A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) 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: Contribution to journalArticle

105 Citations (Scopus)

Abstract

Intensive insulin therapy (IIT) and tight glycaemic control (TGC), particularly in intensive care unit (ICU), are the subjects of increasing and controversial debate in recent years. Model-based TGC has shown potential in delivering safe and tight glycaemic management, all the while limiting hypoglycaemia. A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) model is presented and validated using data from critically ill patients. Two existing glucose-insulin models are reviewed and formed the basis for the ICING model. Model limitations are discussed with respect to relevant physiology, pharmacodynamics and TGC practicality. Model identifiability issues are carefully considered for clinical settings. This article also contains significant reference to relevant physiology and clinical literature, as well as some references to the modeling efforts in this field. Identification of critical constant population parameters was performed in two stages, thus addressing model identifiability issues. Model predictive performance is the primary factor for optimizing population parameter values. The use of population values are necessary due to the limited clinical data available at the bedside in the clinical control scenario. Insulin sensitivity, SI, the only dynamic, time-varying parameter, is identified hourly for each individual. All population parameters are justified physiologically and with respect to values reported in the clinical literature. A parameter sensitivity study confirms the validity of limiting time-varying parameters to SI only, as well as the choices for the population parameters. The ICING model achieves median fitting error of <1% over data from 173 patients (N=42,941h in total) who received insulin while in the ICU and stayed for ≥72h. Most importantly, the median per-patient 1-h ahead prediction error is a very low 2.80% [IQR 1.18, 6.41%]. It is significant that the 75th percentile prediction error is within the lower bound of typical glucometer measurement errors of 7-12%. These results confirm that the ICING model is suitable for developing model-based insulin therapies, and capable of delivering real-time model-based TGC with a very tight prediction error range. Finally, the detailed examination and discussion of issues surrounding model-based TGC and existing glucose-insulin models render this article a mini-review of the state of model-based TGC in critical care.

Original languageEnglish
Pages (from-to)192-205
Number of pages14
JournalComputer Methods and Programs in Biomedicine
Volume102
Issue number2
DOIs
Publication statusPublished - 01 May 2011

Fingerprint

Insulin
Nutrition
Critical Illness
Glucose
Population
Intensive Care Units
Intensive care units
Physiology
Critical Care
Hypoglycemia
Insulin Resistance
Pharmacodynamics
Measurement errors
Therapeutics

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Health Informatics

Cite this

Lin, Jessica ; Abdul Razak, Normy Norfiza ; Pretty, Christopher G. ; Le Compte, Aaron ; Docherty, Paul ; Parente, Jacquelyn D. ; Shaw, Geoffrey M. ; Hann, Christopher E. ; Geoffrey Chase, J. / A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients. In: Computer Methods and Programs in Biomedicine. 2011 ; Vol. 102, No. 2. pp. 192-205.
@article{836ca45ae18b4a3a996c23e6cb8a97cf,
title = "A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients",
abstract = "Intensive insulin therapy (IIT) and tight glycaemic control (TGC), particularly in intensive care unit (ICU), are the subjects of increasing and controversial debate in recent years. Model-based TGC has shown potential in delivering safe and tight glycaemic management, all the while limiting hypoglycaemia. A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) model is presented and validated using data from critically ill patients. Two existing glucose-insulin models are reviewed and formed the basis for the ICING model. Model limitations are discussed with respect to relevant physiology, pharmacodynamics and TGC practicality. Model identifiability issues are carefully considered for clinical settings. This article also contains significant reference to relevant physiology and clinical literature, as well as some references to the modeling efforts in this field. Identification of critical constant population parameters was performed in two stages, thus addressing model identifiability issues. Model predictive performance is the primary factor for optimizing population parameter values. The use of population values are necessary due to the limited clinical data available at the bedside in the clinical control scenario. Insulin sensitivity, SI, the only dynamic, time-varying parameter, is identified hourly for each individual. All population parameters are justified physiologically and with respect to values reported in the clinical literature. A parameter sensitivity study confirms the validity of limiting time-varying parameters to SI only, as well as the choices for the population parameters. The ICING model achieves median fitting error of <1{\%} over data from 173 patients (N=42,941h in total) who received insulin while in the ICU and stayed for ≥72h. Most importantly, the median per-patient 1-h ahead prediction error is a very low 2.80{\%} [IQR 1.18, 6.41{\%}]. It is significant that the 75th percentile prediction error is within the lower bound of typical glucometer measurement errors of 7-12{\%}. These results confirm that the ICING model is suitable for developing model-based insulin therapies, and capable of delivering real-time model-based TGC with a very tight prediction error range. Finally, the detailed examination and discussion of issues surrounding model-based TGC and existing glucose-insulin models render this article a mini-review of the state of model-based TGC in critical care.",
author = "Jessica Lin and {Abdul Razak}, {Normy Norfiza} and Pretty, {Christopher G.} and {Le Compte}, Aaron and Paul Docherty and Parente, {Jacquelyn D.} and Shaw, {Geoffrey M.} and Hann, {Christopher E.} and {Geoffrey Chase}, J.",
year = "2011",
month = "5",
day = "1",
doi = "10.1016/j.cmpb.2010.12.008",
language = "English",
volume = "102",
pages = "192--205",
journal = "Computer Methods and Programs in Biomedicine",
issn = "0169-2607",
publisher = "Elsevier Ireland Ltd",
number = "2",

}

A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) 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.; Geoffrey Chase, J.

