Untangling glycaemia and mortality in critical care

BackgroundHyperglycaemia is associated with adverse outcomes in the intensive care unit, and initial studies suggested outcome benefits of glycaemic control (GC). However, subsequent studies often failed to replicate these results, and they were often unable to achieve consistent, safe control, raising questions about the benefit or harm of GC as well as the nature of the association of glycaemia with mortality and clinical outcomes. In this study, we evaluated if non-survivors are harder to control than survivors and determined if glycaemic outcome is a function of patient condition and eventual outcome or of the glycaemic control provided.MethodsClinically validated, model-based, hour-to-hour insulin sensitivity (SI) and its hour-to-hour variability (%ΔSI) were identified over the first 72 h of therapy in 145 patients (119 survivors, 26 non-survivors). In hypothesis testing, we compared distributions of SI and %ΔSI in 6-hourly blocks for survivors and non-survivors. In equivalence testing, we assessed if differences in these distributions, based on blood glucose measurement error, were clinically significant.ResultsSI level was never equivalent between survivors and non-survivors (95% CI of percentage difference in medians outside ±12%). Non-survivors had higher SI, ranging from 9% to 47% higher overall in 6-h blocks, and this difference became statistically significant as glycaemic control progressed. %ΔSI was equivalent between survivors and non-survivors for all 6-hourly blocks (95% CI of difference in medians within ±12%) and decreased in general over time as glycaemic control progressed.ConclusionsWhereas non-survivors had higher SI levels, variability was equivalent to that of survivors over the first 72 h. These results indicate survivors and non-survivors are equally controllable, given an effective glycaemic control protocol, suggesting that glycaemia level and variability, and thus the association between glycaemia and outcome, are essentially determined by the control provided rather than by underlying patient or metabolic condition.

[1]  Chester Ni,et al.  Acute hyperglycemia impairs IL‐6 expression in humans , 2016, Immunity, inflammation and disease.

[2]  B.W. Bequette,et al.  A Dual-Rate Kalman Filter for Continuous Glucose Monitoring , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

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

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

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

[6]  J. Geoffrey Chase,et al.  Generalisability of a Virtual Trials Method for Glycaemic Control in Intensive Care , 2018, IEEE Transactions on Biomedical Engineering.

[7]  Alain Cariou,et al.  Universal changes in biomarkers of coagulation and inflammation occur in patients with severe sepsis, regardless of causative micro-organism [ISRCTN74215569] , 2004, Critical care.

[8]  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.

[9]  J. Chase,et al.  Complexity of Continuous Glucose Monitoring Data in Critically Ill Patients: Continuous Glucose Monitoring Devices, Sensor Locations, and Detrended Fluctuation Analysis Methods , 2013, Journal of diabetes science and technology.

[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]  J. Geoffrey Chase,et al.  Impact of glucocorticoids on insulin resistance in the critically ill , 2011, Comput. Methods Programs Biomed..

[12]  Dominic S. Lee,et al.  Implementation and evaluation of the SPRINT protocol for tight glycaemic control in critically ill patients: a clinical practice change , 2008, Critical care.

[13]  D. Fry,et al.  Acute hyperglycemia and the innate immune system: Clinical, cellular, and molecular aspects , 2005, Critical care medicine.

[14]  Steen Andreassen,et al.  Decision support for optimized blood glucose control and nutrition in a neurotrauma intensive care unit: preliminary results of clinical advice and prediction accuracy of the Glucosafe system , 2012, Journal of Clinical Monitoring and Computing.

[15]  Stephen Daniel,et al.  Intensive insulin therapy and mortality in critically ill patients , 2008, Critical care.

[16]  S. Goodman Toward Evidence-Based Medical Statistics. 1: The P Value Fallacy , 1999, Annals of Internal Medicine.

[17]  Ilse Vanhorebeek,et al.  Glycemic and nonglycemic effects of insulin: how do they contribute to a better outcome of critical illness? , 2005, Current opinion in critical care.

