Mohammadzadeh Ibrahim, Niroomand Behnaz, Shahnazian Zahra, Ghanbarnia Ramin, Nouri Zahra, Tajerian Amin, Choubineh Tannaz, Najafi Masoud, Mohammadzadeh Shahin, Soltani Reza, Keshavarzi Arya, Keshtkar Abbasali, Mousavinejad Seyed Ali
Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Clin Neurol Neurosurg. 2025 Feb;249:108668. doi: 10.1016/j.clineuro.2024.108668. Epub 2024 Dec 5.
Early prediction of poor outcomes in patients impacted with aneurysmal subarachnoid hemorrhage (aSAH) is crucial for timely intervention and effective management. This systematic review and meta-analysis aimed to evaluate the performance of machine learning (ML) algorithms in predicting poor outcomes in patients with aSAH, assessing their sensitivity, specificity, and other algorithm metrics. A comprehensive search of PubMed, Scopus, Embase, Web of science and Cochrane library conducted to identify eligible studies. We extracted data on sensitivity, specificity, accuracy, precision, F1score and area under the curve (AUC) from the included studies. Out of 2238 studies screened, 12 met our eligibility criteria involving ML approaches in patients with confirmed aSAH. ML algorithms, particularly XGBoost and CatBoost, offer promising performance for predicting poor outcomes in aSAH patients. Meta-analysis was performed on 12 studies resulted in a pooled sensitivity of 0.88 [95 % CI: 0.76-0.94], specificity of 0.78 [95 % CI 0.66-0.86], positive DLR of 3.91 [95 % CI: 2.42-6.30], negative DLR of 0.16 [95 % CI: 0.07-0.34], diagnostic odds ratio of 24.9 [95 % CI: 7.97-77.82], the diagnostic score of 3.21[95 % CI: 2.08-4.35], and the area AUC was 0.82, indicating substantial diagnostic performance. However, conventional LR showed slightly superior predictive function compared to ML algorithms. These findings underscore the potential of ML algorithms to significantly advance the predictability of poor outcomes in patients with aSAH, suggesting that ML can play a critical role in enhancing clinical decision-making.
对动脉瘤性蛛网膜下腔出血（aSAH）患者不良预后进行早期预测，对于及时干预和有效管理至关重要。本系统评价和荟萃分析旨在评估机器学习（ML）算法在预测aSAH患者不良预后方面的性能，评估其敏感性、特异性和其他算法指标。通过全面检索PubMed、Scopus、Embase、Web of science和Cochrane图书馆来识别符合条件的研究。我们从纳入的研究中提取了关于敏感性、特异性、准确性、精确性、F1评分和曲线下面积（AUC）的数据。在筛选的2238项研究中，有12项符合我们的纳入标准，涉及确诊aSAH患者的ML方法。ML算法，特别是XGBoost和CatBoost，在预测aSAH患者不良预后方面表现出良好的性能。对12项研究进行荟萃分析，得出合并敏感性为0.88 [95% CI：0.76 - 0.94]，特异性为0.78 [95% CI 0.66 - 0.86]，阳性似然比为3.91 [95% CI：2.42 - 6.30]，阴性似然比为0.16 [95% CI：0.07 - 0.34]，诊断比值比为24.9 [95% CI：7.97 - 77.82]，诊断评分为3.21[95% CI：2.08 - 4.35]，AUC面积为0.82，表明具有显著的诊断性能。然而，传统逻辑回归与ML算法相比显示出略优的预测功能。这些发现强调了ML算法在显著提高aSAH患者不良预后预测能力方面的潜力，表明ML在加强临床决策中可以发挥关键作用。
