title: The absence of longitudinal data limits the accuracy of high-throughput clinical phenotyping for identifying type 2 diabetes mellitus subjects.
abstract: OBJECTIVE
  To evaluate the impact of insufficient longitudinal data on the accuracy of a high-throughput clinical phenotyping (HTCP) algorithm for identifying (1) patients with type 2 diabetes mellitus (T2DM) and (2) patients with no diabetes.
  
  
  METHODS
  Retrospective study conducted at Mayo Clinic in Rochester, Minnesota. Eligible subjects were Olmsted County residents with ≥1 Mayo Clinic encounter in each of three time periods: (1) 2007, (2) from 1997 through 2006, and (3) before 1997 (N = 54,283). Diabetes relevant electronic medical record (EMR) data about diagnoses, laboratories, and medications were used. We employed the HTCP algorithm to categorize individuals as T2DM cases and non-diabetes controls. Considering the full 11 years (1997-2007) as the gold standard, we compared gold-standard categorizations with those using data for 10 subsequent intervals, ranging from 1998-2007 (10-year data) to 2007 (1-year data). Positive predictive values (PPVs) and false-negative rates (FNRs) were calculated. McNemar tests were used to determine whether categorizations using shorter time periods differed from the gold standard. Statistical significance was defined as P < 0.05.
  
  
  RESULTS
  We identified 2770 T2DM cases and 21,005 controls when the algorithm was applied using 11-year data. Using 2007 data alone, PPVs and FNRs, respectively, were 70% and 25% for case identification and 59% and 67% for control identification. All time frames differed significantly from the gold standard, except for the 10-year period.
  
  
  CONCLUSIONS
  The accuracy of the algorithm reduced remarkably as data were limited to shorter observation periods. This impact should be considered carefully when designing/executing HTCP algorithms.
authors: Institute for Health Informatics, University of Minnesota, Twin Cities, MN, USA.
authors: Wei-Qi
authors: WQ
authors: Wei
authors: Cynthia L
authors: CL
authors: Leibson
authors: Jeanine E
authors: JE
authors: Ransom
authors: Abel N
authors: AN
authors: Kho
authors: Christopher G
authors: CG
authors: Chute
mesh_terms: D000368:Aged; D000465:Algorithms; D016022:Case-Control Studies; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D008910:Minnesota; D010641:Phenotype