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Physiological modeling, tight glycemic control, and the ICU clinician: what are models and how can they affect practice?


Critically ill patients are highly variable in their response to care and treatment. This variability and the search for improved outcomes have led to a significant increase in the use of protocolized care to reduce variability in care. However, protocolized care does not address the variability of outcome due to inter- and intra-patient variability, both in physiological state, and the response to disease and treatment. This lack of patient-specificity defines the opportunity for patient-specific approaches to diagnosis, care, and patient management, which are complementary to, and fit within, protocolized approaches.

Computational models of human physiology offer the potential, with clinical data, to create patient-specific models that capture a patient's physiological status. Such models can provide new insights into patient condition by turning a series of sometimes confusing clinical data into a clear physiological picture. More directly, they can track patient-specific conditions and thus provide new means of diagnosis and opportunities for optimising therapy.

This article presents the concept of model-based therapeutics, the use of computational models in clinical medicine and critical care in specific, as well as its potential clinical advantages, in a format designed for the clinical perspective. The review is presented in terms of a series of questions and answers. These aspects directly address questions concerning what makes a model, how it is made patient-specific, what it can be used for, its limitations and, importantly, what constitutes sufficient validation.

To provide a concrete foundation, the concepts are presented broadly, but the details are given in terms of a specific case example. Specifically, tight glycemic control (TGC) is an area where inter- and intra-patient variability can dominate the quality of care control and care received from any given protocol. The overall review clearly shows the concept and significant clinical potential of using computational models in critical care medicine.

The critically ill patient

Critically ill patients can be defined by the high variability in response to care and treatment. In particular, variability in outcome arises from variability in care and variability in the patient-specific response to care. The greater the variability, the more difficult the patient's management and the more likely a lesser outcome becomes. Hence, the recent increase in importance of protocolized care to minimize the iatrogenic component due to variability in care. Recent articles [1, 2] have noted that protocols are potentially most applicable to groups with well-known clinical pathways and limited comorbidities, where a "one size fits all" approach can be effective. Those outside this group may receive lesser care and outcomes compared with the greater number receiving benefit.

Figure 1 defines this problem in terms of variability in care that protocolized care can reduce, and a different, potentially less reducible, component due to inter- and intra-patient variability in response to treatment. The larger the area, the more difficult the patient can be to manage. Thus, protocolized care reduces only the nonpatient portion of this diagram. Equally, those whose clinical pathway is "straightforward" and can benefit most from protocolized care are likely to have limited inter- and intra-patient variability in response to treatment. Hence, the smallest, least variable case is one in which intra-patient response is reduced or managed in a patient-specific fashion, thus separating the final area into several smaller ones. A focus of this paper is that the model-based methods discussed here can provide patient-specific care that is robust to these intra- and inter-patient variabilities.

Figure 1

Variability in outcome of the critically ill patient defined by variability in response to therapy and variability in care. Shaded area defines the target zone for patient-specific care.

This issue is evident in many areas of care. For example, why are the complications of diabetes and therapeutic anticoagulation a leading cause of death or iatrogenic harm when they are amongst the most highly researched and understood fields in medicine? A PubMed search using the key words "diabetes mellitus" and "anticoagulation" returned 19,008 and 288,774 references, respectively, and a Google search multiplied these numbers to 1.14 M and 9.48 M pages. The collective experience of the drugs used in these conditions also is enormous; insulin, heparin, and warfarin were first used in humans more than 89, 76, and 57 years ago, respectively, and yet despite huge knowledge and experience, management of these conditions is fraught with problems.

What has led to this paradox? If, for example, managing diabetes was as straightforward as popping a few tablets or a daily insulin injection, doctors and patients would not still be struggling to get this right. Unfortunately, patients with diabetes have a widely variable clinical response, both within and between individuals, which often leaves clinicians unsuccessfully grappling with these nonlinear behaviors and responses. The randomized controlled trial (RCT) is regarded as the most reliable instrument on which to base treatment selection. However, recommendations from RCTs are based on overall cohort responses, not individual responses, and therefore cannot provide the necessary patient-specific therapeutic guidance, particularly when variability can have a major impact in titrating treatment.

We examine and review a new, emerging therapeutic approach that provides for individualized care that accounts for intra- and inter-patient variability within an overall protocolized and evidence-based framework. This review is done with reference to the management of glucose intolerance and diabetes in critically ill patients, but the overall approach is readily generalizable to other areas of intensive care medicine.

Physiological and clinical problem

Critically ill patients often experience stress-induced hyperglycemia and high insulin resistance [35] associated with increased morbidity and mortality [68]. Strong counter-regulatory (stress) hormone and proinflammatory immune responses lead to extreme insulin resistance and hyperglycemia, often exacerbated by high carbohydrate nutritional regimes and (relative) insulin deficiency. Inter- and intra-patient variability over different patients and as patient condition evolves make providing consistently tight glycemic control (TGC) across every individual patient a significant challenge, despite the growing use of protocolized care approaches.

This article uses TGC to present how computer models can be used at the bedside, within protocolized care, to provide patient-specific care and thus reduce the impact of intra- and inter-patient variability and provide care (within the shaded lower corner of Figure 1). TGC is a particularly apt example for model-based methods, as intra- and inter-patient variability in response to insulin can be extreme, leading to significant difficulty in providing safe and effective control [9].

