Charts versus Discharge ICD-10 Coding for Sternal Wound Infection Following Coronary Artery Bypass Grafting

Perspect Health Inf Manag. 2015 Jul 1;12(Summer):1e. eCollection 2015.

Abstract

Background: Sternal wound infection (SWI) in patients undergoing coronary artery bypass grafting (CABG) can carry a significant risk of morbidity and mortality. The objective of this work is to describe the methods used to identify cases of SWI in an administrative database and to demonstrate the effectiveness of using an International Classification of Diseases, Tenth Revision (ICD-10) coding algorithm for this purpose.

Methods: ICD-10 codes were used to identify cases of SWI within one year of CABG between April 2002 and November 2009. We randomly chose 200 charts for detailed chart review (100 from each of the groups coded as having SWI and not having SWI) to determine the utility of the ICD-10 coding algorithm.

Results: There were 2,820 patients undergoing CABG. Of these, 264 (9.4 percent) were coded as having SWI. Thirty-eight cases of SWI were identified by chart review. The ICD-10 coding algorithm of T81.3 or T81.4 was able to identify incident SWI with a positive predictive value of 35 percent and a negative predictive value of 97 percent. The agreement between the ICD-10 coding algorithm and presence of SWI remained fair, with an overall kappa coefficient of 0.32 (95 percent confidence interval, 0.22-0.43). The effectiveness of identifying deep SWI cases is also presented.

Conclusions: This article describes an effective algorithm for identifying a cohort of patients with SWI following open sternotomy in large databases using ICD-10 coding. In addition, alternative search strategies are presented to suit researchers' needs.

Keywords: ICD-10 coding algorithm; deep sternal wound infection; sternal wound dehiscence.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Coronary Artery Bypass / adverse effects*
  • Data Mining
  • Documentation / standards
  • Documentation / statistics & numerical data*
  • Female
  • Humans
  • International Classification of Diseases / standards
  • International Classification of Diseases / statistics & numerical data*
  • Male
  • Patient Discharge
  • Reproducibility of Results
  • Surgical Wound Infection / epidemiology*