The glucogram: A new quantitative tool for glycemic analysis in the surgical intensive care unit

Abstract

Background:Glycemic control is an important aspect of patient care in the surgical intensive care unit (SICU). This is a pilot study of a novel glycemic analysis tool – the glucogram. We hypothesize that the glucogram may be helpful in quantifying the clinical significance of acute hyperglycemic states (AHS) and in describing glycemic variability (GV) in critically ill patients.Materials and Methods:Serial glucose measurements were analyzed in SICU patients with lengths of stay (LOS) >30 days. Glucose data were formatted into 12-hour epochs and graphically analyzed using stochastic and momentum indicators. Recorded clinical events were classified as major or minor (control). Examples of major events include cardiogenic shock, acute respiratory failure, major hemorrhage, infection/sepsis, etc. Examples of minor (control) events include non-emergent bedside procedures, blood transfusion given to a hemodynamically stable patient, etc. Positive/negative indicator status was then correlated with AHS and associated clinical events. The conjunction of positive indicator/major clinical event or negative indicator/minor clinical event was defined as clinical “match”. GV was determined by averaging glucose fluctuations (maximal – minimal value within each 12-hour epoch) over time. In addition, event-specific glucose excursion (ESGE) associated with each positive indicator/AHS match (final minus initial value for each occurrence) was calculated. Descriptive statistics, sensitivity/specificity determination, and student's t-test were used in data analysis.Results:Glycemic and clinical data were reviewed for 11 patients (mean SICU LOS 74.5 days; 7 men/4 women; mean age 54.9 years; APACHE II of 17.7 ± 6.44; mortality 36%). A total of 4354 glucose data points (1254 epochs) were analyzed. There were 354 major clinical events and 93 minor (control) events. The glucogram identified AHS/indicator/clinical event “matches” with overall sensitivity of 84% and specificity of 65%. We noted that while the mean GV was greater for non-survivors than for survivors (19.3 mg/dL vs. 10.3 mg/dL, P = 0.02), there was no difference in mean ESGE between survivors (154.7) and non-survivors (160.8, P = 0.67).Conclusions:The glucogram was able to quantify the correlation between AHS and major clinical events with a sensitivity of 84% and a specificity of 65%. In addition, mean GV was nearly two times higher for non-survivors. The glucogram may be useful both clinically (i.e., in the electronic ICU or other “early warning” systems) and as a research tool (i.e., in model development and standardization). Results of this study provide a foundation for further, larger-scale, multi-parametric, prospective evaluations of the glucogram.

Evaluation of a Model for Glycemic Prediction in Critically Ill Surgical Patients

“…Given that acute glycemic excursions have been correlated with adverse clinical events [1], it is reasonable to speculate that models integrating NNM-based glycemic prediction and indicator-based system of “clinical alerts” could further enhance the armamentarium of clinical predictive tools available to the intensivist. Such integrated systems could utilize existing CGM and POC glycemic testing, not only to alert the bedside practitioner to potential glycemic dysregulation within a near-term horizon, but also to alert the critical care team to look for potential underlying events that may incite such glycemic excursions.…”

“…Previous research clearly associates significant physiologic stress and acute hyperglycemic spikes, with elevated glucose levels serving as a form of “physiologic barometer” [1]. It is not surprising, therefore, that acute hyperglycemia is present in 25% or more of severely injured patients [2].…”

“…Unfortunately, this goal can be elusive in trauma patients that require multiple critical care therapies [4]. In addition to maintaining serum glucoses within the relatively narrow therapeutic window described above, it is important to note that glycemic variability also plays an important role as a predictor of survival in critically ill surgical patients, with glycemic variability among non-survivors being twice the variability of survivors [1].…”

Continuous versus bolus tube feeds: Does the modality affect glycemic variability, tube feeding volume, caloric intake, or insulin utilization?

“…Traditionally, GV has been presented using various methods including (a) Standard deviation;[ 25 ] (b) mean daily change in serum glucose (i.e., mean difference between daily maximum and minimum glucose values); (c) the glucose lability index as well as the mean amplitude of glycemic excursion technique. [ 26 ] A variation on the “mean daily change” has been previously presented by our group[ 20 ] and is called “the glucogram.”[ 27 28 ] Here, an epoch-based financial chart is constructed that utilizes the “open-high-low-close” (OHLC, Figure 1 ) paradigm. [ 27 28 ] These measurements are then organized into 12-h epochs, and GV is subsequently calculated by determining serial differences between the maximum and the minimum value for each standardized time period.…”

“…[ 15 16 17 18 ] Subsequent to the early reports, it has been increasingly recognized that GV, and not the glucose levels alone, correlates with increased mortality independent of hypoglycemia, presence of diabetes, organ dysfunction, or treatment. [ 19 20 21 ] GV is an objective measure of glucose fluctuations,[ 20 21 22 ] with previous studies showing that acute glucose gyrations may induce endothelial cell damage, apoptosis, and could be associated with worse clinical outcomes. [ 23 24 ] The primary objective of this study was to determine if there is a difference in GV and insulin utilization among critically ill patients receiving BTF versus CTF via percutaneous endoscopic gastrostomy (PEG) tube.…”