Therapy insight: the effect of tight glycemic control in acute illness

Hyperglycemia commonly occurs in patients who are acutely ill, in a variety of clinical situations. Generally, moderate hyperglycemia in critically ill patients was thought to be beneficial; however, the degree of hyperglycemia on admission and the duration of hyperglycemia during critical illness are now recognized markers of adverse outcome. The use of insulin therapy to maintain normoglycemia for at least a few days improves survival and reduces morbidity in patients who are in a surgical intensive care unit (ICU), as shown by a large, randomized, controlled study. These results were recently confirmed by two studies--a randomized, controlled study of patients in a medical ICU, and a prospective, observational study of a heterogeneous patient population admitted to a mixed medical and surgical ICU. Results of multicenter trials that investigated tight blood-glucose control in critically ill patients are, however, still lacking. While we await those multicenter results, the current evidence favors the control of blood glucose levels in the ICU. Indeed, the studies showed that many lives are saved with this intervention, despite an increased incidence of hypoglycemia. Prevention of glucose toxicity by strict glycemic control (but also other metabolic and nonmetabolic effects of insulin) contribute to these clinical benefits.     

Data entry errors and design for model-based tight glycemic control in critical care

Introduction

Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. Model-based methods and computerized protocols offer the opportunity to improve TGC quality but require human data entry, particularly of blood glucose (BG) values, which can be significantly prone to error. This study presents the design and optimization of data entry methods to minimize error for a computerized and model-based TGC method prior to pilot clinical trials.

Method

To minimize data entry error, two tests were carried out to optimize a method with errors less than the 5%-plus reported in other studies. Four initial methods were tested on 40 subjects in random order, and the best two were tested more rigorously on 34 subjects. The tests measured entry speed and accuracy. Errors were reported as corrected and uncorrected errors, with the sum comprising a total error rate. The first set of tests used randomly selected values, while the second set used the same values for all subjects to allow comparisons across users and direct assessment of the magnitude of errors. These research tests were approved by the University of Canterbury Ethics Committee.

Results

The final data entry method tested reduced errors to less than 1–2%, a 60–80% reduction from reported values. The magnitude of errors was clinically significant and was typically by 10.0 mmol/liter or an order of magnitude but only for extreme values of BG < 2.0 mmol/liter or BG > 15.0–20.0 mmol/liter, both of which could be easily corrected with automated checking of extreme values for safety.

Conclusions

The data entry method selected significantly reduced data entry errors in the limited design tests presented, and is in use on a clinical pilot TGC study. The overall approach and testing methods are easily performed and generalizable to other applications and protocols.     

The different methods of assessing glycemic variability,quality of glycemic control and glycemic risk cannot be interpreted as equivalent in clinical practice

Objective

Several methods are available to calculate glycemic variability (GV), quality of glycemic control (QGC) and glycemic risk (GR). However, clinicians do not easily interpret these data. This study evaluates whether the results of the different methods can be interpreted as equivalent.

Interface design and human factors considerations for model-based tight glycemic control in critical care

Introduction

Tight glycemic control (TGC) has shown benefits but has been difficult to implement. Model-based methods and computerized protocols offer the opportunity to improve TGC quality and compliance. This research presents an interface design to maximize compliance, minimize real and perceived clinical effort, and minimize error based on simple human factors and end user input.

Method

The graphical user interface (GUI) design is presented by construction based on a series of simple, short design criteria based on fundamental human factors engineering and includes the use of user feedback and focus groups comprising nursing staff at Christchurch Hospital. The overall design maximizes ease of use and minimizes (unnecessary) interaction and use. It is coupled to a protocol that allows nurse staff to select measurement intervals and thus self-manage workload.

Results

The overall GUI design is presented and requires only one data entry point per intervention cycle. The design and main interface are heavily focused on the nurse end users who are the predominant users, while additional detailed and longitudinal data, which are of interest to doctors guiding overall patient care, are available via tabs. This dichotomy of needs and interests based on the end user''s immediate focus and goals shows how interfaces must adapt to offer different information to multiple types of users.

Conclusions

The interface is designed to minimize real and perceived clinical effort, and ongoing pilot trials have reported high levels of acceptance. The overall design principles, approach, and testing methods are based on fundamental human factors principles designed to reduce user effort and error and are readily generalizable.     

