Variability of insulin sensitivity during the first 4 days of critical illness: implications for tight glycemic control

Pretty, Christopher G.; Compte, Aaron J. Le; Chase, J. Geoffrey; Shaw, Geoffrey M.; Preiser, Jean‐Charles; Penning, Sophie; Desaive, Thomas

doi:10.1186/2110-5820-2-17

Cited by 79 publications

(89 citation statements)

References 59 publications

(50 reference statements)

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“…However, several studies have failed to repeat these positive results [21][22][23], often with significantly increased hypoglycaemia [11]. The main issue is that ICU patients are highly variable in their response to insulin, particularly in the first 48 hours of stay [24][25][26], which can make managing glycaemia difficult as patient condition evolves [27].…”

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confidence: 99%

Generalisability of a Virtual Trials Method for Glycaemic Control in Intensive Care

Dickson

Stewart

Pretty

et al. 2018

IEEE Trans. Biomed. Eng.

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Abstract-Background: Elevated blood glucose (BG) concentrations (Hyperglycaemia) are a common complication in critically ill patients. Insulin therapy is commonly used to treat hyperglycaemia, but metabolic variability often results in poor BG control and low BG (hypoglycaemia). Objective: This paper presents a modelbased virtual trial method for glycaemic control protocol design, and evaluates its generalisability across different populations. Methods: Model-based insulin sensitivity (SI) was used to create virtual patients from clinical data from three different ICUs in New Zealand, Hungary, and Belgium. Glycaemic results from simulation of virtual patients under their original protocol (self-simulation) and protocols from other units (cross-simulation) were compared. Results: Differences were found between the three cohorts in median SI and inter-patient variability in SI. However, hour-to-hour intra-patient variability in SI was found to be consistent between cohorts. Self and crosssimulation results were found to have overall similarity and consistency, though results may differ in the first 24-48 hours due to different cohort starting BG and underlying SI. Conclusions and significance: Virtual patients and the virtual trial method were found to be generalisable across different ICUs. This virtual trial method is useful for in silico protocol design and testing, given an understanding of the underlying assumptions and limitations of this method.

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confidence: 99%

Generalisability of a Virtual Trials Method for Glycaemic Control in Intensive Care

Dickson

Stewart

Pretty

et al. 2018

IEEE Trans. Biomed. Eng.

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“…Identified values of SI were typically within 12% of the true value when influenced by both sources of error. In contrast, changes in SI greater than 20% were seen with glucocorticoid treatment (Pretty et al, 2010) and improving patient condition over the first 18 hours of ICU stay (Pretty et al, 2012).…”

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confidence: 90%

Impact of sensor and measurement timing errors on model-based insulin sensitivity

Pretty

Signal

Fisk

et al. 2014

Computer Methods and Programs in Biomedicine

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Abstract:A model-based insulin sensitivity parameter (SI) is often used in glucose-insulin system models to define the glycaemic response to insulin. As a parameter identified from clinical data, insulin sensitivity can be affected by blood glucose (BG) sensor error and measurement timing error, which can subsequently impact analyses or glycaemic variability during control. This study assesses the impact of both measurement timing and BG sensor errors on identified values of SI and its hour-to-hour variability within the ICING-type glucose-insulin system models.Retrospective clinical data was used from 270 patients of the Christchurch Hospital intensive care unit (ICU). An error model was created for the Arkray Super-Glucocard II glucometer used in Christchurch from manufacturer supplied data. Timing error was estimated from recent, computerised clinical data. Monte Carlo analysis was used to quantify the impact of these random errors by identifying SI profiles from data incorporating errors and comparing them to the 'true' SI profile (without additional errors) at each patient hour. To consolidate comparisons over the n = 100 Monte Carlo simulations, the width of the interquartile range (IQR) was used for percentage difference from the true SI level and for percentage hour-to-hour variability.The results of the study show that timing errors in isolation have little clinically significant impact on identified SI level or variability. The clinical impact of changes to SI level induced by combined sensor and timing errors is likely to be limited during glycaemic control. Identified values of SI were typically within 12% of the true value when influenced by both sources of error. In contrast, for variability, 95 % of patient hours had an IQR of 34.9%, indicating that for half the simulations the hour-to-hour variability of SI was within ±17.5%.The results of this study indicate that the impact of sensor or timing errors on SI level is unlikely to be clinically significant. The effects are probably overshadowed by physiological factors arising from the critical condition of the patients or other under-modelled or un-modelled dynamics. In contrast, the impact of errors on hour-to-hour SI variability is more pronounced and may impact the way the SI parameter is utilised for control and analysis

