ArticleMolecular neurobiology2019
Machine Learning Analysis of Matricellular Proteins and Clinical Variables for Early Prediction of Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage.
Article in Molecular neurobiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 3 of them syntheses that pooled it.
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The trial behind it
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Who cites it
26 citing papers in PubMed, 3 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- Machine Learning for the Early Prediction of Delayed Cerebral Ischemia in Patients With Subarachnoid Hemorrhage: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Machine Learning Algorithms to Predict Delayed Cerebral Ischemia After Subarachnoid Hemorrhage: A Systematic Review and Meta-analysis.Neurocritical care · 2024Pooled it
- A systematic review of machine learning models for predicting outcomes of stroke with structured data.PloS one · 2020Pooled it
- Cerebral Vasospasm Detection and Delayed Cerebral Ischemia Prediction after Aneurysmal Subarachnoid Hemorrhage: A Scoping Review.Neurocritical care · 2026Article
- Phenotype-Aligned Metabolomics Identifies Plasma Arginine as a Candidate Predictor of Delayed Cerebral Ischemia after Aneurysmal Subarachnoid Hemorrhage.Translational stroke research · 2026Article
- Machine Learning Framework for Ovarian Cancer Diagnostics Using Plasma Lipidomics and Metabolomics.International journal of molecular sciences · 2025Article
- Tenascin-C as a Target for Intervention in Delayed Cerebral Ischemia After Subarachnoid Hemorrhage.Acta neurochirurgica. Supplement · 2025Review
- Impact Exploration of Spatiotemporal Feature Derivation and Selection on Machine Learning-Based Predictive Models for Post-Embolization Cerebral Aneurysm Recanalization.Cardiovascular engineering and technology · 2024Article
- Machine learning predicts cerebral vasospasm in patients with subarachnoid haemorrhage.EBioMedicine · 2024Article
- Predicting who has delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage using machine learning approach: a multicenter, retrospective cohort study.BMC neurology · 2024Article
- Anti-Apoptotic Effects of AMPA Receptor Antagonist Perampanel in Early Brain Injury After Subarachnoid Hemorrhage in Mice.Translational stroke research · 2024Article
- Advances in biomarkers for vasospasm - Towards a future blood-based diagnostic test.World neurosurgery: X · 2024Review
- Accurately predicting the risk of unfavorable outcomes after endovascular coil therapy in patients with aneurysmal subarachnoid hemorrhage: an interpretable machine learning model.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Article
- Article
- Global trend in research of intracranial aneurysm management with artificial intelligence technology: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2024Article
- Effect of Cilostazol on Delayed Cerebral Infarction in Aneurysmal Subarachnoid Hemorrhage Using Explainable Predictive Modeling.Bioengineering (Basel, Switzerland) · 2023Article
- Plasma SPARC Elevation in Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2023Article
- Artificial Intelligence for Neurosurgery : Current State and Future Directions.Journal of Korean Neurosurgical Society · 2023Article
- Machine learning prediction of hematoma expansion in acute intracerebral hemorrhage.Scientific reports · 2022Article
- Predictive modeling in neurocritical care using causal artificial intelligence.World journal of critical care medicine · 2021Review
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Although delayed cerebral ischemia (DCI) is a well-known complication after subarachnoid hemorrhage (SAH), there are no reliable biomarkers to predict DCI development. Matricellular proteins (MCPs) have been reported relevant to DCI and expected to become biomarkers. As machine learning (ML) enables the classification of various input data and the result prediction, the aim of this study was to construct early prediction models of DCI development with clinical variables and MCPs using ML analyses. Early-stage clinical data of 95 SAH patients in a prospective cohort were analyzed and applied to a ML algorithm, random forest, to construct three prediction models: (1) a model with only clinical variables on admission, (2) a model with only plasma levels of MCP (periostin, osteopontin, and galectin-3) at post-onset days 1-3, and (3) a model with both clinical variables on admission and MCP values at days 1-3. The prediction accuracy of the development of DCI, angiographic vasospasm, or cerebral infarction and the importance of each feature were computed. The prediction accuracy of DCI development was 93.9% in model 1, 87.2% in model 2, and 95.1% in model 3, but that of angiographic vasospasm or cerebral infarction was lower. The three most important features in model 3 for DCI were periostin, osteopontin, and galectin-3, followed by aneurysm location. All of the early-stage prediction models of DCI development constructed by ML worked with high accuracy and sensitivity. One-time early-stage measurement of plasma MCPs served for reliable prediction of DCI development, suggesting their potential utility as biomarkers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.