A literature review of intensive insulin therapy and mortality in critically ill patients

Point of care blood glucose devices in the hospital setting

Dysglycemia is common among hospitalized patients. Accurate point-of-care (POC) glucose monitoring is necessary for the safe administration of insulin. Unfortunately, POC glucose meters are not all created equal. Interfering factors such as abnormal hematocrit, abnormal oxygen tension, and oxidizing/reducing substances can lead to inaccurate glucose measurements and result in inappropriate insulin dosing. The introduction of autocorrecting glucose meters has changed the POC testing landscape. Autocorrecting glucose meters provide more accurate measurements and have been associated with improved glycemic control in hospitalized patients. Continuous glucose monitoring has also created interest in using these platforms in at-risk inpatient populations. Future glucose monitoring technologies such as artificial intelligence/machine learning, wearable smart devices, and closed-loop insulin management systems are poised to transform glycemic management. The goal of this review is to provide an overview of glucose monitoring technology, summarize the clinical impact of glucose monitoring accuracy, and highlight emerging and future POC glucose monitoring technologies.

Glycemic control in cardiac surgery: implementing an evidence-based insulin infusion protocol

BACKGROUND

Acute hyperglycemia following cardiac surgery increases the risk of deep sternal wound infection, significant early morbidity, and mortality. Insulin infusion protocols that target tight glycemic control to treat hyperglycemia have been linked to hypoglycemia and increased mortality. Recently published studies examining glycemic control in critical illness and clinical practice guidelines from professional organizations support moderate glycemic control.

OBJECTIVES

To measure critical care nurses' knowledge of glycemic control in cardiac surgery before and after education. To evaluate the safety and effectiveness of an evidence-based insulin infusion protocol targeting moderate glycemic control in cardiac surgery patients.

METHODS

This evidence-based practice change was implemented in the cardiovascular unit in a community teaching hospital. Nurses completed a self-developed questionnaire to measure knowledge of glycemic control. Blood glucose data, collected (retrospectively) from anesthesia end time through 11:59 PM on postoperative day 2, were compared from 2 months before to 2 months after the practice change.

RESULTS

Nurses' knowledge (test scores) increased significantly after education (pretest mean = 53.10, SD = 11.75; posttest mean = 79.10, SD = 12.02; t54 = -8.18, P < .001). Mean blood glucose level after implementation was 148 mg/dL. The incidence of hypoglycemia, 2.09% before and 0.22% after the intervention, was significantly reduced ( $${\hbox{ \chi }}_{1}^{2}$$ [n = 29] = 13.9, P < .001). The percentage of blood glucose levels less than 180 mg/dL was 88.30%.

CONCLUSIONS

Increasing nurses' knowledge of glycemic control and implementing an insulin infusion protocol targeting moderate glycemic control were effective for treating acute hyperglycemia following cardiac surgery with decreased incidence of hypoglycemia.

Inpatient electronic prescribing data can be used to identify 'lost' discharge codes for diabetes

AIM

Accurate assessment of missed discharge codes for diabetes is critical for effective planning of hospital diabetes services. We wished to estimate the frequency of missed discharge diagnostic codes for diabetes and the impact missed codes would have on diabetes-related payments to the hospital.

METHODS

We linked Patient Administration System data to the Prescribing Information and Communication System. We defined diabetes as those having a discharge code for diabetes in the Patient Administration System and those on anti-diabetic medication in the Prescribing Information and Communication System. Based on the two sources, we calculated the estimated missed discharge codes for diabetes using the capture-recapture technique. We generated the Healthcare Resource Group for a given admission before and after correction for the missed code to estimate the impact that correction would make on payments to the hospital.

RESULTS

Among the 171 067 admissions linked, 22 412 (13.1%) had a code for diabetes at discharge. An additional 2706 admissions were classified as having diabetes based on prescription data. The capture-recapture technique estimated there were 4588 (2.7% of all admissions) admissions with diabetes missed by current coding, of which 2706 (60%) would be obtained from prescription data. After adding a diabetes diagnostic code, 12.8% of the missed admissions with diabetes resulted in a change to the Healthcare Resource Group tariff code and payment.

CONCLUSION

The use of electronic prescription data is a simple solution to correct for missed discharge diagnostic codes.

The effect of hyperglycemia and hypoglycemia on glucose transport and expression of glucose transporters in human lymphocytes B and T: an in vitro study

Glucose transport in lymphocytes is regulated by many agents. It is interesting if only changing glucose concentrations in environment involves the impact on glucose uptake. The aims of this study were to investigate the impact of changing glucose concentrations in medium on deoxy-d-glucose uptake and what these conditions impact on the percent of cells with expression of chosen glucose transporters in human lymphocytes B and T. Isolated lymphocytes B and T obtained from healthy subject were cultivated in different concentrations of glucose. The experiments were carried out using tritium labeled deoxy-d-glucose and flow cytometry. In comparison to normoglycemia, hyperglycemia impairs the uptake of deoxy-d-glucose more than hypoglycemia. Lymphocytes B manifest significantly lower uptake of deoxy-d-glucose than lymphocytes T. Lymphocytes incubated in hyperglycemic and hypoglycemic medium show lower percent cells with expression of GLUT 1 and GLUT 3, and higher percent cells with expression of GLUT 4. The incubation of lymphocytes in hyperglycemic and hypoglycemic medium does not stimulate translocation of glucose transporters 3 and 4 to plasma membrane. Study shows that a change in concentration of glucose in incubation environment influence intracellular expression of glucose transporters in a significant part of lymphocytes B and T.

