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Improved surveillance for surgical site infections after orthopedic implantation procedures: extending applications
Maureen K Bolon1, David Hooper, Kurt B Stevenson
1Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
Background:
Screening methods that use automated data may streamline surgical site infection (SSI) surveillance and improve the accuracy and comparability of data on SSIs. We evaluated the use of automated inpatient diagnosis codes and pharmacy data to identify SSIs after arthroplasty.
Methods:
This retrospective cohort study at 8 hospitals involved weighted, random samples of medical records from 2128 total hip arthroplasty (THA) procedures performed from 1 July 2002 through 30 June 2004, and 4194 total knee arthroplasty (TKA) procedures performed from 1 July 2003 through 30 June 2005. We compared routine surveillance with screening of inpatient pharmacy data and diagnoses codes followed by medical record review to confirm SSI status.
Results:
Records from 696 THA and 1009 TKA procedures were reviewed. The SSI rates were nearly double those determined by routine surveillance (1.32% [95% confidence interval, 0.83%-1.81%] vs. 0.75% for THA; 1.83% [95% confidence interval, 1.43%-2.23%] vs. 0.71% for TKA). An inpatient diagnosis code for infection within a year after the operation had substantially higher sensitivity (THA, 89%; TKA, 81%), compared with routine surveillance (THA, 56%; TKA, 39%). Adding antimicrobial exposure of 7 days after the procedure increased the sensitivity (THA, 93%; TKA, 86%). Record review confirmed SSIs after 51% of THAs and 55% of TKAs that met diagnosis code criteria and after 25% of THAs and 39% of TKAs that met antimicrobial exposure and/or diagnosis code criteria.
Conclusions:
Focused surveillance among a subset of patients who met diagnosis code screening criteria with or without the addition of antimicrobial exposure-based screening was more sensitive than routine surveillance for detecting SSIs after arthroplasty and could be an efficient and readily standardized adjunct to traditional methods.
