant predictor of CKD (HR 3.28 [95% CI 2.12; 5.07]) and change in eGFR (estimate: -8.59 [95% CI -10.20; -7.00]).

CONCLUSION

Our findings indicate that traditional, non-modifiable risk factors for kidney disorders apply to insulin-treated patients with T2D at high CV risk.

TRIAL REGISTRATION

NCT01959529 (ClinicalTrials.gov).

Incidence and Prediction of Unstable Blood Glucose Level among Critically Ill Patients: A Cohort Study

PURPOSE

To evaluate the incidence and the prediction of unstable blood glucose level among critically ill patients hospitalized in an intensive care unit.

METHODS

A cohort study was conducted with 62 adult patients hospitalized at an intensive care unit of a hospital located in Minas Gerais, Brazil, between March and July of 2017. Patient's demographic information, along with scores for Simplified Acute Physiology Score III, primary medical diagnosis, discharge status, diagnosis of diabetes and/or sepsis, length of stay, glycemic variability, type of nutrition, types of medications and treatments, and oxygen therapy were collected daily. A daily venous blood sample was collected to measure blood glucose levels during the patient's hospitalization period. Bivariate analysis was used to explore the association among the potential diagnostic indicators and the outcome of unstable blood glucose levels. Multivariate Cox regression was used to identify the potential predictors for the outcome.

FINDINGS

Of the total of 62 participants, 45.1% (n=28) had unstable blood glucose level. Among the 28 patients with unstable blood glucose levels, half of them (n=14, 50%) had hypoglycemia and the other half had hyperglycemia (n=14, 50%). Decreased number of days hospitalized and the use of intensive glucose control with regular insulin were associated with decreased odds of developing hyperglycemia. The presence of mechanical ventilation was associated with a higher risk for the development of hypoglycemia.

CONCLUSIONS

This study provides knowledge and evidence of diagnostic indicators for unstable blood glucose levels that are not currently included in the NANDA-International terminology for the nursing diagnosis Risk for unstable blood glucose level (00179).

IMPLICATIONS FOR NURSING PRACTICE

This study identified important diagnostic indicators that nurses can observe during the assessment to identify patients that are at risk for developing unstable blood glucose level and provide the appropriate care.

Effect of Adding Insulin Glargine on Glycemic Control in Critically Ill Patients Admitted to Intensive Care Units: A Prospective Randomized Controlled Study

OBJECTIVE

We aimed to examine the effects of adding a longer-acting insulin glargine to existing glucose control on reducing blood-glucose fluctuations in an intensive care unit (ICU).

METHODS

A total of 110 patients randomly received adjuvant insulin glargine 15 IU/day (glargine) or placebo (control), in addition to daily infusion of insulin to maintain glucose levels at a target of 140-180 mg/dL. End points were mean and variance of blood glucose and frequency of hypoglycemia, hyperglycemia, ICU stay, and mortality. Data were analyzed with repeated-measures ANOVA and Mann-Whitney U test.

RESULTS

Average daily glucose level was significantly less in the glargine group than controls (P<0.0001), while there was no difference in daily variance in blood glucose between the two groups. The duration of glucose concentrations being within the target range was identical between the glargine and control groups (16.6±4.9 vs 16.4±4.6 hours/day, P=0.844) during the 7 days of admission. The frequency of hypoglycemia was greater in the glargine group and total duration of hyperglycemia (>180 mg/dL) much longer among controls (P<0.001). Similar mortality rates were observed in both groups, while ICU length of stay was 2 days shorter in the glargine group.

CONCLUSION

Addition of insulin glargine to routine protocols more effectively reduces glucose levels and decreases incidence of hyperglycemic episodes and regular insulin usage. This adjustment may be associated with decreases in duration of ICU stay or increases in hypoglycemic events.

Effect of Cucurbita Maxima on Control of Blood Glucose in Diabetic Critically Ill Patients

Purpose: Cucurbita maxima Duchense (C. maxima) has been widely used in China and Mexico as a hypoglycemic plant for controlling blood glucose in diabetic patients. Furthermore, in northwest of Iran, this plant is used traditionally for controlling of diabetes. We examined the effect of C. maxima pulp besides insulin on control of hyperglycemia in diabetic patients admitted to Intensive care unit (ICU).

Methods: Twenty critically ill patients who were admitted to the ICU were enrolled in this study. 5g lyophilized powder of C. maxima was administrated every 12 hours for 3 days. Moreover, blood glucose level and insulin dose were measured every 1-4 hours during 3 days before administration and 3days at the time of C. maxima administration.