In particular, recent randomized trials of TGC have failed to repeat promising early results [1012]. Equally, reduced outcomes due to hyperglycemia, hypoglycemia (if control is poor), and glycemic variability [13, 14], and the overall physiological basis in inflammatory and oxidative stress responses are increasingly understood [1517]. Thus, it seems increasingly clear that protocolization of care alone has not been able to reduce the variability in patient outcomes and that patient-specific solutions that manage inter- and intra-patient variation may be required to determine if TGC offers significant benefit. Hence, this review examines (physiological) model-based methods for TGC as a case example of the patient-specific solutions that are possible and the potential of these methods to improve care.

A series of questions

This review takes the reader through mathematical models in the context of TGC based on a series of clinically focused questions.

What is a mathematical model? Physiological relevance and representation

A mathematical model is a mathematical description of reality. In physiology, such a model underlies a certain number of assumptions about the physical, chemical, and biological processes involved. These mathematical models may vary significantly in their complexity and their objectives. They can range from relatively simple lumped-compartment models [1820] to very complex network representations and finite element models of several million degrees of freedom [21, 22].

For model-based TGC, the models should capture the fundamental underlying physiology as illustrated schematically in Figure 2. In particular, they should capture the transport of exogenous insulin, the production of endogenous insulin, the appearance of endogenous and exogenous carbohydrate as blood glucose, and, critically, both insulin-mediated and insulin-independent uptake of glucose. In addition, insulin-mediated uptake must have the ability to capture inter- and intra-patient variability in the time-varying insulin resistance observed in these patients. The model structure and physiological relevance of Figure 2 is detailed in several references [23, 24] and in the appendix in Additional File 1 with TGC specific modeling details for the interested reader.

Figure 2

Relevant physiology required to create effective models of human metabolism for the critically ill patient. Insulin sensitivity is a whole body parameter representing is the average of the insulin resistance of each particular organ, which are all differentially regulated in stress conditions, and thus the dashed line indicates insulin-mediated uptake. Its value is patient-specific and can vary hourly [48, 73].

In the critical care arena, the use of in silico physiological models is only emerging. However, there are already model-based or model-derived applications for managing sedation [25, 26], cardiovascular diagnosis and therapy [27, 28], mechanical ventilation [29, 30], and the diagnosis of sepsis [31, 32]. Particular to TGC, there are already some attempts at modeling for both understanding and implementing TGC [23, 3342], with a review of many in [43].

What can a model do? Capabilities and limitations

All models have different uses or goals. A model may be used to describe, interpret, predict, or explain [18, 19] a physiological process. Real capabilities depend on the chosen degree of approximation, based on a combination of the knowledge of the physiological processes involved and implementation goal.

However, a model definition is not enough. Model parameter values must be assumed from clinical data or reports, or identified (mathematically) from clinical data. These values determine whether the model is generic to a population or (more) patient-specific with parameters identified from a particular patient's data. In reality, most models are a mixture of both approaches, where patient-specific parameters are identified for those parameters critical to the application.

However, once identified, patient-specific models in particular offer a range of potential opportunities, including, for TGC, the:

  • Simulation of so-called virtual patients [41, 4448] to design [33, 41], analyze [49, 50], or optimize glycemic control methods.

  • Implementation at the bedside for patient-specific care in which patient-specific model parameters are identified in real-time to guide care [3436, 40, 47, 5153].

Equally, metabolic models can be used with patient data to investigate a range of physiological behaviors [5456].

In intensive care, patient-specific metabolic model parameters also have been used as sepsis biomarkers because they can accurately reflect the inflammatory status of the patient and severity of illness [31, 32]. These studies showed that model-based insulin sensitivity alone could provide 70-80% sensitivity and specificity in assessing sepsis compared with a control cohort, yielding a negative predictive value (NPV) greater than 99%, thus clearly identifying periods where antibiotic therapy was not necessary. Such an outcome thus uses model-based physiological insight not otherwise available to provide a novel, non-invasive diagnostic.

Similarly, model-based insulin sensitivity has been used to assess the impact of glucocorticoid therapy on glycemic control [57]. In particular, it has been thought that glucocorticoid therapy would significantly increase insulin requirements in TGC based on the results of studies showing significantly increased insulin resistance when given to healthy individuals. However, this modeling showed the effect to be 5-10 times smaller in ICU patients, to be highly patient-specific depending on patient status, and to (overall) have very little impact on TGC dosing requirements, as a result. The ability to discern patient-specific impacts at the bedside using the model can provide significant insight.

Finally, TGC models can be used to assess the quality of control achieved clinically relative to other protocols using virtual patients [24, 33, 46, 50]. In Suhaimi et al [50] the multi-center Glucontrol trial [12] protocol was evaluated versus the control achieved with the Specialized Relative Insulin and Nutrition Titration (SPRINT) [58] protocol. The model and analysis yielded clear directions on protocol compliance and the importance of understanding nutrition delivery in the provision of TGC. It also was able to show a surprising similarity in the inter- and intra-patient metabolic variability of critically ill patients between the centers and studies compared.

Finally, physiologically relevant computer models have a longer, similar history in the broader diabetes field, primarily for research to gain patho-physiological insight rather than direct use in controlling glycemia [18, 54, 55, 5964].

