Glycemic control in the intensive care unit: A control systems perspective

Control systems; Critical care; Dynamic systems modelling; Glycemic control; ICU; Identifiability; Intensive care; System identification; Automation; Identification (control systems); Productivity; Religious buildings; Systems modelling; Intensive care units

Abstract :

[en] Computers and automation have revolutionised quality and productivity in many industries, but not in medicine. Healthcare costs are thus growing beyond the ability of society to pay for them, multiplied by the impact of increasingly aging populations, and specifically in the demand placed on intensive care unit (ICU) services. Glycemic control is a core ICU therapy with the potential to reduce both mortality and cost, which makes it an area where greater personalisation and automation could play a leading role in improving productivity and care. This review presents the background to the problem and the main issues arising in this area of care from a control systems technology perspective. It then presents a vision of a more automated future with specific goals in the areas of dynamic systems modeling, system identification and control. These areas are then given a state of the art review, mixing both medicine and control systems perspectives. It is concluded by specific recommendations for the field, where control systems expertise can be leveraged to best advantage. © 2019 Elsevier Ltd

Disciplines :

Anesthesia & intensive care

Author, co-author :

Chase, J. G.; Mechanical Engineering, Centre of Bio-Engineering, University of Canterbury, Christchurch, New Zealand

Benyo, B.; Department of Control Engineering and Information Technology, Budapest University of Technology and EconomicsBudapest, Hungary

Desaive, Thomas ; Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Thermodynamique des phénomènes irréversibles

Language :

English

Title :

Glycemic control in the intensive care unit: A control systems perspective

Publication date :

2019

Journal title :

Annual Reviews in Control

ISSN :

1367-5788

Publisher :

Elsevier Ltd

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 24 May 2019

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53 (2 by ULiège)

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203 (1 by ULiège)

