Glucose Control in the ICU: A Continuing Story.
; ; et al
in Journal of diabetes science and technology (2016)
In the present era of near-continuous glucose monitoring (CGM) and automated therapeutic closed-loop systems, measures of accuracy and of quality of glucose control need to be standardized for licensing ... [more ▼]
In the present era of near-continuous glucose monitoring (CGM) and automated therapeutic closed-loop systems, measures of accuracy and of quality of glucose control need to be standardized for licensing authorities and to enable comparisons across studies and devices. Adequately powered, good quality, randomized, controlled studies are needed to assess the impact of different CGM devices on the quality of glucose control, workload, and costs. The additional effects of continuing glucose control on the general floor after the ICU stay also need to be investigated. Current algorithms need to be adapted and validated for CGM, including effects on glucose variability and workload. Improved collaboration within the industry needs to be encouraged because no single company produces all the necessary components for an automated closed-loop system. Combining glucose measurement with measurement of other variables in 1 sensor may help make this approach more financially viable. [less ▲]Detailed reference viewed: 12 (0 ULg)
Using Continuous Glucose Monitoring Data and Detrended Fluctuation Analysis to Determine Patient Condition: A Review.
Thomas, Felicity Louise ; ;
in Journal of diabetes science and technology (2015), 9(6), 1327-35
Patients admitted to critical care often experience dysglycemia and high levels of insulin resistance, various intensive insulin therapy protocols and methods have attempted to safely normalize blood ... [more ▼]
Patients admitted to critical care often experience dysglycemia and high levels of insulin resistance, various intensive insulin therapy protocols and methods have attempted to safely normalize blood glucose (BG) levels. Continuous glucose monitoring (CGM) devices allow glycemic dynamics to be captured much more frequently (every 2-5 minutes) than traditional measures of blood glucose and have begun to be used in critical care patients and neonates to help monitor dysglycemia. In an attempt to obtain a better insight relating biomedical signals and patient status, some researchers have turned toward advanced time series analysis methods. In particular, Detrended Fluctuation Analysis (DFA) has been a topic of many recent studies in to glycemic dynamics. DFA investigates the "complexity" of a signal, how one point in time changes relative to its neighboring points, and DFA has been applied to signals like the inter-beat-interval of human heartbeat to differentiate healthy and pathological conditions. Analyzing the glucose metabolic system with such signal processing tools as DFA has been enabled by the emergence of high quality CGM devices. However, there are several inconsistencies within the published work applying DFA to CGM signals. Therefore, this article presents a review and a "how-to" tutorial of DFA, and in particular its application to CGM signals to ensure the methods used to determine complexity are used correctly and so that any relationship between complexity and patient outcome is robust. [less ▲]Detailed reference viewed: 15 (2 ULg)
Continuous glucose monitoring in newborn infants: how do errors in calibration measurements affect detected hypoglycemia?
Thomas, Felicity Louise ; ; et al
in Journal of diabetes science and technology (2014), 8(3), 543-50
Neonatal hypoglycemia is common and can cause serious brain injury. Continuous glucose monitoring (CGM) could improve hypoglycemia detection, while reducing blood glucose (BG) measurements. Calibration ... [more ▼]
Neonatal hypoglycemia is common and can cause serious brain injury. Continuous glucose monitoring (CGM) could improve hypoglycemia detection, while reducing blood glucose (BG) measurements. Calibration algorithms use BG measurements to convert sensor signals into CGM data. Thus, inaccuracies in calibration BG measurements directly affect CGM values and any metrics calculated from them. The aim was to quantify the effect of timing delays and calibration BG measurement errors on hypoglycemia metrics in newborn infants. Data from 155 babies were used. Two timing and 3 BG meter error models (Abbott Optium Xceed, Roche Accu-Chek Inform II, Nova Statstrip) were created using empirical data. Monte-Carlo methods were employed, and each simulation was run 1000 times. Each set of patient data in each simulation had randomly selected timing and/or measurement error added to BG measurements before CGM data were calibrated. The number of hypoglycemic events, duration of hypoglycemia, and hypoglycemic index were then calculated using the CGM data and compared to baseline values. Timing error alone had little effect on hypoglycemia metrics, but measurement error caused substantial variation. Abbott results underreported the number of hypoglycemic events by up to 8 and Roche overreported by up to 4 where the original number reported was 2. Nova results were closest to baseline. Similar trends were observed in the other hypoglycemia metrics. Errors in blood glucose concentration measurements used for calibration of CGM devices can have a clinically important impact on detection of hypoglycemia. If CGM devices are going to be used for assessing hypoglycemia it is important to understand of the impact of these errors on CGM data. [less ▲]Detailed reference viewed: 14 (2 ULg)
Complexity of continuous glucose monitoring data in critically ill patients: continuous glucose monitoring devices, sensor locations, and detrended fluctuation analysis methods.
