Habibi Mohammad Amin, Rashidi Farhang, Mehrtabar Ehsan, Arshadi Mohammad Reza, Fallahi Mohammad Sadegh, Amirkhani Nikan, Hajikarimloo Bardia, Shafizadeh Milad, Majidi Shahram, Dmytriw Adam A
Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Acta Neurol Belg. 2024 Nov 7. doi: 10.1007/s13760-024-02682-y.
Stroke is a leading cause of death and disability worldwide. Approximately one-third of patients with stroke experienced a second stroke. This study investigates the predictive value of machine learning (ML) algorithms for recurrent stroke.
This study was prepared according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. PubMed, Scopus, Embase, and Web of Science (WOS) were searched until January 1, 2024. The quality assessment of studies was conducted using the QUADAS-2 tool. The diagnostic meta-analysis was conducted to calculate the pooled sensitivity, specificity, diagnostic accuracy, positive and negative diagnostic likelihood ratio (DLR), diagnostic accuracy, diagnostic odds ratio (DOR), and area under of the curve (AUC) by the MIDAS package in STATA V.17.
Twelve studies, comprising 24,350 individuals, were included. The meta-analysis revealed a sensitivity of 71% (95% CI 0.64-0.78) and a specificity of 88% (95% confidence interval (CI) 0.76-0.95). Positive and negative DLR were 5.93 (95% CI 3.05-11.55) and 0.33 (95% CI 0.28-0.39), respectively. The diagnostic accuracy and DOR was 2.89 (95% CI 2.32-3.46) and 18.04 (95% CI 10.21-31.87), respectively. The summary ROC curve indicated an AUC of 0.82 (95% CI 0.78-0.85).
ML demonstrates promise in predicting recurrent strokes, with moderate to high sensitivity and specificity. However, the high heterogeneity observed underscores the need for standardized approaches and further research to enhance the reliability and generalizability of these models. ML-based recurrent stroke prediction can potentially augment clinical decision-making and improve patient outcomes by identifying high-risk patients.
中风是全球死亡和残疾的主要原因。约三分之一的中风患者会经历二次中风。本研究调查机器学习（ML）算法对复发性中风的预测价值。
本研究按照系统评价和Meta分析的首选报告项目（PRISMA）指南进行。检索了PubMed、Scopus、Embase和科学网（WOS），检索截止至2024年1月1日。使用QUADAS-2工具对研究进行质量评估。采用STATA V.17中的MIDAS软件包进行诊断性Meta分析，以计算合并敏感度、特异度、诊断准确性、阳性和阴性诊断似然比（DLR）、诊断准确率、诊断比值比（DOR）以及曲线下面积（AUC）。
纳入了12项研究，共24350名个体。Meta分析显示敏感度为71%（95%可信区间[CI]0.64 - 0.78），特异度为88%（95%可信区间[CI]0.76 - 0.95）。阳性和阴性DLR分别为5.93（95%CI 3.05 - 11.55）和0.33（95%CI 0.28 - 0.39）。诊断准确率和DOR分别为2.89（95%CI 2.32 - 3.46）和18.04（95%CI 10.21 - 31.87）。汇总ROC曲线显示AUC为0.82（95%CI 0.78 - 0.85）。
ML在预测复发性中风方面显示出前景，具有中度至高敏感度和特异度。然而，观察到的高度异质性强调需要标准化方法和进一步研究，以提高这些模型的可靠性和可推广性。基于ML的复发性中风预测可通过识别高危患者潜在地增强临床决策并改善患者预后。