Efficacy and safety of intensive insulin therapy for critically ill neurologic patients: a meta-analysis

BACKGROUND

Whether intensive insulin therapy (IIT) may improve clinical outcomes for patients admitted to intensive care units, especially critically ill neurologic patients, is still debated. In the present study, we performed a meta-analysis of literature comparing the efficacy and safety of IIT and conventional insulin therapy (CIT) for critically ill neurologic patients in terms of mortality, infection rate, neurologic outcome, and hypoglycemia.

METHODS

We searched for published reports of studies of randomized control trials (up to March 10, 2011) of patients admitted to neurologic intensive care units and investigated an IIT (target of blood glucose control <120 mg/dL) with a control of CIT. Data were abstracted by a standardized protocol.

RESULTS

We retrieved reports of five studies involving 924 patients. The risk of mortality, infection rate, and neurologic outcome did not differ with IIT or CIT. However, the incidence of hypoglycemic episodes was significantly higher with IIT than CIT (78.8% vs. 48.9%), with a relative risk of 2.62 (95% confidence interval [CI]: 1.07-6.43; p < 0.04).

CONCLUSIONS

As compared with CIT, IIT may not benefit critically ill neurologic patients in terms of mortality, infection rate, or neurologic outcome and in fact may be associated with increased hypoglycemic complications. Therefore, IIT cannot be recommended over conventional control for critical neurologic disease, but further study is warranted.

[Validation of a glucose meter at an intensive care unit]

INTRODUCTION

Hyperglycemic patients admitted to Intensive care units (ICUs) have higher morbidity and mortality than normoglycemic patients. Blood glucose levels of ICU patients are usually measured with a glucose meter. The aim of this study was to evaluate a glucose meter (StatStrip, Nova Biomedical) to assess its agreement with the standard laboratory method for testing glucose.

MATERIAL AND METHODS

Eighty-nine different samples were collected from patients (76.4% men and 23.6% women) admitted to an ICU from September to December 2010. Each blood sample was collected into two tubes, a lithium heparin tube and an EDTA tube. The total blood aliquot was used to measure glycemia using the glucose meter. The lithium heparin tube was processed at the same time for measuring plasma glucose (Cobas 6000 Analyzer, Roche Diagnostics, SA). Agreement between the two methods was assessed according to the EP-9-A2 Clinical Laboratory Standards Institute guideline.

RESULTS

Mean whole blood glucose level measured by the glucose meter was 126.53+49.28 mg/dL (range, 33.5-431 mg/dL), while mean plasma glucose value measured by the laboratory reference method was 138.13+78.6 mg/dL (range, 43-451 mg/dL). Correlation coefficient was 0.99, with a 95% confidence interval of 0.98 to 0.99. Coefficient of determination (R2) was 0.97, and intraclass correlation coefficient was 0.99 with a 95% CI of 0.98 to 0.99.

CONCLUSIONS

The tested glucose meter (StatStrip) shows a good linear association, precision, and accuracy when compared to the laboratory reference method. This device is adequate for glucose monitoring.

Modeling the glucose regulatory system in extreme preterm infants

BACKGROUND

Premature infants represent a significant proportion of the neonatal intensive care population. Blood glucose homeostasis in this group is often disturbed by immaturity of endogenous regulatory systems and the stress of their condition. Hypo- and hyperglycemia are frequently reported in very low birth weight infants, and more mature infants often experience low levels of glycemia. A model capturing the unique fundamental dynamics of the neonatal glucose regulatory system could be used to develop better blood glucose control methods.

METHODS

A metabolic system model is adapted from adult critical care to the unique physiological case of the neonate. Integral-based fitting methods were used to identify time-varying insulin sensitivity and non-insulin mediated glucose uptake profiles. The clinically important predictive ability of the model was assessed by assuming insulin sensitivity was constant over prediction intervals of 1, 2 and 4h forward and comparing model-simulated versus actual clinical glucose values for all recorded interventions. The clinical data included 1091 glucose measurements over 3567 total patient hours, along with all associated insulin and nutritional infusion data, for N=25 total cases. Ethics approval was obtained from the Upper South A Regional Ethics Committee for this study.

RESULTS

The identified model had a median absolute percentage error of 2.4% [IQR: 0.9-4.8%] between model-fitted and clinical glucose values. Median absolute prediction errors at 1-, 2- and 4-h intervals were 5.2% [IQR: 2.5-10.3%], 9.4% [IQR: 4.5-18.4%] and 13.6% [IQR: 6.3-27.6%] respectively.

CONCLUSIONS

The model accurately captures and predicts the fundamental dynamic behaviors of the neonatal metabolism well enough for effective clinical decision support in glycemic control. The adaptation from adult to a neonatal case is based on the data from the literature. Low prediction errors and very low fitting errors indicate that the fundamental dynamics of glucose metabolism in both premature neonates and critical care adults can be described by similar mathematical models.