bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Benchmark Data Set for Model-Based Glycemic Control in Critical Care
Authors: Chase J. Geoffrey , LeCompte Aaron , Shaw Geoffrey M. , Blakemore Amy , Wong Jason , Lin Jessica , Hann Christopher E. 
Published: January 2014

Background: Hyperglycemia is prevalent in critical care. That tight control saves lives is becoming more clear, but the “how” and “for whom” in repeating the initial results remain elusive. Model-based methods can provide tight, patient-specific control, as well as providing significant insight into the etiology and evolution of this condition. However, it is still often difficult to compare results due to lack of a common benchmark. This article puts forward a benchmark data set for critical care glycemic control in a medical intensive care unit (ICU). Based on clinical patient data from SPecialized Relative Insulin and Nutrition Tables (SPRINT) studies, it provides a benchmark for comparing and analyzing performance in model-based glycemic control. Methods: Data from 20 of the first 150 postpilot patients treated under SPRINT are presented. All patients had longer than a 5-day length of stay (LoS) in the Christchurch ICU. The benchmark data set matches overall patient data and glycemic control results for the entire cohort and this particular LoS >5-day group. The mortality outcome ( n = 3, 15%) also matches SPRINT results for this patient group. Results: Data cover 20 patients and 6372 total patient hours with an average of 339.4 hours per patient. It includes insulin and nutrition inputs along with 4182 blood glucose measurements at an average of 224.3 measurements per patient, averaging a measurement approximately every 1.5 hours (16 per day). Data are available via download in a Microsoft Excel format. A series of cumulative distribution functions and tables are used to summarize data in this article. Conclusion: Model-based methods can provide tighter, more adaptable “one method fits all” solutions using methods that enable patient-specific modeling and control. A benchmark data set will enable easier model and protocol development for groups lacking clinical data, as well as providing a benchmark to compare results of different protocols on a single (virtual) cohort based on real clinical data.
Using Support Vector Machines to Detect Therapeutically Incorrect Measurements by the MiniMed CGMS®
Authors: Bondia Jorge , Tarín Cristina , García-Gabin Winston , Esteve Eduardo , Fernández-Real José Manuel , Ricart Wifredo , Vehí Josep 
Published: January 2014

Background: Current continuous glucose monitors have limited accuracy mainly in the low range of glucose measurements. This lack of accuracy is a limiting factor in their clinical use and in the development of the so-called artificial pancreas. The ability to detect incorrect readings provided by continuous glucose monitors from raw data and other information supplied by the monitor itself is of utmost clinical importance. In this study, support vector machines (SVMs), a powerful statistical learning technique, were used to detect therapeutically incorrect measurements made by the Medtronic MiniMed CGMS®. Methods: Twenty patients were monitored for three days (first day at the hospital and two days at home) using the MiniMed CGMS. After the third day, the monitor data were downloaded to the physician's computer. During the first 12 hours, the patients stayed in the hospital, and blood samples were taken every 15 minutes for two hours after meals and every 30 minutes otherwise. Plasma glucose measurements were interpolated using a cubic method for time synchronization with simultaneous MiniMed CGMS measurements every five minutes, obtaining a total of 2281 samples. A Gaussian SVM classifier trained on the monitor's electrical signal and glucose estimation was tuned and validated using multiple runs of k-fold cross-validation. The classes considered were Clarke error grid zones A+B and C+D+E. Results: After ten runs of ten-fold cross-validation, an average specificity and sensitivity of 92.74% and 75.49%, respectively, were obtained (see Figure 4). The average correct rate was 91.67%. Conclusions: Overall, the SVM performed well, in spite of the somewhat low sensitivity. The classifier was able to detect the time intervals when the monitor's glucose profile could not be trusted due to incorrect measurements. As a result, hypoglycemic episodes missed by the monitor were detected.
Autonomie Mechanisms and Therapeutic Implications of Postural Diabetic Cardiovascular Abnormalities
Authors: Arora Rohit R. , Bulgarelli Robert J. , Ghosh-Dastidar Samanwoy , Colombo Joseph 
Published: January 2014

