Variability of insulin sensitivity during the first 4 days of critical illness

Pretty, Christopher G.; Compte, Aaron Le; Chase, J. Geoffrey; Preiser, J.-C.; Penning, Sophie; Desaive, Thomas

doi:10.1186/cc10774

Cited by 5 publications

(4 citation statements)

References 1 publication

(1 reference statement)

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“…Other types of exercise may have different effects on insulin sensitivity, requiring additional observers. Other factors such as illness, stress, menstruation cycle, sleep apnea, or some medications can also lead to important physiological changes, including changes in insulin sensitivity [52,53]. Therefore, the system is assumed to be working under normal operation for the detection of aerobic exercise.…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems

Beneyto

Puig

Bequette

et al. 2021

Sensors

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The use of automated insulin delivery systems has become a reality for people with type 1 diabetes (T1D), with several hybrid systems already on the market. One of the particularities of this technology is that the patient is in the loop. People with T1D are the plant to control and also a plant operator, because they may have to provide information to the control loop. The most immediate information provided by patients that affects performance and safety are the announcement of meals and exercise. Therefore, to ensure safety and performance, the human factor impact needs to be addressed by designing fault monitoring strategies. In this paper, a monitoring system is developed to diagnose potential patient modes and faults. The monitoring system is based on the residual generation of a bank of observers. To that aim, a linear parameter varying (LPV) polytopic representation of the system is adopted and a bank of Kalman filters is designed using linear matrix inequalities (LMI). The system uncertainty is propagated using a zonotopic-set representation, which allows determining confidence bounds for each of the observer outputs and residuals. For the detection of modes, a hybrid automaton model is generated and diagnosis is performed by interpreting the events and transitions within the automaton. The developed system is tested in simulation, showing the potential benefits of using the proposed approach for artificial pancreas systems.

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Section: Discussionmentioning

confidence: 99%

A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems

Beneyto

Puig

Bequette

et al. 2021

Sensors

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“…However, patient insulin sensitivity (SI), which governs glycaemic response, undergoes complex and highly variable stress-induced changes [13,14]. Thus, safe, effective control of patient G is reliant on effective and accurate prediction of future patient SI [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…Failure to effectively account for variability in future SI can lead to an increased risk of hypoglycaemia, as in [18][19][20][21][22][23][24][25], where 17-29 % of patients had at least one blood glucose reading of less than 2.2 mmol/L. Hypoglycaemia is associated with increased mortality [26][27][28], and often occurs due to large, difficult to predict changes in patient SI over short periods [14,29,30]. These large changes in patient SI are particularly frequent in the first 48 hours post ICU admission [31], where there is a concordant, high rate of hypoglycaemic events and a strong association with mortality [27].…”

Section: Introductionmentioning

confidence: 99%

Multi-input stochastic prediction of insulin sensitivity for tight glycaemic control using insulin sensitivity and blood glucose data

Davidson

Pretty

Uyttendaele

et al. 2019

Computer Methods and Programs in Biomedicine

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Background: Glycaemic control in the intensive care unit is dependent on effective prediction of patient insulin sensitivity (SI). The stochastic targeted (STAR) controller uses a 2D stochastic model for prediction, with current SI as an input and future SI as an output. Methods: This paper develops an extension of the STAR 2D stochastic model into 3D by adding blood glucose (G) as an input. The performance of the 2D and 3D stochastic models is compared over a retrospective cohort of 65,269 data points across 1,525 patients. Results: Under five-fold cross-validation, the 3D model was found to better match the expected potion of data points within, above and below various credible intervals, suggesting it provided a better representation of the underlying probability field. The 3D model was also found to provide an 18.1% narrower 90% credible interval on average, and a narrower 90% credible interval in 96.4% of cases, suggesting it provided more accurate predictions of future SI. Additionally, the 3D stochastic model was found to avoid the undesirable tendency of the 2D model to overestimate SI for patients with high G, and underestimate SI for patients with low G. Conclusions: Overall, the 3D stochastic model is shown to provide clear potential benefits over the 2D model for minimal clinical cost or effort, though further exploration into whether these improvements in SI prediction translate into improved clinical outcomes is required.

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“…Furthermore, some glucose monitoring devices are able to communicate with a continuous insulin pump, thus, resulting in automated diabetes management. , Beyond technological advances, improvements are being made in insulin therapy regimes to control blood glucose levels. − Insights are being gained by examining the influence of different factors in glucose management, such as exercise and diet. In addition, the presence of critical illness or disease (such as cardiac disease), and the impact this has on glucose management is being investigated. − …”

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confidence: 99%

Towards Point-of-Care Insulin Detection

2018

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Good glucose management through an insulin dose regime based on the metabolism of glucose helps millions of people worldwide manage their diabetes. Since Banting and Best extracted insulin, glucose management has improved due to the introduction of insulin analogues that act from 30 minutes to 28 days, improved insulin dose regimes, and portable glucose meters, with a current focus on alternative sampling sites that are less invasive. However, a piece of the puzzle is still missingthe ability to measure insulin directly in a Point-of-Care device. The ability to measure both glucose and insulin concurrently will enable better glucose control by providing an improved estimate for insulin sensitivity, minimizing variability in control, and maximizing safety from hypoglycaemia. However, direct detection of free insulin has provided a challenge due to the size of the molecule, the low concentration of insulin in blood, and the selectivity against interferants in blood. This review summarizes current insulin detection methods from immunoassays to analytical chemistry, and sensors. We also discuss the challenges and potential of each of the methods towards Point-of-Care insulin detection.

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Variability of insulin sensitivity during the first 4 days of critical illness

Cited by 5 publications

References 1 publication

A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems

A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems

Multi-input stochastic prediction of insulin sensitivity for tight glycaemic control using insulin sensitivity and blood glucose data

Towards Point-of-Care Insulin Detection

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