ArticleFrontiers in neurology2026
Prediction model for early neurological deterioration in large artery atherosclerotic stroke.
Article in Frontiers in neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Background: To develop and validate a predictive model for early neurological deterioration (END) in acute ischemic stroke due to large artery atherosclerosis. Methods: We included 433 patients admitted to our hospital between August 2023 and January 2025, randomly split into training (325) and internal validation (108) cohorts (3:1). END was defined as an NIHSS increase of ≥2 points within 7 days. Univariate analysis and LASSO regression selected variables in the training cohort; multivariate logistic regression was used to build the predictive model, which was visualized as a nomogram. Model performance was assessed using ROC curves, calibration curves, and decision curve analysis (DCA). Results: END occurred in 27.1% of the training cohort and 26.9% of the internal validation cohort. Seven variables were identified: neutrophil count, platelet count, lymphocyte count, fasting plasma glucose, total cholesterol, homocysteine, and D-dimer. Six were independent predictors ( Conclusion: The predictive model developed in this study (incorporating neutrophil count, platelet count, lymphocyte count, fasting plasma glucose, total cholesterol, homocysteine, and D-dimer) may serve as a straightforward initial screening tool for early neurological deterioration in patients with acute ischemic stroke due to large artery atherosclerosis. Six of these variables were independent predictors; fasting plasma glucose showed a trend toward association (
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