Generalisability of a Virtual Trials Method for Glycaemic Control in Intensive Care

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.

[1]  R. Hovorka,et al.  Comparison of Three Protocols for Tight Glycemic Control in Cardiac Surgery Patients , 2009, Diabetes Care.

[2]  Timothy W. Evans,et al.  Glucose Control and Mortality in Critically Ill Patients , 2003 .

[3]  Roman Hovorka,et al.  Multicentric, randomized, controlled trial to evaluate blood glucose control by the model predictive control algorithm versus routine glucose management protocols in intensive care unit patients. , 2006, Diabetes care.

[4]  Thomas Desaive,et al.  What Makes Tight Glycemic Control ( TGC ) Tight ? The impact of variability and nutrition in 2 clinical studies , 2011 .

[5]  Miet Schetz,et al.  Outcome benefit of intensive insulin therapy in the critically ill: Insulin dose versus glycemic control* , 2003, Critical care medicine.

[6]  O. Tanner Intensive versus Conventional Glucose Control in Critically Ill Patients , 2009 .

[7]  C. Cobelli,et al.  The university of Virginia/Padova type 1 diabetes simulator matches the glucose traces of a clinical trial. , 2014 .

[8]  Christopher E. Hann,et al.  A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients , 2011, Comput. Methods Programs Biomed..

[9]  Thomas Desaive,et al.  Pilot proof of concept clinical trials of Stochastic Targeted (STAR) glycemic control , 2011, Annals of intensive care.

[10]  J. Geoffrey Chase,et al.  What Makes Tight Glycemic Control Tight? The Impact of Variability and Nutrition in Two Clinical Studies , 2010, Journal of diabetes science and technology.

[11]  Balázs Benyó,et al.  Safety, efficacy and clinical generalization of the STAR protocol: a retrospective analysis , 2016, Annals of Intensive Care.

[12]  Rinaldo Bellomo,et al.  Variability of Blood Glucose Concentration and Short-term Mortality in Critically Ill Patients , 2006, Anesthesiology.

[13]  Scott K Aberegg,et al.  Intensive insulin therapy in the medical ICU. , 2006, The New England journal of medicine.

[14]  Thomas Desaive,et al.  Variability of insulin sensitivity during the first 4 days of critical illness : implications for tight glycemic control , 2012 .

[15]  Thomas Desaive,et al.  Validation of a model-based virtual trials method for tight glycemic control in intensive care , 2010, Biomedical engineering online.

[16]  B. De Moor,et al.  LOGIC-Insulin Algorithm–Guided Versus Nurse-Directed Blood Glucose Control During Critical Illness , 2013, Diabetes Care.

[17]  J. G. Chase,et al.  In silico assessment of a computerized model-based glycaemic control approach in a Belgian medical intensive care unit , 2014 .

[18]  Johan Groeneveld,et al.  A prospective randomised multi-centre controlled trial on tight glucose control by intensive insulin therapy in adult intensive care units: the Glucontrol study , 2009, Intensive Care Medicine.

[19]  G. V. Berghe,et al.  Intensive insulin therapy in critically ill patients. , 2001, The New England journal of medicine.

[20]  H. Chandalia,et al.  STRESS HYPERGLYCAEMIA , 1984, The Lancet.

[21]  Christopher E. Hann,et al.  Model-based glycaemic control in critical care - A review of the state of the possible , 2006, Biomed. Signal Process. Control..

[22]  J. Geoffrey Chase,et al.  Stochastic Model Predictive (STOMP) glycaemic control for the intensive care unit: Development and virtual trial validation , 2015, Biomed. Signal Process. Control..

[23]  Michael Bailey,et al.  Hypoglycemia and outcome in critically ill patients. , 2010, Mayo Clinic proceedings.

[24]  C. Mélot,et al.  Mild hypoglycemia is strongly associated with increased intensive care unit length of stay , 2011, Annals of intensive care.

[25]  James Stephen Krinsley,et al.  Effect of an intensive glucose management protocol on the mortality of critically ill adult patients. , 2004, Mayo Clinic proceedings.

[26]  Interstitial Insulin Kinetic Parameters for a 2-Compartment Insulin Model with Saturable Clearance , 2012 .