[18]  Stephane Heritier,et al.  Intensive versus conventional glucose control in critically ill patients. , 2009, The New England journal of medicine.

[19]  Guido Freckmann,et al.  System accuracy evaluation of 27 blood glucose monitoring systems according to DIN EN ISO 15197. , 2010, Diabetes technology & therapeutics.

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

[21]  E. Adrario,et al.  Glycaemic variability, infections and mortality in a medical-surgical intensive care unit. , 2014, Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine.

[22]  J. Geoffrey Chase,et al.  DISTq: An Iterative Analysis of Glucose Data for Low-Cost, Real-Time and Accurate Estimation of Insulin Sensitivity , 2009, The open medical informatics journal.

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

[24]  K. Polderman,et al.  Intensive insulin therapy: of harm and health, of hypes and hypoglycemia. , 2006, Critical care medicine.

[25]  P. Spronk,et al.  Clinical review: Strict or loose glycemic control in critically ill patients - implementing best available evidence from randomized controlled trials , 2010, Critical care.

[26]  Ziad A Memish,et al.  Intensive versus conventional insulin therapy: A randomized controlled trial in medical and surgical critically ill patients* , 2008, Critical care medicine.

[27]  Liam M. Fisk,et al.  Concurrent Continuous Glucose Monitoring in Critically Ill Patients: Interim Results and Observations , 2013, Journal of diabetes science and technology.

[28]  Srinivasan Rajaraman,et al.  Predictive Monitoring for Improved Management of Glucose Levels , 2007, Journal of diabetes science and technology.

[29]  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..

[30]  J. Geoffrey Chase,et al.  Blood Glucose Controller for Neonatal Intensive Care: Virtual Trials Development and First Clinical Trials , 2009, Journal of diabetes science and technology.

[31]  A. Aljada,et al.  Insulin infusion in acute illness. , 2005, The Journal of clinical investigation.

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

[33]  M. Jeschke,et al.  Impaired Immune Response in Elderly Burn Patients: New Insights Into the Immune-senescence Phenotype , 2016, Annals of surgery.

[34]  D. Bruns,et al.  The Impact of Measurement Frequency on the Domains of Glycemic Control in the Critically Ill-A Monte Carlo Simulation , 2015, Journal of diabetes science and technology.

[35]  Jean-Charles Preiser,et al.  Which factors influence glycemic control in the intensive care unit? , 2010, Current opinion in clinical nutrition and metabolic care.

[36]  J. Chase,et al.  Observation of incretin effects during enteral feed transitions of critically ill patients , 2012 .

[37]  M Schetz,et al.  Intensive insulin therapy in critically ill patients. , 2001, The New England journal of medicine.

[38]  J. Vincent,et al.  Evolution of insulin sensitivity and its variability in out-of-hospital cardiac arrest (OHCA) patients treated with hypothermia , 2014, Critical Care.

[39]  J Geoffrey Chase,et al.  A Benchmark Data Set for Model-Based Glycemic Control in Critical Care , 2008, Journal of diabetes science and technology.

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

[41]  J. Geoffrey Chase,et al.  Development and optimisation of stochastic targeted (STAR) glycaemic control for pre-term infants in neonatal intensive care , 2013, Biomed. Signal Process. Control..

[42]  Brian P. Kavanagh,et al.  Glycemic Control in the ICU , 2010 .

[43]  DJ Lunn,et al.  Fitting dynamic models with forcing functions: Application to continuous glucose monitoring in insulin therapy , 2011, Statistics in medicine.

[44]  Stanley Lemeshow,et al.  Glucose variability and mortality in patients with sepsis* , 2008, Critical care medicine.

[45]  Christopher E. Hann,et al.  Independent cohort cross-validation of the real-time DISTq estimation of insulin sensitivity , 2011, Comput. Methods Programs Biomed..

