Modeling of effect of glucose sensor errors on insulin dosage and glucose bolus computed by LOGIC-Insulin.

BACKGROUND Effective and safe glycemic control in critically ill patients requires accurate glucose sensors and adequate insulin dosage calculators. The LOGIC-Insulin calculator for glycemic control has recently been validated in the LOGIC-1 randomized controlled trial. In this study, we aimed to determine the allowable error for intermittent and continuous glucose sensors, on the basis of the LOGIC-Insulin calculator. METHODS A gaussian simulation model with a varying bias (0%-20%) and CV (-20% to +20%) simulated blood glucose values from the LOGIC-1 study (n = 149 patients) in 10 Monte Carlo steps. A clinical error grid system was developed to compare the simulated LOGIC-Insulin-directed intervention with the nominal intervention (0% bias, 0% CV). The severity of error measuring the clinical effect of the simulated LOGIC-Insulin intervention was graded as type B, C, and D errors. Type D errors were classified as acutely life-threatening (0% probability preferred). RESULTS The probability of all types of errors was lower for continuous sensors compared with intermittent sensors. The maximum total error (TE), defined as the first TE introducing a type B/C/D error, was similar for both sensor types. To avoid type D errors, TEs <15.7% for intermittent sensors and <17.8% for continuous sensors were required. Mean absolute relative difference thresholds for type C errors were 7.1% for intermittent and 11.0% for continuous sensors. CONCLUSIONS Continuous sensors had a lower probability for clinical errors than intermittent sensors at the same accuracy level. These simulations demonstrated the suitability of the LOGIC-Insulin control system for use with continuous, as well as intermittent, sensors.

[1]  D. Bruns,et al.  Effects of measurement frequency on analytical quality required for glucose measurements in intensive care units: assessments by simulation models. , 2014, Clinical chemistry.

[2]  D. Mesotten Continuous glucose sensors for glycaemic control in the ICU: have we arrived? , 2013, Critical Care.

[3]  G. Van den Berghe,et al.  Performance of cassette-based blood gas analyzers to monitor blood glucose and lactate levels in a surgical intensive care setting , 2013, Clinical chemistry and laboratory medicine.

[4]  Roman Hovorka,et al.  Clinical review: Consensus recommendations on measurement of blood glucose and reporting glycemic control in critically ill adults , 2013, Critical Care.

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

[6]  D. Mesotten,et al.  Blood Glucose Measurements in Critically Ill Patients , 2012, Journal of diabetes science and technology.

[7]  R. Hovorka,et al.  Evaluating glycemic control algorithms by computer simulations. , 2011, Diabetes technology & therapeutics.

[8]  B. Kavanagh,et al.  Clinical practice. Glycemic control in the ICU. , 2010, The New England journal of medicine.

[9]  George G Klee,et al.  Glucose meter performance criteria for tight glycemic control estimated by simulation modeling. , 2010, Clinical chemistry.

[10]  Geoffrey M. Shaw,et al.  Hypoglycemia Detection in Critical Care Using Continuous Glucose Monitors: An in Silico Proof of Concept Analysis , 2010, Journal of diabetes science and technology.

[11]  Miet Schetz,et al.  Intensive Insulin Therapy in Critically Ill Patients: NICE-SUGAR or Leuven Blood Glucose Target? , 2009 .

[12]  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.

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

[14]  Bart De Moor,et al.  Towards closed-loop glycaemic control. , 2009 .

[15]  Greet Van den Berghe,et al.  Intensive insulin therapy for patients in paediatric intensive care: a prospective, randomised controlled study , 2009, The Lancet.

[16]  R. Bellomo,et al.  Early blood glucose control and mortality in critically ill patients in Australia* , 2009, Critical care medicine.

[17]  David E Bruns,et al.  Monte Carlo simulation in establishing analytical quality requirements for clinical laboratory tests meeting clinical needs. , 2009, Methods in enzymology.

[18]  David B Sacks,et al.  Tight glucose control in the intensive care unit: are glucose meters up to the task? , 2009, Clinical chemistry.

[19]  Frizo A. L. Janssens,et al.  Statistical Approach of Assessing the Reliability of Glucose Sensors: The GLYCENSIT Procedure , 2008, Journal of diabetes science and technology.

[20]  R. Hovorka,et al.  A simulation model of glucose regulation in the critically ill , 2008, Physiological measurement.

[21]  P. Raskin,et al.  Hyperglycemia and acute coronary syndrome: a scientific statement from the American Heart Association Diabetes Committee of the Council on Nutrition, Physical Activity, and Metabolism. , 2008, Circulation.

[22]  B. De Moor,et al.  Glycemic penalty index for adequately assessing and comparing different blood glucose control algorithms , 2008, Critical care.

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

[24]  C. Bekes Intensive Insulin Protocol Improves Glucose Control and Is Associated with a Reduction in Intensive Care Unit Mortality , 2008 .

[25]  Niels Haverbeke,et al.  Glycemia Prediction in Critically Ill Patients Using an Adaptive Modeling Approach , 2007, Journal of diabetes science and technology.

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

[27]  Brian Hutton,et al.  Reliability of point-of-care testing for glucose measurement in critically ill adults* , 2005, Critical care medicine.

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

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

[30]  J C Boyd,et al.  Quality specifications for glucose meters: assessment by simulation modeling of errors in insulin dose. , 2001, Clinical chemistry.

[31]  D. Altman,et al.  Measuring agreement in method comparison studies , 1999, Statistical methods in medical research.

[32]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[33]  C Cobelli,et al.  Quantitative Estimation of Beta Cell Sensitivity to Glucose in the Intact Organism: A Minimal Model of Insulin Kinetics in the Dog , 1980, Diabetes.

[34]  A. Beckett,et al.  AKUFO AND IBARAPA. , 1965, Lancet.