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Bibliography

Ali, N.A., O'Brien, J.M. Jr., Dungan, K., Phillips, G., Marsh, C.B., Lemeshow, S., et al. Glucose variability and mortality in patients with sepsis. Critical Care Medicine 36:8 (2008), 2316–2321.
Alsweiler, J.M., Harding, J.E., Bloomfield, F.H., Tight glycemic control with insulin in hyperglycemic preterm babies: A randomized controlled trial. Pediatrics 129:4 (2012), 639–647.
Alsweiler, J., Williamson, K., Bloomfield, F., Chase, G., Harding, J., Computer-determined dosage of insulin in the management of neonatal hyperglycaemia (HINT2): Protocol of a randomised controlled trial. BMJ Open, 7(3), 2017, e012982.
Al-Tarifi, A., Abou-Shala, N., Tamim, H.M., Rishu, A.H., Arabi, Y.M., What is the optimal blood glucose target in critically ill patients? A nested cohort study. Annals of Thoracic Medicine 6:4 (2011), 207–211.
Amerling, R., Winchester, J.F., Ronco, C., Guidelines have done more harm than good. Blood Purification 26:1 (2008), 73–76.
Amrein, K., Ellmerer, M., Hovorka, R., Kachel, N., Fries, H., von Lewinski, D., et al. Efficacy and safety of glucose control with Space GlucoseControl in the medical intensive care unit–an open clinical investigation. Diabetes Technology & Therapeutics 14:8 (2012), 690–695.
Amrein, K., Ellmerer, M., Hovorka, R., Kachel, N., Parcz, D., Korsatko, S., et al. Hospital glucose control: Safe and reliable glycemic control using enhanced model predictive control algorithm in medical intensive care unit patients. Diabetes Technology & Therapeutics 12:5 (2010), 405–412.
Arabi, Y.M., Dabbagh, O.C., Tamim, H.M., Al-Shimemeri, A.A., Memish, Z.A., Haddad, S.H., et al. Intensive versus conventional insulin therapy: A randomized controlled trial in medical and surgical critically ill patients. Critical Care Medicine 36:12 (2008), 3190–3197.
Arleth, T., Andreassen, S., Federici, M.O., Benedetti, M.M., A model of the endogenous glucose balance incorporating the characteristics of glucose transporters. Computer Methods and Programs in Biomedicine 62:3 (2000), 219–234.
Bagshaw, S.M., Bellomo, R., Jacka, M.J., Egi, M., Hart, G.K., George, C., et al. The impact of early hypoglycemia and blood glucose variability on outcome in critical illness. Critical Care (London, England), 13(3), 2009, R91.
Bahremand, S., Ko, H.S., Balouchzadeh, R., Felix Lee, H., Park, S., Kwon, G., Neural network-based model predictive control for type 1 diabetic rats on artificial pancreas system. Medical & Biological Engineering & Computing, 2018.
Bally, L., Thabit, H., Hartnell, S., Andereggen, E., Ruan, Y., Wilinska, M.E., et al. Closed-loop insulin delivery for glycemic control in noncritical care. New England Journal of Medicine 379:6 (2018), 547–556.
Barrett, P.H., Bell, B.M., Cobelli, C., Golde, H., Schumitzky, A., Vicini, P., et al. SAAM II: Simulation, analysis, and modeling software for tracer and pharmacokinetic studies. Metabolism 47:4 (1998), 484–492.
Baumol, W.J., De Ferranti, D.M., The cost disease: Why computers get cheaper and health care doesn't. 2012, Yale University Press, New Haven, 249 xxi.
Beardsall, K., Vanhaesebrouck, S., Ogilvy-Stuart, A.L., Vanhole, C., Palmer, C.R., van Weissenbruch, M., et al. Early insulin therapy in very-low-birth-weight infants. New England Journal of Medicine 359:18 (2008), 1873–1884.
Ben-Tal, A., Simplified models for gas exchange in the human lungs. Journal of Theoretical Biology 238:2 (2006), 474–495.
Bequette, B.W., A critical assessment of algorithms and challenges in the development of a closed-loop artificial pancreas. Diabetes Technology & Therapeutics 7:1 (2005), 28–47.
Bergman, R.N., Finegood, D.T., Ader, M., Assessment of insulin sensitivity in vivo. Endocrine Reviews 6:1 (1985), 45–86.
Bergman, R.N., Ider, Y.Z., Bowden, C.R., Cobelli, C., Quantitative estimation of insulin sensitivity. American Journal of Physiology 236:6 (1979), E667–E677.
Biagi, L., Ramkissoon, C.M., Facchinetti, A., Leal, Y., Vehi, J., Modeling the error of the medtronic paradigm veo enlite glucose sensor. Sensors (Basel), 17(6), 2017.
Blaha, J., Barteczko-Grajek, B., Berezowicz, P., Charvat, J., Chvojka, J., Grau, T., et al. Space GlucoseControl system for blood glucose control in intensive care patients–A European multicentre observational study. BMC Anesthesiology, 16, 2016, 8.
Blaha, J., Kopecky, P., Matias, M., Hovorka, R., Kunstyr, J., Kotulak, T., et al. Comparison of three protocols for tight glycemic control in cardiac surgery patients. Diabetes Care 32:5 (2009), 757–761.
Bradley, C., Bowery, A., Britten, R., Budelmann, V., Camara, O., Christie, R., et al. OpenCMISS: A multi-physics & multi-scale computational infrastructure for the VPH/Physiome project. Progress in Biophysics and Molecular Biology 107:1 (2011), 32–47.
Breton, M., Kovatchev, B., Analysis, modeling, and simulation of the accuracy of continuous glucose sensors. Journal of Diabetes Science and Technology 2:5 (2008), 853–862.
Brunkhorst, F.M., Engel, C., Bloos, F., Meier-Hellmann, A., Ragaller, M., Weiler, N., et al. Intensive insulin therapy and pentastarch resuscitation in severe sepsis. New England Journal of Medicine 358:2 (2008), 125–139.
Brunkhorst, F.M., Reinhart, K., Intensive insulin therapy in the ICU: Benefit versus harm?. Intensive Care Medicine, 33(7), 2007, 1302.
Cahill, N.E., Dhaliwal, R., Day, A.G., Jiang, X., Heyland, D.K., Nutrition therapy in the critical care setting: What is “best achievable” practice? An international multicenter observational study. Critical Care Medicine 38:2 (2010), 395–401.
Callegari, T., Caumo, A., Cobelli, C., Bayesian two-compartment and classic single-compartment minimal models: Comparison on insulin modified IVGTT and effect of experiment reduction. IEEE Transactions on Bio-Medical Engineering 50:12 (2003), 1301–1309.
Carson, E.R., Cobelli, C., Modelling methodology for physiology and medicine. Academic Press Series in Biomedical Engineering, 2001, 421 xiv.
Chase, J.G., Desaive, T., Bohe, J., Cnop, M., De Block, C., Gunst, J., et al. Improving glycemic control in critically ill patients: Personalized care to mimic the endocrine pancreas. Critical Care, 22(1), 2018, 182.