Results: The average of glucose level in 3 days before C. maxima administration was 214.9 ± 55.7 mg/dl, while in 3 days during C. maxima administration it was decreased to 178.4 ± 36.1 mg/dl (P<0.001). Additionally, the average insulin dose during 3 days before intervention was 48.05 ± 36.5 IU and during the 3 days of C. maxima administration was decreased to 39.5 ± 27.8 IU (P=0.06).

Conclusion: It seems that C. maxima may decrease high blood glucose level fast and effective in diabetic critically ill patients.

Dysglycemia in the critically ill patient: current evidence and future perspectives

Dysglycemia in critically ill patients (hyperglycemia, hypoglycemia, glycemic variability and time in range) is a biomarker of disease severity and is associated with higher mortality. However, this impact appears to be weakened in patients with previous diabetes mellitus, particularly in those with poor premorbid glycemic control; this phenomenon has been called "diabetes paradox". This phenomenon determines that glycated hemoglobin (HbA1c) values should be considered in choosing glycemic control protocols on admission to an intensive care unit and that patients' target blood glucose ranges should be adjusted according to their HbA1c values. Therefore, HbA1c emerges as a simple tool that allows information that has therapeutic utility and prognostic value to be obtained in the intensive care unit.

Risk of Hypoglycemia Following Hospital Discharge in Patients With Diabetes and Acute Kidney Injury

OBJECTIVE

Hypoglycemia is common in patients with diabetes. The risk of hypoglycemia after acute kidney injury (AKI) is not well defined. The purpose of this study was to compare the risk for postdischarge hypoglycemia among hospitalized patients with diabetes who do and do not experience AKI.

RESEARCH DESIGN AND METHODS

We performed a propensity-matched analysis of patients with diabetes, with and without AKI, using a retrospective national cohort of veterans hospitalized between 2004 and 2012. AKI was defined as a 0.3 mg/dL or 50% increase in serum creatinine from baseline to peak serum creatinine during hospitalization. Hypoglycemia was defined as hospital admission or an emergency department visit for hypoglycemia or as an outpatient blood glucose <60 mg/dL. Time to incident hypoglycemia within 90 days postdischarge was examined using Cox proportional hazards models. Prespecified subgroup analyses by renal recovery, baseline chronic kidney disease, preadmission drug regimen, and HbA1c were performed.

RESULTS

We identified 65,151 propensity score-matched pairs with and without AKI. The incidence of hypoglycemia was 29.6 (95% CI 28.9-30.4) and 23.5 (95% CI 22.9-24.2) per 100 person-years for patients with and without AKI, respectively. After adjustment, AKI was associated with a 27% increased risk of hypoglycemia (hazard ratio [HR] 1.27 [95% CI 1.22-1.33]). For patients with full recovery, the HR was 1.18 (95% CI 1.12-1.25); for partial recovery, the HR was 1.30 (95% CI 1.23-1.37); and for no recovery, the HR was 1.48 (95% CI 1.36-1.60) compared with patients without AKI. Across all antidiabetes drug regimens, patients with AKI experienced hypoglycemia more frequently than patients without AKI, though the incidence of hypoglycemia was highest among insulin users, followed by glyburide and glipizide users, respectively.

CONCLUSIONS

AKI is a risk factor for hypoglycemia in the postdischarge period. Studies to identify risk-reduction strategies in this population are warranted.

Clinical characteristics and outcomes of symptomatic and asymptomatic hypoglycemia in hospitalized patients with diabetes

IMPORTANCE

The frequency and impact of asymptomatic hypoglycemia in hospitalized patients with diabetes is not known.

OBJECTIVE

We determined the clinical characteristics and hospital outcomes of general medicine and surgery patients with symptomatic and asymptomatic hypoglycemia.

RESEARCH DESIGN AND METHODS

Prospective observational study in adult patients with diabetes and blood glucose (BG) <70 mg/dL. Participants were interviewed about signs and symptoms of hypoglycemia using a standardized questionnaire. Precipitating causes, demographics, insulin regimen, and complications data during admission was collected.

