title: Identifying in-hospital venous thromboembolism (VTE): a comparison of claims-based approaches with the Rochester Epidemiology Project VTE cohort.
abstract: BACKGROUND
  Efforts to identify hospital-acquired complications from claims data by applying exclusion rules to discharge diagnosis codes exhibit low positive predictive value (PPV). The PPV improves when a variable is added to each secondary diagnosis to indicate whether the condition was "present-on-admission" (POA) or "hospital-acquired". Such indicator variables will soon be required for Medicare reimbursement. No estimates are available, however, of the proportion of hospital-acquired complications that are missed (sensitivity) using either exclusion rules or indicator variables. We estimated sensitivity, specificity, PPV, and negative predictive value (NPV) of claims-based approaches using the Rochester Epidemiology Project (REP) venous thromboembolism (VTE) cohort as a "gold standard."
  
  
  METHODS
  All inpatient encounters by Olmsted County, Minnesota, residents at Mayo Clinic-affiliated hospitals 1995-1998 constituted the at-risk-population. REP-identified hospital-acquired VTE consisted of all objectively-diagnosed VTE among County residents 1995-1998, whose onset of symptoms occurred during inpatient stays at these hospitals, as confirmed by detailed review of County residents' provider-linked medical records. Claims-based approaches used billing data from these hospitals.
  
  
  RESULTS
  Of 37,845 inpatient encounters, 98 had REP-identified hospital-acquired VTE; 47 (48%) were medical encounters. NPV and specificity were >99% for both claims-based approaches. Although indicator variables provided higher PPV (74%) compared with exclusion rules (35%), the sensitivity for exclusion rules was 74% compared with only 38% for indicator variables. Misclassification was greater for medical than surgical encounters.
  
  
  CONCLUSIONS
  Utility and accuracy of claims data for identifying hospital-acquired conditions, including POA indicator variables, requires close attention be paid by clinicians and coders to what is being recorded.
authors.affiliation: Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester 55905, Minnesota, USA. leibson@mayo.edu
authors.forename: Cynthia L
authors.initials: CL
authors.lastname: Leibson
authors.forename: Jack
authors.initials: J
authors.lastname: Needleman
authors.forename: Peter
authors.initials: P
authors.lastname: Buerhaus
authors.forename: John A
authors.initials: JA
authors.lastname: Heit
authors.forename: L Joseph
authors.initials: LJ
authors.lastname: Melton
authors.forename: James M
authors.initials: JM
authors.lastname: Naessens
authors.forename: Kent R
authors.initials: KR
authors.lastname: Bailey
authors.forename: Tanya M
authors.initials: TM
authors.lastname: Petterson
authors.forename: Jeanine E
authors.initials: JE
authors.lastname: Ransom
authors.forename: Marcelline R
authors.initials: MR
authors.lastname: Harris
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D005260:Female; D006770:Hospitals, Group Practice / Q000191:economics / Q000592:standards* / Q000706:statistics & numerical data; D006801:Humans; D007049:Iatrogenic Disease / Q000453:epidemiology*; D007344:Insurance Claim Reporting / Q000706:statistics & numerical data*; D038801:International Classification of Diseases / Q000706:statistics & numerical data; D008297:Male; D008498:Medical Record Linkage; D006278:Medicare; D008875:Middle Aged; D008910:Minnesota / Q000453:epidemiology; D017063:Outcome Assessment, Health Care / Q000191:economics / Q000379:methods*; D010343:Patient Admission / Q000706:statistics & numerical data; D019984:Quality Indicators, Health Care; D012052:Reimbursement, Incentive; D020379:Risk Adjustment / Q000379:methods; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D014481:United States; D054556:Venous Thromboembolism / Q000145:classification* / Q000191:economics / Q000453:epidemiology / Q000209:etiology