All models have limitations. Limited bedside data and the quality of the mathematical process used to find model parameters from data (identification method) can have a significant impact on identified parameter accuracy and model performance [24, 6466], as well as entailing specific assumptions [23, 24, 46, 67]. In particular, models that are not physiologically relevant [37] or do not have all the necessary physiology relevant to the patient group to which it is applied [6870] can yield inaccurate results. These studies failed to capture the enhanced glycemic production and reduced renal and hepatic clearances, the balance of which can dominate the overall metabolic behavior of the critically ill. Similarly, one can over-model a situation with too much complexity and create models that are not useful for implementation. As a result, their predictive ability and use in control was less effective. Such limitations must be rigorously quantified [23, 57] to understand the quality of answer that any given model can provide.

How do we know that a model is good? Prediction and validation

Making suitable assumptions and choosing a desired degree of approximation do not naturally generate a "good" model. Similarly, being able to find model parameters that ensure it fits a set of clinical data does not make a model valid, except to show that it can capture the dynamics observed clinically. It is critical to validate the model to determine if its performance is acceptable for its intended application.

For designing and/or implementing model-based TGC, where the model is directly used to provide patient-specific advice, it is necessary to ensure the models ability to:

  • For design: predict the overall glycemic outcomes (median and variation) of patients and/or cohorts for a (simulated) protocol [44, 46]

  • For implementation: predict the glycemic outcome of a clinical intervention during a relevant 1- to 4-hour timeframe typical of TGC intervention frequencies [24, 38, 46, 47, 49, 50, 71, 72].

These metrics define validity in its ability to capture patient-specific behaviors to a clinically acceptable level (approximately equivalent to measurement error). Errors thus reflect model limitations.

To date, only two ICU focused metabolic model structures have been validated with respect to individual patient-specific predictions (for implementation and design) [23, 24, 39, 44, 46]. Only one has been validated for cohorts [46].

The specific models in these studies define the critically ill patients by their time varying counter-regulatory and inflammatory status, as seen metabolically via their overall insulin sensitivity or metabolic balance that can vary hourly in acute cases, as illustrated in Figure 2. All other parameters were set at population constants following detailed parametric sensitivity studies based on assessing parameters impact on predictive performance [23, 24, 48]. Hence, the models provide median blood glucose prediction errors for specific interventions that are less than 3-4%. When an independent clinical protocol was simulated on virtual patients created the median cohort and patient glycemia and its variation were captured to within 3% and 5% respectively compared with the original clinical data (see [46] and appendix). Hence, validate the models and modeling approach, as well as show how they capture, through one main parameter, the metabolic dynamism of the critically ill patient.

Why use models? Patient-specific insight and care from available data

The time-scale for decision making in the ICU ranges from 1-2 minutes in acute cases to hours for some therapies, such as mechanical ventilation or TGC. It often requires the synthesis of a wide range of patient-specific data across a number of monitors, assays, and physiological systems. Typically, clinicians apply their experience and intuition to make diagnoses and develop treatment plans, based on how they aggregate that data and how it fits their mental model of what they are observing. More specifically, they are using this data and a mental model to estimate occult physiological variables (i.e., make a diagnosis or determine patient state) and from that developing decisions for treatment. Given the range of experience, intuition, and mental models across clinicians, diagnosis is open to error and care can be quite variable.

A validated and relevant physiological model can create a more consistent, high-resolution physiological picture of the overall physiological system that also is potentially more accurate than the clinician's mental model. In particular, computer models and methods offer the ability to aggregate more data and to discern subtle trends in data that may otherwise be easily missed.

For model-based TGC, the patient-specific model variable that determines patient-specific state and response to therapy is the overall, whole body insulin sensitivity [42, 48, 73]. This value is itself the average of the insulin resistance of each particular organ, each of which is differentially regulated in stress conditions and sets the balance between insulin and nutrition inputs and outcome glycemia. However, given the variations in patient kinetics and levels of these inputs, it is very difficult, if not impossible, for a clinician to review these and arrive at an accurate assessment of its current value. But, without such a value, optimal dosing of insulin, including the effects of insulin saturation, for example, is not possible with any resolution.

Hence, the ability of a validated, physiologically relevant model to provide a patient-specific value and its potential variation in future offers unique insight and potential to optimize interventions that is not otherwise available [48, 72, 73]. Thus, validated, patient-specific models can test these insights and proposed treatments in silico, before application, improving safety. Because they use existing data and can predict accurately they offer the clinician a window on past and present behaviors, as well as a view of how to customize treatment for optimal future behaviors.

What are the differences between computer-based, model-based, and model-derived TGC? The model, the implementation, and the level of patient-specificity

There are an increasing number of computer-based TGC protocols that are not model-based [7479] and thus do not offer the same physiological insight or "picture." They are, more accurately, an extension of protocolized care in that they take a protocol and put it on the computer. Equally, such protocolized care provides a cohort-based approach that is consistent ("one size fits all") but not necessarily patient-specific. Thus, the main element that differentiates a model-based system is the use of a physiologically relevant, validated model to create a patient-specific picture of patient state and provide patient-specific ("one method fits all") advice.

A hybrid path uses what we denote "model-derived" protocols. The only current example of this approach is the SPRINT protocol [58]. This paper-based system was created and optimized in silico by using clinically validated models and virtual patients [33, 80]. However, it provides patient-specific care, based on its design using the model, within the paper-based abstraction used to provide easy uptake in the ICU.

