title: Postmarket Surveillance of Point-of-Care Glucose Meters through Analysis of Electronic Medical Records.
abstract: The electronic medical record (EMR) holds a promising source of data for active postmarket surveillance of diagnostic accuracy, particularly for point-of-care (POC) devices. Through a comparison with prospective bedside and laboratory accuracy studies, we demonstrate the validity of active surveillance via an EMR data mining method [Data Mining EMRs to Evaluate Coincident Testing (DETECT)], comparing POC glucose results to near-in-time central laboratory glucose results.
  
  
  
  The Roche ACCU-CHEK Inform II(®) POC glucose meter was evaluated in a laboratory validation study (n = 73), a prospective bedside intensive care unit (ICU) study (n = 124), and with DETECT (n = 852-27 503). For DETECT, the EMR was queried for POC and central laboratory glucose results with filtering based on of bedside collection timestamps, central laboratory time delays, patient location, time period, absence of repeat testing, and presence of peripheral lines.
  
  
  
  DETECT and the bedside ICU study produced similar estimates of average bias (4.5 vs 5.0 mg/dL) and relative random error (6.3% vs 5.6%), with overlapping CIs. For glucose <100 mg/dL, the laboratory validation study estimated a lower relative random error of 3.6%. POC average bias correlated with central laboratory turnaround times, consistent with 4.8 mg · dL(-1) · h(-1) glycolysis. After glycolysis adjustment, average bias was estimated by the bedside ICU study at -0.4 mg/dL (CI, -1.6 to 0.9) and DETECT at -0.7 (CI, -1.3 to 0.2), and percentage POC results occurring outside Clinical Laboratory Standards Institute quality goals were 2.4% and 4.8%, respectively.
  
  
  
  This study validates DETECT for estimating POC glucose meter accuracy compared with a prospective bedside ICU study and establishes it as a reliable postmarket surveillance methodology.
authors.affiliation: Department of Pathology, leeschro@med.umich.edu.
authors.forename: Lee F
authors.initials: LF
authors.lastname: Schroeder
authors.affiliation: Department of Pathology.
authors.forename: Don
authors.initials: D
authors.lastname: Giacherio
authors.affiliation: Department of Internal Medicine, Division of Metabolism and Endocrinology, Inpatient Hyperglycemia Service, and.
authors.forename: Roma
authors.initials: R
authors.lastname: Gianchandani
authors.affiliation: Department of Anesthesiology, University of Michigan, Ann Arbor, MI;
authors.forename: Milo
authors.initials: M
authors.lastname: Engoren
authors.affiliation: Center for Biomedical Informatics, Stanford University, Stanford, CA.
authors.forename: Nigam H
authors.initials: NH
authors.lastname: Shah
mesh_terms: D001786:Blood Glucose / Q000032:analysis*; D057286:Electronic Health Records; D006801:Humans; D007362:Intensive Care Units; D019095:Point-of-Care Systems; D011358:Product Surveillance, Postmarketing
chemical_list: D001786:Blood Glucose