Background: Cardiovascular autonomic neuropathy (CAN) is a disorder of progressive autonomic dysfunction (AD) associated with diabetes and other chronic diseases. Orthostatic hypotension (OH) is one of the most incapacitating symptoms of CAN and AD. AD in OH can include sympathetic withdrawal (SW). To detect and diagnose SW, parasympathetic and sympathetic changes must be clearly differentiated from each other. This is accomplished by means of the novel autonomic nervous system (ANS) method based on the simultaneous spectral analyses of respiratory activity (RA) and heart rate variability (HRV). Methods: We performed autonomic profiling of 184 (142 females) consecutive, arrhythmia-free patients with type 2 diabetes using the ANX-3.0 autonomic monitoring system. The patient cohort included 86 (64 female) patients for whom an a1-agonist was the only drug changed and increased from one test to the next; 37 (33 female) for whom the a1-agonist was discontinued; and 61 (45 female) who were on an a1-agonist, but for whom no drug changes were made. The tests averaged 3.1 ± 1.4 months apart; midodrine (ProAmatine) was the a1-agonist prescribed. Of the group, 99 patients also had hypertension and 47 also had cardiovascular disease. No patient had supine hypertension. Results: Changes in parameters from the HRV (without respiration) and ANS methods were compared with changes in heart rate and blood pressure (BP) as measured from one test (test N) to the next (test N + 1). SW with a BP drop of less than the clinical definition may be a trend that can be an early indicator of orthostasis. In this study, patients were treated with low-dose, short-term α1-agonist (vasopressor) therapy, which tended to correct the abnormal trend of SW with a drop in BP. Included in the findings was a systolic BP trend in response to vasopressor therapy of an (expected) initial increase in BP followed by an eventual decrease in systolic BP as SW was reversed. Conclusions: The ANS method enables quantitative assessment of CAN by independently and simultaneously quantifying the two branches of the ANS, sympathetic and parasympathetic. The ANS method modifies standard spectral analysis of HRV (without RA analysis) by incorporating spectral analysis of RA. The ANS method appears to model the normal and abnormal responses to upright posture and changes in vasopressor therapy with greater fidelity than the HRV method. Independent, simultaneous assessment of progressive parasympathetic and sympathetic dysfunction, autonomic imbalance, and responses of the two ANS branches to therapy seems to enable early detection and early intervention. Orthostasis, by way of example, illustrates that frequent, sensitive assessments of both ANS branches can improve the negative outcomes associated with CAN.
A Subcutaneous Insulin Pharmacokinetic Model for Computer Simulation in a Diabetes Decision Support Role: Validation and Simulation
Authors: Wong Jason , Chase J. Geoffrey , Hann Christopher E. , Shaw Geoffrey M. , Lotz Thomas F. , Lin Jessica , Le Compte Aaron J. 
Published: January 2014

Objective: The goal of this study was to validate a previously derived and identified physiological subcutaneous (SC) insulin absorption model for computer simulation in a clinical diabetes decision support role using published pharmacokinetic summary measures. Methods: Validation was performed using maximal plasma insulin concentration (Cmax) and time to maximal concentration (tmax) pharmacokinetic summary measures. Values were either reported or estimated from 37 pharmacokinetic studies over six modeled insulin types. A validation comparison was made to equivalent pharmacokinetic summary measures calculated from model generated curves fitted to respective plasma insulin concentration data. The validation result was a measure of goodness of fit. Validation for each reported study was classified into one of four cases. Results: Of 37 model fits, 22 were validated on both the Cmax and the tmax summary measures. Another 6 model fits were partially validated on one measure only due to lack of reporting on the second measure with errors to reported or estimated ranges of <12%. Another 7 studies could not be validated on either measure because of inadequate reported clinical data. Finally, 2 separate model fits to data from the same study failed the validation with 90 and 71% error on tmax only, which was likely caused by protocol-based error. No model fit failed the validation on both measures. Conclusions: A previously derived and identified model was clinically validated for six insulin types using Cmax and tmax summary measures from published pharmacokinetic studies. Hence, this article presents a clinically valid model that accounts for multiple nonlinear effects and six different types of SC insulin in a computationally modest form suitable for use in clinical decision support.
A Pocket-Size Device to Detect Autonomic Neuropathy
Authors: Ejskjaer Niels , Fleischer Jesper , Fleischer Jacob , Jacobsen Poul Erik , Poulsen Per Logstrup , Nygaard Hans 
Published: January 2014