Hence, the critical difference is that model-based methods implicitly enforce a protocol, but, in their patient-specificity, translate the "one size fits all" approach of a fixed protocol to a "one method fits all" patient-specific form of care. For TGC these methods are already (increasingly) proven in both model-derived [44, 58] and model-based [36, 47, 48, 52, 53] formats. Their success is due to their unique ability, when properly modeled and validated, to provide much better, real-time management of both intra- and inter-patient variability that typical non-model-based clinical protocols cannot and, as a result, provide a level of care that is beyond existing clinical protocols.


Models and model-based methods have a lot to offer in a wide range of clinical areas in medicine, and in critical care specifically. Using TGC as an example, they can offer significant physiological insight into patient status and behavior that are not readily available at the bedside or part of the typical, clinical mental model. Hence, they enable the means to develop and implement "one method fits all" patient-specific approaches to diagnosis and care. Their ability to reduce the impact of intra- and inter-patient variability, within a protocolized framework that reduces variability in care, can improve care and outcomes for all patients. Hence, models and model-based methods represent an important area of potentially increasing significance to the practice of critical care medicine, and TGC in particular, in the coming years.


  1. 1.

    Wendon J: Critical care "normality": individualized versus protocolized care. Crit Care Med 2010,38(10 Suppl):S590–599.

    PubMed  Article  Google Scholar 

  2. 2.

    Kavanagh BP, Meyer LJ: Normalizing physiological variables in acute illness: five reasons for caution. Intensive Care Med 2005,31(9):1161–1167. 10.1007/s00134-005-2729-7

    PubMed  Article  Google Scholar 

  3. 3.

    McCowen KC, Malhotra A, Bistrian BR: Stress-induced hyperglycemia. Crit Care Clin 2001,17(1):107–124. 10.1016/S0749-0704(05)70154-8

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Capes SE, Hunt D, Malmberg K, Gerstein HC: Stress hyperglycaemia and increased risk of death after myocardial infarction in patients with and without diabetes: a systematic overview. Lancet 2000,355(9206):773–778. 10.1016/S0140-6736(99)08415-9

    CAS  PubMed  Article  Google Scholar 

  5. 5.

    Black PR, Brooks DC, Bessey PQ, Wolfe RR, Wilmore DW: Mechanisms of insulin resistance following injury. Ann Surg 1982,196(4):420–435. 10.1097/00000658-198210000-00005

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  6. 6.

    Krinsley JS: Association between hyperglycemia and increased hospital mortality in a heterogeneous population of critically ill patients. Mayo Clin Proc 2003,78(12):1471–1478. 10.4065/78.12.1471

    PubMed  Article  Google Scholar 

  7. 7.

    Van den Berghe G, Wouters P, Weekers F, Verwaest C, Bruyninckx F, Schetz M, Vlasselaers D, Ferdinande P, Lauwers P, Bouillon R: Intensive insulin therapy in the critically ill patients. N Engl J Med 2001,345(19):1359–1367. 10.1056/NEJMoa011300

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Waeschle RM, 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. Crit Care (London) 2008,12(5):R129. 10.1186/cc7097

    Article  Google Scholar 

  9. 9.

    Chase JG, Le Compte AJ, Suhaimi F, Shaw GM, Lynn A, Lin J, Pretty CG, Razak N, Parente JD, Hann CE, et al.: Tight glycemic control in critical care. The leading role of insulin sensitivity and patient variability: a review and model-based analysis. Comput Methods Programs Biomed 2010. Online Early Release.

    Google Scholar 

  10. 10.

    Finfer S, Chittock DR, Su SY, Blair D, Foster D, Dhingra V, Bellomo R, Cook D, Dodek P, Henderson WR, et al.: Intensive versus conventional glucose control in critically ill patients. N Engl J Med 2009,360(13):1283–1297.

    PubMed  Article  Google Scholar 

  11. 11.

    Brunkhorst FM, Engel C, Bloos F, Meier-Hellmann A, Ragaller M, Weiler N, Moerer O, Gruendling M, Oppert M, Grond S, et al.: Intensive insulin therapy and pentastarch resuscitation in severe sepsis. N Engl J Med 2008,358(2):125–139. 10.1056/NEJMoa070716

    CAS  PubMed  Article  Google Scholar 

  12. 12.

    Preiser JC, Devos P, Ruiz-Santana S, Melot C, Annane D, Groeneveld J, Iapichino G, Leverve X, Nitenberg G, Singer P, et al.: A prospective randomized 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(10):1738–1748. 10.1007/s00134-009-1585-2

    CAS  PubMed  Article  Google Scholar 

  13. 13.

    Egi M, Bellomo R, Stachowski E, French CJ, Hart G: Variability of blood glucose concentration and short-term mortality in critically ill patients. Anesthesiology 2006,105(2):244–252. 10.1097/00000542-200608000-00006

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Bagshaw SM, Bellomo R, Jacka MJ, Egi M, Hart GK, George C: The impact of early hypoglycemia and blood glucose variability on outcome in critical illness. Crit Care 2009,13(3):R91. 10.1186/cc7921

    PubMed Central  PubMed  Article  Google Scholar 

  15. 15.

    Brownlee M: Biochemistry and molecular cell biology of diabetic complications. Nature 2001,414(6865):813–820. 10.1038/414813a

    CAS  PubMed  Article  Google Scholar 

  16. 16.

    Dandona P, Chaudhuri A, Ghanim H, Mohanty P: Anti-inflammatory effects of insulin and pro-inflammatory effects of glucose: relevance to the management of acute myocardial infarction and other acute coronary syndromes. Rev Cardiovasc Med 2006,7(Suppl 2):S25–34.

