Validation of an Electronic Algorithm to Identify Candidates for Colon Surgical Site Infection Review JA Yegge 1, K Gase 1, M Hohrein 1, H Xu 1, R Khoury 1, H Babcock 2 1 BJC HealthCare, St. Louis MO, 2 Washington University, St. Louis, MO Background/Objectives: In response to an increasing surveillance burden, an electronic algorithm was developed in 2009 to identify candidates for surgical site infection (SSI) review. The algorithm rules look for readmissions after a qualifying procedure in addition to cultures, antibiotic starts and ICD-9 infection codes. The objective of this study was to examine the algorithm’s accuracy as a surveillance method for colon SSIs. Methods: Colon procedures (identified by ICD-9 code) performed in 4th quarter 2012 at 10 adult hospitals in a single healthcare system were included. The medical records were screened by a single clinical abstractor and based on that abstraction a single Infection Preventionist reviewed procedures identified as potentially infected to determine infection status. Specificity, sensitivity, positive and negative predictive values were calculated. Results: 417 colon procedures were identified. The electronic algorithm triggered at least one rule for 115 (28%) procedures; 17 (15% yield) infected cases were confirmed by review. There was one infected case identified in the 302 procedures that did not trigger any rules. Algorithm sensitivity was 94.5% and specificity was 100%. The positive predictive value was 100% and the negative predictive value was 99.75%. Conclusions: These results confirm that the algorithm is highly effective in rejecting true negatives for further evaluation and capturing true positives within the subset identified for infection investigation. Additional refinement of the algorithm rules is needed to decrease the number of procedures that are flagged for review. This will decrease the time the IP spends on chart review while not missing any infected cases. Abstract In response to an increasing surveillance burden, an electronic algorithm was developed in 2009 to identify surgical site infection (SSI) candidates. The algorithm rules look for readmissions after a qualifying procedure in addition to cultures, antibiotic starts and ICD-9 infection codes. The SSI candidates are sent to an Infection Preventionist’s work list for review. The objective of this study was to examine the algorithm’s accuracy as a surveillance method for colon SSIs. Background Every colon procedure (identified by ICD-9 code) performed between 10/1/ /31/2012 at 10 BJC Healthcare system adult hospitals were included. The electronic medical records were screened by a single clinical abstractor. Based on the abstracted information a single Infection Preventionist reviewed all procedures identified as potentially infected to determine infection status using 2012 National Healthcare Safety Network definitions. Specificity, sensitivity, positive and negative predictive values were calculated. Results These results confirm that the algorithm is highly effective in rejecting true negatives for further evaluation. It is also highly effective in capturing true positives within the subset identified for infection investigation. Additional refinement of the algorithm rules is needed to decrease the number of procedures that are flagged for review. This will decrease the time the Infection Preventionist spends on chart review while not missing any infected cases. Conclusions ABSTRACT # MethodsResults 417 colon procedures were identified. The electronic algorithm triggered at least one rule for 115 (28%) procedures; 17 (15% yield) infected cases were confirmed by review. There was one infected case identified in the 302 procedures that did not trigger any rules. The hospital infection Preventionist would therefore not have been alerted to this SSI candidate. Algorithm sensitivity was 94.5% and specificity was 100%. The positive predictive value was 100% and the negative predictive value was 99.75%. 417 procedures identified 115 triggered algorithm 17 confirmed cases 302 did not trigger algorithm 1 confirmed case (not identified by trigger) Infection Preventionist InfectionNo InfectionTotal Algorithm Infection170 No Infection Total TestDefinitionCalculation & Result SensitivityThe probability that the algorithm will include procedures that indicate infection among those with infections 17/(17+1) = 94.45% SpecificityThe fraction of those without infection who were not triggered by the algorithm 399/(399+0) = 100% Positive Predictive Value (PPV) The probability that an SSI was identified by the algorithm17/17 = 100% Negative Predictive Value (NPV) The probability that a non-infected case was not identified as an SSI candidate by the algorithm 399/(399+1) = 99.75% Nothing to Disclose