Dynamic properties of glucose complexity during the course of critical illness: a pilot study

Methods to control the blood glucose (BG) levels of patients in intensive care units (ICU) improve the outcomes. The development of continuous BG levels monitoring devices has also permitted to optimize these processes. Recently it was shown that a complexity loss of the BG signal is linked to poor clinical outcomes. Thus, it becomes essential to decipher this relation to design efficient BG level control methods. In previous studies the BG signal complexity was calculated as a single index for the whole ICU stay. Although, these approaches did not grasp the potential variability of the BG signal complexity. Therefore, we setup this pilot study using a continuous monitoring of central venous BG levels in ten critically ill patients (EIRUS platform, Maquet Critical CARE AB, Solna, Sweden). Data were processed and the complexity was assessed by the detrended fluctuation analysis and multiscale entropy (MSE) methods. Finally, recordings were split into 24 h overlapping intervals and a MSE analysis was applied to each of them. The MSE analysis on time intervals revealed an entropy variation and allowed periodic BG signal complexity assessments. To highlight differences of MSE between each time interval we calculated the MSE complexity index defined as the area under the curve. This new approach could pave the way to future studies exploring new strategies aimed at restoring blood glucose complexity during the ICU stay.

[1]  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.

[2]  Jeffrey M. Hausdorff,et al.  Fractal dynamics in physiology: Alterations with disease and aging , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[3]  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.

[4]  David C Klonoff,et al.  Consensus Statement on Inpatient Use of Continuous Glucose Monitoring , 2017, Journal of diabetes science and technology.

[5]  Claudio Cobelli,et al.  Interstitial Fluid Glucose Is Not Just a Shifted-in-Time but a Distorted Mirror of Blood Glucose: Insight from an In Silico Study , 2016, Diabetes technology & therapeutics.

[6]  S. Svacina,et al.  Glucose Control in the ICU , 2014, Journal of diabetes science and technology.

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

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

[9]  Jun Zheng,et al.  Complexity Change in Cardiovascular Disease , 2017, International journal of biological sciences.

[10]  P. Kalfon,et al.  Continuous glucose monitoring in the ICU: clinical considerations and consensus , 2017, Critical Care.

[11]  Gilles Clermont,et al.  Using what you get: dynamic physiologic signatures of critical illness. , 2015, Critical care clinics.

[12]  R. Hovorka,et al.  Glucose Control in the ICU , 2016, Journal of diabetes science and technology.

[13]  K. Kohnert,et al.  Associations of blood glucose dynamics with antihyperglycemic treatment and glycemic variability in type 1 and type 2 diabetes , 2017, Journal of Endocrinological Investigation.

[14]  Jeffrey M. Hausdorff,et al.  Multiscale entropy analysis of human gait dynamics. , 2003, Physica A.

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

[16]  Roman Hovorka,et al.  Continuous glucose control in the ICU: report of a 2013 round table meeting , 2014, Critical Care.

[17]  H. Stanley,et al.  Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. , 1995, Chaos.

[18]  J. Orme,et al.  Coefficient of glucose variation is independently associated with mortality in critically ill patients receiving intravenous insulin , 2014, Critical Care.

[19]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[20]  I. Mackenzie,et al.  The metrics of glycaemic control in critical care , 2011, Intensive Care Medicine.

[21]  J. Preiser,et al.  International recommendations for glucose control in adult non diabetic critically ill patients , 2010, Critical care.

[22]  A. Kitabchi,et al.  Hyperglycemia: an independent marker of in-hospital mortality in patients with undiagnosed diabetes. , 2002, The Journal of clinical endocrinology and metabolism.

[23]  H. Stanley,et al.  Effect of trends on detrended fluctuation analysis. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  A. Öwall,et al.  Evaluation of a continuous blood glucose monitoring system using a central venous catheter with an integrated microdialysis function. , 2013, Diabetes technology & therapeutics.

[25]  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.

[26]  R. Mazze,et al.  Evaluating the accuracy, reliability, and clinical applicability of continuous glucose monitoring (CGM): Is CGM ready for real time? , 2009, Diabetes technology & therapeutics.