    PubMed  Google Scholar 

  17. 17.

    Langouche L, Vanhorebeek I, Vlasselaers D, Vander Perre S, Wouters PJ, Skogstrand K, Hansen TK, Van den Berghe G: Intensive insulin therapy protects the endothelium of critically ill patients. J Clin Invest 2005,115(8):2277–2286. 10.1172/JCI25385

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  18. 18.

    Carson ER, Cobelli C: Modelling methodology for physiology and medicine. Acad Press Series Biomed Eng 2001, xiv: 421.

    Google Scholar 

  19. 19.

    Keener JP, Sneyd J: Mathematical Physiology. New York: Springer; 1998.

    Google Scholar 

  20. 20.

    Smith BW, Chase JG, Nokes RI, Shaw GM, Wake G: Minimal haemodynamic system model including ventricular interaction and valve dynamics. Med Eng Phys 2004,26(2):131–139. 10.1016/j.medengphy.2003.10.001

    PubMed  Article  Google Scholar 

  21. 21.

    Kerckhoffs RC, Neal ML, Gu Q, Bassingthwaighte JB, Omens JH, McCulloch AD: Coupling of a 3D finite element model of cardiac ventricular mechanics to lumped systems models of the systemic and pulmonic circulation. Ann Biomed Eng 2007,35(1):1–18.

    PubMed Central  PubMed  Article  Google Scholar 

  22. 22.

    Hunter PJ, Nielsen PM, Smaill BH, LeGrice IJ, Hunter IW: An anatomical heart model with applications to myocardial activation and ventricular mechanics. Crit Rev Biomed Eng 1992,20(5–6):403–426.

    CAS  PubMed  Google Scholar 

  23. 23.

    Lin J, Razak NN, Pretty CG, Le Compte A, Docherty P, Parente JD, Shaw GM, Hann CE, Geoffrey Chase J: A physiological Intensive control insulin-nutrition-glucose (ICING) model validated in critically ill patients. Comput Methods Programs Biomed 2011.

    Google Scholar 

  24. 24.

    Hann CE, Chase JG, Lin J, Lotz T, Doran CV, Shaw GM: Integral-based parameter identification for long-term dynamic verification of a glucose-insulin system model. Comput Methods Programs Biomed 2005,77(3):259–270. 10.1016/j.cmpb.2004.10.006

    PubMed  Article  Google Scholar 

  25. 25.

    Rudge AD, Chase JG, Shaw GM, Lee D: Physiological modelling of agitation-sedation dynamics. Med Eng Phys 2006,28(1):49–59. 10.1016/j.medengphy.2005.03.005

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Chase JG, Rudge AD, Shaw GM, Wake GC, Lee D, Hudson IL, Johnston L: Modeling and control of the agitation-sedation cycle for critical care patients. Med Eng Phys 2004,26(6):459–471. 10.1016/j.medengphy.2004.02.001

    PubMed  Article  Google Scholar 

  27. 27.

    Starfinger C, Chase JG, Hann CE, Shaw GM, Lambert P, Smith BW, Sloth E, Larsson A, Andreassen S, Rees S: Prediction of hemodynamic changes towards PEEP titrations at different volemic levels using a minimal cardiovascular model. Comput Methods Programs Biomed 2008,91(2):128–134. 10.1016/j.cmpb.2008.03.004

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Starfinger C, Hann CE, Chase JG, Desaive T, Ghuysen A, Shaw GM: Model-based cardiac diagnosis of pulmonary embolism. Comput Methods Programs Biomed 2007,87(1):46–60. 10.1016/j.cmpb.2007.03.010

    CAS  PubMed  Article  Google Scholar 

  29. 29.

    Sundaresan A, Yuta T, Hann CE, Geoffrey Chase J, Shaw GM: A minimal model of lung mechanics and model-based markers for optimizing ventilator treatment in ARDS patients. Comput Methods Programs Biomed 2009,95(2):166–80. 10.1016/j.cmpb.2009.02.008

    PubMed  Article  Google Scholar 

  30. 30.

    Sundaresan A, Chase JG, Hann CE, Shaw GM: Cardiac output estimation using pulmonary mechanics in mechanically ventilated patients. Biomed Eng Online 2010,9(1):80. 10.1186/1475-925X-9-80

    PubMed Central  PubMed  Article  Google Scholar 

  31. 31.

    Blakemore A, Wang S, LeCompte A, Wong X, Shaw G, Lin J, Lotz T, Hann C, Chase J: Model-based insulin sensitivity as a sepsis diagnostic in critical care. J Diabetes Sci Technol (JoDST) 2008,2(3):468–477.

    Article  Google Scholar 

  32. 32.

    Lin J, Parente JD, Chase JG, Shaw GM, Blakemore AJ, Lecompte AJ, Pretty C, Razak NN, Lee DS, Hann CE, et al.: Development of a model-based clinical sepsis biomarker for critically ill patients. Comput Methods Programs Biomed 2010.

    Google Scholar 

  33. 33.

    Lonergan T, LeCompte A, Willacy M, Chase JG, Shaw GM, Wong XW, Lotz T, Lin J, Hann CE: A simple insulin-nutrition protocol for tight glycemic control in critical illness: development and protocol comparison. Diabetes Technol Ther 2006,8(2):191–206. 10.1089/dia.2006.8.191

    PubMed  Article  Google Scholar 

  34. 34.