Chase, J.G., Le Compte, A.J., Preiser, J.C., Shaw, G.M., Penning, S., Desaive, T., Physiological modeling, tight glycemic control, and the ICU clinician: What are models and how can they affect practice?. Ann Intensive Care, 1(1), 2011, 11.
Chase, J.G., Le Compte, A.J., Suhaimi, F., Shaw, G.M., Lynn, A., Lin, J., et al. Tight glycemic control in critical care-the leading role of insulin sensitivity and patient variability: A review and model-based analysis. Computer Methods and Programs in Biomedicine 102:2 (2011), 156–171.
Chase, J.G., Preiser, J.C., Dickson, J.L., Pironet, A., Chiew, Y.S., Pretty, C.G., et al. Next-generation, personalised, model-based critical care medicine: A state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them. Biomedical Engineering Online [Electronic Resource], 17(1), 2018, 24.
Chase, J.G., Pretty, C.G., Pfeifer, L., Shaw, G.M., Preiser, J.C., Le Compte, A.J., et al. Organ failure and tight glycemic control in the SPRINT study. Critical Care, 14(4), 2010, R154.
Chase, J.G., Shaw, G.M., Lotz, T., LeCompte, A., Wong, J., Lin, J., et al. Model-based insulin and nutrition administration for tight glycaemic control in critical care. Current Drug Delivery 4:4 (2007), 283–296.
Chase, J.G., Shaw, G., Le Compte, A., Lonergan, T., Willacy, M., Wong, X.W., et al. Implementation and evaluation of the SPRINT protocol for tight glycaemic control in critically ill patients: A clinical practice change. Critical Care, 12(2), 2008, R49.
Chase, J.G., Suhaimi, F., Penning, S., Preiser, J.C., Le Compte, A.J., Lin, J., et al. Validation of a model-based virtual trials method for tight glycemic control in intensive care. Biomedical Engineering Online [Electronic Resource], 9, 2010, 84.
Chase, J., Desaive, T., Preiser, J.C., Virtual Patients, Virtual Cohorts, A new way to think about the design and implementation of personalized ICU treatments. Vincent, J.L., (eds.) Annual update in intensive care and emergency medicine, 2016, 435–448.
Chase, J., LeCompte, A., Shaw, G., Blakemore, A., Wong, J., Lin, J., et al. A benchmark data set for model-based glycemic control in critical care. Journal of Diabetes Science and Technology (JoDST) 24:4 (2008), 584–594.
Chase, J., Shaw, G., Wong, X., Lotz, T., Lin, J., Hann, C., Model-based glycaemic control in critical care—A review of the state of the possible. Biomedical Signal Processing and Control 1:1 (2006), 3–21.
Cobelli, C., Pacini, G., Toffolo, G., Sacca, L., Estimation of insulin sensitivity and glucose clearance from minimal model: New insights from labeled IVGTT. American Journal of Physiology 250:5 Pt 1 (1986), E591–E598.
Cohen, M.J., Use of models in identification and prediction of physiology in critically ill surgical patients. British Journal of Surgery 99:4 (2012), 487–493.
Cooling, M.T., Hunter, P., The CellML metadata framework 2.0 specification. Journal of Integrative Bioinformatics, 12(2), 2015, 260.
Cueni-Villoz, N., Devigili, A., Delodder, F., Cianferoni, S., Feihl, F., Rossetti, A.O., et al. Increased blood glucose variability during therapeutic hypothermia and outcome after cardiac arrest. Critical Care Medicine 39:10 (2011), 2225–2231.
Dalla Man, C., Camilleri, M., Cobelli, C., A system model of oral glucose absorption: Validation on gold standard data. Ieee Transactions on Bio-Medical Engineering 53:12 Pt 1 (2006), 2472–2478.
Dalla Man, C., Caumo, A., Cobelli, C., The oral glucose minimal model: Estimation of insulin sensitivity from a meal test. Ieee Transactions on Biomedical Engineering 49:5 (2002), 419–429.
Dalla Man, C., Micheletto, F., Lv, D., Breton, M., Kovatchev, B., Cobelli, C., The UVA/PADOVA Type 1 diabetes simulator: New features. Journal of Diabetes Science and Technology 8:1 (2014), 26–34.
Dalla Man, C., Yarasheski, K.E., Caumo, A., Robertson, H., Toffolo, G., Polonsky, K.S., et al. Insulin sensitivity by oral glucose minimal models: Validation against clamp. American Journal of Physiology. Endocrinology and Metabolism 289:6 (2005), E954–E959.
Dassau, E., Palerm, C.C., Zisser, H., Buckingham, B.A., Jovanovic, L., Doyle, F.J., In silico evaluation platform for artificial pancreatic beta-cell development–a dynamic simulator for closed-loop control with hardware-in-the-loop. Diabetes Technology & Therapeutics 11:3 (2009), 187–194.
Davidson, P.C., Steed, R.D., Bode, B.W., Glucommander: A computer-directed intravenous insulin system shown to be safe, simple, and effective in 120,618 h of operation. Diabetes Care 28:10 (2005), 2418–2423.
De La Rosa, C., Donado, J.H., Restrepo, A.H., Quintero, A.M., Gonzalez, L.G., Saldarriaga, N.E., et al. Strict glycaemic control in patients hospitalised in a mixed medical and surgical intensive care unit: A randomised clinical trial. Critical Care, 12(5), 2008, R120.
Devreese, K., Leroux-Roels, G., Laboratory assessment of five glucose meters designed for self-monitoring of blood glucose concentration. European Journal of Clinical Chemistry and Clinical Biochemistry 31:12 (1993), 829–837.
Dickson, J.L., Chase, J.G., Lynn, A., Shaw, G.M., Model-based glycaemic control: Methodology and initial results from neonatal intensive care. Biomedizinische Technik. Biomedical Engineering, 2016.
Dickson, J.L., Lynn, A.M., Shaw, G.M., Chase, J.G., Safe and effective glycaemic control in premature infants: observational clinical results from the computerised STAR-GRYPHON protocol. Archives of Disease in Childhood Fetal & Neonatal 104:2 (2019), F204–F211.
Dickson, J.L., Stewart, K.W., Pretty, C.G., Flechet, M., Desaive, T., Penning, S., et al. Generalisability of a virtual trials method for glycaemic control in intensive care. IEEE Transactions on Bio-Medical Engineering 65:7 (2018), 1543–1553.
Docherty, P.D., Chase, J.G., David, T., Characterisation of the iterative integral parameter identification method. Medical and Biological Engineering and Computing, 2012, 1–8.
