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Year : 2011  |  Volume : 1  |  Issue : 1  |  Page : 5-12

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

1 Department of Surgery, Division of Critical Care, Trauma, and Burn, The Ohio State University Medical Center, Columbus, OH 43210, USA
2 Department of Endocrinology, Diabetes, and Metabolism, The Ohio State University Medical Center, Columbus, OH 43210, USA
3 Information Warehouse, The Ohio State University Medical Center, Columbus, OH 43210, USA

Correspondence Address:
SPA Stawicki
Department of Surgery, Division of Critical Care, Trauma, and Burn, The Ohio State University Medical Center, 395 West 12th Avenue, Suite 634, Columbus, OH 43210
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2229-5151.79275

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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.

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