[27]  John A Myburgh,et al.  Hypoglycemia and risk of death in critically ill patients. , 2012, The New England journal of medicine.

[28]  Christopher E. Hann,et al.  Tight glycemic control in critical care - The leading role of insulin sensitivity and patient variability: A review and model-based analysis , 2011, Comput. Methods Programs Biomed..

[29]  J. Preiser,et al.  Time in blood glucose range 70 to 140 mg/dl >80% is strongly associated with increased survival in non-diabetic critically ill adults , 2015, Critical Care.

[30]  R. Bellomo,et al.  Glycemic control in the ICU. , 2011, Chest.

[31]  Christopher E. Hann,et al.  Stochastic modelling of insulin sensitivity variability in critical care , 2006, Biomed. Signal Process. Control..

[32]  F. Cerra,et al.  Applied nutrition in ICU patients. A consensus statement of the American College of Chest Physicians. , 1997, Chest.

[33]  J. Geoffrey Chase,et al.  STAR Development and Protocol Comparison , 2012, IEEE Transactions on Biomedical Engineering.

[34]  Thomas Desaive,et al.  Organ failure and tight glycemic control in the SPRINT study , 2010, Critical care.

[35]  Christopher E. Hann,et al.  Integral-based parameter identification for long-term dynamic verification of a glucose-insulin system model , 2005, Comput. Methods Programs Biomed..

[36]  J. Geoffrey Chase,et al.  Characterisation of the iterative integral parameter identification method , 2011, Medical & Biological Engineering & Computing.

[37]  Rolf Rossaint,et al.  Intensive insulin therapy and pentastarch resuscitation in severe sepsis. , 2008, The New England journal of medicine.

[38]  J. Chase,et al.  Glycemic Levels in Critically Ill Patients: Are Normoglycemia and Low Variability Associated with Improved Outcomes? , 2012, Journal of diabetes science and technology.

[39]  Christopher E. Hann,et al.  Stochastic modelling of insulin sensitivity and adaptive glycemic control for critical care , 2008, Comput. Methods Programs Biomed..

[40]  Roman Hovorka,et al.  In Silico Testing—Impact on the Progress of the Closed Loop Insulin Infusion for Critically Ill Patients Project , 2008, Journal of diabetes science and technology.

[41]  A. Malhotra,et al.  Stress-induced hyperglycemia. , 2001, Critical care clinics.

[42]  Thomas Lotz,et al.  A simple insulin-nutrition protocol for tight glycemic control in critical illness: development and protocol comparison. , 2006, Diabetes technology & therapeutics.

[43]  A. Forbes,et al.  ESPEN Guidelines on Parenteral Nutrition: intensive care. , 2006, Clinical nutrition.

[44]  J. Chase,et al.  Glucose control positively influences patient outcome: A retrospective study. , 2015, Journal of critical care.

[45]  P. Marik,et al.  Stress hyperglycemia: an essential survival response! , 2013, Critical care medicine.

[46]  James Stephen Krinsley,et al.  Association between hyperglycemia and increased hospital mortality in a heterogeneous population of critically ill patients. , 2003, Mayo Clinic proceedings.

[47]  Thomas Desaive,et al.  A graphical method for practical and informative identifiability analyses of physiological models: A case study of insulin kinetics and sensitivity , 2011, Biomedical engineering online.

[48]  G. Van den Berghe,et al.  Effect of intensive insulin therapy on insulin sensitivity in the critically ill. , 2007, The Journal of clinical endocrinology and metabolism.

[49]  J Geoffrey Chase,et al.  Targeted glycemic reduction in critical care using closed-loop control. , 2005, Diabetes technology & therapeutics.

[50]  C. Pretty Analysis, classification and management of insulin sensitivity variability in a glucose-insulin system model for critical illness , 2012 .

[51]  J. Dickson,et al.  Humans are Horribly Variable , 2014 .

[52]  Rinaldo Bellomo,et al.  The impact of early hypoglycemia and blood glucose variability on outcome in critical illness , 2009, Critical care.

[53]  Liu Xinbing,et al.  Intensive insulin therapy for the critically ill patients with stress hyperglycemia , 2008 .

[54]  Christopher E. Hann,et al.  A glucose-insulin pharmacodynamic surface modeling validation and comparison of metabolic system models , 2009, Biomed. Signal Process. Control..