Accurate glycemic control (AGC) is difficult due to excessive hypoglycemia risk. Stochastic TARgeted (STAR) glycemic control forecasts changes in insulin sensitivity to calculate a range of glycemic outcomes for an insulin intervention, creating a risk framework to improve safety and performance. An improved, simplified STAR framework was developed to reduce light hypoglycemia and clinical effort, while improving nutrition rates and performance. Blood glucose (BG) levels are targeted to 80-145 mg/dL, using insulin and nutrition control for 1-3 h interventions. Insulin changes are limited to +3U/h and nutrition to ±30% of goal rate (minimum 30%). All targets and rate change limits are clinically specified and generalizable. Clinically validated virtual trials were run on using clinical data from 371 patients (39841 h) from the Specialized Relative Insulin and Nutrition Tables (SPRINT) cohort. Cohort and per-patient results are compared to clinical SPRINT data, and virtual trials of three published protocols. Performance was measured as time within glycemic bands, and safety by patients with severe (BG < 40 mg/dL) and mild (%BG < 72 mg/dL) hypoglycemia. Pilot trial results from the first ten patients (1486 h) are included to support the in-silico findings. In both virtual and clinical trials, mild hypoglycemia was below 2% versus 4% for SPRINT. Severe hypoglycemia was reduced from 14 (SPRINT) to 6 (STAR), and 0 in the pilot trial. AGC was tighter than both SPRINT clinical data and in-silico comparison protocols, with 91% BG within the specified target (80-145 mg/dL) in virtual trials and 89.4% in pilot trials. Clinical effort (measurements) was reduced from 16.2/day to 11.8/day (13.5/day in pilot trials). This STAR framework provides safe AGC with significant reductions in hypoglycemia and clinical effort due to stochastic forecasting of patient variation-a unique risk-based approach. Initial pilot trials validate the in-silico design methods and resulting protocol, all of which can be generalized to suit any given clinical environment.
Critically ill patients often experience stress-induced hyperglycaemia, which results in increased morbidity and mortality. Glycaemic control (GC) can be implemented in the intensive care unit (ICU) to safely manage hyperglycaemia. Two protocols SPRINT and STAR, have been implemented in the Christchurch ICU, and have been successful in treating hyperglycaemia while decreasing the risk of hypoglycaemia. This paper presents a new GC protocol that implements the probabilistic, stochastic forecasting methods of STAR, while formalizing the control methodology using model predictive control (MPC) theory to improve the ability to tune the dynamic response of the controller. This Stochastic Model Predictive (STOMP) controller predicts the response to a given insulin/nutrition intervention, and attributes weighted penalty values to several key performance metrics. The controller thus chooses an intervention at each hour that minimizes the sum of these penalties over a prediction window of 6 hours, which is twice as long as the 3-hour window used in STAR. Clinically validated virtual trials were used to evaluate the relative performance of STOMP. Results showed STOMP was able to obtain results very
BackgroundThe metabolism of critically ill patients evolves dynamically over time. Post critical insult, levels of counter-regulatory hormones are significantly elevated, but decrease rapidly over the first 12–48 hours in the intensive care unit (ICU). These hormones have a direct physiological impact on insulin sensitivity (SI). Understanding the variability of SI is important for safely managing glycaemic levels and understanding the evolution of patient condition. The objective of this study is to assess the evolution of SI over the first two days of ICU stay, and using this data, propose a separate stochastic model to reduce the impact of SI variability during glycaemic control using the STAR glycaemic control protocol.MethodsThe value of SI was identified hourly for each patient using a validated physiological model. Variability of SI was then calculated as the hour-to-hour percentage change in SI. SI was examined using 6 hour blocks of SI to display trends while mitigating the effects of noise. To reduce the impact of SI variability on achieving glycaemic control a new stochastic model for the most variable period, 0–18 hours, was generated. Virtual simulations were conducted using an existing glycaemic control protocol (STAR) to investigate the clinical impact of using this separate stochastic model during this period of increased metabolic variability.ResultsFor the first 18 hours, over 80% of all SI values were less than 0.5 × 10-3 L/mU.min, compared to 65% for >18 hours. Using the new stochastic model for the first 18 hours of ICU stay reduced the number of hypoglycaemic measurements during virtual trials. For time spent below 4.4, 4.0, and 3.0 mmol/L absolute reductions of 1.1%, 0.8% and 0.1% were achieved, respectively. No severe hypoglycaemic events (BG < 2.2 mmol/L) occurred for either case.ConclusionsSI levels increase significantly, while variability decreases during the first 18 hours of a patients stay in ICU. Virtual trials, using a separate stochastic model for this period, demonstrated a reduction in variability and hypoglycaemia during the first 18 hours without adversely affecting the overall level of control. Thus, use of multiple models can reduce the impact of SI variability during model-based glycaemic control.
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
The quick dynamic insulin sensitivity test (DISTq) can yield an insulin sensitivity result immediately after a 30-min clinical protocol. The test uses intravenous boluses of 10 g glucose and 1 U insulin at t = 1 and 11 min, respectively, and measures glucose levels in samples taken at t = 0, 10, 20, and 30 min. The low clinical cost of the protocol is enabled via robust model formulation and a series of population-derived relationships that estimate insulin pharmacokinetics as a function of insulin sensitivity (SI). Fifty individuals underwent the gold standard euglycaemic clamp (EIC) and DISTq within an eight-day period. SI values from the EIC and two DISTq variants (four-sample DISTq and two-sample DISTq30) were compared with correlation, Bland-Altman and receiver operator curve analyses. DISTq and DISTq30 correlated well with the EIC [R = 0.76 and 0.75, and receiver operator curve c-index = 0.84 and 0.85, respectively]. The median differences between EIC and DISTq/DISTq30 SI values were 13% and 22%, respectively. The DISTq estimation method predicted individual insulin responses without specific insulin assays with relative accuracy and thus high equivalence to EIC SI values was achieved. DISTq produced very inexpensive, relatively accurate immediate results, and can thus enable a number of applications that are impossible with established SI tests.
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