title: Performance of a Random Glucose Case-Finding Strategy to Detect Undiagnosed Diabetes.
abstract: BACKGROUND
  Random glucose <200 mg/dL is associated with undiagnosed diabetes but not included in screening guidelines. This study describes a case-finding approach using non-diagnostic random glucose values to identify individuals in need of diabetes testing and compares its performance to current screening guidelines.
  
  
  METHODS
  In 2015, cross-sectional data from non-fasting adults without diagnosed diabetes or prediabetes (N=7,161) in the 2007-2012 National Health and Nutrition Examination Surveys were analyzed. Random glucose and survey data were used to assemble the random glucose, American Diabetes Association (ADA), and U.S. Preventive Services Task Force (USPSTF) screening strategies and predict diabetes using hemoglobin A1c criteria.
  
  
  RESULTS
  Using random glucose ≥100 mg/dL to select individuals for diabetes testing was 81.6% (95% CI=74.9%, 88.4%) sensitive, 78% (95% CI=76.6%, 79.5%) specific and had an area under the receiver operating curve (AROC) of 0.80 (95% CI=0.78, 0.83) to detect undiagnosed diabetes. Overall performance of ADA (AROC=0.59, 95% CI=0.58, 0.60), 2008 USPSTF (AROC=0.62, 95% CI=0.59, 0.65), and 2015 USPSTF (AROC=0.64, 95% CI=0.61, 0.67) guidelines was similar. The random glucose strategy correctly identified one case of undiagnosed diabetes for every 14 people screened, which was more efficient than ADA (number needed to screen, 35), 2008 USPSTF (44), and 2015 USPSTF (32) guidelines.
  
  
  CONCLUSIONS
  Using random glucose ≥100 mg/dL to identify individuals in need of diabetes screening is highly sensitive and specific, performing better than current screening guidelines. Case-finding strategies informed by random glucose data may improve diabetes detection. Further evaluation of this strategy's effectiveness in real-world clinical practice is needed.
authors.affiliation: Division of General Internal Medicine, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, Texas; Division of Outcomes and Health Services Research, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas. Electronic address: michael.bowen@utsouthwestern.edu.
authors.forename: Michael E
authors.initials: ME
authors.lastname: Bowen
authors.affiliation: Division of Outcomes and Health Services Research, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas.
authors.forename: Lei
authors.initials: L
authors.lastname: Xuan
authors.affiliation: Division of Endocrinology, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, Texas; Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas.
authors.forename: Ildiko
authors.initials: I
authors.lastname: Lingvay
authors.affiliation: Division of General Internal Medicine, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, Texas; Division of Outcomes and Health Services Research, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas.
authors.forename: Ethan A
authors.initials: EA
authors.lastname: Halm
mesh_terms: D003430:Cross-Sectional Studies; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis*; D005260:Female; D006442:Glycated Hemoglobin / Q000032:analysis*; D006801:Humans; D008297:Male; D008403:Mass Screening / Q000706:statistics & numerical data*; D008875:Middle Aged; D009749:Nutrition Surveys; D011236:Prediabetic State / Q000175:diagnosis*; D015995:Prevalence; D012307:Risk Factors
chemical_list: D006442:Glycated Hemoglobin