Second pilot trials of the STAR-Liege protocol for tight glycemic control in critically ill patients

ABSTRACT: BACKGROUND: Critically ill patients often present increased insulin resistance and stress-induced hyperglycemia. Tight glycemic control aims to reduce blood glucose (BG) levels and variability while ensuring safety from hypoglycemia. This paper presents the results of the second Belgian clinical trial using the customizable STAR framework in a target-to-range control approach. The main objective is reducing measurement frequency while maintaining performance and safety of the glycemic control. METHODS: The STAR-Liege 2 (SL2) protocol targeted the 100--140 mg/dL glycemic band and offered 2-hourly and 3-hourly interventions. Only insulin rates were adjusted, and nutrition inputs were left to the attending clinicians. This protocol restricted the forecasted risk of BG < 90 mg/dL to a 5% level using a stochastic model of insulin sensitivity to assess patient-specific responses to insulin and its future likely variability to optimize insulin interventions. The clinical trial was performed at the Centre Hospitalier Universitaire de Liege and included 9 patients. Results are compared to 24-hour pre-trial and 24-hour post-trial, but also to the results of the first pilot trial performed in Liege, STAR-Liege 1 (SL1). This trial was approved by the Ethics Committee of the Medical Faculty of the University of Liege (Liege, Belgium). RESULTS: During the SL2 trial, 91 measurements were taken over 194 hours. BG levels were tightly distributed: 54.9% of BG within 100--140 mg/dL, 40.7% were [GREATER-THAN OR EQUAL TO] 140 mg/dL and 4.4% were < 100 mg/dL with no BG < 70 mg/dL. Comparing these results with 24-hour pre-trial and post-trial shows that SL2 reduced high and low BG levels and reduced glycemic variability. Nurses selected 3-hourly measurement only 5 of 16 times and overrode 12% of 91 recommended interventions (35% increased insulin rates and 65% decreased insulin rates). SL1 and SL2 present similar BG levels distribution (p > 0.05) with significantly reduced measurement frequency for SL2 (p < 0.05). CONCLUSIONS: The SL2 protocol succeeded in reducing clinical workload while maintaining safety and effectiveness of the glycemic control. SL2 was also shown to be safer and tighter than hospital control. Overall results validate the efficacy of significantly customizing the STAR framework.     

Validation of a glucose meter at an intensive care unit

IntroductionHyperglycemic 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 methodsEighty-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.ResultsMean 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.ConclusionsThe 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.     

Insulin Glargine in the intensive care unit: A model-based clinical trial design

IntroductionCurrent successful AGC (Accurate Glycemic Control) protocols require extra clinical effort and are impractical in less acute wards where patients are still susceptible to stress-induced hyperglycemia. Long-acting insulin Glargine has the potential to be used in a low effort controller. However, potential variability in efficacy and length of action prevent direct in-hospital use in an AGC framework for less acute wards.MethodClinically validated virtual trials based on data from stable ICU patients from the SPRINT cohort who would be transferred to such an approach are used to develop a 24-h AGC protocol robust to different Glargine potencies (1.0×, 1.5× and 2.0× regular insulin) and initial dose sizes (dose = total insulin over prior 12, 18 and 24 h). Glycemic control in this period is provided only by varying nutritional inputs. Performance is assessed as %BG in the 4.0–8.0 mmol/L band and safety by %BG < 4.0 mmol/L.ResultsThe final protocol consisted of Glargine bolus size equal to insulin over the previous 18 h. Compared to SPRINT there was a 6.9–9.5% absolute decrease in mild hypoglycemia (%BG < 4.0 mmol/L) and up to a 6.2% increase in %BG between 4.0 and 8.0 mmol/L. When the efficacy is known (1.5× assumed) there were reductions of: 27% BG measurements, 59% insulin boluses, 67% nutrition changes, and 6.3% absolute in mild hypoglycemia.ConclusionBased on current understanding of Glargine behaviour, a robust protocol for a 24–48 clinical trial has been designed to safely investigate possible differences in efficacy and kinetics of Glargine in a critically ill population. This protocol is a first step towards developing a Glargine-based protocol for less acute wards. Ensuring robustness to variability in Glargine efficacy directly affects the performance and safety that can be obtained.     

