Identifying in-hospital venous thromboembolism (VTE): a comparison of claims-based approaches with the Rochester Epidemiology Project VTE cohort

Med Care. 2008 Feb;46(2):127-32. doi: 10.1097/MLR.0b013e3181589b92.

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.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Cohort Studies
  • Female
  • Hospitals, Group Practice / economics
  • Hospitals, Group Practice / standards*
  • Hospitals, Group Practice / statistics & numerical data
  • Humans
  • Iatrogenic Disease / epidemiology*
  • Insurance Claim Reporting / statistics & numerical data*
  • International Classification of Diseases / statistics & numerical data
  • Male
  • Medical Record Linkage
  • Medicare
  • Middle Aged
  • Minnesota / epidemiology
  • Outcome Assessment, Health Care / economics
  • Outcome Assessment, Health Care / methods*
  • Patient Admission / statistics & numerical data
  • Quality Indicators, Health Care*
  • Reimbursement, Incentive
  • Risk Adjustment / methods
  • Risk Assessment
  • Sensitivity and Specificity
  • United States
  • Venous Thromboembolism / classification*
  • Venous Thromboembolism / economics
  • Venous Thromboembolism / epidemiology
  • Venous Thromboembolism / etiology