FEASIBILITY OF AN INTENSIVE CONTROL INSULIN-NUTRITION GLUCOSE MODEL ‘ICING' WITH MALAYSIAN CRITICALLY-ILL PATIENT
DOI:
https://doi.org/10.22159/ijpps.2016v8s2.15218Keywords:
Glucose-insulin model, TGC, Malaysian critically-ill, model-based controlAbstract
A clinically verified patient-specific glucose-insulin metabolic model known as ICING is used to account for time-varying insulin sensitivity. ICING was developed and validated from critically-ill patients with various medical conditions in the intensive care unit in Christchurch Hospital, New Zealand. Hence, it is interesting and vital to analyse the compatibility of the model once fitted to Malaysian critically-ill data. Results were assessed in terms of percentage of model-fit error, both by cohort and per-patient analysis. The ICING model accomplished median fitting error of<1% over data from 63 patients. Most importantly, the median per-patients is at a low fitting error of 0.34% and per cohort is 0.35%. These results provide a promising avenue for near future simulations of developing tight glycaemic control protocol in the Malaysian intensive care unit.
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References
JG Chase, AJ Le Compte, F Suhaimi, GM Shaw, A Lynn, J Lin, et al. Tight glycemic control in critical care-the leading role of insulin sensitivity and patient variability: a review and model-based analysis.†Computer Methods Programs Biomed 2011;102:156–71.
JS Krinsley. Glycemic variability: a strong independent predictor of mortality in critically ill patients.†Crit Care Med 2008;36:3008–13.
JG Chase, AJ Le Compte, JC Preiser, GM Shaw, S Penning, T Desaive. Physiological modeling, tight glycemic control, and the ICU clinician: what are models and how can they affect practice? Ann Intensive Care 2011;1:11.
L Ward, J Steel, A Le Compte, A Evans, CS Tan, S Penning, et al. Interface design and human factors considerations for model-based tight glycemic control in critical care. J Diabetes Sci Technol 2012;6:125–34.
AJ Le Compte, DS Lee, JG Chase, J Lin, A Lynn, GM Shaw. Blood glucose prediction using stochastic modeling in neonatal intensive care. IEEE Trans Biomed Eng 2010;57:509–18.
J Lin, NN Razak, CG Pretty, A Le Compte, P Docherty, JD Parente, et al. A physiological intensive control insulin-nutrition-glucose (ICING) model validated in critically ill patients. Comput Methods Programs Biomed 2011;102:192–205.
CE Hann, JG Chase, J Lin, T Lotz, CV Doran, GM Shaw. Integral-based parameter identification for long-term dynamic verification of a glucose-insulin system model. Comput Methods Programs Biomed 2005;77:259–70.