ArticleAJNR. American journal of neuroradiology2021
Prediction of Clinical Outcome in Patients with Large-Vessel Acute Ischemic Stroke: Performance of Machine Learning versus SPAN-100.
Article in AJNR. American journal of neuroradiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic Review of Machine Learning Applied to the Secondary Prevention of Ischemic Stroke.Journal of medical systems · 2024Pooled it
- Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis.Frontiers in neurology · 2023Pooled it
- Development and validation of the ANGS score for predicting long-term outcomes in acute ischemic stroke patients receiving intravenous thrombolysis.BMC medical informatics and decision making · 2026Article
- Article
- A new nomogram for predicting 90-day outcomes of intravenous thrombolysis in patients with acute ischaemic stroke.Frontiers in neurology · 2025Article
- Prediction of Ischemic Stroke Functional Outcomes from Acute-Phase Noncontrast CT and Clinical Information.Radiology · 2024Article
- Predicting the clinical prognosis of acute ischemic stroke using machine learning: an application of radiomic biomarkers on non-contrast CT after intravascular interventional treatment.Frontiers in neuroinformatics · 2024Article
- Machine learning and acute stroke imaging.Journal of neurointerventional surgery · 2023Review
- Evaluation of Blood Biomarkers and Parameters for the Prediction of Stroke Survivors' Functional Outcome upon Discharge Utilizing Explainable Machine Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Prognostic estimation for acute ischemic stroke patients undergoing mechanical thrombectomy within an extended therapeutic window using an interpretable machine learning model.Frontiers in neuroinformatics · 2023Article
- Explainable machine learning for long-term outcome prediction in two-center stroke patients after intravenous thrombolysis.Frontiers in neuroscience · 2023Article
- End to end stroke triage using cerebrovascular morphology and machine learning.Frontiers in neurology · 2023Article
- Prognostic nomogram for the outcomes in acute stroke patients with intravenous thrombolysis.Frontiers in neuroscience · 2022Article
- Interpretable Machine Learning Modeling for Ischemic Stroke Outcome Prediction.Frontiers in neurology · 2022Article
- Tissue clock-guided prediction and intervention of futile recanalization: towards precision therapy in mechanical thrombectomy.Frontiers in neurologyReview
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11 authors.
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No grant is acknowledged in the PubMed record.
Abstract
background and purposeTraditional statistical models and pretreatment scoring systems have been used to predict the outcome for acute ischemic stroke patients (AIS). Our aim was to select the most relevant features in terms of outcome prediction on the basis of machine learning algorithms for patients with acute ischemic stroke and to compare the performance between multiple models and the Stroke Prognostication Using Age and National Institutes of Health Stroke Scale (SPAN-100) index model. MATERIALS AND
methodsA retrospective multicenter cohort of 1431 patients with acute ischemic stroke was subdivided into recanalized and nonrecanalized patients. Extreme Gradient Boosting machine learning models were built to predict the mRS score at 90 days using clinical, imaging, combined, and best-performing features. Feature selection was performed using the relative weight and frequency of occurrence in the models. The model with the best performance was compared with the SPAN-100 index model using area under the receiver operating curve analysis.
resultsIn 3 groups of patients, the baseline NIHSS was the most significant predictor of outcome among all the parameters, with relative weights of 0.36∼0.69; ischemic core volume on CTP ranked as the most important imaging biomarker with relative weights of 0.29∼0.47. The model with the best-performing features had a better performance than the other machine learning models. The area under the curve of the model with the best-performing features was higher than SPAN-100 model and reached statistical significance for the total (
conclusionsMachine learning-based feature selection can identify parameters with higher performance in outcome prediction. Machine learning models with the best-performing features, especially advanced CTP data, had superior performance of the recovery outcome prediction for patients with stroke at admission in comparison with SPAN-100.
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