Tight glycemic control in critical care – The leading role of insulin sensitivity and patient variability: A review and model-based analysis

https://doi.org/10.1016/j.cmpb.2010.11.006Get rights and content

Abstract

Tight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial successful TGC trials that reduced mortality and other outcomes has proven difficult with more failures than successes. Hence, there has been growing debate over the necessity of TGC, its goals, the risk of severe hypoglycemia, and target cohorts.

This paper provides a review of TGC via new analyses of data from several clinical trials, including SPRINT, Glucontrol and a recent NICU study. It thus provides both a review of the problem and major background factors driving it, as well as a novel model-based analysis designed to examine these dynamics from a new perspective. Using these clinical results and analysis, the goal is to develop new insights that shed greater light on the leading factors that make TGC difficult and inconsistent, as well as the requirements they thus impose on the design and implementation of TGC protocols.

A model-based analysis of insulin sensitivity using data from three different critical care units, comprising over 75,000 h of clinical data, is used to analyse variability in metabolic dynamics using a clinically validated model-based insulin sensitivity metric (SI). Variation in SI provides a new interpretation and explanation for the variable results seen (across cohorts and studies) in applying TGC. In particular, significant intra- and inter-patient variability in insulin resistance (1/SI) is seen be a major confounder that makes TGC difficult over diverse cohorts, yielding variable results over many published studies and protocols. Further factors that exacerbate this variability in glycemic outcome are found to include measurement frequency and whether a protocol is blind to carbohydrate administration.

Introduction

Critically ill patients often experience stress-induced hyperglycemia and high insulin resistance [1], [2], [3], [4], [5]. It is strongly associated with increased mortality [6], [7], [8], [9], [10]. Hyperglycemia is also associated with increases in other negative clinical outcomes, including infection [11], sepsis and septic shock [10], [12], [13], myocardial infarction [2], and polyneuropathy and multi-organ failure [3], [14].

In specific, the effect of a strong counter-regulatory (stress) hormone response in stimulating endogenous glucose production (EGP) and inhibiting insulin production and/or action, is further aggravated by the similar impact of a strong pro-inflammatory immune responses [15], [16], [17]. Thus, both factors significantly increase effective insulin resistance. Absolute and relative insulin deficiency is a further cause. Finally, high glucose content nutritional regimes exacerbate hyperglycemia and thus mortality [18], [19], [20], [21], [22], [23], whereas reducing glucose intake from all sources has reduced glycemic levels [19], [22], [24], [25], [26] and can alleviate the impact of the hyperglycemic counter-regulatory response that drives the problem [1], [4], [27], [28]. Equally, insulin, with TGC, can ameliorate these inflammatory responses and improve insulin sensitivity and glycemic response [17], [29], [30], [31].

The problem is thus summarised as a strong counter-regulatory hormone driven stress response that induces significant insulin resistance and can antagonise insulin production and action. Coupled with unsuppressed EGP and potentially excessive nutritional inputs, high blood glucose is inevitable. Dynamic patients whose condition, and thus insulin resistance, evolves regularly and sometimes acutely, provide a further challenge to providing consistently tight TGC across every individual patient in a cohort. Coupled with clinical burden in measuring frequently, and large swings in blood glucose are inevitable without the ability to adapt. Thus, the overall problem becomes one of managing a highly dynamic cohort, with minimal effort or intervention, which also displays significant variability both between and within patients. Considered generically, this definition is a classic dynamic systems and control problem definition that can be readily addressed if the major driving factors can be accurately modeled and understood.

Van den Berghe et al. [3], obtained significant mortality reductions for a cardiovascular surgery cohort, as well as reducing other outcomes and treatments. It was matched by the retrospective study of Krinsley [32]. A later study by Van den Berghe et al. [33] was less successful with a more dynamic medical ICU cohort. The SPRINT (Specialised Relative Insulin and Nutrition Titration) study obtained significant mortality reductions for a medical ICU cohort controlling both nutrition and insulin inputs [34], [35], which is a unique approach in the field [36]. These studies showed reductions of 17–42% in mortality for patients whose length of ICU stay was 3–5 days or longer. They were matched by equally impressive reductions in cost per patient treated [37], [38], and in reduced clinical incidence of sepsis, polyneuropathy and organ failure [36], [39].

However, other studies did not get a similar result [40], [41], [42], [43], [44], [45], [46], with some stopped for safety due to hypoglycemia [44] or unintended protocol violations [46]. The recent NICE-SUGAR study [45] reported an increase in mortality in the TGC arm with a lower glycemic target, but was also subject to criticism of its treatment approach, analysis and randomisation methods [47], [48], [49], [50]. The meta-analysis that followed the publication of the NICE-SUGAR study showed that most studies failed to achieve a result either way, but also had significantly variable numbers of centres, patients, target cohorts and ICU types [43]. Thus, overall comparisons are difficult, making it almost impossible to assess which factors are associated with successful TGC.