Zinc and glycemic control: A meta-analysis of randomised placebo controlled supplementation trials in humans

BackgroundImpaired zinc metabolism is prominent in chronic disorders including cardiovascular disease and diabetes. Zinc has the potential to affect glucose homeostasis in animals and humans and hence impact the risk of type 2 diabetes mellitus.MethodsA systematic review and meta-analysis of randomised placebo controlled trials was conducted to determine the effect of zinc supplementation on fasting blood glucose, HbA1c, serum insulin and serum zinc concentrations. Relevant studies for inclusion were identified from a literature search of electronic databases up to July 2011.ResultsFourteen reports (n = 3978 subjects) were included in the meta-analysis. In the overall analysis, a small but statistically significant reduction in fasting glucose concentrations was observed (?0.19 ± 0.08 mmol/L, P = 0.013) after zinc supplementation. HbA1c tended to decrease in zinc-supplemented individuals (?0.64 ± 0.36%, P = 0.072). No significant effect was observed for serum insulin concentrations. Plasma zinc concentrations increased significantly following supplementation (+4.03 ± 0.81 μmol/L, P = 0.001). In secondary analyses of participants with chronic metabolic disease (types 1 and 2 diabetes mellitus, metabolic syndrome and obesity), zinc supplementation produced a greater reduction in glucose concentrations (?0.49 ± 0.11 mmol/L, P = 0.001) compared to the effect that was observed in healthy participants.ConclusionThe significant albeit modest reduction in glucose concentrations and tendency for a decrease in HbA1c following zinc supplementation suggest that zinc may contribute to the management of hyperglycemia in individuals with chronic metabolic disease.     

Evaluation of a model-based method for designing water quality observations

An observational design technique supporting water quality modelling was reviewed and evaluated. The practical feasibility and mathematical sensitivity of the method were analysed for Lake Kuortaneenjärvi, Western Finland. A sampling programme to estimate characteristic rate parameters for competition between the nitrogen-fixing cyanobacter Aphanizomenon flos-aquae and eukaryotic phytoplankton was produced and the robustness of the design with respect to parameters and input was studied using the Monte Carlo technique. Optimal timing of observations appeared to be more sensitive to noise in the parameter values than to stochasticity in the model input, which consisted of data on the nutrient loading and climatic conditions of the lake. The perturbated parameter values favoured the weighting of phytoplankton dynamics, whereas the perturbations of the input shifted the design towards cyanobacterial dominance. It was concluded that this was because correct timing of algal succession was the most crucial element in successful estimation. Considerable rationalization of the use of measurement resources can be achieved by adopting the design procedure. This was also demonstrated for Lake Kuortaneenjärvi.     

Validation of a new model-based tracking technique for measuring three-dimensional, in vivo glenohumeral joint kinematics

