Abstract
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.
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Abbreviations
- BG:
-
Blood glucose
- CGM:
-
Continuous glucose monitoring
- COPD:
-
Chronic obstructive pulmonary disease
- CVC:
-
Central venous catheter
- DFA:
-
Detrended fluctuation analysis
- ICU:
-
Intensive care unit
- MSE:
-
Multiscale entropy
- CI:
-
Complexity index
- SampEn:
-
Sample entropy
References
Vanhorebeek I, Langouche L, Van den Berghe G. Tight blood glucose control with insulin in the ICU: facts and controversies. Chest. 2007;132:268–78. https://doi.org/10.1378/chest.06-3121.
Umpierrez GE, Isaacs SD, Bazargan N, et al. Hyperglycemia: an independent marker of in-hospital mortality in patients with undiagnosed diabetes. J Clin Endocrinol Metab. 2002;87:978–82. https://doi.org/10.1210/jcem.87.3.8341.
van den Berghe G, Wouters P, Weekers F, et al. Intensive insulin therapy in critically ill patients. N Engl J Med. 2001;345:1359–67. https://doi.org/10.1056/NEJMoa011300.
Brunkhorst FM, Engel C, Bloos F, et al. Intensive insulin therapy and pentastarch resuscitation in severe sepsis. N Engl J Med. 2008;358:125–39. https://doi.org/10.1056/NEJMoa070716.
Preiser J-C, Devos P, Ruiz-Santana S, 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. Intensive Care Med. 2009;35:1738–48. https://doi.org/10.1007/s00134-009-1585-2.
NICE-SUGAR Study Investigators, Finfer S, Liu B, et al. Hypoglycemia and risk of death in critically ill patients. N Engl J Med. 2012;367:1108–18. https://doi.org/10.1056/NEJMoa1204942.
Ichai C, Preiser J-C. Société Française d’Anesthésie-Réanimation. International recommendations for glucose control in adult non diabetic critically ill patients. Crit Care. 2010;14:R166. https://doi.org/10.1186/cc9258.
Finfer S, Wernerman J, Preiser J-C, et al. Clinical review: consensus recommendations on measurement of blood glucose and reporting glycemic control in critically ill adults. Crit Care. 2013;17:229. https://doi.org/10.1186/cc12537.
Krinsley JS, Chase JG, Gunst J, et al. Continuous glucose monitoring in the ICU: clinical considerations and consensus. Crit Care. 2017;21:197. https://doi.org/10.1186/s13054-017-1784-0.
Vanhorebeek I, Gunst J, Van den Berghe G. Critical care management of stress-induced hyperglycemia. Curr DiabRep. 2018;18:17. https://doi.org/10.1007/s11892-018-0988-2.
Wallia A, Umpierrez GE, Rushakoff RJ, et al. Consensus statement on inpatient use of continuous glucose monitoring. J Diabetes Sci Technol. 2017;11:1036–44. https://doi.org/10.1177/1932296817706151.
Preiser J-C, Chase JG, Hovorka R, et al. Glucose control in the ICU. J Diabetes Sci Technol. 2016;10:1372–81. https://doi.org/10.1177/1932296816648713.
Wernerman J, Desaive T, Finfer S, et al. Continuous glucose control in the ICU: report of a 2013 round table meeting. Crit Care. 2014;18:226. https://doi.org/10.1186/cc13921.
Egi M, Bellomo R, Stachowski E, et al. Variability of blood glucose concentration and short-term mortality in critically ill patients. Anesthesiology. 2006;105:244–52.
Lanspa MJ, Dickerson J, Morris AH, et al. Coefficient of glucose variation is independently associated with mortality in critically ill patients receiving intravenous insulin. Crit Care. 2014;18:R86. https://doi.org/10.1186/cc13851.
Mackenzie IMJ, Whitehouse T, Nightingale PG. The metrics of glycaemic control in critical care. Intensive Care Med. 2011;37:435–43. https://doi.org/10.1007/s00134-010-2103-2.
Lundelin K, Vigil L, Bua S, et al. Differences in complexity of glycemic profile in survivors and nonsurvivors in an intensive care unit: a pilot study. Crit Care Med. 2010;38:849–54. https://doi.org/10.1097/CCM.0b013e3181ce49cf.