Docherty, P.D., Chase, J.G., Lotz, T.F., Desaive, T., A graphical method for practical and informative identifiability analyses of physiological models: A case study of insulin kinetics and sensitivity. Biomedical Engineering Online 10:1 (2011), 1–20.
Docherty, P.D., Chase, J.G., Lotz, T.F., Hann, C.E., Shaw, G.M., Berkeley, J.E., et al. Independent cohort cross-validation of the real-time DISTq estimation of insulin sensitivity. Computer Methods and Programs in Biomedicine 102:2 (2011), 94–104.
Docherty, P.D., Chase, J.G., Morenga, L., Lotz, T.F., Berkeley, J., Shaw, G., et al. A spectrum of dynamic insulin sensitivity test protocols. Journal of Diabetes Science and Technology, 5(6), 2011, 1499.
Dombovy, M.L., U.S. health care in conflict–Part I. The challenges of balancing cost, quality and access. Physician Executive 28:4 (2002), 43–47.
Donati, A., Damiani, E., Domizi, R., Botticelli, L., Castagnani, R., Gabbanelli, V., et al. Glycaemic variability, infections and mortality in a medical-surgical intensive care unit. Crit Care Resusc 16:1 (2014), 13–23.
Dubois, J., Van Herpe, T., van Hooijdonk, R.T., Wouters, R., Coart, D., Wouters, P., et al. Software-guided versus nurse-directed blood glucose control in critically ill patients: The LOGIC-2 multicenter randomized controlled clinical trial. Critical Care, 21(1), 2017, 212.
Dungan, K.M., Braithwaite, S.S., Preiser, J.C., Stress hyperglycaemia. Lancet 373:9677 (2009), 1798–1807.
Economist, T., Patient, heal thyself. The economis, 2011, The Economist, London, UK, 2.
Egi, M., Bellomo, R., Stachowski, E., French, C.J., Hart, G.K., Taori, G., et al. Hypoglycemia and outcome in critically ill patients. Mayo Clinic Proceedings 85:3 (2010), 217–224.
Egi, M., Bellomo, R., Stachowski, E., French, C.J., Hart, G., Variability of blood glucose concentration and short-term mortality in critically ill patients. Anesthesiology 105:2 (2006), 244–252.
Egi, M., Finfer, S., Bellomo, R., Glycemic control in the ICU. Chest 140:1 (2011), 212–220.
Evans, A., Le Compte, A., Tan, C.S., Ward, L., Steel, J., Pretty, C.G., et al. Targeted, stochastic (STAR) glycemic control: Design, safety, and performance. Journal of Diabetes Science and Technology 6:1 (2012), 102–115.
Evans, A., Shaw, G.M., Le Compte, A., Tan, C.S., Ward, L., Steel, J., et al. Pilot proof of concept clinical trials of Stochastic Targeted (STAR) glycemic control. Annals of Intensive Care, 1(1), 2011, 38.
Facchinetti, A., Del Favero, S., Sparacino, G., Castle, J.R., Ward, W.K., Cobelli, C., Modeling the glucose sensor error. IEEE Transactions on Bio-Medical Engineering 61:3 (2014), 620–629.
Fahy, B.G., Sheehy, A.M., Coursin, D.B., Glucose control in the intensive care unit. Critical Care Medicine 37:5 (2009), 1769–1776.
Ferenci, T., Benyo, B., Kovacs, L., Fisk, L., Shaw, G.M., Chase, J.G., Daily evolution of insulin sensitivity variability with respect to diagnosis in the critically ill. Plos One, 8(2), 2013, e57119.
Fernandez, A., Sturmberg, J., Lukersmith, S., Madden, R., Torkfar, G., Colagiuri, R., et al. Evidence-based medicine: Is it a bridge too far?. Health Res Policy Syst, 13, 2015, 66.
Ferrannini, E., Mari, A., How to measure insulin sensitivity. Journal of Hypertension 16:7 (1998), 895–906.
Investigators, N.-S.S., Finfer, S., Liu, B., Chittock, D.R., Norton, R., Myburgh, J.A., et al. Hypoglycemia and risk of death in critically ill patients. New England Journal of Medicine 367:12 (2012), 1108–1118.
Finfer, S., Chittock, D.R., Su, S.Y., Blair, D., Foster, D., Dhingra, V., et al. Intensive versus conventional glucose control in critically ill patients. New England Journal of Medicine 360:13 (2009), 1283–1297.
Fisk, L., Lecompte, A., Penning, S., Desaive, T., Shaw, G., Chase, G., STAR development and protocol comparison. IEEE Transactions on Bio-Medical Engineering 59:12 (2012), 3357–3364.
Garny, A., Hunter, P.J., OpenCOR: A modular and interoperable approach to computational biology. Frontiers in Physiology, 6, 2015, 26.
Griesdale, D.E., de Souza, R.J., van Dam, R.M., Heyland, D.K., Cook, D.J., Malhotra, A., et al. Intensive insulin therapy and mortality among critically ill patients: A meta-analysis including NICE-SUGAR study data. Cmaj 180:8 (2009), 821–827.
Gunst, J., Van den Berghe, G., Blood glucose control in the ICU: Don't throw out the baby with the bathwater!. Intensive Care Medicine 42:9 (2016), 1478–1481.
Halpern, N.A., Can the costs of critical care be controlled?. Current Opinion in Critical Care 15:6 (2009), 591–596.
Halpern, S.D., ICU capacity strain and the quality and allocation of critical care. Current Opinion in Critical Care 17:6 (2011), 648–657.
Hann, C.E., Chase, J.G., Lin, J., Lotz, T., Doran, C.V., Shaw, G.M., Integral-based parameter identification for long-term dynamic verification of a glucose-insulin system model. Computer Methods and Programs in Biomedicine 77:3 (2005), 259–270.
Hewson, M., Nawadra, V., Oliver, J., Odgers, C., Plummer, J., Simmer, K., Insulin infusions in the neonatal unit: Delivery variation due to adsorption. Journal of Paediatrics and Child Health 36:3 (2000), 216–220.
Hovorka, R., Closed-loop insulin delivery: From bench to clinical practice. Nature Reviews Endrocrinology 7:7 (2011), 385–395.
Hovorka, R., Canonico, V., Chassin, L.J., Haueter, U., Massi-Benedetti, M., Federici, M.O., et al. Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiological Measurement 25:4 (2004), 905–920.
Hovorka, R., Chassin, L.J., Ellmerer, M., Plank, J., Wilinska, M.E., A simulation model of glucose regulation in the critically ill. Physiological Measurement 29:8 (2008), 959–978.
Hovorka, R., Chassin, L.J., Wilinska, M.E., Virtual type 1 diabetic treated by CSII: Model description. WC2003, Sydney, Australia, 2003.
Hovorka, R., Kremen, J., Blaha, J., Matias, M., Anderlova, K., Bosanska, L., et al. Blood glucose control by a model predictive control algorithm with variable sampling rate versus a routine glucose management protocol in cardiac surgery patients: A randomized controlled trial. Journal of Clinical Endocrinology and Metabolism 92:8 (2007), 2960–2964.
