Tight glycemic control in critical care – The leading role of insulin sensitivity and patient variability: A review and model-based analysis
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:
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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].
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Mortality increases with blood glucose variability, independent of mean or median value achieved by any form of glycemic control [60], [61], [62], [63].
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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:
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Nutritional inputs (carbohydrate content in specific and endogenous glucose production).
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Insulin (endogenous and exogenous).
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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
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2021, Clinical NutritionCitation 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.