[46]  J. Donado,et al.  Strict glycaemic control in patients hospitalised in a mixed medical and surgical intensive care unit: a randomised clinical trial , 2008, Critical care.

[47]  C. Pretty,et al.  Does the achievement of an intermediate glycemic target reduce organ failure and mortality? A post hoc analysis of the Glucontrol trial. , 2014, Journal of critical care.

[48]  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.

[49]  Y. Arabi,et al.  What is the optimal blood glucose target in critically ill patients? A nested cohort study , 2011, Annals of thoracic medicine.

[50]  J Geoffrey Chase,et al.  Using Continuous Glucose Monitoring Data and Detrended Fluctuation Analysis to Determine Patient Condition , 2015, Journal of diabetes science and technology.

[51]  C. Mélot,et al.  Mild hypoglycemia is independently associated with increased mortality in the critically ill , 2011, Critical care.

[52]  Brenda G Fahy,et al.  Glucose control in the intensive care unit , 2009, Critical care medicine.

[53]  Rajvir Singh,et al.  Association of time in blood glucose range with outcomes following cardiac surgery , 2015, BMC Anesthesiology.

[54]  G. Van den Berghe,et al.  Blood glucose control in the ICU: don’t throw out the baby with the bathwater! , 2016, Intensive Care Medicine.

[55]  Simona O. Butler,et al.  Relationship Between Hyperglycemia and Infection in Critically Ill Patients , 2005, Pharmacotherapy.

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

[57]  D. Dent,et al.  Intensive insulin protocol improves glucose control and is associated with a reduction in intensive care unit mortality. , 2007, Journal of the American College of Surgeons.

[58]  C. Madl,et al.  Glycemic variability and glucose complexity in critically ill patients: a retrospective analysis of continuous glucose monitoring data , 2012, Critical Care.

[59]  Boris Kovatchev,et al.  Analysis, Modeling, and Simulation of the Accuracy of Continuous Glucose Sensors , 2008, Journal of diabetes science and technology.

[60]  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..

[61]  J. Geoffrey Chase,et al.  Impact of sensor and measurement timing errors on model-based insulin sensitivity , 2014, Comput. Methods Programs Biomed..

[62]  P. Marik,et al.  Original ResearchCritical Care MedicineToward Understanding Tight Glycemic Control in the ICU: A Systematic Review and Metaanalysis , 2010 .

[63]  W. Kenneth Ward,et al.  Modeling the Glucose Sensor Error , 2014, IEEE Transactions on Biomedical Engineering.

[64]  Michael Quintel,et al.  The impact of the severity of sepsis on the risk of hypoglycaemia and glycaemic variability , 2008, Critical care.

[65]  Paul D Docherty,et al.  Design and Clinical Pilot Testing of the Model-Based Dynamic Insulin Sensitivity and Secretion Test (DISST) , 2010, Journal of diabetes science and technology.

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

[67]  G. Van den Berghe,et al.  Metabolic, endocrine, and immune effects of stress hyperglycemia in a rabbit model of prolonged critical illness. , 2003, Endocrinology.

[68]  J. Preiser,et al.  Glycemic control: please agree to disagree , 2016, Intensive Care Medicine.

[69]  B. Bistrian,et al.  Intensive insulin therapy in critically ill patients. , 2002, The New England journal of medicine.

[70]  J. Chase,et al.  The dynamic insulin sensitivity and secretion test--a novel measure of insulin sensitivity. , 2011, Metabolism: clinical and experimental.

[71]  R. Manfro,et al.  Interleukin-6 Is a Better Predictor of Mortality as Compared to C-Reactive Protein, Homocysteine, Pentosidine and Advanced Oxidation Protein Products in Hemodialysis Patients , 2008, Blood Purification.

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

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

[74]  G. Van den Berghe,et al.  Intensive insulin therapy in the medical ICU. , 2006, The New England journal of medicine.

