Kent Stewart

Zusammenfassung

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 model-based 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 cross-simulation results were found to have overall similarity and consistency, though results may differ in the first 24-48 h 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.

Zusammenfassung

In this paper the insulin sensitivity profile and the diffusion term as stepwise functions were determined for the grey box variant of the ICING (Intensive Control Insulin-Nutrition-Glucose) model used for virtual trial methodology. The suggested technique can separate system noise, which was earlier lumped into the insulin sensitivity profile itself. In this way one can get smaller residual for the model fitting as well as more precise value for the insulin sensitivity. This fact can improve the prediction process estimating insulin sensitivity value for the future time span and therefore it can ensure better clinical treatment. Maximum likelihood method has been employed to compute the values of the insulin sensitivity profile and the diffusion term for every half hour intervals. The global optimization problem was solved using genetic algorithm, simulated annealing method and Nelder-Mead procedure employing parallel computation. The application of the grey box model was able to reduce the errors sometimes with even more than 50%. According to the likelihood ratio test, the stochastic variant of the ICING model yielded significant improvement comparing to the white box one.

Zusammenfassung

Extending safe, effective glycemic control to the general wards requires a simple approach using subcutaneous (SC) insulin. However, this approach can increase relative risk compared to intravenous insulin due to the increased variability of SC insulin appearance. This paper evaluates the accuracy of a SC plasma insulin model and optimizes its parameters using measured plasma insulin data from 6 less acute critical care patients treated with SC insulin. The SC plasma insulin model used captures the dynamics of regular SC insulin well. However, there appears to be a positive bias leading to an overall median IQR residual error of -28.3 -37 - 19 mU/L. The optimized model reduced the RMS residual error by 20-70% for each patient. The distinct inter- and intra-patient, and cohort variation seen in this data highlights the importance to of understanding how SC insulin appearance dynamics may be affected by the subject condition.