Shoulder motion is complex and significant research efforts have focused on measuring glenohumeral joint motion. Unfortunately, conventional motion measurement techniques are unable to measure glenohumeral joint kinematics during dynamic shoulder motion to clinically significant levels of accuracy. The purpose of this study was to validate the accuracy of a new model-based tracking technique for measuring three-dimensional, in vivo glenohumeral joint kinematics. We have developed a model-based tracking technique for accurately measuring in vivo joint motion from biplane radiographic images that tracks the position of bones based on their three-dimensional shape and texture. To validate this technique, we implanted tantalum beads into the humerus and scapula of both shoulders from three cadaver specimens and then recorded biplane radiographic images of the shoulder while manually moving each specimen's arm. The position of the humerus and scapula were measured using the model-based tracking system and with a previously validated dynamic radiostereometric analysis (RSA) technique. Accuracy was reported in terms of measurement bias, measurement precision, and overall dynamic accuracy by comparing the model-based tracking results to the dynamic RSA results. The model-based tracking technique produced results that were in excellent agreement with the RSA technique. Measurement bias ranged from -0.126 to 0.199 mm for the scapula and ranged from -0.022 to 0.079 mm for the humerus. Dynamic measurement precision was better than 0.130 mm for the scapula and 0.095 mm for the humerus. Overall dynamic accuracy indicated that rms errors in any one direction were less than 0.385 mm for the scapula and less than 0.374 mm for the humerus. These errors correspond to rotational inaccuracies of approximately 0.25 deg for the scapula and 0.47 deg for the humerus. This new model-based tracking approach represents a non-invasive technique for accurately measuring dynamic glenohumeral joint motion under in vivo conditions. The model-based technique achieves accuracy levels that far surpass all previously reported non-invasive techniques for measuring in vivo glenohumeral joint motion. This technique is supported by a rigorous validation study that provides a realistic simulation of in vivo conditions and we fully expect to achieve these levels of accuracy with in vivo human testing. Future research will use this technique to analyze shoulder motion under a variety of testing conditions and to investigate the effects of conservative and surgical treatment of rotator cuff tears on dynamic joint stability.     

Computational assessment of model-based wave separation using a database of virtual subjects

The quantification of arterial wave reflection is an important area of interest in arterial pulse wave analysis. It can be achieved by wave separation analysis (WSA) if both the aortic pressure waveform and the aortic flow waveform are known. For better applicability, several mathematical models have been established to estimate aortic flow solely based on pressure waveforms. The aim of this study is to investigate and verify the model-based wave separation of the ARCSolver method on virtual pulse wave measurements.The study is based on an open access virtual database generated via simulations. Seven cardiac and arterial parameters were varied within physiological healthy ranges, leading to a total of 3325 virtual healthy subjects. For assessing the model-based ARCSolver method computationally, this method was used to perform WSA based on the aortic root pressure waveforms of the virtual patients. As a reference, the values of WSA using both the pressure and flow waveforms provided by the virtual database were taken.The investigated parameters showed a good overall agreement between the model-based method and the reference. Mean differences and standard deviations were −0.05 ± 0.02 AU for characteristic impedance, −3.93 ± 1.79 mmHg for forward pressure amplitude, 1.37 ± 1.56 mmHg for backward pressure amplitude and 12.42 ± 4.88% for reflection magnitude.The results indicate that the mathematical blood flow model of the ARCSolver method is a feasible surrogate for a measured flow waveform and provides a reasonable way to assess arterial wave reflection non-invasively in healthy subjects.     

Validation of a model-based inverse kinematics approach based on wearable inertial sensors

Abstract

Wearable inertial measurement units (IMUs) are a promising solution to human motion estimation. Using IMUs 3D orientations, a model-driven inverse kinematics methodology to estimate joint angles is presented. Estimated joint angles were validated against encoder-measured kinematics (robot) and against marker-based kinematics (passive mechanism). Results are promising, with RMS angular errors respectively lower than 3 and 6?deg over a minimum range of motion of 50?deg (robot) and 160?deg (passive mechanism). Moreover, a noise robustness analysis revealed that the model-driven approach reduces the effects of experimental noises, making the proposed technique particularly suitable for application in human motion analysis.     

Challenges in implementing model-based phase I designs in a grant-funded clinical trials unit

Background

For a clinical trials unit to run its first model-based, phase I trial, the statistician, chief investigator, and trial manager must all acquire a new set of skills. These trials also require a different approach to funding and data collection.

Challenges and discussion

From the statisticians’ viewpoint, we highlight what is needed to move from running rule-based, early-phase trials to running a model-based phase I study as we experienced it in our trials unit located in the United Kingdom. Our example is CHARIOT, a dose-finding trial using the time-to-event continual reassessment method. It consists of three stages and aims to discover the maximum tolerated dose of the combination of radiotherapy, chemotherapy, and the ataxia telangiectasia mutated Rad3-related inhibitor M6620 (previously known as VX-970) in patients with oesophageal cancer. We present the challenges we faced in designing this trial and how we overcame them as a way of demystifying the conduct of a model-based trial in a grant-funded clinical trials unit.

