The use of automated data to identify complications and comorbidities of diabetes: a validation study

J Clin Epidemiol. 1999 Mar;52(3):199-207. doi: 10.1016/s0895-4356(98)00161-9.


We evaluated the accuracy of administrative data for identifying complications and comorbidities of diabetes using International Classification of Diseases, 9th edition, Clinical Modification and Current Procedural Terminology codes. The records of 471 randomly selected diabetic patients were reviewed for complications from January 1, 1993 to December 31, 1995; chart data served to validate automated data. The complications with the highest sensitivity determined by a diagnosis in the medical records identified within +/-60 days of the database date were myocardial infarction (95.2%); amputation (94.4%); ischemic heart disease (90.3%); stroke (91.2%); osteomyelitis (79.2%); and retinal detachment, vitreous hemorrhage, and vitrectomy (73.5%). With the exception of amputation (82.9%), positive predictive value was low when based on a diagnosis identified within +/-60 days of the database date but increased with relaxation of the time constraints to include confirmation of the condition at any time during 1993-1995: ulcers (88.5%); amputation (85.4%); and retinal detachment, vitreous hemorrhage and vitrectomy (79.8%). Automated data are useful for ascertaining potential cases of some diabetic complications but require confirmatory evidence when they are to be used for research purposes.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Age Distribution
  • Aged
  • Algorithms
  • Cohort Studies
  • Comorbidity
  • Diabetes Complications*
  • Diabetes Mellitus / epidemiology*
  • Female
  • Humans
  • Male
  • Medical Records
  • Medical Records Systems, Computerized / standards*
  • Middle Aged
  • Outcome Assessment, Health Care / standards*
  • Predictive Value of Tests
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Sex Distribution
  • Washington / epidemiology