Permanent neonatal diabetes mellitus: same mutation, different glycemic control with sulfonylurea therapy on long-term follow-up

Permanent neonatal diabetes mellitus (PNDM) is a rare condition presenting before six months of age. Mutations in the genes encoding the ATP-sensitive potassium (KATP) channel are the most common causes. Sulfonylurea (SU) therapy leads to dramatic improvement in diabetes control and quality of life in most patients who carry these mutations. Here, we report the long-term follow-up results of two siblings with PNDM who were treated with insulin until ABCC8 gene mutation was identified, and were successfully transferred to oral SU therapy. After 3.5 years of follow-up on SU, one patient had a very good response, while the other one had a poor response. Bad compliance to diet was thought to be the most probable reason for poor glycemic control in this patient. In conclusion, molecular genetic diagnosis in all patients with PNDM is recommended. Compliance to treatment should be an important aspect of the follow-up of these patients.     

Importance of glycemic control on the course of glomerular filtration rate in type 2 diabetes with hypertension and microalbuminuria under tight blood pressure control

Background and aimsTo evaluate the role of glycemic control on the evolution of glomerular filtration rate (GFR) in type 2 diabetes (T2DM) with mild-moderate hypertension under tight blood pressure control, and to address the current controversy whether diabetic nephropathy worsens, independently of blood pressure, proportionally to HbA1c at any physiological level or only when HbA1c is above a 7.5–8% threshold.Methods and resultsT2DM (N = 127) during early stage diabetic nephropathy characterized by microalbuminuria were followed during a 2 year multicenter study. Individual GFR profiles were accurately obtained by ⁵¹Cr – EDTA bolus injections and analyzed with linear statistical mixed-effects models. GFR at baseline was significantly negatively correlated with age and plasma creatinine concentration (P ≤ 0.0001), and GFR declined, on average, by 4.0 ml/min 1.73 m²/year (P = 0.001). A significant correlation was found between individual GFR decline rate and average systolic (SBP) and diastolic (DBP) blood pressures (−0.254 (0.736) ml/min 1.73 m²/year per mmHg increase in SBP (DBP), P = 0.041 (0.014)) and % of glycated hemoglobin (HbA1c) (−1.78 ml/min 1.73 m²/year per % increase in HbA1c, P = 0.048). This implies a 44% increase/reduction in GFR decline rate for 1% HbA1c increase/reduction around 7.0% (i.e. 5.79 and 2.24 ml/min 1.73 m²/year at 8% and 6% HbA1c, respectively).ConclusionsThis study demonstrates that, despite tight blood pressure control, an accurate glycemic control till very low patterns of HbA1c (from 10–11% to 5–6%) is needed to delay the progression of GFR decay in Mediterranean T2DM in south Europe with microalbuminuria.     

1，5-脱水葡萄糖醇：可能是更好反映短期血糖控制水平的标志

目的初步建立1,5-脱水葡萄糖醇（1,5-AG）在健康人群中的参考范围,并探讨1,5-AG与短期血糖控制水平的相关性。方法2010年3至7月在北京古城和苹果园社区及解放军总医院门诊体检人群筛选不同年龄健康受试者281名,其中男143名,女138名,年龄20-87岁,用酶偶联-两点法测定空腹血清1,5-AG,建立1,5-AG的正常参考值。同期随机选取门诊2型糖尿病（T2DM）患者38例,其中男女各19例,年龄37-74岁,给予重组赖脯胰岛素25治疗12周,每周规律监测餐前、餐后2h指尖血糖用以计算平均血糖（MBG）,测定0、2、4、8、12周的1,5-AG、糖化血红蛋白（HbAlc）和糖化血清白蛋白（GA）,比较各阶段不同血糖监测指标的变化,并行Pearson相关分析。结果健康人群的参考值范围：整体人群68-251μmol／L;男性80-267μmol／L,女性66-206μmol／L。健康人群1,5-AG水平号性别有关,男性高于女性;与年龄、体质指数（BMI）均无关。T2DM患者中,1,5-AG、HbAlc、GA与MBG均呈显著相关性（r=-0．491、0．563、0．422,均P〈0．01）;1,5-AG与2周内的MBG相关性最高（r=-0．675,P〈0．01）,HbAlc与MBG的相关性维持时间最长。2周时1,5-AG升高了18．8％,而HbAlc和GA只分别降低了3．2％、6．2％。到12周,1,5-AG变化幅度为65．2％,仍明显高于HbAlc、GA的降低率（16．1％、21．1％）。T2DM患者个体的1,5-AG、HbAlc、GA、MBG随时间变化的一致性较好。结论1,5-AG的正常参考值：整体人群68-251μmol／L;男性80。267μmol／L,女性66-206μmol／L;1,5-AG作为短期血糖控制程度的标志,可能优于HbAlc和GA。     

Diabetes in pregnancy and infant mortality: Link with glycemic control

Aims

To compare the clinical and glycemic profile as well as pregnancy complications and infant mortality among diabetic mothers in Indonesia.