Conclusions

Although we appreciate that undertaking model-based trials requires additional time and effort, they are feasible to implement and, once suitable tools such as guiding publications and document templates become available, the design and set-up process will be easier and more efficient.

     

Hypothermia in a surgical intensive care unit

Background

Inadvertent hypothermia is not uncommon in the immediate postoperative period and it is associated with impairment and abnormalities in various organs and systems that can lead to adverse outcomes. The aim of this study was to estimate the prevalence, the predictive factors and outcome of core hypothermia on admission to a surgical ICU.

Methods

All consecutive 185 adult patients who underwent scheduled or emergency noncardiac surgery admitted to a surgical ICU between April and July 2004 were admitted to the study. Tympanic membrane core temperature (Tc) was measured before surgery, on arrival at ICU and every two hours until 6 hours after admission. The following variables were also recorded: age, sex, body weight and height, ASA physical status, type of surgery, magnitude of surgical procedure, anesthesia technique, amount of intravenous fluids administered during anesthesia, use of temperature monitoring and warming techniques, duration of the anesthesia, ICU length of stay, hospital length of stay and SAPS II score. Patients were classified as either hypothermic (Tc ≤ 35°C) or normothermic (Tc> 35°C). Univariate analysis and multiple regression binary logistic with an odds ratio (OR) and its 95% Confidence Interval (95%CI) were used to compare the two groups of patients and assess the relationship between each clinical predictor and hypothermia. Outcome measured as ICU length of stay and mortality was also assessed.

Results

Prevalence of hypothermia on ICU admission was 57.8%. In univariate analysis temperature monitoring, use of warming techniques and higher previous body temperature were significant protective factors against core hypothermia. In this analysis independent predictors of hypothermia on admission to ICU were: magnitude of surgery, use of general anesthesia or combined epidural and general anesthesia, total intravenous crystalloids administrated and total packed erythrocytes administrated, anesthesia longer than 3 hours and SAPS II scores. In multiple logistic regression analysis significant predictors of hypothermia on admission to the ICU were magnitude of surgery (OR 3.9, 95% CI, 1.4–10.6, p = 0.008 for major surgery; OR 3.6, 95% CI, 1.5–9.0, p = 0.005 for medium surgery), intravenous administration of crystalloids (in litres) (OR 1.4, 95% CI, 1.1–1.7, p = 0.012) and SAPS score (OR 1.0, 95% CI 1.0–1.7, p = 0.014); higher previous temperature in ward was a significant protective factor (OR 0.3, 95% CI 0.1–0.7, p = 0.003). Hypothermia was neither a risk factor for hospital mortality nor a predictive factor for staying longer in ICU.

Conclusion

The prevalence of patient hypothermia on ICU arrival was high. Hypothermia at time of admission to the ICU was not an independent factor for mortality or for staying longer in ICU.     