; Thomas, Felicity Louise ; et al
in Journal of diabetes science and technology (2013), 7(6), 1492-506
BACKGROUND: Critically ill patients often experience high levels of insulin resistance and stress-induced hyperglycemia, which may negatively impact outcomes. However, evidence surrounding the causes of ... [more ▼]
BACKGROUND: Critically ill patients often experience high levels of insulin resistance and stress-induced hyperglycemia, which may negatively impact outcomes. However, evidence surrounding the causes of negative outcomes remains inconclusive. Continuous glucose monitoring (CGM) devices allow researchers to investigate glucose complexity, using detrended fluctuation analysis (DFA), to determine whether it is associated with negative outcomes. The aim of this study was to investigate the effects of CGM device type/calibration and CGM sensor location on results from DFA. METHODS: This study uses CGM data from critically ill patients who were each monitored concurrently using Medtronic iPro2s on the thigh and abdomen and a Medtronic Guardian REAL-Time on the abdomen. This allowed interdevice/calibration type and intersensor site variation to be assessed. Detrended fluctuation analysis is a technique that has previously been used to determine the complexity of CGM data in critically ill patients. Two variants of DFA, monofractal and multifractal, were used to assess the complexity of sensor glucose data as well as the precalibration raw sensor current. Monofractal DFA produces a scaling exponent (H), where H is inversely related to complexity. The results of multifractal DFA are presented graphically by the multifractal spectrum. RESULTS: From the 10 patients recruited, 26 CGM devices produced data suitable for analysis. The values of H from abdominal iPro2 data were 0.10 (0.03-0.20) higher than those from Guardian REAL-Time data, indicating consistently lower complexities in iPro2 data. However, repeating the analysis on the raw sensor current showed little or no difference in complexity. Sensor site had little effect on the scaling exponents in this data set. Finally, multifractal DFA revealed no significant associations between the multifractal spectrums and CGM device type/calibration or sensor location. CONCLUSIONS: Monofractal DFA results are dependent on the device/calibration used to obtain CGM data, but sensor location has little impact. Future studies of glucose complexity should consider the findings presented here when designing their investigations. [less ▲]Detailed reference viewed: 16 (4 ULg)
Data Entry Errors and Design for Model-Based Tight Glycemic Control in Critical Care
; ; et al
in Journal of Diabetes Science and Technology (2012)Detailed reference viewed: 14 (8 ULg)
Stochastic Targeted (STAR) Glycemic Control - Design, Safety and Performance
; ; et al
in Journal of Diabetes Science and Technology (2012)Detailed reference viewed: 19 (3 ULg)
Interface Design and Human Factors Consideration for Model-Based Tight Glycemic Control in Critical Care
; ; et al
in Journal of Diabetes Science and Technology (2012)Detailed reference viewed: 23 (2 ULg)
What makes tight glycemic control tight? The impact of variability and nutrition in two clinical studies.