Brunner R, Adelsmayr G, Herkner H, et al. Glycemic variability and glucose complexity in critically ill patients: a retrospective analysis of continuous glucose monitoring data. Crit Care. 2012;16:R175. https://doi.org/10.1186/cc11657.
Engoren M, Schwann TA, Habib RH. Hyperglycemia, hypoglycemia, and glycemic complexity are associated with worse outcomes after surgery. J Crit Care. 2014. https://doi.org/10.1016/j.jcrc.2014.03.014.
Milo R, Shen-Orr S, Itzkovitz S, et al. Network motifs: simple building blocks of complex networks. Science. 2002;298:824–7. https://doi.org/10.1126/science.298.5594.824.
Goldberger AL. Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside. Lancet. 1996;347:1312–4.
Goldberger AL, Amaral LAN, Hausdorff JM, et al. Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci USA. 2002;99:2466–72. https://doi.org/10.1073/pnas.012579499.
Crenier L, Lytrivi M, Van Dalem A, et al. Glucose complexity estimates insulin resistance in either nondiabetic individuals or in type 1 diabetes. J Clin Endocrinol Metab. 2016;101:1490–7. https://doi.org/10.1210/jc.2015-4035.
van Hooijdonk RTM, Abu-Hanna A, Schultz MJ. Glycemic variability is complex—is glucose complexity variable? Crit Care. 2012;16:178. https://doi.org/10.1186/cc11834.
Schierenbeck F, Nijsten MWN, Franco-Cereceda A, Liska J. Introducing intravascular microdialysis for continuous lactate monitoring in patients undergoing cardiac surgery: a prospective observational study. Crit Care. 2014;18:R56. https://doi.org/10.1186/cc13808.
Costa M, Goldberger AL, Peng CK. Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett. 2002;89:068102.
Schierenbeck F, Öwall A, Franco-Cereceda A, Liska J. Evaluation of a continuous blood glucose monitoring system using a central venous catheter with an integrated microdialysis function. Diabetes Technol Ther. 2013;15:26–31. https://doi.org/10.1089/dia.2012.0169.
Costa MD, Henriques T, Munshi MN, et al. Dynamical glucometry: use of multiscale entropy analysis in diabetes. Chaos. 2014;24:033139. https://doi.org/10.1063/1.4894537.
Peng CK, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos. 1995;5:82–7. https://doi.org/10.1063/1.166141.
Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol. 2000;278:H2039–49.
R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2017.
Ihlen EAF. Introduction to multifractal detrended fluctuation analysis in matlab. Front Physiol. 2012;3:141. https://doi.org/10.3389/fphys.2012.00141.
Goldberger AL, Amaral LA, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101:E215–20.
Costa M, Goldberger AL, Peng CK. Multiscale entropy analysis of biological signals. Phys Rev E Stat Nonlinear Soft Matter Phys. 2005;71:021906.
Ogata H, Tokuyama K, Nagasaka S et al (2007) Long-range correlated glucose fluctuations in diabetes. Methods Inf Med 46:222–226
Ogata H, Tokuyama K, Nagasaka S et al (2006) Long-range negative correlation of glucose dynamics in humans and its breakdown in diabetes mellitus. Am J Physiol Regul Integr Comp Physiol 291:R1638–R1643. https://doi.org/10.1152/ajpregu.00241.2006
Hu K, Ivanov PC, Chen Z, et al. Effect of trends on detrended fluctuation analysis. Phys Rev E. 2001;64:011114. https://doi.org/10.1103/PhysRevE.64.011114.
Yentes JM, Hunt N, Schmid KK, et al. The appropriate use of approximate entropy and sample entropy with short data sets. Ann Biomed Eng. 2013;41:349–65. https://doi.org/10.1007/s10439-012-0668-3.
Cobelli C, Schiavon M, Man CD, 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. Diabetes Technol Ther. 2016. https://doi.org/10.1089/dia.2016.0112.
Signal M, Thomas F, Shaw GM, Chase JG. Complexity of continuous glucose monitoring data in critically ill patients: continuous glucose monitoring devices, sensor locations, and detrended fluctuation analysis methods. J Diabetes Sci Technol. 2013;7:1492–506.
