Validity of International Classification of Diseases, Ninth Revision, Clinical Modification Codes for Acute Renal Failure

J Am Soc Nephrol. 2006 Jun;17(6):1688-94. doi: 10.1681/ASN.2006010073. Epub 2006 Apr 26.

Abstract

Administrative and claims databases may be useful for the study of acute renal failure (ARF) and ARF that requires dialysis (ARF-D), but the validity of the corresponding diagnosis and procedure codes is unknown. The performance characteristics of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for ARF were assessed against serum creatinine-based definitions of ARF in 97,705 adult discharges from three Boston hospitals in 2004. For ARF-D, ICD-9-CM codes were compared with review of medical records in 150 patients with ARF-D and 150 control patients. As compared with a diagnostic standard of a 100% change in serum creatinine, ICD-9-CM codes for ARF had a sensitivity of 35.4%, specificity of 97.7%, positive predictive value of 47.9%, and negative predictive value of 96.1%. As compared with review of medical records, ICD-9-CM codes for ARF-D had positive predictive value of 94.0% and negative predictive value of 90.0%. It is concluded that administrative databases may be a powerful tool for the study of ARF, although the low sensitivity of ARF codes is an important caveat. The excellent performance characteristics of ICD-9-CM codes for ARF-D suggest that administrative data sets may be particularly well suited for research endeavors that involve patients with ARF-D.

Publication types

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

MeSH terms

  • Acute Kidney Injury / classification*
  • Acute Kidney Injury / diagnosis
  • Aged
  • Diagnosis-Related Groups
  • Female
  • Forms and Records Control
  • Hospital Records
  • Humans
  • Insurance Claim Reporting
  • International Classification of Diseases*
  • Logical Observation Identifiers Names and Codes
  • Male
  • Medical Records
  • Middle Aged
  • Models, Statistical
  • Retrospective Studies