    Chase JG, Shaw GM, Lin J, Doran CV, Hann C, Lotz T, Wake GC, Broughton B: Targeted glycemic reduction in critical care using closed-loop control. Diabetes Technol Ther 2005,7(2):274–282. 10.1089/dia.2005.7.274

    PubMed  Article  Google Scholar 

  35. 35.

    Blaha J, Kopecky P, Matias M, Hovorka R, Kunstyr J, Kotulak T, Lips M, Rubes D, Stritesky M, Lindner J, et al.: Comparison of three protocols for tight glycemic control in cardiac surgery patients. Diabetes Care 2009,32(5):757–761. 10.2337/dc08-1851

    PubMed Central  PubMed  Article  Google Scholar 

  36. 36.

    Plank J, Blaha J, Cordingley J, Wilinska ME, Chassin LJ, Morgan C, Squire S, Haluzik M, Kremen J, Svacina S, et al.: Multicentric, randomized, controlled trial to evaluate blood glucose control by the model predictive control algorithm versus routine glucose management protocols in intensive care unit patients. Diabetes Care 2006,29(2):271–276. 10.2337/diacare.29.02.06.dc05-1689

    PubMed  Article  Google Scholar 

  37. 37.

    Van Herpe T, Espinoza M, Pluymers B, Goethals I, Wouters P, Van den Berghe G, De Moor B: An adaptive input-output modeling approach for predicting the glycemia of critically ill patients. Physiol Meas 2006,27(11):1057–1069. 10.1088/0967-3334/27/11/001

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Pielmeier U, Andreassen S, Nielsen BS, Chase JG, Haure P: A simulation model of insulin saturation and glucose balance for glycemic control in ICU patients. Comput Methods Programs Biomed 2010,97(3):211–222. 10.1016/j.cmpb.2009.06.004

    PubMed  Article  Google Scholar 

  39. 39.

    Pielmeier U, Andreassen S, Juliussen B, Chase JG, Nielsen BS, Haure P: The Glucosafe system for tight glycemic control in critical care: a pilot evaluation study. J Crit Care 2010,25(1):97–104. 10.1016/j.jcrc.2009.10.003

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Hovorka R, Kremen J, Blaha J, Matias M, Anderlova K, Bosanska L, Roubicek T, Wilinska ME, Chassin LJ, Svacina S, 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. J Clin Endocrinol Metab 2007,92(8):2960–2964. 10.1210/jc.2007-0434

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Wilinska ME, Chassin L, Hovorka R: In silico testing--impact on the progress of the closed loop insulin infusion for critically ill patients project. J Diabetes Sci Technol 2008,2(3):417–423.

    PubMed Central  PubMed  Article  Google Scholar 

  42. 42.

    Hovorka R, Chassin LJ, Ellmerer M, Plank J, Wilinska ME: A simulation model of glucose regulation in the critically ill. Physiol Meas 2008,29(8):959–978. 10.1088/0967-3334/29/8/008

    PubMed  Article  Google Scholar 

  43. 43.

    Chase J, Shaw GM, Wong XW, Lotz T, Lin J, Hann CE: Model-based glycaemic control in critical care - a review of the state of the possible. Biomed Signal Process Control 2006,1(1):3–21. 10.1016/j.bspc.2006.03.002

    Article  Google Scholar 

  44. 44.

    Chase JG, Shaw GM, Lotz T, LeCompte A, Wong J, Lin J, Lonergan T, Willacy M, Hann CE: Model-based insulin and nutrition administration for tight glycaemic control in critical care. Curr Drug Deliv 2007,4(4):283–296. 10.2174/156720107782151223

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Chase J, LeCompte A, Shaw G, Blakemore A, Wong J, Lin J, Hann C: A benchmark data set for model-based glycemic control in critical care. J Diabetes Sci Technol (JoDST) 2008,24(4):584–594.

    Article  Google Scholar 

  46. 46.

    Chase JG, Suhaimi F, Penning S, Preiser JC, Le Compte AJ, Lin J, Pretty CG, Shaw GM, Moorhead KT, Desaive T: Validation of a model-based virtual trials method for tight glycemic control in intensive care. Biomed Eng Online 2010, 9: 84. 10.1186/1475-925X-9-84

    PubMed Central  PubMed  Article  Google Scholar 

  47. 47.

    LeCompte A, Chase J, Lynn A, Hann C, Shaw G, Wong X, Lin J: Blood glucose controller for neonatal intensive care: virtual trials development and 1st clinical trials. J Diabetes Sci Technol (JoDST) 2009,3(5):1066–1081.

    Article  Google Scholar 

  48. 48.

    Lin J, Lee D, Chase JG, Shaw GM, Le Compte A, Lotz T, Wong J, Lonergan T, Hann CE: Stochastic modelling of insulin sensitivity and adaptive glycemic control for critical care. Comput Methods Programs Biomed 2008,89(2):141–152. 10.1016/j.cmpb.2007.04.006

    PubMed  Article  Google Scholar 

  49. 49.

    Pielmeier U, Chase J, Andreassen S, Haure P, Nielsen B, Shaw G: Prediction validation of two glycaemic control models in critical care. 17th IFAC World Congress (IFAC WC2008): July 6–11 2008; Seoul, Korea 2008, 8074–8079.

    Google Scholar 

  50. 50.

    Suhaimi F, Le Compte A, Preiser JC, Shaw GM, Massion P, Radermecker R, Pretty C, Lin J, Desaive T, Chase JG: What makes tight glycemic control (TGC) tight? The impact of variability and nutrition in 2 clinical studies. J Diabetes Sci Technol 2010,4(2):284–298.