[27]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[28]  Z. Struzik,et al.  Long-range negative correlation of glucose dynamics in humans and its breakdown in diabetes mellitus. , 2006, American journal of physiology. Regulatory, integrative and comparative physiology.

[29]  Jun Zheng,et al.  Entropy change of biological dynamics in COPD , 2017, International journal of chronic obstructive pulmonary disease.

[30]  R. Habib,et al.  Hyperglycemia, hypoglycemia, and glycemic complexity are associated with worse outcomes after surgery. , 2014, Journal of critical care.

[31]  D. Gough,et al.  Blood glucose dynamics. , 2008, Diabetes technology & therapeutics.

[32]  Teresa Henriques,et al.  Dynamical glucometry: use of multiscale entropy analysis in diabetes. , 2014, Chaos.

[33]  W. Tamborlane,et al.  A tale of two compartments: interstitial versus blood glucose monitoring. , 2009, Diabetes technology & therapeutics.

[34]  G. Van den Berghe,et al.  Tight blood glucose control with insulin in the ICU: facts and controversies. , 2007, Chest.

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

[36]  A. Goldberger Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside , 1996, The Lancet.

[37]  G. Van den Berghe,et al.  Critical Care Management of Stress-Induced Hyperglycemia , 2018, Current Diabetes Reports.

[38]  B. Corvilain,et al.  Glucose Complexity Estimates Insulin Resistance in Either Nondiabetic Individuals or in Type 1 Diabetes. , 2016, The Journal of clinical endocrinology and metabolism.

[39]  Chung-Kang Peng,et al.  Multiscale Entropy Analysis of Center-of-Pressure Dynamics in Human Postural Control: Methodological Considerations , 2015, Entropy.

[40]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

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

[42]  J. Wernerman,et al.  Continuous glucose monitoring by intravenous microdialysis , 2010, Acta anaesthesiologica Scandinavica.

[43]  Jin‐Tae Kim,et al.  Continuous glucose monitoring system in the operating room and intensive care unit: any difference according to measurement sites? , 2017, Journal of Clinical Monitoring and Computing.

[44]  Madalena Costa,et al.  Multiscale entropy analysis of biological signals. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[45]  F. Möller,et al.  Evaluation of a continuous blood glucose monitoring system using central venous microdialysis , 2011, Journal of diabetes science and technology.

[46]  Samuel M. Brown,et al.  Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients? , 2014, Critical Care.

[47]  Pin-Fan Chen,et al.  Decreased complexity of glucose dynamics in diabetes: evidence from multiscale entropy analysis of continuous glucose monitoring system data. , 2014, American journal of physiology. Regulatory, integrative and comparative physiology.

[48]  A. Abu-Hanna,et al.  Glycemic variability is complex - is glucose complexity variable? , 2012, Critical Care.

[49]  J Nilsson,et al.  Principles of digital sampling of a physiologic signal. , 1993, Electroencephalography and clinical neurophysiology.

[50]  Espen A. F. Ihlen,et al.  Introduction to Multifractal Detrended Fluctuation Analysis in Matlab , 2012, Front. Physio..

[51]  Jan Liska,et al.  Introducing intravascular microdialysis for continuous lactate monitoring in patients undergoing cardiac surgery: a prospective observational study , 2014, Critical Care.

[52]  N. Pørksen,et al.  The in vivo regulation of pulsatile insulin secretion , 2002, Diabetologia.

[53]  Nathaniel H. Hunt,et al.  The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets , 2012, Annals of Biomedical Engineering.

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

[55]  Xiaoli Ping,et al.  Decreased complexity of glucose dynamics preceding the onset of diabetes in mice and rats , 2017, PloS one.

[56]  A. Seely,et al.  Continuous multiorgan variability analysis to track severity of organ failure in critically ill patients. , 2013, Journal of critical care.

[57]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[58]  N. K. Skjaervold,et al.  Some oscillatory phenomena of blood glucose regulation: An exploratory pilot study in pigs , 2018, PloS one.

[59]  Z R Struzik,et al.  Long-range Correlated Glucose Fluctuations in Diabetes , 2007, Methods of Information in Medicine.

[60]  S. Shen-Orr,et al.  Network motifs: simple building blocks of complex networks. , 2002, Science.