; ; Preiser, Jean-Charles et al
in Journal of Diabetes Science and Technology (2010), 4(2), 284-98
INTRODUCTION: Tight glycemic control (TGC) remains controversial while successful, consistent, and effective protocols remain elusive. This research analyzes data from two TGC trials for root causes of ... [more ▼]
INTRODUCTION: Tight glycemic control (TGC) remains controversial while successful, consistent, and effective protocols remain elusive. This research analyzes data from two TGC trials for root causes of the differences achieved in control and thus potentially in glycemic and other outcomes. The goal is to uncover aspects of successful TGC and delineate the impact of differences in cohorts. METHODS: A retrospective analysis was conducted using records from a 211-patient subset of the GluControl trial taken in Liege, Belgium, and 393 patients from Specialized Relative Insulin Nutrition Titration (SPRINT) in New Zealand. Specialized Relative Insulin Nutrition Titration targeted 4.0-6.0 mmol/liter, similar to the GluControl A (N = 142) target of 4.4-6.1 mmol/liter. The GluControl B (N = 69) target was 7.8-10.0 mmol/liter. Cohorts were matched by Acute Physiology and Chronic Health Evaluation II score and percentage males (p > .35); however, the GluControl cohort was slightly older (p = .011). Overall cohort and per-patient comparisons (median, interquartile range) are shown for (a) glycemic levels achieved, (b) nutrition from carbohydrate (all sources), and (c) insulin dosing for this analysis. Intra- and interpatient variability were examined using clinically validated model-based insulin sensitivity metric and its hour-to-hour variation. RESULTS: Cohort blood glucose were as follows: SPRINT, 5.7 (5.0-6.6) mmol/liter; GluControl A, 6.3 (5.3-7.6) mmol/liter; and GluControl B, 8.2 (6.9-9.4) mmol/liter. Insulin dosing was 3.0 (1.0-3.0), 1.5 (0.5-3), and 0.7 (0.0-1.7) U/h, respectively. Nutrition from carbohydrate (all sources) was 435.5 (259.2-539.1), 311.0 (0.0-933.1), and 622.1 (103.7-1036.8) kcal/day, respectively. Median per-patient results for blood glucose were 5.8 (5.3-6.4), 6.4 (5.9-6.9), and 8.3 (7.6-8.8) mmol/liter. Insulin doses were 3.0 (2.0-3.0), 1.5 (0.8-2.0), and 0.5 (0.0-1.0) U/h. Carbohydrate administration was 383.6 (207.4-497.7), 103.7 (0.0-829.4), and 207.4 (0.0-725.8) kcal/day. Overall, SPRINT gave approximately 2x more insulin with a 3-4x narrower, but generally non-zero, range of nutritional input to achieve equally TGC with less hypoglycemia. Specialized Relative Insulin Nutrition Titration had much less hypoglycemia (<2.2 mmol/liter), with 2% of patients, compared to GluControl A (7.7%) and GluControl B (2.9%), indicating much lower variability, with similar results for glucose levels <3.0 mmol/liter. Specialized Relative Insulin Nutrition Titration also had less hyperglycemia (>8.0 mmol/liter) than groups A and B. GluControl patients (A+B) had a approximately 2x wider range of insulin sensitivity than SPRINT. Hour-to-hour variation was similar. Hence GluControl had greater interpatient variability but similar intrapatient variability. CONCLUSION: Protocols that dose insulin blind to carbohydrate administration can suffer greater outcome glycemic variability, even if average cohort glycemic targets are met. While the cohorts varied significantly in model-assessed insulin resistance, their variability was similar. Such significant intra- and interpatient variability is a further significant cause and marker of glycemic variability in TGC. The results strongly recommended that TGC protocols be explicitly designed to account for significant intra- and interpatient variability in insulin resistance, as well as specifying or having knowledge of carbohydrate administration to minimize variability in glycemic outcomes across diverse cohorts and/or centers. [less ▲]Detailed reference viewed: 54 (9 ULg)