Zhang Haofuzi, Zou Peng, Luo Peng, Jiang Xiaofan
Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
J Med Internet Res. 2025 Jan 20;27:e54121. doi: 10.2196/54121.
Delayed cerebral ischemia (DCI) is a primary contributor to death after subarachnoid hemorrhage (SAH), with significant incidence. Therefore, early determination of the risk of DCI is an urgent need. Machine learning (ML) has received much attention in clinical practice. Recently, some studies have attempted to apply ML models for early noninvasive prediction of DCI. However, systematic evidence for its predictive accuracy is still lacking.
The aim of this study was to synthesize the prediction accuracy of ML models for DCI to provide evidence for the development or updating of intelligent detection tools.
PubMed, Cochrane, Embase, and Web of Science databases were systematically searched up to May 18, 2023. The risk of bias in the included studies was assessed using PROBAST (Prediction Model Risk of Bias Assessment Tool). During the analysis, we discussed the performance of different models in the training and validation sets.
We finally included 48 studies containing 16,294 patients with SAH and 71 ML models with logistic regression as the main model type. In the training set, the pooled concordance index (C index), sensitivity, and specificity of all the models were 0.786 (95% CI 0.737-0.835), 0.77 (95% CI 0.69-0.84), and 0.83 (95% CI 0.75-0.89), respectively, while those of the logistic regression models were 0.770 (95% CI 0.724-0.817), 0.75 (95% CI 0.67-0.82), and 0.71 (95% CI 0.63-0.78), respectively. In the validation set, the pooled C index, sensitivity, and specificity of all the models were 0.767 (95% CI 0.741-0.793), 0.66 (95% CI 0.53-0.77), and 0.78 (95% CI 0.71-0.84), respectively, while those of the logistic regression models were 0.757 (95% CI 0.715-0.800), 0.59 (95% CI 0.57-0.80), and 0.80 (95% CI 0.71-0.87), respectively.
ML models appear to have relatively desirable power for early noninvasive prediction of DCI after SAH. However, enhancing the prediction sensitivity of these models is challenging. Therefore, efficient, noninvasive, or minimally invasive low-cost predictors should be further explored in future studies to improve the prediction accuracy of ML models.
PROSPERO (CRD42023438399); https://tinyurl.com/yfuuudde.
迟发性脑缺血（DCI）是蛛网膜下腔出血（SAH）后导致死亡的主要原因，发病率较高。因此，尽早确定DCI风险迫在眉睫。机器学习（ML）在临床实践中备受关注。最近，一些研究尝试将ML模型用于DCI的早期无创预测。然而，其预测准确性的系统证据仍然缺乏。
本研究旨在综合ML模型对DCI的预测准确性，为智能检测工具的开发或更新提供证据。
截至2023年5月18日，系统检索了PubMed、Cochrane、Embase和Web of Science数据库。使用PROBAST（预测模型偏倚风险评估工具）评估纳入研究的偏倚风险。在分析过程中，我们讨论了不同模型在训练集和验证集中的表现。
我们最终纳入了48项研究，共16294例SAH患者以及71个以逻辑回归为主要模型类型的ML模型。在训练集中，所有模型的合并一致性指数（C指数）、敏感性和特异性分别为0.786（95%CI 0.737-0.835）、0.77（95%CI 0.69-0.84）和0.83（95%CI 0.75-0.89），而逻辑回归模型的相应指标分别为0.770（95%CI 0.724-0.817）、0.75（95%CI 0.67-0.82）和0.71（95%CI 0.63-0.78）。在验证集中，所有模型的合并C指数、敏感性和特异性分别为0.767（95%CI 0.741-0.793）、0.66（95%CI 0.53-0.77）和0.78（95%CI 0.71-0.84），而逻辑回归模型的相应指标分别为0.757（95%CI 0.715-0.800）、0.59（95%CI 0.57-0.80）和0.80（95%CI 0.71-0.87）。
ML模型似乎对SAH后DCI的早期无创预测具有相对理想的能力。然而，提高这些模型的预测敏感性具有挑战性。因此，未来研究应进一步探索高效、无创或微创低成本预测指标，以提高ML模型的预测准确性。
PROSPERO（CRD42023438399）；https://tinyurl.com/yfuuudde 。
