Objective:To screening the possible risk factors for surgical site infection, identify the independent risk factors for surgical site infection in neurosurgery, then develop a predictive system with those independent risk factors, as an efficient tool of risk prediction for surgical site infection in neurosurgery.Method:1DataThe possible risk factors which had reported in articles were collected and classified into five types:patient characteristics, operative characteristics, hospital condition, medical staff and medicine characteristics. Then the SSI Risk Factors Questionnaire was designed, and information what we needed were filled in the questionnaires.2Data analysisThe x2test was used to evaluate the correlation between variables and surgical site infection, and P<0.1was considered to be statistically significant. To identify independent risk factors that were associated with surgical site infection, a multivariate forward logistic regression analysis was used to adjust for confounders and screening independent risk factors. When P<0.05, the variable was entered in the model; if P>0.1, the variable was removed.3The predictive system establishment and assessment of risk prediction abilityIndependent risk factors were used to build up the predictive system. A receiver operating characteristic (ROC) curve was constructed to evaluate the accuracy of risk prediction comparing the calculated probabilities of SSI with the actual occurrence. The performance of the predictive system was compared with that of the NNIS and SENIC indexe using the area under the ROC curve to evaluate the accuracy of risk prediction.Result:A total of214patients fulfilled our inclusion criteria, and18episodes of surgical site infection were identified. The infection incidence was8.41%. The8independent risk factors for surgical site infection included number of the incision (OR5.242;95%CI0.843-32.603), timing of skin preparation (OR5.736;95%CI1.222-26.921), course of prophylactic antibacterial (OR6.068;95%CI0.872-42.236), preoperative hospitalization time (OR11.799;95%CI2.119-65.688), age (OR11.907;95%CI1.533-92.458), diabetes (OR14.576;95%CI1.833-115.907), consecutive operation (OR19.052;95%CI2.517-144.233), endocranium incision(OR48.231;95%CI3.308-703.302).The predictive system showed an area of0.964(P<0.001) under the ROC curve, revealing good accuracy or good predictive power of the test to detect patients with. SSI.Conclusion:1The indepandent risk factors for surgical site infection in neurosurgery were the number of the incision≥2, the time between skin preparation and operation>2h, prolonged use of antibiotics, preoperative hospitalization time>3d, the age of patient>70years, diabetic, consecutive operation and endocranium incision.2Incidence of surgical site infection was classified by the score of the predictive system, and a linear increase in incidence levels was observed to the extent that the score increase. Compared the ROC curves derived from the probabilities estimated for the predictive system and for the NNIS and SENIC indexe, the area under the curve was larger for the predictive system. It showed that the predictive system had a good function to detect patients with SSI.