Machine learning models in the prediction of chronic or shunt-dependent hydrocephalus following subarachnoid hemorrhage: A systematic review and meta-analysis.

Abstract

PurposeChronic or shunt-dependent hydrocephalus is a frequent consequence of subarachnoid hemorrhage (SAH) with an unclear pathophysiology, making treatment challenging. Despite favorable outcomes following cerebrospinal fluid (CSF) diversion, high-risk surgical interventions remain necessary in some cases. Accurate prediction of chronic or shunt-dependent hydrocephalus in SAH patients can play an important role in their management. This systematic review and meta-analysis assessed the predictive performance of machine learning (ML) models in forecasting chronic or shunt-dependent hydrocephalus following SAH.MethodsA systematic search of PubMed, Embase, Scopus, and Web of Science was conducted. ML or deep learning (DL)-based models that predicted chronic or shunt-dependent hydrocephalus following SAH were included. To avoid bias, only the data of the best-performance model, which was defined by the highest area under the curve (AUC) of the models, were extracted. The pooled AUC, accuracy (ACC), sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated using the R program.ResultsSix studies with 2096 individuals were included. The AUC, ACC, sensitivity, and specificity ranged from 0.8 to 0.92, 0.72 to 0.9, 0.73 to 0.85, and 0.7 to 0.92. The meta-analysis showed a pooled AUC of 0.83 (95%CI: 0.81-0.84) and ACC of 0.79 (95%CI: 0.66-0.91). The meta-analysis revealed a pooled sensitivity of 0.8 (95%CI: 0.73-0.85), specificity of 0.79 (95%CI: 0.68-0.86), and DOR of 12.13 (95%CI: 8.2-17.96) for predictive performance of these models.ConclusionML-based models showed encouraging predictive performance in forecasting chronic or shunt-dependent hydrocephalus following SAH.