Identification of patients with diabetic macular edema from claims data: a validation study.

Journal Article (Journal Article;Multicenter Study)

OBJECTIVE: To assess the validity of an algorithm for identifying patients with diabetic macular edema (DME) using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes in administrative billing data from a convenience sample of physician offices. METHODS: A convenience sample of 12 general ophthalmologists and 10 retina specialists applied prespecified algorithms based on ICD-9-CM diagnosis codes to the billing claims of their practices and selected the associated medical records. Four ophthalmologists abstracted data from the medical records, which were then compared with the coded diagnoses. Main outcome measures were sensitivity, specificity, and the kappa statistic for the DME algorithm (a combination of codes 250.xx and 362.53), treating medical record documentation of DME as the standard criterion. RESULTS: The DME algorithm had a sensitivity of 0.88 and a specificity of 0.96 for identifying DME. Excellent agreement was noted between the algorithm and the medical records (kappa = 0.84). The algorithm performed less well in identifying patients with a diagnosis of clinically significant DME (sensitivity, 0.86; specificity, 0.84; kappa = 0.64). CONCLUSIONS: The results of this pilot study suggest that patients with DME can be identified accurately in claims data using ICD-9-CM diagnosis codes. Application of this algorithm could improve investigations of disease prevalence and disease burden and provide an efficient means of assessing care and interventions.

Full Text

Duke Authors

Cited Authors

  • Bearelly, S; Mruthyunjaya, P; Tzeng, JP; Suñer, IJ; Shea, AM; Lee, JT; Kowalski, JW; Curtis, LH; Schulman, KA; Lee, PP

Published Date

  • July 2008

Published In

Volume / Issue

  • 126 / 7

Start / End Page

  • 986 - 989

PubMed ID

  • 18625948

Electronic International Standard Serial Number (EISSN)

  • 1538-3601

Digital Object Identifier (DOI)

  • 10.1001/archopht.126.7.986


  • eng

Conference Location

  • United States