Risk prediction models of intracranial infection after neurosurgical craniotomy：a systematic review

Objective To systematically evaluate the risk prediction models of patients after craniotomy, so as to provide reference for clinical selection of appropriate risk assessment models. Methods CKNI, WangFang Data, VIP, CBM, PubMed, Embase, Web of Science, Cochrane Library, CINAHL Completa were searched by computer. The search time limit was from the establishment of the database to February 2024. Literature screening and data extraction were performed by two researchers independently. The risk of bias and applicability of the literature were assessed using the PROBAST tool. Results A total of 12 studies were included, with a total sample size of 5165 cases and 1175 events of intracranial infection. the area under the curve (AUC) of the prediction model ranged from 0.774 to 0.911, and the AUC of each study was > 0.8, indicating that the prediction performance was good, but the overall risk of bias of the included studies was high, mainly due to the differences in study subjects, evaluation methods, predictors, and modeling methods. Conclusions The prediction models of intracranial infection risk in patients after craniotomy have good discrimination and applicability, some of the prediction models have significant methodological defects and high risk of bias. In the future, it should be developed and verified in strict accordance with the risk of bias reporting standards, so as to form a risk early warning system with low risk of bias and high feasibility.