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Assessing specific secondary ICD-9-CM codes as potential predictors of surgical site infections
Jessica West1, Yosef Khan, David M Murray
1Division of Infectious Diseases, College of Medicine, The Ohio State University, 410 West 10th Ave., Columbus, OH 43210, USA.
Background:
Public reporting and reduced Medicare payments because of health care-associated infections have resulted in the consideration of administrative discharge codes as markers of health care-associated infections. This study aims to determine whether specific secondary ICD-9-CM infection codes linked to cases from a large data set of surgical procedures are predictors of surgical site infections (SSIs).
Methods:
All patients undergoing 1 of 9 surgical procedures from January 1, 2005, through December 31, 2005, at a large academic medical center and who were assigned a secondary ICD-9-CM infection code at discharge were eligible for study inclusion. All cases were reviewed to determine the presence of SSIs. Logistic regression was used to determine which secondary codes were predictors of SSIs.
Results:
Among 75 secondary infection codes applied at discharge to 454 patients, only 1 code (998.59) appeared to be reliably associated with SSIs. Two other general infection codes (996.63 and 996.67) and 1 specific infection code (320.3) may also have utility.
Conclusion:
Administrative coding data do not perform well to identify SSIs. Some general secondary infection codes, however, may have the potential to be utilized in screening algorithms of electronic health data to assist in SSI surveillance.
Insights
Administrative discharge codes are poor predictors of surgical site infections (SSIs). While some general infection codes show potential, current coding data is not reliable for SSI identification.
Area of Science:
- Healthcare Informatics
- Infectious Disease Epidemiology
- Surgical Outcomes Research
Background:
- Healthcare-associated infections (HAIs) impact public reporting and Medicare payments.
- Administrative discharge codes are being evaluated as potential markers for HAIs.
- The study investigates the utility of secondary ICD-9-CM infection codes in predicting surgical site infections (SSIs).
Purpose of the Study:
- To determine if specific secondary ICD-9-CM infection codes predict surgical site infections (SSIs).
- To assess the reliability of administrative discharge codes for identifying SSIs.
Main Methods:
- Retrospective study of patients undergoing 9 specific surgical procedures from January 1, 2005, to December 31, 2005.
- Inclusion criteria: patients with secondary ICD-9-CM infection codes at discharge.
- Logistic regression analysis to identify predictive secondary infection codes for SSIs.
Main Results:
- Out of 75 secondary infection codes, only one (998.59) showed a reliable association with SSIs.
- Codes 996.63, 996.67, and 320.3 demonstrated potential utility in predicting SSIs.
- Overall, administrative coding data demonstrated poor performance in identifying SSIs.
Conclusions:
- Administrative coding data are not effective for identifying surgical site infections (SSIs).
- Certain general secondary infection codes may be useful in screening algorithms for electronic health data.
- Potential application in enhancing SSI surveillance systems using electronic health records.
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