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Machine Learning for Predicting Poor Outcomes in Aneurysmal Subarachnoid Hemorrhage: A Systematic Review and Meta-Analysis Involving 8445 Participants Publisher Pubmed



Mohammadzadeh I1 ; Niroomand B1 ; Shahnazian Z1 ; Ghanbarnia R1 ; Nouri Z1 ; Tajerian A2 ; Choubineh T3 ; Najafi M4 ; Mohammadzadeh S1 ; Soltani R5 ; Keshavarzi A1 ; Keshtkar A6 ; Mousavinejad SA1
Authors
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Authors Affiliations
  1. 1. Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  2. 2. School of Medicine, Arak University of Medical Sciences, Arak, Iran
  3. 3. Department of Computer (Computer engineering), North Tehran Branch, Islamic Azad University, Tehran, Iran
  4. 4. School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
  5. 5. Department of Biology and Anatomical Sciences, School of Medicine, Shahid Beheshti University of Medical Sciences, Iran
  6. 6. Department of Disaster and Emergency Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

Source: Clinical Neurology and Neurosurgery Published:2025


Abstract

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. © 2024 Elsevier B.V.
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