ArticleFrontiers in neurology2025
A predictive model for early neurological deterioration after intravenous thrombolysis in patients with ischemic stroke.
Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- [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
- Association between stress hyperglycemia ratio and early neurological deterioration after acute ischemic stroke: a retrospective cohort study.BMC neurology · 2026Article
- Predictive value of net water uptake for early neurological deterioration after mechanical thrombectomy in acute ischemic stroke with large vessel occlusion.Frontiers in neurology · 2025Article
- Interpretable machine learning for predicting early neurological deterioration in symptomatic intracranial atherosclerotic stenosis.Frontiers in neurology · 2025Article
- Early antiplatelet treatment for minor stroke following thrombolysis: Insights from the Early Antiplatelet for Minor Stroke following Thrombolysis trial.Brain circulationArticle
- What's New in Emergencies, Trauma, and Shock: Nomograms in Emergency Medicine.Journal of emergencies, trauma, and shockArticle
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7 authors.
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Abstract
Objective: Intravenous thrombolysis (IVT) is the treatment of choice for acute ischemic stroke (AIS), but some patients develop early neurological deterioration (END) within 24 h after IVT. Therefore, we aimed to identify predictors of END in AIS patients following treatment with IVT. Methods: We retrospectively analyzed the clinical data of 621 AIS patients who received IVT with recombinant tissue-type plasminogen activator (rt-PA) at the Stroke Centre of the People's Hospital of Lu'an City, China, from July 2018 to July 2023. Clinical data, including demographic characteristics, clinical assessment results, underlying diseases, and laboratory indices, were collected at the time of admission. The patients were divided into training and validation cohorts, after which LASSO regression was applied to select the most important predictor variables, and multivariate logistic regression was used to construct a nomogram. The discriminative power of the model was determined by calculating the area under the curve (AUC), and calibration and decision curve analyses (DCA) were performed. Results: The platelet-to-lymphocyte ratio (PLR) (OR 1.01, 95% CI 1.01-1.01, Conclusion: This predictive model can be used for the early identification of the risk of developing END after IVT and development of active interventions to improve the prognosis of AIS.
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