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| EDITORIAL |
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| Year : 2011 | Volume
: 1
| Issue : 1 | Page : 3-4 |
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Whats new in critical illness and injury science ? Mapping and tracking glucose levels in critical patients
Nicole Fox, Mark J Seamon
Department of Surgery, Division of Trauma, Cooper University Hospital, 3 Cooper Plaza, Camden, NJ 08103
| Date of Web Publication | 12-Apr-2011 |
Correspondence Address:
Nicole Fox
3 Cooper Plaza, Camden, NJ 08103
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/2229-5151.79274
How to cite this article:
Fox N, Seamon MJ. Whats new in critical illness and injury science ? Mapping and tracking glucose levels in critical patients. Int J Crit Illn Inj Sci 2011;1:3-4 |
How to cite this URL:
Fox N, Seamon MJ. Whats new in critical illness and injury science ? Mapping and tracking glucose levels in critical patients. Int J Crit Illn Inj Sci [serial online] 2011 [cited 2023 Apr 1];1:3-4. Available from: https://www.ijciis.org/text.asp?2011/1/1/3/79274 |
Stress-induced hyperglycemia in critically ill patients was recognized over 150 years ago. The physiologic basis for this is multifactorial. During the acute and chronic phases of critical illness, hepatic glucose production and serum insulin levels are increased while peripheral glucose uptake is impaired, indicating a state of overall insulin resistance. For decades, "stress hyperglycemia" was regarded as a beneficial response in non-diabetic patients and intervention was only considered necessary if blood glucose levels were excessively elevated. In 2001 Van den Berghe and colleagues challenged this practice with the results of a prospective, randomized trial published in the New England Journal of Medicine, comparing intensive insulin therapy (blood glucose levels of 80-110 mg/dL) with conventional treatment (blood glucose levels of 180-200 mg/dL). [1] A total of 1,548 surgical intensive care unit patients were enrolled. All patients were receiving mechanical ventilation and the majority (over 60%) were post-operative cardiac surgery patients. The authors found that intensive insulin therapy significantly reduced morbidity and mortality in this population.
In 2006, Van den Berghe et al. repeated their original study in a population of 1,200 medical ICU patients. [2] Patients were again randomized to either intensive (80-110 mg/dL) or conventional (180-200 mg/dL) insulin therapy. The results of this study demonstrated a reduction in morbidity, specifically a decrease in acute kidney injury, earlier weaning from the ventilator and earlier ICU and hospital discharge. However, there was no mortality benefit in the intensive therapy group. Mortality was decreased, however, in a subgroup requiring 3 or more days of critical care. As hospitals modified their glycemic control strategies based on these studies, critics continued to debate appropriate glucose targets. In 2009, the NICE-SUGAR investigators responded to this controversy with a prospective, randomized trial of 6,104 ICU patients requiring >3 days of treatment. [3] They found that intensive glucose control (80-108 mg/dL) actually increased mortality. Furthermore, blood glucose target levels of <180 mg/dL resulted in a lower mortality than the previously defined target of 80-108 mg/dL. This report and others have underscored one potential adverse effect of intensive insulin therapy-hypoglycemia. There is mounting evidence to suggest that repeated episodes of hypoglycemia increase both morbidity and mortality although a causal relationship has not been firmly established. As a result of these studies, the most current recommendations, published by the American College of Physicians in 2011, recommend a target blood glucose level of 140-200 mg/dL for ICU patients. [4]
Subsequent research sought to further define the relationship between blood glucose levels and mortality. It was evident that elevated glucose levels were associated with adverse clinical outcomes, but the exact mechanism and ideal glucose target remained unclear. In an attempt to reconcile the contradictory results of the studies outlined above, focus shifted to the role of factors other than mean glucose levels and their effect on clinical outcomes. Variability in glycemic control is one factor of specific interest to researchers. Glucose variability (GV) is believed to increase oxidative stress, mitochondrial damage, neuronal damage and coagulation activity. The most frequently used indicator used to measure GV in the literature is the standard deviation (SD) of blood glucose levels per patient.