Hovorka, R., Shojaee-Moradie, F., Carroll, P.V., Chassin, L.J., Gowrie, I.J., Jackson, N.C., et al. Partitioning glucose distribution/transport, disposal, and endogenous production during IVGTT. American Journal of Physiology. Endocrinology and Metabolism 282:5 (2002), E992–1007.
Hunter, P.J., Crampin, E.J., Nielsen, P.M., Bioinformatics, multiscale modeling and the IUPS Physiome Project. Briefings in Bioinformatics 9:4 (2008), 333–343.
Hunter, P., Chapman, T., Coveney, P.V., de Bono, B., Diaz, V., Fenner, J., et al. A vision and strategy for the virtual physiological human: 2012 update. Interface Focus, 3(2), 2013, 20130004.
Jakobsson, T., Shulman, R., Gill, H., Taylor, K., The impact of insulin adsorption onto the infusion sets in the adult intensive care unit. Journal of Diabetes Science and Technology 3:1 (2009), 213–214.
Jamaludin, U.K., Docherty, P.D., Geoffrey Chase, J., Shaw, G.M., Impact of haemodialysis on insulin kinetics of acute kidney injury patients in critical care. Journal of Medical and Biological Engineering 35:1 (2015), 125–133.
Juneja, R., Golas, A.A., Carroll, J., Nelson, D., Abad, V.J., Roudebush, C.P., et al. Safety and effectiveness of a computerized subcutaneous insulin program to treat inpatient hyperglycemia. Journal of Diabetes Science and Technology 2:3 (2008), 384–391.
Kanderian, S.S., Weinzimer, S.A., Steil, G.M., The identifiable virtual patient model: Comparison of simulation and clinical closed-loop study results. Journal of Diabetes Science and Technology 6:2 (2012), 371–379.
Kansal, A.R., Modeling approaches to type 2 diabetes. Diabetes Technology & Therapeutics 6:1 (2004), 39–47.
Kauffmann, R.M., Hayes, R.M., Buske, B.D., Norris, P.R., Campion, T.R. Jr., Dortch, M., et al. Increasing blood glucose variability heralds hypoglycemia in the critically ill. Journal of Surgical Research 170:2 (2011), 257–264.
Keener, J.P., Sneyd, J., Mathematical physiology. Interdisciplinary applied mathematics v. 8. 1998, Springer, New York, 766 viii.
Koch, A., Gressner, O.A., Sanson, E., Tacke, F., Trautwein, C., Serum resistin levels in critically ill patients are associated with inflammation, organ dysfunction and metabolism and may predict survival of non-septic patients. Critical Care (London, England), 13(3), 2009, R95.
Kovatchev, B.P., Breton, M., Man, C.D., Cobelli, C., In silico preclinical trials: A proof of concept in closed-loop control of type 1 diabetes. Journal of Diabetes Science and Technology 3:1 (2009), 44–55.
Krinsley, J.S., Effect of an intensive glucose management protocol on the mortality of critically ill adult patients. Mayo Clinic Proceedings 79:8 (2004), 992–1000.
Krinsley, J.S., Glycemic variability: A strong independent predictor of mortality in critically ill patients. Critical Care Medicine 36:11 (2008), 3008–3013.
Krinsley, J.S., Bruns, D.E., Boyd, J.C., The impact of measurement frequency on the domains of glycemic control in the critically ill–a Monte Carlo simulation. Journal of Diabetes Science and Technology 9:2 (2015), 237–245.
Krinsley, J.S., Preiser, J.C., Time in blood glucose range 70 to 140 mg/dl >80% is strongly associated with increased survival in non-diabetic critically ill adults. Critical Care (London, England), 19, 2015, 179.
Krinsley, J.S., Schultz, M.J., Spronk, P.E., Harmsen, R.E., van Braam Houckgeest, F., van der Sluijs, J.P., et al. Mild hypoglycemia is independently associated with increased mortality in the critically ill. Critical Care, 15(4), 2011, R173.
Langouche, L., Vander Perre, S., Wouters, P.J., D'Hoore, A., Hansen, T.K., Van den Berghe, G., Effect of intensive insulin therapy on insulin sensitivity in the critically ill. Journal of Clinical Endocrinology and Metabolism 92:10 (2007), 3890–3897.
Laviano, A., Aghilone, F., Colagiovanni, D., Fiandra, F., Giambarresi, R., Tordiglione, P., et al. Metabolic and clinical effects of the supplementation of a functional mixture of amino acids in cerebral hemorrhage. Neurocrit Care 14:1 (2011), 44–49.
Le Compte, A.J., Chase, J.G., Lynn, A., Hann, C.E., Shaw, G.M., Lin, J., Development of blood glucose control for extremely premature infants. Computer Methods and Programs in Biomedicine 102:2 (2011), 181–191.
Le Compte, A.J., Lee, D.S., Chase, J.G., Lin, J., Lynn, A., Shaw, G.M., Blood glucose prediction using stochastic modeling in neonatal intensive care. IEEE Transactions on Bio-Medical Engineering 57:3 (2010), 509–518.
Le Compte, A.J., Lynn, A.M., Lin, J., Pretty, C.G., Shaw, G.M., Chase, J.G., Pilot study of a model-based approach to blood glucose control in very-low-birthweight neonates. Bmc Pediatrics, 12, 2012, 117.
Le Compte, A.J., Pretty, C.G., Lin, J., Shaw, G.M., Lynn, A., Chase, J.G., Impact of variation in patient response on model-based control of glycaemia in critically ill patients. Computer Methods and Programs in Biomedicine, 2011.
Le Compte, A., Chase, J.G., Russell, G., Lynn, A., Hann, C., Shaw, G., et al. Modeling the glucose regulatory system in extreme preterm infants. Computer Methods and Programs in Biomedicine, 2010.
Le Compte, A., Chase, J., Lynn, A., Hann, C., Shaw, G., Wong, X., et al. Blood glucose controller for neonatal intensive care: Virtual trials development and 1st clinical trials. Journal of Diabetes Science and Technology (JoDST) 3:5 (2009), 1066–1081.
Lehmann, E.D., Deutsch, T., AIDA2: A Mk. II automated insulin dosage advisor. Journal of Biomedical Engineering 15 (1993), 201–211.
Lin, J., Lee, D., Chase, J.G., Shaw, G.M., Hann, C.E., Lotz, T., et al. Stochastic modelling of insulin sensitivity variability in critical care. Biomedical Signal Processing and Control 1:3 (2006), 229–242.
Lin, J., Lee, D., Chase, J.G., Shaw, G.M., Le Compte, A., Lotz, T., et al. Stochastic modelling of insulin sensitivity and adaptive glycemic control for critical care. Computer Methods and Programs in Biomedicine 89:2 (2008), 141–152.
Lin, J., Parente, J.D., Chase, J.G., Shaw, G.M., Blakemore, A.J., LeCompte, A.J., et al. Development of a model-based clinical sepsis biomarker for critically ill patients. Computer Methods and Programs in Biomedicine 102:2 (2011), 149–155.