强化控糖后加用盐酸吡格列酮治疗2型糖尿病的临床疗效观察

目的观察强化控糖后加用盐酸吡格列酮治疗2型糖尿病的临床疗效。方法 60例使用口服降糖药物治疗的血糖控制不佳的2型糖尿病患者,入院后先进行胰岛素泵强化控糖治疗,患者血糖达到目标值后（FPG〈7.0 mmol/L,2 h PG〈10.0 mmol/L）,改为三餐前门冬胰岛素联合睡前甘精胰岛素继续强化治疗,1周后按1：1的比例随机分为两组,一组继续使用三餐前门冬胰岛素联合睡前甘精胰岛素治疗（对照组）,一组在三餐前门冬胰岛素联合睡前甘精胰岛素的基础上加用盐酸吡格列酮30 mg/日（治疗组）。1～4周我院住院治疗,5～12周门诊随访,若出现FPG及2 h PG明显下降或低血糖反应,则减少胰岛素用量。观察加用盐酸吡格列酮治疗前以及治疗12周时FPG、2 h PG、HbAlc、胰岛素用量、甘油三酯（TG）、总胆固醇（TC）、高密度脂蛋白胆固醇（HDL-C）、低密度脂蛋白胆固醇（LDL-C）、体重指数的变化情况。结果 （1）治疗组胰岛素用量明显下降,与加用盐酸吡格列酮治疗前有明显差异（P〈0.05）;对照组的胰岛素用量治疗前后无明显差异（P〉0.05）;（2）治疗组与对照组加用盐酸吡格列酮治疗前后FPG、2hPG均无明显差异（P〉0.05）。（3）治疗组HbAlc有所下降,与加用盐酸吡格列酮治疗前有明显差异（P〈0.05）,对照组HbAlc也有所下降,差异明显（P〈0.05）,但治疗后两组比较无明显差异（P〉0.05）。（4）加用盐酸吡格列酮后,治疗组TG下降,HDL-C升高,与同组治疗前相比,差异明显（P〈0.05）,对照组与治疗前相比差异不明显,组间比较差异有显著性（P〈0.05）。结论在强化控糖血糖达标后,加用盐酸吡格列酮继续治疗,可以明显降低患者胰岛素的使用剂量,提高患者的依从性,并且能改善血脂代谢紊乱,对于减少心血管并发症的发生有一定益处。     

Analysis of a Gibbs sampler method for model-based clustering of gene expression data

MOTIVATION: Over the last decade, a large variety of clustering algorithms have been developed to detect coregulatory relationships among genes from microarray gene expression data. Model-based clustering approaches have emerged as statistically well-grounded methods, but the properties of these algorithms when applied to large-scale data sets are not always well understood. An in-depth analysis can reveal important insights about the performance of the algorithm, the expected quality of the output clusters, and the possibilities for extracting more relevant information out of a particular data set. RESULTS: We have extended an existing algorithm for model-based clustering of genes to simultaneously cluster genes and conditions, and used three large compendia of gene expression data for Saccharomyces cerevisiae to analyze its properties. The algorithm uses a Bayesian approach and a Gibbs sampling procedure to iteratively update the cluster assignment of each gene and condition. For large-scale data sets, the posterior distribution is strongly peaked on a limited number of equiprobable clusterings. A GO annotation analysis shows that these local maxima are all biologically equally significant, and that simultaneously clustering genes and conditions performs better than only clustering genes and assuming independent conditions. A collection of distinct equivalent clusterings can be summarized as a weighted graph on the set of genes, from which we extract fuzzy, overlapping clusters using a graph spectral method. The cores of these fuzzy clusters contain tight sets of strongly coexpressed genes, while the overlaps exhibit relations between genes showing only partial coexpression. AVAILABILITY: GaneSh, a Java package for coclustering, is available under the terms of the GNU General Public License from our website at http://bioinformatics.psb.ugent.be/software     

A mixed model-based variance estimator for marginal model analyses of cluster randomized trials

Generalized estimating equations (GEE) are used in the analysis of cluster randomized trials (CRTs) because: 1) the resulting intervention effect estimate has the desired marginal or population-averaged interpretation, and 2) most statistical packages contain programs for GEE. However, GEE tends to underestimate the standard error of the intervention effect estimate in CRTs. In contrast, penalized quasi-likelihood (PQL) estimates the standard error of the intervention effect in CRTs much better than GEE but is used less frequently because: 1) it generates an intervention effect estimate with a conditional, or cluster-specific, interpretation, and 2) PQL is not a part of most statistical packages. We propose taking the variance estimator from PQL and re-expressing it as a sandwich-type estimator that could be easily incorporated into existing GEE packages, thereby making GEE useful for the analysis of CRTs. Using numerical examples and data from an actual CRT, we compare the performance of this variance estimator to others proposed in the literature, and we find that our variance estimator performs as well as or better than its competitors.     

