Objective To establish a predictive model for postoperative intracranial infection in patients with brain tumors and analyze the impact of intracranial infection on the prognosis of patients.Methods The clinical data of 1 352 patients with intracranial tumor surgery admitted to Linfen People's Hospital from January 2015 to December 2022 were analyzed retrospectively.According to whether the patients developed intracranial infection after surgery or not,they were divided into intracranial infection group(n=52)and control group(n=1 300).The clinical characteristics of the two groups of patients were compared,and the risk factors for postoperatively intracranial infection in patients with craniocerebral tumor were analyzed.Based on the relevant risk factors,a Nomogram prediction model was established.Meanwhile,the prognosis of the intracranial infection group was analyzed.Results Multivariate logistic regression analysis showed that diabetes,craniotomy,operation time,postoperative cerebrospinal fluid leakage and postoperative cerebral hemorrhage were independent influencing factors of postoperative intracranial infection in patients with brain tumors.The data set was randomly divided into a training set and a verification set.Diabetes,craniotomy,operation time,postoperative cerebrospinal fluid leakage and postoperative cerebral hemorrhage were included in the prediction model.Nomogram,clinical decision curves,calibration curves and receiver operating characteristic(ROC)curves were drawn.The area under curve(AUC)of ROC in the training set was 0.849(95％CI=0.763-0.934),and the AUC of ROC in the validation set was 0.838(95％CI=0.732-0.943).In the validation set,the model was subjected to the Hosmer-Lemeshow Goodness-of-Fit Test,with a chi square value of 14.399 and a P value of 0.072,which indicated that this model had good reliability.Compared with the control group,the hospital mortality rate in the intracranial infection group was significantly higher(15.38％vs 1.54％,P＜0.001).Conclusions The prediction model established in this study can accurately identify patients at high risk of intracranial infection in patients with intracranial tumors after surgery.
维普
万方数据
