Continuous Glucose Monitoring: Real-Time Algorithms for Calibration, Filtering, and Alarms

Bequette, B. Wayne

doi:10.1177/193229681000400222

Cited by 169 publications

(114 citation statements)

References 62 publications

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“…Thus, while better measurements are always beneficial, measurement error itself does not appear to have a greater impact than the TGC protocol design or its ability to manage inter-and intra-patient variability. [142][143][144][145]). For TGC in the ICU this area was also reviewed in [66].…”

Section: The Impact Of Measurement Frequency and Error And Implicatimentioning

confidence: 99%

Tight glycemic control in critical care – The leading role of insulin sensitivity and patient variability: A review and model-based analysis

Chase

Compte

Suhaimi

et al. 2011

Computer Methods and Programs in Biomedicine

123

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Tight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial successful TGC trials that reduced mortality and other outcomes has proven difficult with more failures than successes. Hence, there has been growing debate over the necessity of TGC, its goals, the risk of severe hypoglycemia, and target cohorts.This paper provides a review of TGC via new analyses of data from several clinical trials, including SPRINT, Glucontrol and a recent NICU study. It thus provides both a review of the problem and major background factors driving it, as well as a novel model-based analysis designed to examine these dynamics from a new perspective. Using these clinical results and analysis, the goal is to develop new insights that shed greater light on the leading factors that make TGC difficult and inconsistent, as well as the requirements they thus impose on the design and implementation of TGC protocols.A model-based analysis of insulin sensitivity using data from three different critical care units, comprising over 75,000 hours of clinical data, is used to analyse variability in metabolic dynamics using a clinically validated model-based insulin sensitivity metric (S I ). Variation in S I provides a new interpretation and explanation for the variable results seen (across cohorts and studies) in applying TGC. In particular, significant intra-and inter-patient variability in insulin resistance (1/ S I ) is seen be a major confounder that makes TGC difficult over diverse cohorts, yielding variable results over many published studies and protocols. Further factors that exacerbate this variability in glycemic outcome are found to include measurement frequency and whether a protocol is blind to carbohydrate administration.

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Section: The Impact Of Measurement Frequency and Error And Implicatimentioning

confidence: 99%

Tight glycemic control in critical care – The leading role of insulin sensitivity and patient variability: A review and model-based analysis

Chase

Compte

Suhaimi

et al. 2011

Computer Methods and Programs in Biomedicine

123

View full text Add to dashboard Cite

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“…Both problems can result in sensors that fail to detect hypoglycemia and underestimate hyperglycemia. The problems have been discussed at length in scientific literature, with studies looking to correct for OS current focusing primarily on calibration (e.g., one-versus two-point 1,2 ) and studies looking to address the delay focusing on signal processing algorithms (e.g., model-based deconvolution 3,4 and Kalman filtering [5][6][7][8] ). The origin of the OS current has not been determined definitively, although it has long been known that the OS observed in vitro is not necessarily the same as that observed in vivo, indicating that the biological environment contributes some unknown factor or factors.…”

Section: Introductionmentioning

confidence: 99%

Use of Subcutaneous Interstitial Fluid Glucose to Estimate Blood Glucose: Revisiting Delay and Sensor Offset