Validation of a simplified method for muscle volume assessment

The present study investigated the validity of a simplified muscle volume assessment that uses only the maximum anatomical cross-sectional area (ACSA_max), the muscle length (L_M) and a muscle-specific shape factor for muscle volume calculation ( Albracht et al., 2008, J Biomech 41, 2211–2218). The validation on the example of the triceps surae (TS) muscles was conducted in two steps. First L_M, ACSA_max, muscle volume and shape factor were calculated from magnet resonance image muscle reconstructions of the soleus (SO), gastrocnemius medialis (GM) and lateralis (GL) of a group of untrained individuals (n=13), endurance (n=9) and strength trained (n=10) athletes. Though there were significant differences in the muscle dimensions, the shape factors were similar across groups and were in average 0.497±0.026, 0.596±0.030, and 0.556±0.041 for the SO, GM and GL respectively. In a second step, the shape factors were applied to an independent recreationally active group (n=21) to compare the muscle volume assessed by the simplified method to the results from whole muscle reconstructions. There were no significant differences between the volumes assessed by the two methods. In conclusion, assessing TS muscle volume on the basis of the reported shape factors is valid across populations and the root mean square differences to whole muscle reconstruction of 7.9%, 4.8% and 8.3% for SO, GM and GL show that the simplified method is sensitive enough to detect changes in muscle volume in the context of degeneration, atrophy or hypertrophy.     

Validation of an official control method for enumeration of authorised probiotic yeast in animal feed

An official control method in the framework of Council Directive 70/524/EEC for probiotic yeast used as feed additives was validated in a collaborative study by twenty laboratories in 12 European Countries. A pour plate method following ISO 7954 using chloramphenicol glucose yeast extract (CGYE) and a plate count method using CHROMagar Candida were used. Precision data in terms of repeatability (r) and reproducibility (R) of the method using different feeding stuffs and three inoculation levels were determined. Yeast was present in the samples in mixtures with other probiotic feed additives at a lower, a higher concentration or not present. The enumeration of yeast on CGYE agar showed for the lower and higher concentration a RSD(r) of 2.4-4.9% and a RSD(R) of 7.7-8%, respectively and was preferred by the majority of labs. CHROMagar Candida had a RSD(r) of 1.9-2.8% and a RSD(R) of 1.9-5.9%. For routine analysis the use of the pour plate technique is recommended. CHROMagar Candida can be used for confirmation of the species Saccharomyces cerevisiae. The methods are not recommended for mineral feeds. The results from this study are intended for consideration for adoption as CEN and ISO standards.     

A comparison of a maximum exertion method and a model-based, sub-maximum exertion method for normalizing trunk EMG

The problem with normalizing EMG data from patients with painful symptoms (e.g., low back pain) is that such patients may be unwilling or unable to perform maximum exertions. Furthermore, the normalization to a reference signal, obtained from a maximal or sub-maximal task, tends to mask differences that might exist as a result of pathology. Therefore, we presented a novel method (GAIN method) for normalizing trunk EMG data that overcomes both problems. The GAIN method does not require maximal exertions (MVC) and tends to preserve distinct features in the muscle recruitment patterns for various tasks. Ten healthy subjects performed various isometric trunk exertions, while EMG data from 10 muscles were recorded and later normalized using the GAIN and MVC methods. The MVC method resulted in smaller variation between subjects when tasks were executed at the three relative force levels (10%, 20%, and 30% MVC), while the GAIN method resulted in smaller variation between subjects when the tasks were executed at the three absolute force levels (50 N, 100 N, and 145 N). This outcome implies that the MVC method provides a relative measure of muscle effort, while the GAIN-normalized data gives an estimate of the absolute muscle force. Therefore, the GAIN-normalized data tends to preserve the differences between subjects in the way they recruit their muscles to execute various tasks, while the MVC-normalized data will tend to suppress such differences. The appropriate choice of the EMG normalization method will depend on the specific question that an experimenter is attempting to answer.