Use of the glycemic index for weight loss and glycemic control: A review of recent evidence

This article summarizes current findings regarding the use of low-glycemic index (GI) diets for weight loss and type 2 diabetes control. Results from cross-sectional studies evaluating the association between dietary GI and body mass index had equivocal results, especially when dietary fiber was included in the model. Of five prospective cohort studies, two reported increased risk of type 2 diabetes diagnosis with higher dietary GI or glycemic load (GL). Risk of type 2 diabetes appeared to have a stronger association with carbohydrate intake or GL than with GI. Evidence from intervention studies using a low-GI approach for weight loss produced inconsistent results, especially for longer-term studies. In intervention studies with type 2 diabetes patients, consumption of a low-GI diet resulted in lower hemoglobin A_1c concentrations in participants of shorter-term studies. Recent evidence adds to the controversy regarding the effectiveness of consuming low-GI diets for glycemic control and weight reduction.     

Lipohypertrophy in insulin injecting patients with diabetes mellitus: an under-recognized barrier for glycemic control

International Journal of Diabetes in Developing Countries - Lipohypertrophy is the one of the commonest local complications that significantly affects glycemic control in patients of diabetes...     

Paradoxical increase in ventricular torsion and systolic torsion rate in type I diabetic patients under tight glycemic control.

OBJECTIVES: This study sought to characterize the early features of diabetic cardiomyopathy by magnetic resonance imaging (MRI) tagging. BACKGROUND: The earliest manifestations of diabetic cardiomyopathy have not been well established, especially under tight glycemic management. We hypothesized that torsion measurements would identify subclinical contractile alterations in type I diabetics with normal left ventricular ejection fraction, mass, blood pressure, and aggressive glycemic control. We also sought to characterize the influence of elevated resting heart rates (HRs) of diabetics on torsion. METHODS: Sixteen patients with type I diabetes and 10 control patients underwent cine and tagged MRI with a 1.5-T scanner. Torsion, strain, and their rates were measured. To quantify the influence of chronotropic and inotropic stimulation on torsion, nine healthy volunteers underwent MRI tagging at rest, after atropine injection, and after exercise. RESULTS: Diabetic patients (hemoglobin A1c, 6.8 +/- 0.4%) had a higher resting HR (77.0 +/- 12.4 beats/min vs. 59.0 +/- 5.6 beats/min; p < 0.01), higher maximal torsion by 23% (3.5 +/- 0.9 degrees/cm vs. 2.7 +/- 0.4 degrees/cm; p < 0.01) and higher maximal systolic torsion rate (TR-s) by 25% (0.013 +/- 0.003 degrees/cm/s vs. 0.010 +/- 0.002 degrees/cm/s, p = 0.01). Torsion did not significantly change with chronotropic stimulation (p = 0.30). CONCLUSIONS: In diabetics under tight glycemic control, we observed a surprising increase in torsion and TR-s unrelated to chronotropic influences of HR. We propose that increased torsion and TR-s could represent early predictive markers of the propensity to cardiac dysfunction in asymptomatic type I diabetics. Furthermore, these findings seem fundamental to the diabetic state itself and unaccounted for by other comorbidities.     

Benefits and risks for intensive glycemic control in patients with diabetes mellitus

Overweight, obesity, pre-diabetes and diabetes have become epidemic in most of Western society. An estimated 25.8 million United States adults have diabetes and some 79 million have prediabetes and are thus at high risk for future development of diabetes. Appropriate treatment of the ABCs of diabetes [A1C, blood pressure and cholesterol (dyslipidemia)] can reduce the risk for the development and progression of diabetic complications. This paper reviews some of the research studies that support treatment goals established by the American Association of Clinical Endocrinologists/American College of Endocrinology and the American Diabetes Association. Multiple studies have demonstrated that intensive glycemic control will reduce the risk for diabetes microvascular and neuropathic disease, but none showed decreased macrovascular disease events during the initial phase of the trials, although benefit was seen in long-term follow-up of the Diabetes Control and Complications Trial and the United Kingdom Prospective Diabetes Study. The American Association of Clinical Endocrinologists/American College of Endocrinology and the American Diabetes Association goals for glycemia informed by these studies indicate the importance of individualizing targets for patients based on factors including the duration of diabetes, presence of acute and chronic complications and life expectancy. Writing groups convened by these organizations have also developed treatment algorithms to help clinicians appropriately use both lifestyle and pharmacotherapy interventions to safely achieve glycemic targets.