Background: Diabetic autonomic neuropathy (DAN) is a very frequent complication in the diabetic population (type 1 and type 2 diabetes), and patients may suffer debilitating symptoms from various organ systems. In the less symptomatic and even in the asymptomatic condition it severely impacts health. Testing for DAN is currently time-consuming and costly due to the technical setups available today, therefore the examination may not be offered regularly. The purpose of this study was to evaluate the clinical performance of a pocket-size device for detecting DAN by measuring heart rate variability (HRV). Method: Ten healthy young males and eight type 1 diabetes patients suffering symptomatic DAN were selected. The standardized spectral analysis equipment VariaPulse TF3® (Sima Media, Olumouc, Czechoslovakia) was used as a reference method for evaluating a prototype of the pocket-size device according to a specified protocol. HRV, inhalation/exhalation ratio (E:I) (deep breathing test), and 30:15 ratio (response going from lying to standing) were measured using both methods. Statistical calculations were performed. Results: The correlation between the two devices was R2 = 0.98 and R2 = 0.81 when 30:15 ratio and E:I were measured, respectively. Bland-Altman plots showed suitable agreement between the two devices, substantiated by 95% limits of agreement of the differences of ±0.014 and ±0.033 when 30:15 ratio and E:I were measured, respectively. Conclusions: The pocket-size device was fully interchangeable with the hitherto-used, research-based setup. It proved highly suitable for ambulatory testing of autonomic nervous function and may facilitate screening for DAN according to Danish and international recommendations.
Electromagnetic Environmental Effects Testing of Medical Devices Including Those Used for the Treatment of Diabetes
Authors: Herkert Ralph M. 
Published: January 2014

Background: Electromagnetic emissions from technologies that surround us can produce interference with implanted and externally worn medical devices. Electromagnetic environmental effects (E3) testing of medical devices at the Georgia Tech Research Institute (GTRI) began almost four decades ago and continues to incorporate new devices and new sources of electromagnetic emissions as they are developed and become available. The GTRI Medical Device Test Center provides real-world exposure fields to identify interactions and help manufacturers prevent disruptions from the environments in which their devices must function. Methods: Typically, the medical device is mounted in or on a torso simulator containing a saline solution that simulates the electrical characteristics of the body. The torso simulator and the device under test are then moved through the fields generated by production security and logistical system technologies using a computer-controlled positioning system. These tests are conducted with different orientations of the medical device to the electromagnetic source, simulating the way in which device wearers interact with these systems in representative situations. Results: Particular E3 test results measured on specific devices in the GTRI Medical Device Test Center are proprietary; however, the results of tests to date with current medical devices used for the treatment of diabetes have been encouraging. These devices have included implantable and externally worn insulin infusion pumps and continuous glucose monitoring systems from different manufacturers. Conclusion: Since E3 tests of diabetes treatment devices to date in the test center have centered on devices from only a few of the many current manufacturers, further testing is warranted. In addition, increased functionality, which is being added to existing devices, will create new possibilities for interference in the future.
Agreement between Glucose Trends Derived from Three Simultaneously Worn Continuous Glucose Sensors
Authors: King Allen B. , Armstrong Dana , Chu Caroline , Sharma Manoj , Price David 
Published: January 2014