    PubMed Central  PubMed  Article  Google Scholar 

  51. 51.

    Chase JG, Shaw GM, Lin J, Doran CV, Hann C, Robertson MB, Browne PM, Lotz T, Wake GC, Broughton B: Adaptive bolus-based targeted glucose regulation of hyperglycaemia in critical care. Med Eng Phys 2005,27(1):1–11. 10.1016/j.medengphy.2004.08.006

    PubMed  Article  Google Scholar 

  52. 52.

    Wong XW, Singh-Levett I, Hollingsworth LJ, Shaw GM, Hann CE, Lotz T, Lin J, Wong OS, Chase JG: A novel, model-based insulin and nutrition delivery controller for glycemic regulation in critically ill patients. Diabetes Technol Ther 2006,8(2):174–190. 10.1089/dia.2006.8.174

    CAS  PubMed  Article  Google Scholar 

  53. 53.

    Wong XW, Chase JG, Shaw GM, Hann CE, Lotz T, Lin J, Singh-Levett I, Hollingsworth LJ, Wong OS, Andreassen S: Model predictive glycaemic regulation in critical illness using insulin and nutrition input: a pilot study. Med Eng Phys 2006,28(7):665–681. 10.1016/j.medengphy.2005.10.015

    CAS  PubMed  Article  Google Scholar 

  54. 54.

    Dalla Man C, Caumo A, Basu R, Rizza R, Toffolo G, Cobelli C: Minimal model estimation of glucose absorption and insulin sensitivity from oral test: validation with a tracer method. Am J Physiol Endocrinol Metab 2004,287(4):E637-E643. 10.1152/ajpendo.00319.2003

    CAS  PubMed  Article  Google Scholar 

  55. 55.

    Dalla Man C, Campioni M, Polonsky KS, Basu R, Rizza RA, Toffolo G, Cobelli C: Two-hour seven-sample oral glucose tolerance test and meal protocol: minimal model assessment of beta-cell responsivity and insulin sensitivity in nondiabetic individuals. Diabetes 2005,54(11):3265–3273. 10.2337/diabetes.54.11.3265

    CAS  PubMed  Article  Google Scholar 

  56. 56.

    Thorsteinsson B: Kinetic models for insulin disappearance from plasma in man. Dan Med Bull 1990,37(2):143–153.

    CAS  PubMed  Google Scholar 

  57. 57.

    Pretty C, Chase JG, Lin J, Shaw GM, Le Compte A, Razak N, Parente JD: Impact of glucocorticoids on insulin resistance in the critically ill. Comput Methods Programs Biomed 2011.

    Google Scholar 

  58. 58.

    Chase JG, Shaw G, Le Compte A, Lonergan T, Willacy M, Wong XW, Lin J, Lotz T, Lee D, Hann C: Implementation and evaluation of the SPRINT protocol for tight glycaemic control in critically ill patients: a clinical practice change. Crit Care 2008,12(2):R49. 10.1186/cc6868

    PubMed Central  PubMed  Article  Google Scholar 

  59. 59.

    Bergman RN, Prager R, Volund A, Olefsky JM: Equivalence of the insulin sensitivity index in man derived by the minimal model method and the euglycemic glucose clamp. J Clin Invest 1987,79(3):790–800. 10.1172/JCI112886

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  60. 60.

    Kovatchev BP, Breton M, Man CD, Cobelli C: In Silico Preclinical Trials: a proof of concept in closed-loop control of type 1 diabetes. J Diabetes Sci Technol (Online) 2009,3(1):44–55.

    Article  Google Scholar 

  61. 61.

    Lehmann ED, Deutsch T: A physiological model of glucose-insulin interaction in type 1 diabetes mellitus. J Biomed Eng 1992,14(3):235–242. 10.1016/0141-5425(92)90058-S

    CAS  PubMed  Article  Google Scholar 

  62. 62.

    Lehmann ED: AIDA: a computer-based interactive educational diabetes simulator. Diabetes Educator 1998,24(3):341. 10.1177/014572179802400309

    CAS  PubMed  Article  Google Scholar 

  63. 63.

    Arleth T, Andreassen S, Federici MO, Benedetti MM: A model of the endogenous glucose balance incorporating the characteristics of glucose transporters. Comput Methods Programs Biomed 2000,62(3):219–234. 10.1016/S0169-2607(00)00069-9

    CAS  PubMed  Article  Google Scholar 

  64. 64.

    Cobelli C, Caumo A, Omenetto M: Minimal model SG overestimation and SI underestimation: improved accuracy by a Bayesian two-compartment model. Am J Physiol 1999,277(3 Pt 1):E481–488.

    CAS  PubMed  Google Scholar 

  65. 65.

    Hann C, Chase J, Ypma M, Elfring J, Nor N, Lawrence P, Shaw G: The impact of parameter identification methods on drug therapy control in an intensive care unit. Open Med Inform J 2008, 2: 92–104. 10.2174/1874431100802010092

    PubMed Central  PubMed  Article  Google Scholar 

  66. 66.

    Pillonetto G, Sparacino G, Cobelli C: Numerical non-identifiability regions of the minimal model of glucose kinetics: superiority of Bayesian estimation. Math Biosci 2003,184(1):53–67. 10.1016/S0025-5564(03)00044-0

    CAS  PubMed  Article  Google Scholar 

  67. 67.

