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

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

doi:10.1177/1932296815592410

Cited by 12 publications

(14 citation statements)

References 58 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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“…Diabetes mellitus [1] is one of the pandemics characterized by chronic hyperglycemia [2], which seriously threatens human physical and mental health and quality of life. Since no cure has been developed for this disease, maintaining euglycemia through diet, exercise and pharmacological management is a way to prevent diabetic complications [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…Since no cure has been developed for this disease, maintaining euglycemia through diet, exercise and pharmacological management is a way to prevent diabetic complications [3][4][5]. Currently, continuous glucose monitoring systems (CGMS) are increasingly popular, since they can reflect fluctuation of blood glucose (BG), easily perceive asymptomatic hypoglycemia and relieve the pain of sticking fingers frequently for people with diabetes [2,6,7].…”

Section: Introductionmentioning

confidence: 99%

Design of a Sandwich Hierarchically Porous Membrane with Oxygen Supplement Function for Implantable Glucose Sensor

et al. 2020

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This study aims to develop an oxygen regeneration layer sandwiched between multiple porous polyurethanes (PU) to improve the performance of implantable glucose sensors. Sensors were prepared by coating electrodes with platinum nanoparticles, Nafion, glucose oxidase and sandwich hierarchically porous membrane with an oxygen supplement function (SHPM-OS). The SHPM-OS consisted of a hierarchically porous structure synthesized by polyethylene glycol and PU and a catalase (Cat) layer that was coated between hierarchical membranes and used to balance the sensitivity and linearity of glucose sensors, as well as reduce the influence of oxygen deficiency during monitoring. Compared with the sensitivity and linearity of traditional non-porous (NO-P) sensors (35.95 nA/mM, 0.9987, respectively) and single porous (SGL-P) sensors (45.3 nA /mM, 0.9610, respectively), the sensitivity and linearity of the SHPM-OS sensor was 98.45 nA/mM and 0.9989, respectively, which was more sensitive with higher linearity. The sensor showed a response speed of five seconds and a relative sensitivity of 90% in the first 10 days and remained 78% on day 20. This sensor coated with SHPM-OS achieved rapid responses to changes of glucose concentration while maintaining high linearity for long monitoring times. Thus, it may reduce the difficulty of back-end hardware module development and assist with effective glucose self-management for people with diabetes.

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“…In general, changes between each individual CGM measure should be relatively small. The distribution of these changes can be used to determine unlikely or outlying changes in sensor output [ 40 , 41 ]. A large number or cluster of such extreme point-to-point changes can indicate sensor failure or a marked change in subcutaneous tissue condition, such as with oedema.…”

Section: Cgm Technology For Neonatal Carementioning

confidence: 99%

Continuous glucose monitoring in neonates: a review

McKinlay

Chase

Dickson

et al. 2017

matern health, neonatol and perinatol

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Continuous glucose monitoring (CGM) is well established in the management of diabetes mellitus, but its role in neonatal glycaemic control is less clear. CGM has provided important insights about neonatal glucose metabolism, and there is increasing interest in its clinical use, particularly in preterm neonates and in those in whom glucose control is difficult. Neonatal glucose instability, including hypoglycaemia and hyperglycaemia, has been associated with poorer neurodevelopment, and CGM offers the possibility of adjusting treatment in real time to account for individual metabolic requirements while reducing the number of blood tests required, potentially improving long-term outcomes. However, current devices are optimised for use at relatively high glucose concentrations, and several technical issues need to be resolved before real-time CGM can be recommended for routine neonatal care. These include: 1) limited point accuracy, especially at low or rapidly changing glucose concentrations; 2) calibration methods that are designed for higher glucose concentrations of children and adults, and not for neonates; 3) sensor drift, which is under-recognised; and 4) the need for dynamic and integrated metrics that can be related to long-term neurodevelopmental outcomes. CGM remains an important tool for retrospective investigation of neonatal glycaemia and the effect of different treatments on glucose metabolism. However, at present CGM should be limited to research studies, and should only be introduced into routine clinical care once benefit is demonstrated in randomised trials.

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

Cited by 12 publications

References 58 publications

Untangling glycaemia and mortality in critical care

Untangling glycaemia and mortality in critical care

Design of a Sandwich Hierarchically Porous Membrane with Oxygen Supplement Function for Implantable Glucose Sensor

Continuous glucose monitoring in neonates: a review

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