Complexity of Continuous Glucose Monitoring Data in Critically Ill Patients: Continuous Glucose Monitoring Devices, Sensor Locations, and Detrended Fluctuation Analysis Methods

Signal, Matthew; Thomas, Felicity; Chase, J. Geoffrey

doi:10.1177/193229681300700609

Cited by 15 publications

(14 citation statements)

References 35 publications

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“…Equally, there is not the strong physiological evidence that would support this association which exists for the other metrics considered, and there are questions about its proper use in analysing continuous glucose data to create these associations [85, 86]. …”

Section: Discussionmentioning

confidence: 99%

Untangling glycaemia and mortality in critical care

et al. 2017

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BackgroundHyperglycaemia is associated with adverse outcomes in the intensive care unit, and initial studies suggested outcome benefits of glycaemic control (GC). However, subsequent studies often failed to replicate these results, and they were often unable to achieve consistent, safe control, raising questions about the benefit or harm of GC as well as the nature of the association of glycaemia with mortality and clinical outcomes. In this study, we evaluated if non-survivors are harder to control than survivors and determined if glycaemic outcome is a function of patient condition and eventual outcome or of the glycaemic control provided.MethodsClinically validated, model-based, hour-to-hour insulin sensitivity (SI) and its hour-to-hour variability (%ΔSI) were identified over the first 72 h of therapy in 145 patients (119 survivors, 26 non-survivors). In hypothesis testing, we compared distributions of SI and %ΔSI in 6-hourly blocks for survivors and non-survivors. In equivalence testing, we assessed if differences in these distributions, based on blood glucose measurement error, were clinically significant.ResultsSI level was never equivalent between survivors and non-survivors (95% CI of percentage difference in medians outside ±12%). Non-survivors had higher SI, ranging from 9% to 47% higher overall in 6-h blocks, and this difference became statistically significant as glycaemic control progressed. %ΔSI was equivalent between survivors and non-survivors for all 6-hourly blocks (95% CI of difference in medians within ±12%) and decreased in general over time as glycaemic control progressed.ConclusionsWhereas non-survivors had higher SI levels, variability was equivalent to that of survivors over the first 72 h. These results indicate survivors and non-survivors are equally controllable, given an effective glycaemic control protocol, suggesting that glycaemia level and variability, and thus the association between glycaemia and outcome, are essentially determined by the control provided rather than by underlying patient or metabolic condition.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-017-1725-y) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Untangling glycaemia and mortality in critical care

et al. 2017

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“…However, the results from an initial investigation by Signal et al could not find a relationship between the multifractal spectrum and patient outcome. 49 …”

Section: B-producing the Multifractal Spectrummentioning

confidence: 99%

“…50 For example, Signal et al 49 found the Hurst coefficient varied more between retrospective and real time devices, than between survivors and nonsurvivors. This third study of glucose complexity in the critically ill, by Signal et al 49 did test the fractal structure of the CGM signals with n > 500. It found that the CGM traces used for this analysis had a multifractal composition.…”

Section: Dfa Cgm and The Icu-clinical Implementationmentioning

confidence: 99%

“…Signal et al 49 produced an interesting example of the potential effect of sensor performance on DFA results. A patient had three identical sensors inserted, all of which appeared to agree and track a similar glucose profile.…”

Section: Dfa Cgm and The Icu-clinical Implementationmentioning

confidence: 99%

“…Consistency of device type and calibration method 49 2. Consistency of sensor insertion location on the body 3.…”

Section: Dfa Cgm and The Icu-clinical Implementationmentioning

confidence: 99%

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Using Continuous Glucose Monitoring Data and Detrended Fluctuation Analysis to Determine Patient Condition

Thomas

Signal

Chase

2015

J Diabetes Sci Technol

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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.

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Comparison of subcutaneous and intravenous continuous glucose monitoring accuracy in an operating room and an intensive care unit

et al. 2015

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Although we have used an intravenous continuous glucose monitor for blood glucose management, a previous study reported that a subcutaneous continuous glucose monitor was also reliable for use in critically ill patients. The aim of this study was to compare the subcutaneous and intravenous continuous glucose monitors. This was an observational trial (UMIN-CTR, ID:000013338). We included patients who were admitted to our intensive care units (ICU) after hepato-biliary pancreatic surgery. Continuous blood glucose measurement was performed from the beginning of the operation to ICU discharge using the intravenous continuous monitor STG-55 (Nikkiso, Tokyo, Japan) and the subcutaneous continuous monitor iPro2 (Medtronic Japan, Tokyo, Japan). The STG-55 measured the glucose level in real time, and the iPro2 measured this every 5 min. We compared glucose levels obtained using the two devices every 5 min using a Bland-Altman plot and a regression analyses. A total of 3592 comparative samples in 15 cases were analyzed. The mean glucose level measured using the STG-55 was 139 ± 21 mg/dl, and that measured using the iPro2 was 144 ± 31 mg/dl. A linear regression line had the equation of the form y = 0.225x + 106. The coefficient of determination was 0.11, and the F-test significance level was set as p < 0.01. The mean of the differences was -5.2 mg/dl, with a 95 % agreement limit of -67 to + 57 mg/dL. The percent error was 44 %. In conclusion, the current study suggests that subcutaneous and intravenous continuous glucose monitoring was not highly correlated during either surgery or ICU stay.

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Complexity of Continuous Glucose Monitoring Data in Critically Ill Patients: Continuous Glucose Monitoring Devices, Sensor Locations, and Detrended Fluctuation Analysis Methods

Cited by 15 publications

References 35 publications

Untangling glycaemia and mortality in critical care

Untangling glycaemia and mortality in critical care

Using Continuous Glucose Monitoring Data and Detrended Fluctuation Analysis to Determine Patient Condition

Comparison of subcutaneous and intravenous continuous glucose monitoring accuracy in an operating room and an intensive care unit

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