In 2008, Krinsley et al. conducted a retrospective review of 3,252 adult ICU patients to investigate the relationship between GV and mortality. [5] The deviation from each patient's mean glucose level was calculated to reflect GV. The authors found that increasing GV was an independent predictor of mortality. In 2010, Hermanides et al. retrospectively reviewed 5,278 adult ICU patients using two measures of variability, mean absolute glucose change per hour and SD, and determined that high GV was strongly associated with ICU and in-hospital mortality. [6] In addition, the combination of high GV and high mean glucose values in this subset of patients was associated with the highest ICU mortality. More recently, GV and mortality was evaluated in a systematic review published by Eslami et al. in Intensive Care Medicine. [7] Twelve studies, including those outlined above, met inclusion criteria and 13 different indicators were used to measure GV. SD and the "hyper-hypo," a binary variable, were the two most common indicators used. All of the studies reported a significant association between GV and mortality. The authors, however, concluded that "the independent association between GV and mortality is still unsettled" and noted a lack of standardization among variability indicators.
In this issue of the International Journal of Critical Illness and Injury Science, Stawicki and colleagues take the concept of glycemic variability a step further and hypothesize that acute hyperglycemic states (AHS) are associated with major clinical events. In order to measure GV they do not rely on previously established methods, but rather introduce an innovative new tool-the glucogram-which uses graphical indicators to display and interpret glycemic data. Eleven patients with ICU lengths of stay > 30 days were included. Glycemic data was formatted into 12-hour time periods and acute hyperglycemic states were subsequently correlated with major and minor clinical events. After analyzing 4354 glucose data points from these 11 patients, 354 major events and 93 minor events were identified. Utilizing their new GV assessment tool, Stawicki et al. confirm the association between increased GV and mortality demonstrated in previous studies. As a measurement tool, the glucogram had a sensitivity of 84% and a specificity of 65% for correlating acute hyperglycemic states and major clinical events.
Eslami and colleagues concluded in their systematic review that correlation between GV and adverse clinical outcomes may not imply causation. [7] There are many potential confounders and it is difficult to determine whether GV itself is harmful or simply a reflection of the underlying severity of illness. The present article does not address this issue either. In addition, little clinical information is provided about the 11 patients. Reason for admission to the ICU, gender, age and APACHE II scores are included but previous history of diabetes mellitus was not clarified. Although AHS were accompanied by positive "indicator spikes" the majority of the time, more information is required to draw firm conclusions. For example, in the instances where major clinical events are not accompanied by indicator spikes, what was the nature and timing of these events? What about the impact of acute hypoglycemic events on outcomes?
It is clear that the intent of Stawicki et al. was not to firmly establish the relationship between AHS and clinical events or mortality. Their interest is in whether or not the glucogram is an appropriate and widely applicable tool to assess GV. As outlined by Eslami et al., a standardized measurement tool for GV is essential, so the introduction of the glucogram is both timely and necessary. [7] Applying financial analysis tools to clinical medicine is a novel concept and represents the first biomedical application of these specific tools. As the authors state, one of the strengths of using a tool such as the glucogram is that subtle changes in clinical status may be detected even if the raw data does not necessarily reflect an obvious change. In addition, the use of graphical indicators is a way to standardize measurements and trend analysis, in effect, alleviating the heterogeneity that has complicated the interpretation of previous studies. Overall, the glucogram appears to be a viable method for assessing GV and represents a novel new tool to add to our armamentarium. We look forward to the broader implementation of the glucogram in a larger subset of patients.
 References | |  |
| 1. | Van den Berghe G, Wouters P, Weekers F, Verwaest C, Bruyninckx F, Schetz M, et al. Intensive insulin therapy in critically ill patients. N Engl J Med 2001;345:1359-67. 
[PUBMED] [FULLTEXT] |
| 2. | Van den Berghe G, Wilmer A, Hermans G, Meersseman W, Wouters PJ, et al. Intensive insulin therapy in the medical ICU. N Engl J Med 2006;354:449-61.
[PUBMED] [FULLTEXT] |
| 3. | NICE-SUGAR Study Investigators. Intensive versus conventional glucose control in critically ill patients. N Engl J Med 2009;360:1283-97.
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| 4. | Qaseem A, Humphrey LL, Chou R, Snow V, Shekelle P. Use of intensive insulin therapy for the management of glycemic control in hospitalized patients: A clinical practice guideline from the American college of physicians. Ann Intern Med 2011;154:260-7.
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| 5. | Krinsley J. Glycemic variability: A strong independent predictor of mortality in critically ill patients. Crit Care Med 2008;36:3008-13.
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| 6. | Hermanides J, Vriesendorp T, Bosman R. Glucose variability is associated with intensive care unit mortality. Crit Care Med 2010;38:838-42.
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| 7. | Eslami S, Taherzadeh Z, Schultz M, Abu-Hanna A. Glucose variability measures and their effect on mortality: A systematic review. Intensive Care Med 2011.
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