Lin, J., Razak, N.N., Pretty, C.G., Le Compte, A., Docherty, P., Parente, J.D., et al. A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients. Computer Methods and Programs in Biomedicine 102:2 (2011), 192–205.
Lonergan, T., Compte, A.L., Willacy, M., Chase, J.G., Shaw, G.M., Hann, C.E., et al. A pilot study of the SPRINT protocol for tight glycemic control in critically Ill patients. Diabetes Technology & Therapeutics 8:4 (2006), 449–462.
Lonergan, T., LeCompte, A., Willacy, M., Chase, J.G., Shaw, G.M., Wong, X.W., et al. A simple insulin-nutrition protocol for tight glycemic control in critical illness: Development and protocol comparison. Diabetes Technology & Therapeutics 8:2 (2006), 191–206.
Lotz, T.F., Chase, J.G., McAuley, K.A., Lee, D.S., Lin, J., Hann, C.E., et al. Transient and steady-state euglycemic clamp validation of a model for glycemic control and insulin sensitivity testing. Diabetes Technology & Therapeutics 8:3 (2006), 338–346.
Lotz, T.F., Chase, J.G., McAuley, K.A., Shaw, G.M., Docherty, P.D., Berkeley, J.E., et al. Design and clinical pilot testing of the model-based Dynamic Insulin Sensitivity and Secretion Test (DISST). Journal of Diabetes Science and Technology 4:6 (2010), 1408–1423.
Man, C.D., Breton, M.D., Cobelli, C., Physical activity into the meal glucose-insulin model of type 1 diabetes: In silico studies. Journal of Diabetes Science and Technology 3:1 (2009), 56–67.
Mari, A., Pacini, G., Brazzale, A.R., Ahren, B., Comparative evaluation of simple insulin sensitivity methods based on the oral glucose tolerance test. Diabetologia 48:4 (2005), 748–751.
Marik, P.E., Tight glycemic control in acutely ill patients: Low evidence of benefit, high evidence of harm!. Intensive Care Medicine 42:9 (2016), 1475–1477.
Marik, P.E., Precision Glycemic Control in the ICU. Critical Care Medicine 44:7 (2016), 1433–1434.
Marik, P.E., Bellomo, R., Stress hyperglycemia: An essential survival response!. Critical Care (London, England), 17(2), 2013, 305.
Marik, P.E., Preiser, J.C., Toward understanding tight glycemic control in the ICU: A systematic review and metaanalysis. Chest 137:3 (2010), 544–551.
Marik, P.E., Raghavan, M., Stress-hyperglycemia, insulin and immunomodulation in sepsis. Intensive Care Medicine 30:5 (2004), 748–756.
Marvin, M.R., Inzucchi, S.E., Besterman, B.J., Computerization of the Yale insulin infusion protocol and potential insights into causes of hypoglycemia with intravenous insulin. Diabetes Technology & Therapeutics 15:3 (2013), 246–252.
McAuley, K.A., Berkeley, J.E., Docherty, P.D., Lotz, T.F., Te Morenga, L.A., Shaw, G.M., et al. The dynamic insulin sensitivity and secretion test–a novel measure of insulin sensitivity. Metabolism, 2011.
McCowen, K.C., Malhotra, A., Bistrian, B.R., Stress-induced hyperglycemia. Critical Care Clinics 17:1 (2001), 107–124.
Micklethwait, J., Taming Leviathan. The economist, 2011, The Economist, London, UK, 6.
Mowery, N.T., Gunter, O.L., Dossett, L.A., Dortch, M.J., Morris, J.A. Jr., May, A.K., et al. Failure to achieve euglycemia despite aggressive insulin control signals abnormal physiologic response to trauma. Journal of Critical Care 26:3 (2011), 295–302.
Naylor, A.R., Interventions for carotid artery disease: Time to confront some 'inconvenient truths'. Expert Review of Cardiovascular Therapy 5:6 (2007), 1053–1063.
Nickerson, D.P., Ladd, D., Hussan, J.R., Safaei, S., Suresh, V., Hunter, P.J., et al. Using CellML with OpenCMISS to Simulate Multi-Scale Physiology. Frontiers in Bioengineering and Biotechnology, 2, 2014, 79.
OECD. FOCUS on Health Spending @ OECD Health Statistics. OECD Health Statistics, 2015, 1–8 2015, 2015. July 2015.
Omar, A.S., Salama, A., Allam, M., Elgohary, Y., Mohammed, S., Tuli, A.K., et al. Association of time in blood glucose range with outcomes following cardiac surgery. BMC Anesthesiology, 15, 2015, 14.
Orsini, J., Blaak, C., Yeh, A., Fonseca, X., Helm, T., Butala, A., et al. Triage of patients consulted for ICU admission during times of ICU-Bed shortage. Journal of Clinical Medicine 6:6 (2014), 463–468.
Pachler, C., Plank, J., Weinhandl, H., Chassin, L.J., Wilinska, M.E., Kulnik, R., et al. Tight glycaemic control by an automated algorithm with time-variant sampling in medical ICU patients. Intensive Care Medicine 34:7 (2008), 1224–1230.
Pappada, S.M., Cameron, B.D., Rosman, P.M., Bourey, R.E., Papadimos, T.J., Olorunto, W., et al. Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes. Diabetes Technology & Therapeutics 13:2 (2011), 135–141.
Parker, R.S., Doyle, F.J. 3rd, Control-relevant modeling in drug delivery. Advanced Drug Delivery Reviews 48:2–3 (2001), 211–228.
Penning, S., Chase, J.G., Preiser, J.C., Pretty, C.G., Signal, M., Melot, C., et al. Does the achievement of an intermediate glycemic target reduce organ failure and mortality? A post hoc analysis of the Glucontrol trial. Journal of Critical Care 29:3 (2014), 374–379.
Penning, S., Pretty, C., Preiser, J.C., Shaw, G.M., Desaive, T., Chase, J.G., Glucose control positively influences patient outcome: A retrospective study. Journal of Critical Care 30:3 (2015), 455–459.
Pielmeier, U., Andreassen, S., Juliussen, B., Chase, J.G., Nielsen, B.S., Haure, P., The Glucosafe system for tight glycemic control in critical care: A pilot evaluation study. Journal of Critical Care 25:1 (2010), 97–104.
Pielmeier, U., Andreassen, S., Nielsen, B.S., Chase, J.G., Haure, P., A simulation model of insulin saturation and glucose balance for glycemic control in ICU patients. Computer Methods and Programs in Biomedicine 97:3 (2010), 211–222.
Pielmeier, U., Rousing, M.L., Andreassen, S., Nielsen, B.S., Haure, P., 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. Journal of Clinical Monitoring and Computing 26:4 (2012), 319–328.
Pillonetto, G., Caumo, A., Sparacino, G., Cobelli, C., A new dynamic index of insulin sensitivity. IEEE Transactions on Bio-Medical Engineering 53:3 (2006), 369–379.
Pironet, A., Desaive, T., Kosta, S., Lucas, A., Paeme, S., Collet, A., et al. A multi-scale cardiovascular system model can account for the load-dependence of the end-systolic pressure-volume relationship. Biomedical Engineering Online, 12, 2013, 8.