Cengiz E, Tamborlane WV. A tale of two compartments: interstitial versus blood glucose monitoring. Diabetes Technol Ther. 2009;11(Suppl 1):11–6. https://doi.org/10.1089/dia.2009.0002.
Song I-K, Lee J-H, Kang J-E, et al. Continuous glucose monitoring system in the operating room and intensive care unit: any difference according to measurement sites? J Clin Monit Comput. 2017;31:187–94. https://doi.org/10.1007/s10877-015-9804-6.
Schierenbeck F, Franco-Cereceda A, Liska J. Evaluation of a continuous blood glucose monitoring system using central venous microdialysis. J Diabetes Sci Technol. 2012;6:1365–71.
Rooyackers O, Blixt C, Mattsson P, Wernerman J. Continuous glucose monitoring by intravenous microdialysis. Acta Anaesthesiol Scand. 2010;54:841–7. https://doi.org/10.1111/j.1399-6576.2010.02264.x.
Gow B, Peng C-K, Wayne P, Ahn A. Multiscale entropy analysis of center-of-pressure dynamics in human postural control: methodological considerations. Entropy. 2015;17:7926–47. https://doi.org/10.3390/e17127849.
Pørksen N. The in vivo regulation of pulsatile insulin secretion. Diabetologia. 2002;45:3–20. https://doi.org/10.1007/s001250200001.
Rahaghi FN, Gough DA. Blood glucose dynamics. Diabetes Technol Therapeutics. 2008;10:81–94. https://doi.org/10.1089/dia.2007.0256.
Nilsson J, Panizza M, Hallett M. Principles of digital sampling of a physiologic signal. Electroencephalogr Clin Neurophysiol. 1993;89:349–58.
Chen J-L, Chen P-F, Wang H-M. Decreased complexity of glucose dynamics in diabetes: evidence from multiscale entropy analysis of continuous glucose monitoring system data. Am J Physiol Regul Integr Comp Physiol. 2014;307:R179–83. https://doi.org/10.1152/ajpregu.00108.2014.
Costa M, Peng CK, Goldberger AL, Hausdorff JM. Multiscale entropy analysis of human gait dynamics. Physica A. 2003;330:53–60. https://doi.org/10.1016/j.physa.2003.08.022.
Green GC, Bradley B, Bravi A, Seely AJE. Continuous multiorgan variability analysis to track severity of organ failure in critically ill patients. J Crit Care. 2013;28:879.e1–11. https://doi.org/10.1016/j.jcrc.2013.04.001.
Zhang XD, Pechter D, Yang L, et al. Decreased complexity of glucose dynamics preceding the onset of diabetes in mice and rats. PLoS ONE. 2017;12:e0182810. https://doi.org/10.1371/journal.pone.0182810.
Jin Y, Chen C, Cao Z, et al. Entropy change of biological dynamics in COPD. Int J Chron Obstruct Pulmon Dis. 2017;12:2997–3005. https://doi.org/10.2147/COPD.S140636.
Chen C, Jin Y, Lo IL, et al. Complexity change in cardiovascular disease. Int J Biol Sci. 2017;13:1320–8. https://doi.org/10.7150/ijbs.19462.
Kohnert K-D, Heinke P, Vogt L, et al. Associations of blood glucose dynamics with antihyperglycemic treatment and glycemic variability in type 1 and type 2 diabetes. J Endocrinol Investig. 2017;40:1201–7. https://doi.org/10.1007/s40618-017-0682-2.
Seely AJE, Bravi A, Herry C, et al. Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients? Crit Care. 2014;18:R65. https://doi.org/10.1186/cc13822.
Holder AL, Clermont G. Using what you get: dynamic physiologic signatures of critical illness. Crit Care Clin. 2015;31:133–64. https://doi.org/10.1016/j.ccc.2014.08.007.
Skjaervold NK, Knai K, Elvemo N. Some oscillatory phenomena of blood glucose regulation: An exploratory pilot study in pigs. PLoS ONE. 2018;13:e0194826. https://doi.org/10.1371/journal.pone.0194826.
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Godat, E., Preiser, JC., Aude, JC. et al. Dynamic properties of glucose complexity during the course of critical illness: a pilot study. J Clin Monit Comput 34, 361–370 (2020). https://doi.org/10.1007/s10877-019-00299-8
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DOI: https://doi.org/10.1007/s10877-019-00299-8