Intensive control insulin-nutrition-glucose model validated in critically ill patients

Jessica Lin, Normy Norfiza Abdul Razak, Christopher G. Pretty, Aaron Le Compte, Paul Docherty, Jacquelyn D. Parente, Geoffrey M. Shaw, Christopher E. Hann, J. Geoffrey Chase

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) Model is presented and validated using data from critically ill patients. Glucose utilisation and its endogenous production in particular, are more distinctly expressed. A more robust glucose absorption model through ingestion is also added. Finally, this model also includes explicit pathways of insulin kinetics, clearance and utilisation. Identification of critical constant population parameters is carried out parametrically, optimising one hour forward prediction errors, while avoiding model identifiability issues. The identified population values are pG = 0.006 min-1, EGPb = 1.16 mmol/min and nI = 0.003 min-1, all of which are within reported physiological ranges. Insulin sensitivity, SI, is identified hourly for each individual. All other model parameters are kept at well-known population values or functions of body weight or surface area. A sensitivity study confirms the validity of limiting time-varying parameters to SI only. The model achieves median fitting error <1% in data from 173 patients (N = 42,941 hrs in total) who received insulin while in the Intensive Care Unit (ICU) and stayed for more than 72 hrs. Most importantly, the median per-patient one-hour ahead prediction error is a very low 2.80% [IQR 1.18, 6.41%]. It is significant that the 75 th percentile prediction error is now within the lower bound of typical glucometer measurement errors of 7-12%. This result further confirms that the model is suitable for developing model-based insulin therapies, and capable of delivering tight blood glucose control, in a real-time model based control framework with a tight prediction error range.

Original languageEnglish
Title of host publicationUKACC International Conference on CONTROL 2010
Pages644-649
Number of pages6
Volume2010
Edition4
DOIs
Publication statusPublished - 01 Dec 2010
EventUKACC International Conference on CONTROL 2010 - Coventry, United Kingdom
Duration: 07 Sep 201010 Sep 2010

Other

OtherUKACC International Conference on CONTROL 2010
CountryUnited Kingdom
CityCoventry
Period07/09/1010/09/10

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Insulin
Nutrition
Glucose
Intensive care units
Measurement errors
Blood
Kinetics

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Lin, J., Abdul Razak, N. N., Pretty, C. G., Le Compte, A., Docherty, P., Parente, J. D., ... Chase, J. G. (2010). Intensive control insulin-nutrition-glucose model validated in critically ill patients. In UKACC International Conference on CONTROL 2010 (4 ed., Vol. 2010, pp. 644-649). [0357] https://doi.org/10.1049/ic.2010.0357
Lin, Jessica ; Abdul Razak, Normy Norfiza ; Pretty, Christopher G. ; Le Compte, Aaron ; Docherty, Paul ; Parente, Jacquelyn D. ; Shaw, Geoffrey M. ; Hann, Christopher E. ; Chase, J. Geoffrey. / Intensive control insulin-nutrition-glucose model validated in critically ill patients. UKACC International Conference on CONTROL 2010. Vol. 2010 4. ed. 2010. pp. 644-649
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Lin, J, Abdul Razak, NN, Pretty, CG, Le Compte, A, Docherty, P, Parente, JD, Shaw, GM, Hann, CE & Chase, JG 2010, Intensive control insulin-nutrition-glucose model validated in critically ill patients. in UKACC International Conference on CONTROL 2010. 4 edn, vol. 2010, 0357, pp. 644-649, UKACC International Conference on CONTROL 2010, Coventry, United Kingdom, 07/09/10. https://doi.org/10.1049/ic.2010.0357

Intensive control insulin-nutrition-glucose model validated in critically ill patients. / Lin, Jessica; Abdul Razak, Normy Norfiza; Pretty, Christopher G.; Le Compte, Aaron; Docherty, Paul; Parente, Jacquelyn D.; Shaw, Geoffrey M.; Hann, Christopher E.; Chase, J. Geoffrey.

UKACC International Conference on CONTROL 2010. Vol. 2010 4. ed. 2010. p. 644-649 0357.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) Model is presented and validated using data from critically ill patients. Glucose utilisation and its endogenous production in particular, are more distinctly expressed. A more robust glucose absorption model through ingestion is also added. Finally, this model also includes explicit pathways of insulin kinetics, clearance and utilisation. Identification of critical constant population parameters is carried out parametrically, optimising one hour forward prediction errors, while avoiding model identifiability issues. The identified population values are pG = 0.006 min-1, EGPb = 1.16 mmol/min and nI = 0.003 min-1, all of which are within reported physiological ranges. Insulin sensitivity, SI, is identified hourly for each individual. All other model parameters are kept at well-known population values or functions of body weight or surface area. A sensitivity study confirms the validity of limiting time-varying parameters to SI only. The model achieves median fitting error <1% in data from 173 patients (N = 42,941 hrs in total) who received insulin while in the Intensive Care Unit (ICU) and stayed for more than 72 hrs. Most importantly, the median per-patient one-hour ahead prediction error is a very low 2.80% [IQR 1.18, 6.41%]. It is significant that the 75 th percentile prediction error is now within the lower bound of typical glucometer measurement errors of 7-12%. This result further confirms that the model is suitable for developing model-based insulin therapies, and capable of delivering tight blood glucose control, in a real-time model based control framework with a tight prediction error range.

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Lin J, Abdul Razak NN, Pretty CG, Le Compte A, Docherty P, Parente JD et al. Intensive control insulin-nutrition-glucose model validated in critically ill patients. In UKACC International Conference on CONTROL 2010. 4 ed. Vol. 2010. 2010. p. 644-649. 0357 https://doi.org/10.1049/ic.2010.0357