title: Statistical transformation and the interpretation of inpatient glucose control data from the intensive care unit.
abstract: Glucose control can be problematic in critically ill patients. We evaluated the impact of statistical transformation on interpretation of intensive care unit inpatient glucose control data. Point-of-care blood glucose (POC-BG) data derived from patients in the intensive care unit for 2011 was obtained. Box-Cox transformation of POC-BG measurements was performed, and distribution of data was determined before and after transformation. Different data subsets were used to establish statistical upper and lower control limits. Exponentially weighted moving average (EWMA) control charts constructed from April, October, and November data determined whether out-of-control events could be identified differently in transformed versus nontransformed data. A total of 8679 POC-BG values were analyzed. POC-BG distributions in nontransformed data were skewed but approached normality after transformation. EWMA control charts revealed differences in projected detection of out-of-control events. In April, an out-of-control process resulting in the lower control limit being exceeded was identified at sample 116 in nontransformed data but not in transformed data. October transformed data detected an out-of-control process exceeding the upper control limit at sample 27 that was not detected in nontransformed data. Nontransformed November results remained in control, but transformation identified an out-of-control event less than 10 samples into the observation period. Using statistical methods to assess population-based glucose control in the intensive care unit could alter conclusions about the effectiveness of care processes for managing hyperglycemia. Further study is required to determine whether transformed versus nontransformed data change clinical decisions about the interpretation of care or intervention results.
authors.affiliation: Department of Information Technology, Mayo Clinic, Scottsdale, AZ, USA.
authors.forename: George E
authors.initials: GE
authors.lastname: Saulnier
authors.affiliation: Department of Information Technology, Mayo Clinic, Scottsdale, AZ, USA.
authors.forename: Janna C
authors.initials: JC
authors.lastname: Castro
authors.affiliation: Division of Endocrinology, Mayo Clinic, Scottsdale, AZ, USA cook.curtiss@mayo.edu.
authors.forename: Curtiss B
authors.initials: CB
authors.lastname: Cook
mesh_terms: D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000378:metabolism*; D016638:Critical Illness; D003627:Data Interpretation, Statistical; D006801:Humans; D006943:Hyperglycemia / Q000097:blood / Q000175:diagnosis* / Q000628:therapy; D007297:Inpatients / Q000706:statistics & numerical data*; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D019095:Point-of-Care Systems / Q000706:statistics & numerical data*; D000067716:Point-of-Care Testing / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D012189:Retrospective Studies
chemical_list: D015415:Biomarkers; D001786:Blood Glucose
keywords: diabetes; hospital; hyperglycemia; inpatient; statistical process control