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Machine Learning Models in the Prediction of Chronic or Shunt-Dependent Hydrocephalus Following Subarachnoid Hemorrhage: A Systematic Review and Meta-Analysis Publisher



Hajikarimloo B1 ; Mohammadzadeh I2 ; Habibi MA3 ; Tos SM1 ; Asgarzadeh A4 ; Tajvidi M5 ; Aghajani S6 ; Hashemi R7 ; Kooshki A8
Authors
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Authors Affiliations
  1. 1. Department of Neurological Surgery, University of Virginia, Charlottesville, VA, United States
  2. 2. Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  3. 3. Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
  4. 4. Ardabil University of Medical Sciences, Ardabil, Iran
  5. 5. Student Research Committee, Abadan University Of Medical Sciences, Abadan, Iran
  6. 6. Department of Medicine, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  7. 7. Department of Neurological Surgery, Shohada Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  8. 8. Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran

Source: Neuroradiology Journal Published:2025


Abstract

Purpose: Chronic or shunt-dependent hydrocephalus is a frequent consequence of subarachnoid hemorrhage (SAH) with an unclear pathophysiology, making treatment challenging. Despite favorable outcomes following cerebrospinal fluid (CSF) diversion, high-risk surgical interventions remain necessary in some cases. Accurate prediction of chronic or shunt-dependent hydrocephalus in SAH patients can play an important role in their management. This systematic review and meta-analysis assessed the predictive performance of machine learning (ML) models in forecasting chronic or shunt-dependent hydrocephalus following SAH. Methods: A systematic search of PubMed, Embase, Scopus, and Web of Science was conducted. ML or deep learning (DL)-based models that predicted chronic or shunt-dependent hydrocephalus following SAH were included. To avoid bias, only the data of the best-performance model, which was defined by the highest area under the curve (AUC) of the models, were extracted. The pooled AUC, accuracy (ACC), sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated using the R program. Results: Six studies with 2096 individuals were included. The AUC, ACC, sensitivity, and specificity ranged from 0.8 to 0.92, 0.72 to 0.9, 0.73 to 0.85, and 0.7 to 0.92. The meta-analysis showed a pooled AUC of 0.83 (95%CI: 0.81–0.84) and ACC of 0.79 (95%CI: 0.66–0.91). The meta-analysis revealed a pooled sensitivity of 0.8 (95%CI: 0.73–0.85), specificity of 0.79 (95%CI: 0.68–0.86), and DOR of 12.13 (95%CI: 8.2–17.96) for predictive performance of these models. Conclusion: ML-based models showed encouraging predictive performance in forecasting chronic or shunt-dependent hydrocephalus following SAH. © The Author(s) 2025.
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