Background: Sensors detect the rate and direction of glucose trend. They need to be accurate and reproducible as could be evidenced by strong agreement between multiple sensors. We evaluated this relationship through simultaneously worn glucose sensors using several methods of slope analysis. Methods: Ten type 1 diabetic, insulin pump-treated subjects were studied while simultaneously wearing three CGMS® Gold sensors each. Sensors were placed in the right abdomen (reference), left abdomen, and left upper arm. Sensors were calibrated and chronologically aligned. Data were only interpreted and included if there were 24 hours of data simultaneously obtained from all three sensors. Results: Using a two-point derived slope, increasing the duration of the trend from 5 to 60 minutes improved agreement between sensors. Using a 20-minute rolling average trend (using every 5-minute glucose value during the 20 minutes) improved the agreement to 94.3%. Finally, using whichever of the two comparator sensor rolling average trends was closest to the reference (better of two), the agreement improved to 98.2%. However, for these trend analysis methods, when the absolute reference rate of change was more than 1 mg/dl/min, the agreement decreased. Even with the best analysis approach, at an absolute reference sensor rate of change of >2 mg/dl/min, the agreement between sensors was only 40.0%. Conclusion: Despite several methods of analysis, trend agreement from multiple sensors diminishes as the absolute rate of change of reference glucose increases.
Effects of Pulsatile Subcutaneous Injections of Insulin Lispro on Plasma Insulin Concentration Levels
Authors: Chan Alice , Breton Marc D. , Kovatchev Boris P. 
Published: January 2014

Background: Most insulin pumps used for the treatment of diabetes perform subcutaneous insulin injections by pulses. The purpose of this work is to analyze the effects of pulsatile injections of modern insulins on plasma insulin levels compared with a continuous insulin infusion. Method: We simulate pulsatile implementations of a basal rate profile over a day on a type 1 diabetes mellitus patient using insulin lispro. Pulse periods were varied between 1 and 60 min, and random pump errors were included, modeled as white noise, 1/f noise, or 1/f2 noise with relative standard deviations up to 10% of the pump output. Results: Oscillations in plasma insulin caused by the pulsatile injections were not significant with respect to the global variations for pulse periods below 15 min. This cutoff period was found to be robust to random pump errors with standard deviations up to 10% of the pump output and hence solely determined by the insulin kinetics. Additionally, we showed that the pulse period achieving the best implementation of a continuous profile is an increasing function of the error variance for a given type of noise. Conclusions: Our findings support that continuous insulin infusion can be implemented by a pulsatile injection of insulin as infrequent as a pulse every 15 min without significant effects on plasma insulin levels. If clinically confirmed, this result would have important consequences on the design and in silico testing of automated insulin treatment strategies, as increased delivery intervals imply higher accuracy of insulin delivery and facilitated implementations of closed-loop control algorithms.
Percutaneous Window Chamber Method for Chronic Intravital Microscopy of Sensor—Tissue Interactions
Authors: Koschwanez Heidi E. , Klitzman Bruce , Reichert W. Monty 
Published: January 2014

Background: A dorsal, two-sided skin-fold window chamber model was employed previously by Gough in glucose sensor research to characterize poorly understood physiological factors affecting sensor performance. We have extended this work by developing a percutaneous one-sided window chamber model for the rodent dorsum that offers both a larger subcutaneous area and a less restrictive tissue space than previous animal models. Method A surgical procedure for implanting a sensor into the subcutis beneath an acrylic window (15 mm diameter) is presented. Methods to quantify changes in the microvascular network and red blood cell perfusion around the sensors using noninvasive intravital microscopy and laser Doppler flowmetry are described. The feasibility of combining interstitial glucose monitoring from an implanted sensor with intravital fluorescence microscopy was explored using a bolus injection of fluorescein and dextrose to observe real-time mass transport of a small molecule at the sensor-tissue interface. Results: The percutaneous window chamber provides an excellent model for assessing the influence of different sensor modifications, such as surface morphologies, on neovascularization using real-time monitoring of the microvascular network and tissue perfusion. However, the tissue response to an implanted sensor was variable, and some sensors migrated entirely out of the field of view and could not be observed adequately. Conclusions: A percutaneous optical window provides direct, real-time images of the development and dynamics of microvascular networks, microvessel patency, and fibrotic encapsulation at the tissue—sensor interface. Additionally, observing microvessels following combined bolus injections of a fluorescent dye and glucose in the local sensor environment demonstrated a valuable technique to visualize mass transport at the sensor surface.
Reliability of Lightguide Spectrophotometry (O2C®) for the Investigation of Skin Tissue Microvascular Blood Flow and Tissue Oxygen Supply in Diabetic and Nondiabetic Subjects
Authors: Forst Thomas , Hohberg Cloth , Tarakci Eda , Forst Senait , Kann Peter , Pfützner Andreas 
Published: January 2014