Hypoglycemia, as noted, is also a major cause of TGC difficulty, as it stopped the neonatal NIRTURE TGC study [51], and was a factor in stopping both VISEP and Glucontrol [44], [46]. Almost all studies report increased hypoglycemia with intensive TGC [43], excepting SPRINT [34]. One recent study links hypoglycemia in the first 24 h of stay, for those patients who stay longer than 24 h, as a factor for increased risk of death [52] although this was not the case in SPRINT. Thus, hypoglycemia and hyperglycemia are risk factors, and fear of hypoglycemia in particular has thus driven recent doubts about the role of TGC.

Hence, overall, there is significant controversy around TGC and its application [53], [54], [55]. This paper posits that it is a lack of understanding of both the problem and the patient-specific dynamics that hinder clarity on all of these issues. More specifically, it reviews the basic known physiological and clinical aspects of TGC, in terms of their impact on glycemia and thus outcome. The first outcomes are recommendations on the analysis of current and future (or where possible prior) studies with a focus on determining the patient-specific or per-patient results to ascertain if tight control was truly achieved across a cohort or just for a selected sub-group or sample. This paper then further analyses the role of patient specific metabolic status in terms of the ability to achieve TGC and as a source of significant variability in TGC glycemic, safety and mortality outcomes. Thus, the overall paper reviews both effects and causes of the difficulty in applying TGC in critical care and does so from a metabolic model-based perspective. The goal is to provide a review and a new analysis framework from which new insights into this problem and how to implement effective, repeatable solutions.

Section snippets

The interrelationship of glycemia, TGC, patients and outcome

The following facts are well reported in this area:

  • Mortality increases with mean, maximum, and minimum and/or range of blood glucose in a range of cohorts [6], [7], [8], [56], [57], [58], [59].

  • Mortality increases with blood glucose variability, independent of mean or median value achieved by any form of glycemic control [60], [61], [62], [63].

  • Blood glucose levels over 7.0–8.0 mmol/L reduce and/or eliminate the effectiveness immune response to infection [15], [17], [64].

However, the failure of

Insulin sensitivity, patient variability and impact on TGC

Glycemia, both level and variability, in the critically ill broadly reflects patient condition. More specifically, the more critically ill the patient is, the more variable and greater their glycemia (e.g. [2], [6], [7], [60], [63], [66], [79], [80]). However, glycemia merely reflects three main factors:

  • Nutritional inputs (carbohydrate content in specific and endogenous glucose production).

  • Insulin (endogenous and exogenous).

  • Insulin sensitivity (SI hereafter) and its variability, which controls

Conclusions

The field of critical care has seen a great deal of debate over TGC therapy. How to implement it, benefits, safety, the cohorts most likely to benefit, and the most effective and/or safe glycemic targets.

This article uses a mixture of review and model-based analysis to analyse the state of tight glycemic control in critical care. The study uses data from two major trials and three very different cohorts to emphasise the generality of the analysis across age (neonatal versus adult), primary

References (148)

  • K.W. Jones et al.

    Hyperglycemia predicts mortality after CABG: postoperative hyperglycemia predicts dramatic increases in mortality after coronary artery bypass graft surgery

    J. Diabetes Complications

    (2008)
  • J. Chase et al.

    Model-based glycaemic control in critical care – a review of the state of the possible

    Biomed. Signal Process. Control

    (2006)
  • P.A. Goldberg et al.

    Improving glycemic control in the cardiothoracic intensive care unit: clinical experience in two hospital settings

    J. Cardiothorac. Vasc. Anesth.

    (2004)
  • Y. Sakr et al.

    Sepsis and organ system failure are major determinants of post-intensive care unit mortality

    J. Crit. Care

    (2008)
  • J. Lin et al.

    Stochastic modelling of insulin sensitivity variability in critical care

    Biomed. Signal Process. Control

    (2006)
  • J. Lin et al.

    Stochastic modelling of insulin sensitivity and adaptive glycemic control for critical care

    Comput. Methods Programs Biomed.

    (2008)
  • C.E. Hann et al.

    Integral-based parameter identification for long-term dynamic verification of a glucose–insulin system model

    Comput. Methods Programs Biomed.

    (2005)
  • T.F. Lotz et al.

    Monte Carlo analysis of a new model-based method for insulin sensitivity testing

    Comput. Methods Programs Biomed.

    (2008)
  • J.G. Chase et al.

    Adaptive bolus-based targeted glucose regulation of hyperglycaemia in critical care

    Med. Eng. Phys.

    (2005)
  • X.W. Wong et al.

    Model predictive glycaemic regulation in critical illness using insulin and nutrition input: a pilot study

    Med. Eng. Phys.

    (2006)
  • G. Van den Berghe et al.

    Intensive insulin therapy in the critically ill patients

    N. Engl. J. Med.

    (2001)
  • A. Thorell et al.

    Intensive insulin treatment in critically ill trauma patients normalizes glucose by reducing endogenous glucose production

    J. Clin. Endocrinol. Metab.

    (2004)
  • A.M. Laird et al.

    Relationship of early hyperglycemia to mortality in trauma patients

    J. Trauma

    (2004)
  • E. Jeremitsky et al.