In: Computer Methods and Programs in Biomedicine, Vol. 102, No. 2, 01.05.2011, p. 192-205.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients

AU - Lin, Jessica

AU - Abdul Razak, Normy Norfiza

AU - Pretty, Christopher G.

AU - Le Compte, Aaron

AU - Docherty, Paul

AU - Parente, Jacquelyn D.

AU - Shaw, Geoffrey M.

AU - Hann, Christopher E.

AU - Geoffrey Chase, J.

PY - 2011/5/1

Y1 - 2011/5/1

N2 - Intensive insulin therapy (IIT) and tight glycaemic control (TGC), particularly in intensive care unit (ICU), are the subjects of increasing and controversial debate in recent years. Model-based TGC has shown potential in delivering safe and tight glycaemic management, all the while limiting hypoglycaemia. A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) model is presented and validated using data from critically ill patients. Two existing glucose-insulin models are reviewed and formed the basis for the ICING model. Model limitations are discussed with respect to relevant physiology, pharmacodynamics and TGC practicality. Model identifiability issues are carefully considered for clinical settings. This article also contains significant reference to relevant physiology and clinical literature, as well as some references to the modeling efforts in this field. Identification of critical constant population parameters was performed in two stages, thus addressing model identifiability issues. Model predictive performance is the primary factor for optimizing population parameter values. The use of population values are necessary due to the limited clinical data available at the bedside in the clinical control scenario. Insulin sensitivity, SI, the only dynamic, time-varying parameter, is identified hourly for each individual. All population parameters are justified physiologically and with respect to values reported in the clinical literature. A parameter sensitivity study confirms the validity of limiting time-varying parameters to SI only, as well as the choices for the population parameters. The ICING model achieves median fitting error of <1% over data from 173 patients (N=42,941h in total) who received insulin while in the ICU and stayed for ≥72h. Most importantly, the median per-patient 1-h ahead prediction error is a very low 2.80% [IQR 1.18, 6.41%]. It is significant that the 75th percentile prediction error is within the lower bound of typical glucometer measurement errors of 7-12%. These results confirm that the ICING model is suitable for developing model-based insulin therapies, and capable of delivering real-time model-based TGC with a very tight prediction error range. Finally, the detailed examination and discussion of issues surrounding model-based TGC and existing glucose-insulin models render this article a mini-review of the state of model-based TGC in critical care.

AB - Intensive insulin therapy (IIT) and tight glycaemic control (TGC), particularly in intensive care unit (ICU), are the subjects of increasing and controversial debate in recent years. Model-based TGC has shown potential in delivering safe and tight glycaemic management, all the while limiting hypoglycaemia. A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) model is presented and validated using data from critically ill patients. Two existing glucose-insulin models are reviewed and formed the basis for the ICING model. Model limitations are discussed with respect to relevant physiology, pharmacodynamics and TGC practicality. Model identifiability issues are carefully considered for clinical settings. This article also contains significant reference to relevant physiology and clinical literature, as well as some references to the modeling efforts in this field. Identification of critical constant population parameters was performed in two stages, thus addressing model identifiability issues. Model predictive performance is the primary factor for optimizing population parameter values. The use of population values are necessary due to the limited clinical data available at the bedside in the clinical control scenario. Insulin sensitivity, SI, the only dynamic, time-varying parameter, is identified hourly for each individual. All population parameters are justified physiologically and with respect to values reported in the clinical literature. A parameter sensitivity study confirms the validity of limiting time-varying parameters to SI only, as well as the choices for the population parameters. The ICING model achieves median fitting error of <1% over data from 173 patients (N=42,941h in total) who received insulin while in the ICU and stayed for ≥72h. Most importantly, the median per-patient 1-h ahead prediction error is a very low 2.80% [IQR 1.18, 6.41%]. It is significant that the 75th percentile prediction error is within the lower bound of typical glucometer measurement errors of 7-12%. These results confirm that the ICING model is suitable for developing model-based insulin therapies, and capable of delivering real-time model-based TGC with a very tight prediction error range. Finally, the detailed examination and discussion of issues surrounding model-based TGC and existing glucose-insulin models render this article a mini-review of the state of model-based TGC in critical care.

UR - http://www.scopus.com/inward/record.url?scp=79955154658&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79955154658&partnerID=8YFLogxK

U2 - 10.1016/j.cmpb.2010.12.008

DO - 10.1016/j.cmpb.2010.12.008

M3 - Article

VL - 102

SP - 192

EP - 205

JO - Computer Methods and Programs in Biomedicine

JF - Computer Methods and Programs in Biomedicine

SN - 0169-2607

IS - 2

ER -