Citations

Background: We presented a concept for a tear glucose sensor system in an article by Bishop and colleagues in this issue of Journal of Diabetes Science and Technology . A unique solution to collect tear fluid and measure glucose was developed. Individual components were selected, tested, and optimized, and system error modeling was performed. Further data on prototype testing are now provided. Methods: An integrated fluidics portion of the prototype was designed, cast, and tested. A sensor was created using screen-printed sensors integrated with a silicone rubber fluidics system and absorbent polyurethane foam. A simulated eye surface was prepared using fluid-saturated poly(2-hydroxyethyl methacrylate) sheets, and the disposable prototype was tested for both reproducibility at 0, 200, and 400 μ M glucose ( n = 7) and dynamic range of glucose detection from 0 to 1000 μ M glucose. Results: From the replicated runs, an established relative standard deviation of 15.8% was calculated at 200 μ M and a lower limit of detection was calculated at 43.4 μ M . A linear dynamic range was demonstrated from 0 to 1000 μ M with an R 2 of 99.56%. The previously developed model predicted a 14.9% variation. This compares to the observed variance of 15.8% measured at 200 μ M glucose. Conclusion: With the newly designed fluidics component, an integrated tear glucose prototype was assembled and tested. Testing of this integrated prototype demonstrated a satisfactory lower limit of detection for measuring glucose concentration in tears and was reproducible across a physiological sampling range. The next step in the device design process will be initial animal studies to evaluate the current prototype for factors such as eye irritation, ease of use, and correlation with blood glucose.

Gastroparesis and other types of gastric dysfunction result in substantial morbidity in diabetes patients. The pathophysiology of these disorders is incompletely understood. This article reviews techniques applicable to the assessment of gastric function in diabetes patients, including the measurement of emptying, accommodation, and contractility. Available treatment options are also reviewed, including novel yet unapproved serotonin 5-HT 4 agonist pharmacological treatments, as well as the role of endoscopic, surgical, and device treatments of gastroparesis.

Background: Treating hyperglycemia promotes better outcomes among inpatients. Knowledge deficits about management of inpatient diabetes are prevalent among resident physicians, which may affect the care of a substantial number of these patients. Methods: A computer-based training (CBT) curriculum on inpatient diabetes and hyperglycemia was developed and implemented for use by resident physicians and focuses on several aspects of the management of inpatient diabetes and hyperglycemia: (1) review of importance of inpatient glucose control, (2) overview of institution-specific data, (3) triaging and initial admission actions for diabetes or hyperglycemia, (4) overview of pharmacologic management, (5) insulin-dosing calculations and ordering simulations, (6) review of existing policies and procedures, and (7) discharge planning. The curriculum was first provided as a series of lectures, then formatted and placed on the institutional intranet as a CBT program. Results: Residents began using the inpatient CBT in September 2008. By August 2009, a total of 29 residents had participated in CBT: 8 in family medicine, 12 in internal medicine, and 9 in general surgery. Most of the 29 residents confirmed that module content met stated objectives, considered the information valuable to their inpatient practices, and believed that the quality of the online modules met expectations. The majority reported that the modules took just the right amount of time to complete (typically 30 min each). Conclusions: Improvement in inpatient diabetes care requires continuous educational efforts. The CBT format and curriculum content were well accepted by the resident physicians. Ongoing assessment must determine whether resident practice patterns are influenced by such training.

Hyperglycemia in the critically ill is a well-known phenomenon, even in those without known diabetes. The stress response is due to a complex interplay between counter-regulatory hormones, cytokines, and changes in insulin sensitivity. Illness/infection, overfeeding, medications (e.g., corticosteroids), insufficient insulin, and/or volume depletion can be additional contributors. Acute hyperglycemia can adversely affect fluid balance (through glycosuria and dehydration), immune and endothelial function, inflammation, and outcome. While there are several insulin infusion protocols that are able to safely and effectively treat hyperglycemia, the bulk of accumulated evidence does not support a causal relationship between acute hyperglycemia and adverse outcomes in the medical intensive care unit. Meta-analysis of randomized controlled trials suggests there is no benefit to tightening glucose control to normal levels compared to a reasonable and achievable goal of 140 to 180 mg/dl. There is a significantly increased risk of hypoglycemia. Although there is some evidence that patients without known diabetes have worse outcomes than those with known diabetes, more recent evidence is conflicting. Glycemic control in critically ill patients should not be neglected, as studies have not tested tight versus no/poor control, but tight versus good control. A moderate approach to managing critical illness hyperglycemia seems most prudent at this juncture. Future research should ascertain whether there are certain subgroups of patients that would benefit from tighter glycemic goals. It also remains to be seen if tight glucose control is beneficial once hypoglycemia is minimized with technological advances such as continuous glucose monitoring systems.

Patients with dysglycemia related to known or unrecognized diabetes, stress hyperglycemia, or hypoglycemia in the presence or absence of exogenous insulin routinely require care during the perioperative period or critical illness. Recent single and multicenter studies, a large multinational study, and three meta-analyses evaluated the safety of routine tight glycemic control (80–110 mg/dl) in critically ill adults. Results led to a call for more modest treatment goals (initiation of insulin at a blood glucose >180 mg/dl with a goal of ∼150 mg/dl). In this symposium, an international group of multidisciplinary experts discusses the role of tight glycemic control, glucose measurement technique and its accuracy, glucose variability, hypoglycemia, and innovative methods to facilitate glucose homeostasis in this heterogeneous patient population.