RESULTS

Among 250 patients with hypoglycemia, 112 (44.8%) patients were asymptomatic and 138 (55.2%) had symptomatic hypoglycemia. Patients with asymptomatic hypoglycemia were older (59±11 years vs 54.8±13 years, p=0.003), predominantly males (63% vs 48%, p=0.014), and had lower admission glycosylated hemoglobin (8.2%±2.6 % vs 9.1±2.9%, p=0.006) compared with symptomatic patients. Compared with symptomatic patients, those with asymptomatic hypoglycemia had higher mean BG during the episode (60.0±8 mg/dL vs 53.8±11 mg/dL, p<0.001). In multivariate analysis, male gender (OR 2.08, 95% CI 1.13 to 3.83, p=0.02) and age >65 years (OR 4.01, 95% CI 1.62 to 9.92, p=0.02) were independent predictors of asymptomatic hypoglycemia. There were no differences in clinical outcome, composite of hospital complications (27% vs 22%, p=0.41) or in-hospital length of stay (8 days (IQR 4-14) vs 7 days (IQR 5-15), p=0.92)) between groups.

CONCLUSIONS

Asymptomatic hypoglycemia was common among insulin-treated patients with diabetes but was not associated with worse clinical outcome compared with patients with symptomatic hypoglycemia. Older age and male gender were independent risk factors for asymptomatic hypoglycemia.

Hypoglycemia Reduction Strategies in the ICU

PURPOSE OF REVIEW

We reviewed the strategies associated with hypoglycemia risk reduction among critically ill non-pregnant adult patients.

RECENT FINDINGS

Hypoglycemia in the ICU has been associated with increased mortality in a number of studies. Insulin dosing and glucose monitoring rules, response to impending hypoglycemia, use of computerization, and attention to modifiable factors extrinsic to insulin algorithms may affect the risk for hypoglycemia. Recurring use of intravenous (IV) bolus doses of insulin in insulin-resistant cases may reduce reliance upon higher IV infusion rates. In order to reduce the risk for hypoglycemia in the ICU, caregivers should define responses to interruption of continuous carbohydrate exposure, incorporate transitioning strategies upon initiation and interruption of IV insulin, define modifications of antihyperglycemic therapy in the presence of worsening renal function or chronic kidney disease, and anticipate the effects traceable to other medications and substances. Institutional and system-wide quality improvement efforts should assign priority to hypoglycemia prevention.

Consensus Statement on Inpatient Use of Continuous Glucose Monitoring

In June 2016, Diabetes Technology Society convened a panel of US experts in inpatient diabetes management to discuss the current and potential role of continuous glucose monitoring (CGM) in the hospital. This discussion combined with a literature review was a follow-up to a meeting, which took place in May 2015. The panel reviewed evidence on use of CGM in 3 potential inpatient scenarios: (1) the intensive care unit (ICU), (2) non-ICU, and (3) transitioning outpatient CGM use into the hospital setting. Panel members agreed that data from limited studies and theoretical considerations suggested that use of CGM in the hospital had the potential to improve patient clinical outcomes, and in particular reduction of hypoglycemia. Panel members discussed barriers to widespread adoption of CGM, which patients would benefit most from use of this technology, and what type of outcome studies are needed to guide use of CGM in the inpatient setting.

Validity of bedside blood glucose measurement in critically ill patients with intensive insulin therapy

Background and Aims:

There have been variable results on the practice of tight glycemic control, and studies have demonstrated that point-of-care (POC) glucometers have variable accuracy. Glucometers must be accurate, and many variables can affect blood glucose levels. The purpose of this study was to determine the difference between blood glucose concentrations obtained from POC glucometers and laboratory results in critically ill patients with intensive insulin therapy.

Materials and Methods:

This was a descriptive study which enrolled 300 critically ill patients. Four samples of arterial blood were collected and analyzed at the bedside with the POC glucometer and also in the central laboratory to obtain the blood glucose level. To define the effect of various factors on this relation, we noted the levels of hemoglobin (Hb), PaO₂, body temperature, bilirubin, history of drug usage, and sepsis.

Results:

There were not any significant differences between blood sugar levels using laboratory and glucometer methods of measurements. There was a good and significant correlation between glucose levels between two methods (r = 0.81, P < 0.001). Among evaluated factors (body temperature, bilirubin level, blood pressure, Hb level, PaO₂, sepsis, and drugs) which added one by one in model, just drugs decreased the correlation more than others (r = 0.78).

Conclusions:

The results of POC glucometer differ from laboratory glucose concentrations, especially in critically ill patients with unstable hemodynamic status while receiving several drugs. This may raise the concern about using POC devices for tight glycemic control in critically ill patients. These results should be interpreted with caution because of the large variation of accuracy among different glucometer devices.