BackgroundCurrently, the diagnosis of post-neurosurgical intracranial infection is mainly dependent on cerebrospinal fluid (CSF) bacterial culture which has the disadvantages of time-consuming, low detection rate and easy to be affected by other factors. These disadvantages bring some difficulties to early diagnosis. Therefore, it is very important to construct a nomogram model to predict the risk of infection to provide a basis for early diagnosis and treatment. MethodThis retrospective study analyzed post-neurosurgical patient data from the Fourth Affiliated Hospital of Harbin Medical University between January 2019 and September 2023. The patients were randomly assigned in an 8:2 ratio into the training cohort and the internal validation cohort. In the training cohort, initial screening of relevant indices was conducted via univariate analysis. Subsequently, the least absolute shrinkage and selection operator (Lasso) logistic regression identified significant potential risk factors for inclusion in the nomogram model. The model's discriminative ability was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), and its calibration was evaluated through calibration plots. The clinical utility of the model was determined using decision curve analysis (DCA) and further validated by the internal validation cohort. ResultsMultivariate logistic regression analysis of the training cohort identified seven independent risk factors for postoperative intracranial infection: duration of postoperative external drainage (odds ratio [OR] 1.19, P=0.005), continued fever (OR 2.11, P=0.036), CSF turbidity (OR 2.73, P=0.014), CSF pressure (OR 1.01, P=0.018), CSF total protein level (OR 1.26, P=0.026), CSF glucose concentration (OR 0.74, P=0.029), and postoperative serum albumin level (OR 0.84, P<0.001). Using these variables to construct the final model. The AUC value of the model was 0.868 in the training cohort and 0.900 in the internal validation cohort. Calibration and the DCA curve indicated high accuracy and clinical benefit of the nomogram, findings that were corroborated in the validation cohort. ConclusionThis study successfully developed a novel nomogram for predicting postoperative intracranial infection, demonstrating excellent predictive performance. It offers a pragmatic tool for early diagnosis of intracranial infection.