Polderman, K.H., Girbes, A.R., Intensive insulin therapy: Of harm and health, of hypes and hypoglycemia. Critical Care Medicine 34:1 (2006), 246–248.
Preiser, J.C., Devos, P., Chiolero, R., Which factors influence glycemic control in the intensive care unit?. Current Opinion in Clinical Nutrition and Metabolic Care 13:2 (2010), 205–210.
Preiser, J.C., Devos, P., Ruiz-Santana, S., Melot, C., Annane, D., Groeneveld, J., 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 Medicine 35:10 (2009), 1738–1748.
Preiser, J.C., Straaten, H.M., Glycemic control: Please agree to disagree. Intensive Care Medicine 42:9 (2016), 1482–1484.
Pretty, C.G., Le Compte, A.J., Chase, J.G., Shaw, G.M., Preiser, J.C., Penning, S., et al. Variability of insulin sensitivity during the first 4 days of critical illness: Implications for tight glycemic control. Ann Intensive Care, 2(1), 2012, 17.
Pretty, C.G., Signal, M., Fisk, L., Penning, S., Le Compte, A., Shaw, G.M., et al. Impact of sensor and measurement timing errors on model-based insulin sensitivity. Computer Methods and Programs in Biomedicine 114:3 (2014), e79–e86.
Pretty, C., Chase, J.G., Lin, J., Shaw, G.M., Le Compte, A., Razak, N., et al. Impact of glucocorticoids on insulin resistance in the critically ill. Computer Methods and Programs in Biomedicine 102:2 (2011), 172–180.
Reed, C.C., Stewart, R.M., Sherman, M., Myers, J.G., Corneille, M.G., Larson, N., et al. Intensive insulin protocol improves glucose control and is associated with a reduction in intensive care unit mortality. Journal of the American College of Surgeons 204:5 (2007), 1048–1054 discussion 1054-5.
Reifman, J., Rajaraman, S., Gribok, A., Ward, W.K., Predictive monitoring for improved management of glucose levels. Journal of Diabetes Science and Technology 1:4 (2007), 478–486.
Rice, T.W., Gluttony in the intensive care unit: Time to push back from the consensus table. American Journal of Respiratory and Critical Care Medicine 187:3 (2013), 223–224.
Sah Pri, A., Chase, J.G., Pretty, C.G., Shaw, G.M., Preiser, J.C., Vincent, J.L., et al. Evolution of insulin sensitivity and its variability in out-of-hospital cardiac arrest (OHCA) patients treated with hypothermia. Critical Care, 18(5), 2014, 586.
Schultz, M.J., Harmsen, R.E., Spronk, P.E., Clinical review: Strict or loose glycemic control in critically ill patients–implementing best available evidence from randomized controlled trials. Critical Care, 14(3), 2010, 223.
Shorr, A.F., An update on cost-effectiveness analysis in critical care. Current Opinion in Critical Care 8:4 (2002), 337–343.
Signal, M., Fisk, L., Shaw, G.M., Chase, J.G., Concurrent continuous glucose monitoring in critically ill patients: Interim results and observations. Journal of Diabetes Science and Technology 7:6 (2013), 1652–1653.
Signal, M., Gottlieb, R., Le Compte, A., Chase, J.G., Continuous glucose monitoring and trend accuracy: News about a trend compass. Journal of Diabetes Science and Technology 8:5 (2014), 986–997.
Signal, M., Le Compte, A., Shaw, G.M., Chase, J.G., Glycemic levels in critically ill patients: Are normoglycemia and low variability associated with improved outcomes?. Journal of Diabetes Science and Technology 6:5 (2012), 1030–1037.
Signal, M., Pretty, C.G., Chase, J.G., Le Compte, A., Shaw, G.M., Continuous glucose monitors and the burden of tight glycemic control in critical care: Can they cure the time cost?. Journal of Diabetes Science and Technology 4:3 (2010), 625–635.
Smith, N., Waters, S., Hunter, P., Clayton, R., The cardiac physiome: Foundations and future prospects for mathematical modelling of the heart. Progress in Biophysics and Molecular Biology, 104(1–3), 2011, 1.
Solnica, B., Naskalski, J.W., Sieradzki, J., The evaluation of analytical performance of the Precision G point-of-care glucometer. Clinical Chemistry and Laboratory Medicine 39:12 (2001), 1283–1286.
Solnica, B., Naskalski, J.W., Sieradzki, J., Analytical performance of glucometers used for routine glucose self-monitoring of diabetic patients. Clinica Chimica Acta 331:1–2 (2003), 29–35.
Stewart, K.W., Chase, J.G., Pretty, C.G., Shaw, G.M., Nutrition delivery, workload and performance in a model-based ICU glycaemic control system. Computer Methods and Programs in Biomedicine 166 (2018), 9–18.
Stewart, K.W., Chase, J.G., Pretty, C.G., Shaw, G.M., Nutrition delivery of a model-based ICU glycaemic control system. Ann Intensive Care, 8(1), 2018, 4.
Stewart, K.W., Pretty, C.G., Tomlinson, H., Thomas, F.L., Homlok, J., Noemi, S.N., et al. Safety, efficacy and clinical generalization of the STAR protocol: A retrospective analysis. Ann Intensive Care, 6(1), 2016, 24.
Suhaimi, F., Le Compte, A., Preiser, J.C., Shaw, G.M., Massion, P., Radermecker, R., et al. What makes tight glycemic control (TGC) tight? The impact of variability and nutrition in 2 clinical studies. Journal of Diabetes Science and Technology 4:2 (2010), 284–298.
Target Investigators, f.t.A.C.T.G., Energy-dense versus routine enteral nutrition in the critically ill. New England Journal of Medicine 379:19 (2018), 1823–1834.
Tawhai, M.H., Bates, J.H.T., Multi-scale lung modeling. Journal of Applied Physiology 110:5 (2011), 1466–1472.
Tawhai, M.H., Burrowes, K.S., Hoffman, E.A., Computational models of structure-function relationships in the pulmonary circulation and their validation. Experimental Physiology 91:2 (2006), 285–293.
Tawhai, M.H., Hoffman, E.A., Lin, C.-L., The lung physiome: Merging imaging-based measures with predictive computational models. Wiley Interdisciplinary Reviews: Systems Biology and Medicine 1:1 (2009), 61–72.
Tawhai, M.H., Lin, C.-L., Image-based modeling of lung structure and function. Journal of Magnetic Resonance Imaging 32:6 (2010), 1421–1431.
Thomas, F., Pretty, C.G., Fisk, L., Shaw, G.M., Chase, J.G., Desaive, T., Reducing the impact of insulin sensitivity variability on glycaemic outcomes using separate stochastic models within the STAR glycaemic protocol. Biomedical Engineering Online, 13, 2014, 43.
