Accuracy of administrative data for identification of patients with infective endocarditis

Int J Cardiol. 2016 Dec 1;224:162-164. doi: 10.1016/j.ijcard.2016.09.030. Epub 2016 Sep 17.


Background: Infective endocarditis is associated with high morbidity and mortality rates that have plateaued over recent decades. Research to improve outcomes for these patients is limited by the rarity of this condition. Therefore, we sought to validate administrative database codes for the diagnosis of infective endocarditis.

Methods: We conducted a retrospective validation study of International Classification of Diseases (ICD-10-CM) codes for infective endocarditis against clinical Duke criteria (definite and probable) at a large acute care hospital between October 1, 2013 and June 30, 2015. To identify potential cases missed by ICD-10-CM codes, we also screened the hospital's valvular heart surgery database and the microbiology laboratory database (the latter for patients with bacteremia due to organisms commonly causing endocarditis).

Results: Using definite Duke criteria or probable criteria with clinical suspicion as the reference standard, the ICD-10-CM codes had a sensitivity (SN) of 0.90 (95% confidence interval (CI), 0.81-0.95), specificity (SP) of 1 (95% CI, 1-1), positive predictive value (PPV) of 0.78 (95% CI, 0.68-0.85) and negative predictive value (NPV) of 1 (95% CI, 1-1). Restricting the case definition to definite Duke criteria resulted in an increase in SN to 0.95 (95% CI, 0.86-0.99) and a decrease in PPV to 0.6 (95% CI, 0.49-0.69), with no change in specificity.

Conclusion: ICD-10-CM codes can accurately identify patients with infective endocarditis, and so administrative databases offer a potential means to study this infection over large jurisdictions, and thereby improve the prediction, diagnosis, treatment and prevention of this rare but serious infection.

Keywords: Administrative data; Infective endocarditis; Validation.

Publication types

  • Validation Study

MeSH terms

  • Canada
  • Data Accuracy*
  • Databases, Factual* / standards
  • Databases, Factual* / statistics & numerical data
  • Endocarditis / diagnosis*
  • Humans
  • International Classification of Diseases
  • Quality Improvement
  • Retrospective Studies