Background and Aims: Skin microvascular assessment has progressed to an important evaluation in patients with diabetes mellitus. This study was done to evaluate a new device using micro-lightguide spectrophotometry in the assessment of skin microvascular function. Material and Methods: Twenty nondiabetic subjects (age 46.6 ± 14.8 years; mean ± SD) and 20 diabetic patients (age 59.4 ± 8.4 years) participated in repeated microvascular measurements using micro-lightguide spectrophotometry. This technique allows simultaneous, noninvasive measurement of microvascular blood flow and hemoglobin oxygenation (SO2) at the same anatomical area in different tissue layers. A skin probe was placed on nonhairy skin at the thenar eminence of the left hand for the measurement of SO2, and the postischemic reactive hyperemia response (PRH) was measured in skin and underlying muscle tissue. Results: Repeated measurements in PRH revealed a good correlation at the superficial skin layer ( r = 0.97, p < 0.0001) with a coefficient of variation at 9.2 ± 1.7% and at the superficial muscle layer ( r = 0.80, p < 0.0002) with a coefficient of variation at 9.7 ± 1.5%. A slightly weaker correlation was observed for the SO2 measurement at the skin layer ( r = 0.69 ± p < 0.0001) with a coefficient of variation at 17.5 ± 3.8% and at the muscle layer ( r = 0.48; p = 0.0016) with a coefficient of variation at 18.1 ± 10.5%. Conclusions: Lightguide spectrophotometry is an easy, noninvasive, and reliable method for simultaneous measurement of superficial microvascular blood flow by laser Doppler fluxmetry and skin oxygenation by spectrophotometry. Further studies are required to clarify the validity of these measures in special patient populations such as diabetes mellitus with specified microvascular complications.
Signal-Response Modeling of Partial Hormone Feedback Networks
Authors: Johnson Michael L. , Veldhuis Paula P. , Evans William S. 
Published: January 2014

Background: Endocrine feedback control networks are typically complex and contain multiple hormones, pools, and compartments. The hormones themselves commonly interact via multiple pathways and targets within the networks, and a complete description of such relationships may involve hundreds of parameters. In addition, it is often difficult, if not impossible, to collect experimental data pertaining to every component within the network. Therefore, the complete simultaneous analysis of such networks is challenging. Nevertheless, an understanding of these networks is critical for furthering our knowledge of hormonal regulation in both physiologic and pathophysiologic conditions. Methods: We propose a novel approach for the analysis of dose-response relationships of subsets of hormonal feedback networks. The algorithm and signal-response quantification (SRQuant) software is based on convolution integrals, and tests whether several discretely measured input signals can be individually delayed, spread in time, transformed, combined, and discretely convolved with an elimination function to predict the time course of the concentration of an output hormone. Signal-response quantification is applied to examples from the endocrine literature to demonstrate its applicability to the analysis of the different endocrine networks. Results: In one example, SRQuant determines the dose-response relationship by which one hormone regulates another, highlighting its advantages over other traditional methods. In a second example, for the first time (to the best of our knowledge), we show that the secretion of glucagon may be jointly controlled by the β and the δ cells. Conclusion: We have developed a novel convolution integral-based approach, algorithm, and software (SRQuant) for the analysis of dose-response relationships within subsets of complex endocrine feedback control networks.