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Updated: May 2, 2026
The Rabbit Blood-shunt Model for the Study of Acute and Late Sequelae of Subarachnoid Hemorrhage: Technical Aspects
Published on: October 2, 2014
The efficacy of machine learning algorithms in evaluating factors associated with shunt-dependent hydrocephalus after
Parisa Javadnia1, Nila Salimi2, Bita Shokri2
1Department of Neurosurgery, Iran University of Medical Sciences, Tehran, Iran.
Insights
Machine learning models show promise in identifying patients at risk for chronic shunt-dependent hydrocephalus after subarachnoid hemorrhage. Incorporating more risk factors may improve predictive accuracy for these complex cases.
Area of Science:
- Neurosurgery
- Medical Informatics
- Data Science
Background:
- Identifying risk factors for chronic shunt-dependent hydrocephalus (CSDH) after spontaneous subarachnoid hemorrhage (SAH) is clinically significant for patient management.
- Early recognition of high-risk patients is crucial for optimizing treatment strategies and improving outcomes.
Purpose of the Study:
- To systematically review and meta-analyze the efficacy of machine learning (ML) algorithms in predicting CSDH post-SAH.
- To assess the performance metrics of ML models, including sensitivity, accuracy, and specificity, in analyzing relevant datasets.
Main Methods:
- A systematic review of five major databases (PubMed, Scopus, Cochrane Library, Embase, Web of Science) was performed.
- Studies utilizing ML for CSDH prediction post-SAH were included, with data extracted on ML techniques, input features, and performance metrics (e.g., AUC-ROC, accuracy, sensitivity, specificity).
- Two independent reviewers performed data extraction and organization.
Main Results:
- Five studies met the inclusion criteria, analyzing ML models for CSDH post-SAH.
- The pooled area under the receiver operating characteristic curve (AUC-ROC) across models was 0.79 (95% CI: 0.78-0.81), indicating moderate performance.
- Models using more than 10 input features demonstrated a higher pooled AUC-ROC (0.82) compared to those with fewer features (0.78).
Conclusions:
- Machine learning algorithms show potential in identifying factors associated with CSDH development following spontaneous SAH.
- Utilizing a larger number of relevant risk factors in ML models may enhance their performance in identifying high-risk patient profiles.
- ML offers a valuable tool for analyzing complex clinical datasets to improve understanding and prediction of CSDH post-SAH.
Abstract:
The identification of factors associated with chronic shunt-dependent hydrocephalus (CSDH) following spontaneous subarachnoid hemorrhage (SAH) remains challenging, despite numerous studies. Early recognition of patients at higher risk for requiring shunt placement is important for optimizing management strategies. This systematic review and meta-analysis evaluated the efficacy of machine learning (ML) algorithms in analyzing datasets related to CSDH post-SAH, assessing performance metrics such as sensitivity, accuracy, and specificity. A systematic review was conducted across five databases (PubMed, Scopus, Cochrane Library, Embase, and Web of Science) to identify studies employing ML to analyze factors associated with CSDH following SAH. Data extraction included ML techniques, input features, and performance metrics such as area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, specificity, precision, and F1 score. Two independent reviewers extracted and organized the data, including details on machine learning models, validation processes, and metrics. Out of 993 reviewed studies, five met the inclusion criteria for analyzing ML models in relation to CSDH post-SAH. The pooled AUC-ROC across these models was 0.79 (95% CI: 0.78-0.81), with moderate heterogeneity (I² = 42.58%, Q (19) = 34.79, p = 0.01). No significant differences in AUC-ROC were observed between linear, tree-based, and deep learning models (Q (2) = 0.99, p = 0.61). Studies utilizing fewer than 10 input features showed a lower pooled AUC-ROC of 0.78, whereas those with more than 10 features achieved a higher AUC-ROC of 0.82, with heterogeneity of 7.52% and 66.53%, respectively. Machine learning algorithms can assist in identifying factors associated with the development of chronic hydrocephalus following spontaneous SAH through analysis of clinical datasets. Incorporating a greater number of relevant risk factors may further improve the performance of these ML models in understanding high-risk patient profiles.
