The Surgical Site Infection Risk Score (SSIRS): A Model to Predict the Risk of Surgical Site Infections

摘要

Surgical site infections (SSI) are an important cause of peri-surgical morbidity with risks that vary extensively between patients and surgeries. Quantifying SSI risk would help identify candidates most likely to benefit from interventions to decrease the risk of SSI.Methods: We randomly divided all surgeries recorded in the National Surgical Quality Improvement Program from 2010 intoa derivation and validation population. We used multivariate logistic regression to determine the independent associationof patient and surgical covariates with the risk of any SSI (including superficial, deep, and organ space SSI) within 30 days ofsurgery. To capture factors particular to specific surgeries, we developed a surgical risk score specific to all surgeries havinga common first 3 numbers of their CPT code.Results: Derivation (n = 181 894) and validation (n = 181 146) patients were similar for all demographics, past medicalhistory, and surgical factors. Overall SSI risk was 3.9%. The SSI Risk Score (SSIRS) found that risk increased with patient factors(smoking, increased body mass index), certain comorbidities (peripheral vascular disease, metastatic cancer, chronic steroiduse, recent sepsis), and operative characteristics (surgical urgency; increased ASA class; longer operation duration; infectedwounds; general anaesthesia; performance of more than one procedure; and CPT score). In the validation population, theSSIRS had good discrimination (c-statistic 0.800, 95% CI 0.795–0.805) and calibration.Conclusion: SSIRS can be calculated using patient and surgery information to estimate individual risk of SSI for a broadrange of surgery types.