    The impact of hyperglycemia on patients with severe brain injury

    J. Trauma

    (2005)
  • R.G. Branco et al.

    Glucose level and risk of mortality in pediatric septic shock

    Pediatr. Crit. Care Med.

    (2005)
  • B.R. Bistrian

    Hyperglycemia and infection which is the chicken and which is the egg?

    JPEN J. Parenter. Enteral. Nutr.

    (2001)
  • U.N. Das

    Insulin in sepsis and septic shock

    J. Assoc. Physicians India

    (2003)
  • P.E. Marik et al.

    Stress-hyperglycemia, insulin and immunomodulation in sepsis

    Intensive Care Med.

    (2004)
  • L. Langouche et al.

    The role of insulin therapy in critically ill patients

    Treat Endocrinol.

    (2005)
  • J.M. Fernandez-Real et al.

    CD14 monocyte receptor, involved in the inflammatory cascade, and insulin sensitivity

    J. Clin. Endocrinol. Metab.

    (2003)
  • A. Koch et al.

    Serum resistin levels in critically ill patients are associated with inflammation, organ dysfunction and metabolism and may predict survival of non-septic patients

    Crit Care.

    (2009)
  • F. Weekers et al.

    Metabolic, endocrine, and immune effects of stress hyperglycemia in a rabbit model of prolonged critical illness

    Endocrinology

    (2003)
  • J.F. Patino et al.

    Hypocaloric support in the critically ill

    World J Surg.

    (1999)
  • C. Weissman

    Nutrition in the intensive care unit

    Crit. Care

    (1999)
  • A.M. Woolfson

    Control of blood glucose during nutritional support in ill patients

    Intensive Care Med.

    (1980)
  • M. Elia et al.

    Enteral nutritional support and use of diabetes-specific formulas for patients with diabetes: a systematic review and meta-analysis

    Diabetes Care

    (2005)
  • P.H. der Voort et al.

    Intravenous glucose intake independently related to intensive care unit and hospital mortality: an argument for glucose toxicity in critically ill patients

    Clin. Endocrinol. (Oxf)

    (2006)
  • C.L. Ahrens et al.

    Effect of low-calorie parenteral nutrition on the incidence and severity of hyperglycemia in surgical patients: a randomized, controlled trial

    Crit. Care Med.

    (2005)
  • H. Kim et al.

    Association of hyperglycemia and markers of hepatic dysfunction with dextrose infusion rates in Korean patients receiving total parenteral nutrition

    Am. J. Health Syst. Pharm.

    (2003)
  • B.J. Krajicek et al.

    Potentially important contribution of dextrose used as diluent to hyperglycemia in hospitalized patients

    Diabetes Care

    (2005)
  • M.O. Larsen et al.

    High-fat high-energy feeding impairs fasting glucose and increases fasting insulin levels in the Gottingen minipig – results from a pilot study

  • M.G. Jeschke et al.

    Insulin treatment improves the systemic inflammatory reaction to severe trauma

    Ann. Surg.

    (2004)
  • I. Vanhorebeek et al.

    Glycemic and nonglycemic effects of insulin: how do they contribute to a better outcome of critical illness?

    Curr. Opin. Crit. Care

    (2005)
  • L. Langouche et al.

    Effect of intensive insulin therapy on insulin sensitivity in the critically ill

    J. Clin. Endocrinol. Metab.

    (2007)
  • G. Van den Berghe et al.

    Intensive insulin therapy in the medical ICU

    N. Engl. J. Med.

    (2006)
  • J.G. Chase et al.

    Implementation and evaluation of the SPRINT protocol for tight glycaemic control in critically ill patients: a clinical practice change

    Crit. Care

    (2008)
  • T. Lonergan et al.

    A pilot study of the SPRINT protocol for tight glycemic control in critically Ill patients

    Diabetes Technol. Ther.

    (2006)
  • J.G. Chase et al.

    Organ failure and tight glycemic control in the SPRINT study

    Crit. Care

    (2010)
  • G. Van den Berghe et al.

    Analysis of healthcare resource utilization with intensive insulin therapy in critically ill patients

    Crit. Care Med.

    (2006)
  • J.S. Krinsley et al.

    Cost analysis of intensive glycemic control in critically ill adult patients

    Chest

    (2006)
  • Cited by (109)

    • The goldilocks problem: Nutrition and its impact on glycaemic control

      2021, Clinical Nutrition
      Citation Excerpt :

      Equivalence in SI variability with other cohorts, combined with the lowest percentage of insulin treatments above the saturation range (6–8U/mL) [27–29], suggests this performance edge is primarily due to STAR's ability to modulate nutrition to account for dynamic patient-specific insulin sensitivity. In particular, modulation of nutrition reduces the number of riskier high insulin dose treatments, such as those in the insulin-saturation range [49]. Equivalence in SI variability implies equal challenge to glycaemic control protocols, as variability is the driver of uncertainty and risk.

    View all citing articles on Scopus
    View full text