机器学习用于蛛网膜下腔出血患者延迟性脑缺血的早期预测：系统评价与Meta分析

Machine Learning for the Early Prediction of Delayed Cerebral Ischemia in Patients With Subarachnoid Hemorrhage: Systematic Review and Meta-Analysis.

作者信息

Zhang Haofuzi, Zou Peng, Luo Peng, Jiang Xiaofan

机构信息

Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.

出版信息

J Med Internet Res. 2025 Jan 20;27:e54121. doi: 10.2196/54121.

DOI:10.2196/54121

PMID:39832368

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11791451/

Abstract

BACKGROUND

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.

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

TRIAL REGISTRATION

PROSPERO (CRD42023438399); https://tinyurl.com/yfuuudde.

摘要

背景

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。

目的

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。

方法

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。

结果

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）。