Validation of an Electronic Surveillance Algorithm to Identify Patients With Post-Operative Surgical Site Infections Using National Surgical Quality Improvement Program Pediatric Data.

BACKGROUND

Surgical site infections (SSIs) are common, but data related to these infections maybe difficult to capture. We developed an electronic surveillance algorithm to identify patients with SSIs. Our objective was to validate our algorithm by comparing it with our institutional National Surgical Quality Improvement Program Pediatric (NSQIP Peds) data.

METHODS

We applied our algorithm to our institutional NSQIP Peds 2015-2017 cohort. The algorithm consisted of the presence of a diagnosis code for post-operative infection or the presence of 4 criteria: diagnosis code for infection, antibiotic administration, positive culture, and readmission/surgery related to infection. We compared the algorithm's SSI rate to the NSQIP Peds identified SSI. Algorithm performance was assessed using sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and Cohen's kappa. The charts of discordant patients were reviewed to understand limitations of the algorithm.

RESULTS

Of 3879 patients included, 2.5% had SSIs by NSQIP Peds definition and 1.9% had SSIs by our algorithm. Our algorithm achieved a sensitivity of 44%, specificity of 99%, NPV of 99%, PPV of 59%, and Cohen's kappa of 0.5. Of the 54 false negatives, 37% were diagnosed/treated as outpatients, 31% had tracheitis, and 17% developed SSIs during their post-operative admission. Of the 30 false positives, 33% had an infection at index surgery and 33% had SSIs related to other surgeries/procedures.

CONCLUSIONS

Our algorithm achieved high specificity and NPV compared with NSQIP Peds reported SSIs and may be useful when identifying SSIs in patient populations that are not actively monitored for SSIs.