    Cobelli C, Bettini F, Caumo A, Quon MJ: Overestimation of minimal model glucose effectiveness in presence of insulin response is due to undermodeling. Am J Physiol 1998,275(6 Pt 1):E1031–1036.

    CAS  PubMed  Google Scholar 

  68. 68.

    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 2006, 1: 5432–5435.

    PubMed  Article  Google Scholar 

  69. 69.

    Chee F, Fernando TL, Savkin AV, van Heeden V: Expert PID control system for blood glucose control in critically ill patients. IEEE Trans Inf Technol Biomed 2003,7(4):419–425. 10.1109/TITB.2003.821326

    PubMed  Article  Google Scholar 

  70. 70.

    Doran CV, Hudson NH, Moorhead KT, Chase JG, Shaw GM, Hann CE: Derivative weighted active insulin control modelling and clinical trials for ICU patients. Medical Engineering & Physics 2004,26(10):855–866. 10.1016/j.medengphy.2004.08.004

    Article  Google Scholar 

  71. 71.

    Le Compte A, Chase JG, Russell G, Lynn A, Hann C, Shaw G, Wong XW, Blakemore A, Lin J: Modeling the glucose regulatory system in extreme preterm infants. Comput Methods Programs Biomed 2010.

    Google Scholar 

  72. 72.

    Le Compte AJ, Lee DS, Chase JG, Lin J, Lynn A, Shaw GM: Blood glucose prediction using stochastic modeling in neonatal intensive care. IEEE Trans Biomed Eng 2010,57(3):509–518.

    PubMed  Article  Google Scholar 

  73. 73.

    Lin J, Lee DS, Chase JG, Hann CE, Lotz T, Wong XW: Stochastic modelling of insulin sensitivity variability in critical care. Biomed Signal Process Control 2006, 1: 229–242. 10.1016/j.bspc.2006.09.003

    Article  Google Scholar 

  74. 74.

    Davidson PC, Steed RD, Bode BW: Glucommander: a computer-directed intravenous insulin system shown to be safe, simple, and effective in 120,618 h of operation. Diabetes Care 2005,28(10):2418–2423. 10.2337/diacare.28.10.2418

    CAS  PubMed  Article  Google Scholar 

  75. 75.

    Vogelzang M, Zijlstra F, Nijsten MW: Design and implementation of GRIP: a computerized glucose control system at a surgical intensive care unit. BMC Med Inform Decis Mak 2005,5(38):10.

    Google Scholar 

  76. 76.

    Cochran S, Miller E, Dunn K: EndoTool software for tight glucose control for critically ill patients. Crit Care Med 2006,34(Suppl 2):A68.

    Article  Google Scholar 

  77. 77.

    Thomas AN, Marchant AE, Ogden MC, Collin S: Implementation of a tight glycaemic control protocol using a web-based insulin dose calculator. Anaesthesia 2005,60(11):1093–1100. 10.1111/j.1365-2044.2005.04375.x

    CAS  PubMed  Article  Google Scholar 

  78. 78.

    Eslami S, Abu-Hanna A, de Jonge E, de Keizer NF: Tight glycemic control and computerized decision-support systems: a systematic review. Intensive Care Med 2009,35(9):1505–1517. 10.1007/s00134-009-1542-0

    PubMed Central  PubMed  Article  Google Scholar 

  79. 79.

    Morris AH, Orme J Jr, Truwit JD, Steingrub J, Grissom C, Lee KH, Li GL, Thompson BT, Brower R, Tidswell M, et al.: A replicable method for blood glucose control in critically Ill patients. Crit Care Med 2008,36(6):1787–1795. 10.1097/CCM.0b013e3181743a5a

    CAS  PubMed  Article  Google Scholar 

  80. 80.

    Lonergan T, Compte AL, Willacy M, Chase JG, Shaw GM, Hann CE, Lotz T, Lin J, Wong XW: A pilot study of the SPRINT protocol for tight glycemic control in critically Ill patients. Diabetes Technol Ther 2006,8(4):449–462. 10.1089/dia.2006.8.449

    PubMed  Article  Google Scholar 

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Financial support provided by:

Aaron Le Compte: New Zealand Tertiary Education Commission and NZ Foundation for Research Science and Technology Post-Doctoral Fellowship Grant

Sophie Penning: FNRS (Fonds National de la Recherche Scientifique) Research Fellow

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Corresponding authors

Correspondence to J Geoffrey Chase or Thomas Desaive.

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Competing interests

The authors declare that they have no competing interests.

Authors' contributions

JGC, GS, TD, JCP, SP, and ALC conceived and developed the review and written manuscript. All authors approved the final manuscript.

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Appendix: Metabolic System Model and Insulin Sensitivity (SI)

Additional file 1: . This file contains a full description of the metabolic system model equations, their validation and physiological validity, the methods to identify the model-based insulin sensitivity (SI) parameter, its correlation to gold-standard tests, and, finally, the definition and application of stochastic models of model-based insulin sensitivity (SI). (DOC 966 KB)

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Chase, J., Le Compte, A.J., Preiser, JC. et al. Physiological modeling, tight glycemic control, and the ICU clinician: what are models and how can they affect practice?. Ann. Intensive Care 1, 11 (2011).

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  • Virtual Patient
  • Glycemic Variability
  • Tight Glycemic Control
  • Physiological Insight
  • Extreme Insulin Resistance