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

[76]  Deborah J. Cook,et al.  Intensive insulin therapy and mortality among critically ill patients: a meta-analysis including NICE-SUGAR study data , 2009, Canadian Medical Association Journal.

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

[78]  J. Geoffrey Chase,et al.  Stochastic Targeted (STAR) Glycemic Control: Design, Safety, and Performance , 2012, Journal of diabetes science and technology.

[79]  Liam M. Fisk,et al.  Reducing the impact of insulin sensitivity variability on glycaemic outcomes using separate stochastic models within the STAR glycaemic protocol , 2014, Biomedical engineering online.

[80]  P. Marik Tight glycemic control in acutely ill patients: low evidence of benefit, high evidence of harm! , 2016, Intensive Care Medicine.

[81]  Matthew Signal,et al.  Continuous Glucose Monitors and the Burden of Tight Glycemic Control in Critical Care: Can They Cure the Time Cost? , 2010, Journal of diabetes science and technology.

[82]  Dominic S. Lee,et al.  Transient and steady-state euglycemic clamp validation of a model for glycemic control and insulin sensitivity testing. , 2006, Diabetes technology & therapeutics.

[83]  Ilse Vanhorebeek,et al.  The Role of Insulin Therapy in Critically I11 Patients , 2005, Treatments in endocrinology.

[84]  Teresa Honrubia,et al.  Differences in complexity of glycemic profile in survivors and nonsurvivors in an intensive care unit: A pilot study* , 2010, Critical care medicine.

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

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

[87]  P. Marik Precision Glycemic Control in the ICU. , 2016, Critical care medicine.

[88]  Patrick R. Norris,et al.  Increasing blood glucose variability heralds hypoglycemia in the critically ill. , 2011, The Journal of surgical research.

[89]  G. Shaw,et al.  Impact of Haemodialysis on Insulin Kinetics of Acute Kidney Injury Patients in Critical Care , 2015, Journal of medical and biological engineering.

[90]  J. Krinsley,et al.  Glycemic variability: A strong independent predictor of mortality in critically ill patients* , 2008, Critical care medicine.

[91]  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.

[92]  B. Pedersen,et al.  Effect of hyperglycemia and hyperinsulinemia on the response of IL-6, TNF-alpha, and FFAs to low-dose endotoxemia in humans. , 2004, American journal of physiology. Endocrinology and metabolism.

[93]  José Manuel Fernández-Real,et al.  CD14 monocyte receptor, involved in the inflammatory cascade, and insulin sensitivity. , 2003, The Journal of clinical endocrinology and metabolism.

[94]  A. Randolph,et al.  Innate Immune Function and Mortality in Critically Ill Children With Influenza: A Multicenter Study* , 2013, Critical care medicine.

[95]  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.

[96]  V. Nerurkar,et al.  Impaired Virus Clearance, Compromised Immune Response and Increased Mortality in Type 2 Diabetic Mice Infected with West Nile Virus , 2012, PloS one.

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

[98]  Christopher E. Hann,et al.  Monte Carlo analysis of a new model-based method for insulin sensitivity testing , 2008, Comput. Methods Programs Biomed..

[99]  Margarita R. Cabrera-Cancio Infections and the Compromised Immune Status in the Chronically Critically Ill Patient: Prevention Strategies , 2012, Respiratory Care.

[100]  M. Weigand,et al.  Design of a prospective clinical study on the agreement between the Continuous GlucoseMonitor, a novel device for CONTinuous ASSessment of blood GLUcose levels, and the RAPIDLab® 1265 blood gas analyser: The CONTASSGLU study , 2012, BMC Anesthesiology.

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

[102]  K. Reinhart,et al.  Intensive insulin therapy in the ICU: benefit versus harm? , 2007, Intensive Care Medicine.

[103]  Renda Soylemez Wiener,et al.  Ill Adults: A Meta-analysis Benefits and Risks of Tight Glucose Control in Critically , 2010 .