Severely burned patients benefit from intensive insulin therapy (IIT) for tight glycemic control (TGC). The authors evaluated the clinical impact of automatic correction of hematocrit and ascorbic acid interference for bedside glucose monitoring performance in critically ill burn patients. The performance of two point-of-care glucose monitoring systems (GMSs): 1) GMS1, an autocorrecting device, and 2) GMS2, a noncorrecting device were compared. Sixty remnant arterial blood samples were collected in a prospective observational study to evaluate hematocrit and ascorbic acid effects on GMS1 vs GMS2 accuracy paired against a plasma glucose reference. Next, we enrolled 12 patients in a pilot randomized controlled trial. Patients were randomized 1:1 to receive IIT targeting a TGC interval of 111 to 151 mg/dl and guided by either GMS1 or GMS2. GMS bias, mean insulin rate, and glycemic variability were calculated. In the prospective study, GMS1 results were similar to plasma glucose results (mean bias, −0.75 [4.0] mg/dl; n = 60; P = .214). GMS2 results significantly differed from paired plasma glucose results (mean bias, −5.66 [18.7] mg/dl; n = 60; P = .048). Ascorbic acid therapy elicited significant GMS2 performance bias (29.2 [27.2]; P < .001). Randomized controlled trial results reported lower mean bias (P < .001), glycemic variability (P < .05), mean insulin rate (P < .001), and frequency of hypoglycemia (P < .001) in the GMS1 group than in the GMS2 group. Anemia and high-dose ascorbic acid therapy negatively impact GMS accuracy and TGC in burn patients. Automatic correction of confounding factors improves glycemic control. Further studies are warranted to determine outcomes associated with accurate glucose monitoring during IIT.
From the *Department of Pathology and Laboratory Medicine, School of Medicine, and †Department of Biomedical Engineering, University of California, Davis.
This study was supported by National Center for Advancing Translational Sciences, National Institutes of Health (K30 Mentored Clinical Research Training Program Scholarship), the glycemic variability computational engine developed by the Department of Biomedical Engineering Senior Design, and a National Institutes for Biomedical Imaging and Bioengineering R25 grant.
The authors declare no conflict of interest.
Address correspondence to Nam K. Tran, PhD, MS, Department of Pathology and Laboratory Medicine, School of Medicine, University of California, Davis, Davis, California 95616.