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“…The time variable parameter curves were based on previously published articles, meaning the variation of these parameters were kept within previously measured physiologic bounds. [23][24][25] Although these variable curves were not derived from actual ICU patients, the clinical situation that could account for each of these curves is described in the supplement. Through combining the resulting 5 insulin sensitivity parameters, 4 insulin halflife parameters and 4 volumes of distribution parameters a total of 80 unique "patients" were created.…”

Section: Methodsmentioning

confidence: 99%

In Silico Testing of an Artificial-Intelligence-Based Artificial Pancreas Designed for Use in the Intensive Care Unit Setting

DeJournett

2016

J Diabetes Sci Technol

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The importance of glucose control in the intensive care unit (ICU) setting was first demonstrated by Furnary 1 and later confirmed by Van den Berghe 2 in a large, prospective, randomized control study. However, almost all of the clinical studies that have been published since then have struggled to achieve superior glucose control while avoiding the glucose metrics that actually increase mortality rates-hypoglycemia, hyperglycemia, variability, and low time in range.3-6 A Japanese study that utilized a closed loop glucose control system that minimized hypoglycemia while at the same time achieving a high percentage of time in range showed the true promise of effective glucose control.7 To achieve tight glucose control while at the same time minimizing glucose metrics that increase mortality rates, a closed loop system will need to be created. This system should mimic the workings of the native system which have been previously reviewed. 8 To date, most attempts at creating a closed loop glucose control system for use in the ICU setting have utilized the standard engineering control techniques of proportional integral derivative (PID) or model predictive control (MPC). [9][10][11][12] PID controllers typically control to a set point and are challenged by the fact they rely solely on insulin for control, thus they do not take advantage of the counter-regulatory effects of intravenous (IV) glucose. MPC controllers rely on a complex mathematical model of the glucose-insulin system. MPC model parameters are refitted every 1 to 4 hours, in an iterative fashion, based on the difference between the predicted and measured glucose level. Control recommendations are made from the resulting best fit model. MPC controllers can be designed to control to a set point or to a desired range and Background: Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates which should in turn lead to decreased health care expenditures. Current ICU-based glucose controllers are mathematically derived, and tend to be based on proportional integral derivative (PID) or model predictive control (MPC). Artificial intelligence (AI)-based closed loop glucose controllers may have the ability to achieve control that improves on the results achieved by either PID or MPC controllers. Method:We conducted an in silico analysis of an AI-based glucose controller designed for use in the ICU setting. This controller was tested using a mathematical model of the ICU patient's glucose-insulin system. A total of 126 000 unique 5-day simulations were carried out, resulting in 107 million glucose values for analysis. Results:For the 7 control ranges tested, with a sensor error of ±10%, the following average results were achieved: (1) time in control range, 94.2%, (2) time in range 70-140 mg/dl, 97.8%, (3) time in hyperglycemic range (>140 mg/dl), 2.1%, and (4) time in hypoglycemic range (<70 mg/dl), 0.09%. In addition, the average coefficient of variation (CV) was 11.1%. Conclusions:This in sili...

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Variability of insulin sensitivity during the first 4 days of critical illness: implications for tight glycemic control

Cited by 79 publications

References 59 publications

Generalisability of a Virtual Trials Method for Glycaemic Control in Intensive Care

Generalisability of a Virtual Trials Method for Glycaemic Control in Intensive Care

Impact of sensor and measurement timing errors on model-based insulin sensitivity

In Silico Testing of an Artificial-Intelligence-Based Artificial Pancreas Designed for Use in the Intensive Care Unit Setting

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