Javadnia Parisa, Salimi Nila, Shokri Bita, Ramazani Yousef, Moradinazar Mehdi, Khaledian Neda, Alimohammadi Ehsan
Department of Neurosurgery, Iran University of Medical Sciences, Tehran, Iran.
Department of Neurosurgery, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Neurosurg Rev. 2025 Sep 1;48(1):629. doi: 10.1007/s10143-025-03773-x.
The identification of factors associated with chronic shunt-dependent hydrocephalus (CSDH) following spontaneous subarachnoid hemorrhage (SAH) remains challenging, despite numerous studies. Early recognition of patients at higher risk for requiring shunt placement is important for optimizing management strategies. This systematic review and meta-analysis evaluated the efficacy of machine learning (ML) algorithms in analyzing datasets related to CSDH post-SAH, assessing performance metrics such as sensitivity, accuracy, and specificity. A systematic review was conducted across five databases (PubMed, Scopus, Cochrane Library, Embase, and Web of Science) to identify studies employing ML to analyze factors associated with CSDH following SAH. Data extraction included ML techniques, input features, and performance metrics such as area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, specificity, precision, and F1 score. Two independent reviewers extracted and organized the data, including details on machine learning models, validation processes, and metrics. Out of 993 reviewed studies, five met the inclusion criteria for analyzing ML models in relation to CSDH post-SAH. The pooled AUC-ROC across these models was 0.79 (95% CI: 0.78-0.81), with moderate heterogeneity (I² = 42.58%, Q (19) = 34.79, p = 0.01). No significant differences in AUC-ROC were observed between linear, tree-based, and deep learning models (Q (2) = 0.99, p = 0.61). Studies utilizing fewer than 10 input features showed a lower pooled AUC-ROC of 0.78, whereas those with more than 10 features achieved a higher AUC-ROC of 0.82, with heterogeneity of 7.52% and 66.53%, respectively. Machine learning algorithms can assist in identifying factors associated with the development of chronic hydrocephalus following spontaneous SAH through analysis of clinical datasets. Incorporating a greater number of relevant risk factors may further improve the performance of these ML models in understanding high-risk patient profiles.
尽管已有大量研究，但确定自发性蛛网膜下腔出血（SAH）后与慢性分流依赖型脑积水（CSDH）相关的因素仍具有挑战性。早期识别需要进行分流置管的高风险患者对于优化管理策略至关重要。本系统评价和荟萃分析评估了机器学习（ML）算法在分析与SAH后CSDH相关的数据集方面的有效性，评估了诸如敏感性、准确性和特异性等性能指标。对五个数据库（PubMed、Scopus、Cochrane图书馆、Embase和Web of Science）进行了系统评价，以确定采用ML分析SAH后与CSDH相关因素的研究。数据提取包括ML技术、输入特征以及性能指标，如受试者操作特征曲线下面积（AUC-ROC）、准确性、敏感性、特异性、精确性和F1分数。两名独立的评审员提取并整理了数据，包括机器学习模型、验证过程和指标的详细信息。在993项综述研究中，有五项符合分析与SAH后CSDH相关的ML模型的纳入标准。这些模型的合并AUC-ROC为0.79（95%CI：0.78-0.81），具有中度异质性（I² = 42.58%，Q(19)=34.79，p = 0.01）。在线性、基于树的和深度学习模型之间，未观察到AUC-ROC有显著差异（Q(2)=0.99，p = 0.61）。使用少于10个输入特征的研究显示合并AUC-ROC较低，为0.78，而使用超过10个特征的研究则实现了较高的AUC-ROC，为0.82，异质性分别为7.52%和66.53%。机器学习算法可通过分析临床数据集，协助识别自发性SAH后慢性脑积水发展相关的因素。纳入更多相关风险因素可能会进一步提高这些ML模型在了解高风险患者概况方面的性能。
