ArticleClinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
Machine Learning Models for Predicting Early Neurological Deterioration and Risk Classification of Acute Ischemic Stroke.
Article in Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis. 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.
- Risk prediction models for early neurological deterioration after intravenous thrombolysis in acute ischemic stroke patients: a systematic review and meta-analysis.BMC neurology · 2026Pooled it
- Prediction models for early neurological deterioration in patients with acute ischemic stroke: a systematic review and critical appraisal.Frontiers in neurologyPooled it
- Article
- [A prediction model for early neurological improvement after intravenous thrombolysis in patients with acute ischemic stroke].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Article
- Predicting early neurological deterioration in acute branch atheromatous disease without reperfusion therapy: a machine learning model.Frontiers in neuroscience · 2026Article
- Development and Validation of a Web-Based Machine Learning Model for Predicting Early Neurological Deterioration Following Stroke Thrombolysis: Multicenter Study.Journal of medical Internet research · 2025Article
- LncRNA SERPINB9P1 Mitigates Cerebral Injury Induced by Oxygen‒Glucose Deprivation/Reoxygenation by Interacting with HSPA2.Molecular neurobiology · 2025Article
- A clinical-radiomics nomogram to predict early neurological deterioration in patients with stroke undergoing intravenous thrombolysis.Journal of the Chinese Medical Association : JCMA · 2025Article
- A Nomogram Incorporating Intracranial Atherosclerosis Score for Predicting Early Neurological Deterioration in Minor Stroke Patients With Type 2 Diabetes Mellitus.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- The Role of Mature Brain-Derived Neurotrophic Factor and Its Precursor in Predicting Early-Onset Insomnia in Stroke Patients Experiencing Early Neurological Deterioration.Nature and science of sleep · 2025Article
- Machine learning-based predictive model for the development of thrombolysis resistance in patients with acute ischemic stroke.BMC neurology · 2024Article
- Machine learning-based prediction of early neurological deterioration after intravenous thrombolysis for stroke: insights from a large multicenter study.Frontiers in neurology · 2024Article
- Optimizing early neurological deterioration prediction in acute ischemic stroke patients following intravenous thrombolysis: a LASSO regression model approach.Frontiers in neuroscience · 2024Article
- Enhancing stroke-associated pneumonia prediction in ischemic stroke: An interpretable Bayesian network approach.Digital healthArticle
- Risk stratification for stroke in acute persistent vertigo: development and internal validation of a multivariable prediction model.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to create machine learning models for predicting early neurological deterioration and risk classification in acute ischemic stroke (AIS) before intravenous thrombolysis (IVT). The study included 704 AIS patients categorized into END and non-END groups. The least absolute shrinkage and selection operator (LASSO) regression was employed to select the best predictors from clinical indicators, leading to the creation of Model 1. Univariate and multivariate logistic regression analyses identified independent predictive factors for END from inflammatory cell ratios. These factors were combined with clinical indicators, forming Model 2. Receiver operating characteristic (ROC) curves assessed the models' predictive performance. Key variables for Model 1 included the NIHSS score, systolic blood pressure, and lymphocyte percentage. Neutrophil-to-Lymphocyte ratio, Platelet-to-Neutrophil ratio, and Platelet-to-Lymphocyte ratio independently predicted END. Model 1 exhibited moderate predictive ability (AUC 0.721 in training, AUC 0.635 in test). Model 2, which integrated clinical indicators and inflammatory cell ratios, demonstrated strong performance in both training (AUC 0.862) and test (AUC 0.816). Machine learning models, combining clinical indicators and inflammatory cell ratios before IVT, accurately predict END and associated risk in AIS.
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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.