Toffolo, G., Cobelli, C., The hot IVGTT two-compartment minimal model: An improved version. American Journal of Physiology. Endocrinology and Metabolism 284:2 (2003), E317–E321.
Trajanoski, Z., Regittnig, W., Wach, P., Simulation studies on neural predictive control of glucose using the subcutaneous route. Computer Methods and Programs in Biomedicine 56:2 (1998), 133–139.
Trajanoski, Z., Wach, P., Neural predictive controller for insulin delivery using the subcutaneous route. IEEE Transactions on Bio-Medical Engineering 45:9 (1998), 1122–1134.
Trajanoski, Z., Wach, P., Neural predictive controller for insulin delivery using the subcutaneous route. IEEE Transactions on Bio-Medical Engineering 45:9 (1998), 1122–1134.
Treggiari, M.M., Karir, V., Yanez, N.D., Weiss, N.S., Daniel, S., Deem, S.A., Intensive insulin therapy and mortality in critically ill patients. Critical Care, 12(1), 2008, R29.
Truog, R.D., Brock, D.W., Cook, D.J., Danis, M., Luce, J.M., Rubenfeld, G.D., et al. Task force on values, and C. rationing in critical, rationing in the intensive care unit. Critical Care Medicine 34:4 (2006), 958–963 quiz 971.
Turnheim, K., Waldhausl, W.K., Essentials of insulin pharmacokinetics. Wiener Klinische Wochenschrift 100:3 (1988), 65–72.
Uyttendaele, V., Dickson, J.L., Shaw, G.M., Desaive, T., Chase, J.G., Untangling glycaemia and mortality in critical care. Critical Care, 21(1), 2017, 152.
Uyttendaele, V., Knopp, J.L., Stewart, K.W., Desaive, T., Benyó, B., Szabo-Nemedi, N., et al. A 3D insulin sensitivity prediction model enables more patient-specific prediction and model-based glycaemic control. Biomedical Signal Processing and Control 46 (2018), 192–200.
Van den Berghe, G., Wilmer, A., Hermans, G., Meersseman, W., Wouters, P.J., Milants, I., et al. Intensive Insulin Therapy in the Medical ICU. New England Journal of Medicine 354:5 (2006), 449–461.
Van den Berghe, G., Wouters, P., Weekers, F., Verwaest, C., Bruyninckx, F., Schetz, M., et al. Intensive insulin therapy in the critically ill patients. New England Journal of Medicine 345:19 (2001), 1359–1367.
van Exel, J., Baker, R., Mason, H., Donaldson, C., Brouwer, W., EuroVa, Q.T., Public views on principles for health care priority setting: Findings of a European cross-country study using Q methodology. Social Science & Medicine 126 (2015), 128–137.
Van Herpe, T., De Moor, B., Van den Berghe, G., Towards closed-loop glycaemic control. Best Practice &Amp; Research. Clinical Anaesthesiology 23:1 (2009), 69–80.
Van Herpe, T., Espinoza, M., Haverbeke, N., Moor, B.D., den Berghe, G.V., Glycemia prediction in critically ill patients using an adaptive modeling approach. Journal of Diabetes Science and Technology 1:3 (2007), 348–356.
Van Herpe, T., Espinoza, M., Pluymers, B., Goethals, I., Wouters, P., Van den Berghe, G., et al. An adaptive input-output modeling approach for predicting the glycemia of critically ill patients. Physiological Measurement 27:11 (2006), 1057–1069.
Van Herpe, T., Mesotten, D., Wouters, P.J., Herbots, J., Voets, E., Buyens, J., et al. LOGIC-insulin algorithm-guided versus nurse-directed blood glucose control during critical illness: The LOGIC-1 single-center randomized, controlled clinical trial. Diabetes Care 36:2 (2013), 189–194.
Van Herpe, T., Pluymers, B., Espinoza, M., Van den Berghe, G., De Moor, B., A minimal model for glycemia control in critically ill patients. Conf Proc IEEE Eng Med Biol Soc, 1, 2006, 5432–5435.
Viceconti, M., Cobelli, C., Haddad, T., Himes, A., Kovatchev, B., Palmer, M., In silico assessment of biomedical products: The conundrum of rare but not so rare events in two case studies. The Proceedings of the Institution of Mechanical Engineers, Part H 231:5 (2017), 455–466.
Villet, S., Chiolero, R.L., Bollmann, M.D., Revelly, J.P., Cayeux, R.N.M., Delarue, J., et al. Negative impact of hypocaloric feeding and energy balance on clinical outcome in ICU patients. Clinical Nutrition 24:4 (2005), 502–509.
Visentin, R., Man, C.D., Cobelli, C., One-day Bayesian cloning of Type 1 diabetes subjects: Toward a single-day UVA/Padova type 1 diabetes simulator. IEEE Transactions on Bio-Medical Engineering 63:11 (2016), 2416–2424.
Vodovotz, Y., Billiar, T.R., In silico modeling: Methods and applications to trauma and sepsis. Critical Care Medicine 41:8 (2013), 2008–2014.
Waeschle, R.M., Moerer, O., Hilgers, R., Herrmann, P., Neumann, P., Quintel, M., The impact of the severity of sepsis on the risk of hypoglycaemia and glycaemic variability. Critical Care, 12(5), 2008, R129.
Wiener, R.S., Wiener, D.C., Larson, R.J., Benefits and risks of tight glucose control in critically ill adults: A meta-analysis. Jama 300:8 (2008), 933–944.
Wilinska, M.E., Blaha, J., Chassin, L.J., Cordingley, J.J., Dormand, N.C., Ellmerer, M., et al. Evaluating glycemic control algorithms by computer simulations. Diabetes Technology & Therapeutics 13:7 (2011), 713–722.
Wilinska, M.E., Chassin, L.J., Acerini, C.L., Allen, J.M., Dunger, D.B., Hovorka, R., Simulation environment to evaluate closed-loop insulin delivery systems in type 1 diabetes. Journal of Diabetes Science and Technology 4:1 (2010), 132–144.
Wilinska, M.E., Chassin, L., Hovorka, R., In silico testing—Impact on the progress of the closed loop insulin infusion for critically Ill patients project. Journal of Diabetes Science and Technology 2:3 (2008), 417–423.
Wischmeyer, P.E., The evolution of nutrition in critical care: How much, how soon?. Critical Care, 17(Suppl 1), 2013, S7.
Yu, T., Lloyd, C.M., Nickerson, D.P., Cooling, M.T., Miller, A.K., Garny, A., et al. The physiome model repository 2. Bioinformatics 27:5 (2011), 743–744.
Zhou, T., Dickson, J.L., Geoffrey Chase, J., Autoregressive modeling of drift and random error to characterize a continuous intravascular glucose monitoring sensor. Journal of Diabetes Science and Technology 12:1 (2018), 90–104.
Zhou, T., Dickson, J.L., Shaw, G.M., Chase, J.G., Continuous glucose monitoring measures can be used for glycemic control in the ICU: An in-silico study. Journal of Diabetes Science and Technology 12:1 (2018), 7–19.