Rebrin

Sheppard

Steil

2010

J Diabetes Sci Technol

128

117

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Background: Estimates for delays in the interstitial fluid (ISF) glucose response to changes in blood glucose (BG) differ substantially among research groups. We review these findings along with arguments that continuous glucose monitoring (CGM) devices used to measure ISF delay contribute to the variability. We consider the impact of the ISF delay and review approaches to correct for it, including strategies pursued by the manufacturers of these devices. The focus on how the manufacturers have approached the problem is motivated by the observation that clinicians and researchers are often unaware of how the existing CGM devices process the ISF glucose signal. Methods: Numerous models and simulations were used to illustrate problems related to measurement and correction of ISF glucose delay. Results: We find that (1) there is no evidence that the true physiologic ISF glucose delay is longer than 5–10 min and that the values longer than this can be explained by delays in CGM filtering routines; (2) the primary impact of the true ISF delay is on sensor calibration algorithms, making it difficult to estimate calibration factors and offset (OS) currents; (3) inaccurate estimates of the sensor OS current result in overestimation of sensor glucose at low values, making it difficult to detect hypoglycemia; (4) many device companies introduce nonlinear components into their filters, which can be expected to confound attempts by investigators to reconstruct BG using linear deconvolution; and (5) algorithms advocated by academic groups are seldom compared to algorithms pursued by industry, making it difficult to ascertain their value. Conclusions: The absence of any direct comparisons between existing and new algorithms for correcting ISF delay and sensor OS current is, in part, due to the difficulty in extracting relevant details from industry patents and/or extracting unfiltered sensor signals from industry products. The model simulation environment, where all aspects of the signal can be derived, may be more appropriate for developing new filtering and calibration strategies. Nevertheless, clinicians, academic researchers, and the industry would benefit from collaborating when evaluating those strategies.

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“…In particular, it opens the possibility of designing ILR algorithms that collect statistics over longer time durations than is currently done. Other applications that might benefit from StreamQRE include glucose monitoring [69], [70], where a mobile device periodically or continuously measures a diabetic's blood glucose and performs 4 XC is a high-level C-based programming language.…”

Section: Discussionmentioning

confidence: 99%

Real-Time Decision Policies With Predictable Performance

et al. 2018

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As methods and tools for Cyber-Physical Systems grow in capabilities and use, one-size-fits-all solutions start to show their limitations. In particular, tools and languages for programming an algorithm or modeling a CPS that are specific to the application domain are typically more usable, and yield better performance, than general-purpose languages and tools. In the domain of cardiac arrhythmia monitoring, a small, implantable medical device continuously monitors the patient's cardiac rhythm and delivers electrical therapy when needed. The algorithms executed by these devices are streaming algorithms, so they are best programmed in a streaming language that allows the programmer to reason about the incoming data stream as the basic object, rather than force her to think about lower-level details like state maintenance and minimization. Because these devices are resource-constrained, it is useful if the programming language allowed predictable performance in terms of processing runtime and energy consumption, or more general costs. StreamQRE is a declarative streaming programming language, with an efficient and portable implementation and strong theoretical guarantees. In particular, its evaluation algorithm guarantees constant cost (runtime, memory, energy) per data item, and also calculates upper bounds on the per-item cost. Such an estimate of the cost allows early exploration of the algorithmic possibilities, while maintaining a handle on worst-case performance, on the basis of which hardware can be designed and algorithms can be tuned. Abstract-As methods and tools for Cyber-Physical Systems grow in capabilities and use, one-size-fits-all solutions start to show their limitations. In particular, tools and languages for programming an algorithm or modeling a CPS that are specific to the application domain are typically more usable, and yield better performance, than general-purpose languages and tools. In the domain of cardiac arrhythmia monitoring, a small, implantable medical device continuously monitors the patient's cardiac rhythm and delivers electrical therapy when needed. The algorithms executed by these devices are streaming algorithms, so they are best programmed in a streaming language that allows the programmer to reason about the incoming data stream as the basic object, rather than force her to think about lower-level details like state maintenance and minimization. Because these devices are resource-constrained, it is useful if the programming language allowed predictable performance in terms of processing runtime and energy consumption, or more general costs. StreamQRE is a declarative streaming programming language, with an efficient and portable implementation and strong theoretical guarantees. In particular, its evaluation algorithm guarantees constant cost (runtime, memory, energy) per data item, and also calculates upper bounds on the per-item cost. Such an estimate of the cost allows early exploration of the algorithmic possibilities, while maintaining a handle on worst-case per...

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Continuous Glucose Monitoring: Real-Time Algorithms for Calibration, Filtering, and Alarms

Cited by 169 publications

References 62 publications

Tight glycemic control in critical care – The leading role of insulin sensitivity and patient variability: A review and model-based analysis

Tight glycemic control in critical care – The leading role of insulin sensitivity and patient variability: A review and model-based analysis

Use of Subcutaneous Interstitial Fluid Glucose to Estimate Blood Glucose: Revisiting Delay and Sensor Offset

Real-Time Decision Policies With Predictable Performance

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