There is a relative dearth of studies designed to elucidate genetic variation that can explain differences in the response to diabetes pharmacotherapy. When designing such studies, appropriate consideration of the various nongenetic variables that can affect the treatment response is necessary. In addition, disease stage and prior pharmacotherapy also influence drug efficacy. Selecting the appropriate genetic variant to test in such studies is also important, and common variants (known to be functional or otherwise) in a given candidate locus should be tested for the effect on the treatment response. Finally, an appropriate measure of treatment response is necessary to enable detection of pharmacogenetic effects. Perhaps prior to undertaking such studies, smaller studies utilizing well-characterized, homogenous populations with normal glucose tolerance or prediabetes (to avoid the problem of disease effects on treatment response) and surrogate measures of response such as insulin secretion should be completed.

Background: Hypoglycemia is often cited as a barrier to achieving inpatient glycemic targets. We sought to characterize hypoglycemic events in our institution by work-shift cycle and by specific treatment area. Methods: Capillary (bedside) and blood (laboratory) glucose values of <70 mg/dl for patients with either a known diagnosis of diabetes or with evidence of hyperglycemia were abstracted from our laboratory database for hospitalizations between October 1, 2007, and February 3, 2008. Hypoglycemic events were analyzed by 12 h nursing work-shift cycles (day shift, 07:00 to 18:59; night shift, 19:00 to 06:59) and by the six medical, surgical, and intensive care areas in the hospital (designated areas 1 to 6). Results: We identified 206 individual patients with either diabetes or hyperglycemia (mean age, 67 years; 56% men; 83% white) who had 423 hypoglycemic events. There were 78% more hypoglycemic events during the night shift ( n = 271 events in 128 individual patients) than during the day shift ( n = 152 events in 96 individual patients). Most of the night-shift hypoglycemic measurements were detected between 04:00 and 04:59 or 06:00 and 06:59. The mean hypoglycemic level was comparable between shifts ( p = .79) and across the six inpatient areas. The number of hypoglycemic events per person increased with lengths of hospital stay >5 days. The prevalence of hypoglycemia varied across patient care areas within the hospital, with most (28%) detected in one area of the hospital. Conclusion: There are temporal and geographic patterns in the occurrence of hypoglycemia among patients with diabetes or hyperglycemia in our hospital. Further study should focus on the reasons underlying these variations so that specific interventions can address the risk of hypoglycemia during peak times and places.

Background: This article reviews the performance of our hospital's inpatient insulin pump policy. Methods: Twenty-five hospital admissions of 21 unique patients receiving outpatient insulin pump therapy were reviewed. Results: Between November 1, 2005, and November 30, 2006, there were 25 hospital admissions involving 21 patients receiving outpatient insulin pump therapy. The average age and duration of diabetes among these 21 patients was 50 and 29 years, respectively; 67% were women, 90% had type 1 diabetes, and all were white. The mean length of hospital stay was 4 days, and the average reported length of insulin pump therapy was 4 years. Patients in 16 of the admissions were identified as candidates for continued use of the insulin pump during the hospital stay. Over 90% of patients remaining on the insulin pump had documentation by nursing of the presence of the pump at the time of admission; 100% of the patients had an admission glucose recorded; 88% had a record of signed patient consent; 81% had evidence of completed preprinted insulin pump orders; 75% received a required endocrine consultation; and 75% of cases had documentation of completed bedside flow sheet. A high frequency of both hypoglycemic and hyperglycemic events occurred in the patients; however, no adverse events were related directly to the insulin pump. Conclusions: Insulin pump therapy can be safely continued in the hospital setting. While staff compliance with required procedures was high, there was still room for improvement. More data are needed, however, on whether this method of insulin delivery is effective for controlling hyperglycemia in hospitalized patients.

We present an overview of strategies our institution has taken to understand the state of its inpatient diabetes management. We first describe how we utilized information systems to assess inpatient glycemic control and insulin management in noncritically ill patients and discuss our findings regarding mean bedside glucose levels, the prevalence and frequency hypoglycemic and hyperglycemic events, the patterns of insulin therapy, and evidence of inpatient clinical inertia. We also review the development of a survey to determine practitioner attitudes and beliefs about inpatient diabetes. Results of this survey study found that, in general, practitioners believed in the importance of controlling hyperglycemia but were not comfortable with many aspects of inpatient diabetes care, particularly with the use of insulin. Finally, we suggest steps to follow in developing a quality-improvement program for hospitals.

Background: A simulation model of the glucose—insulin system in normal life conditions can be very useful in diabetes research, e.g., testing insulin infusion algorithms and decision support systems and assessing glucose sensor performance and patient and student training. A new meal simulation model has been proposed that incorporates state-of-the-art quantitative knowledge on glucose metabolism and its control by insulin at both organ/tissue and whole-body levels. This article presents the interactive simulation software GIM (glucose insulin model), which implements this model. Methods: The model is implemented in MATLAB, version 7.0.1, and is designed with a windows interface that allows the user to easily simulate a 24-hour daily life of a normal, type 2, or type 1 diabetic subject. A Simulink version is also available. Three meals a day are considered. Both open- and closed-loop controls are available for simulating a type 1 diabetic subject. Results: Software options are described in detail. Case studies are presented to illustrate the potential of the software, e.g., compare a normal subject vs an insulin-resistant subject or open-loop vs closed-loop insulin infusion in type 1 diabetes treatment. Conclusions: User-friendly software that implements a state-of-the-art physiological model of the glucose-insulin system during a meal has been presented. The GIM graphical interface makes its use extremely easy for investigators without specific expertise in modeling.