ArticleFrontiers in neurology2025
Early heart rate predicts 3-month outcomes in acute ischemic stroke patients receiving intravenous thrombolysis: a machine learning approach.
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 1 paper.
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1 citing paper in PubMed.
- Multidimensional machine learning for early neurological deterioration prediction in acute ischemic stroke.Frontiers in medicine · 2026Article
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7 authors.
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Abstract
Background: The predictive role of early heart rate (HR) dynamics in acute ischemic stroke patients (AIS) receiving intravenous thrombolysis (IVT) remains unclear. This study aimed to evaluate whether HR variability within 24 h post-IVT predicts early neurological deterioration (END) and 3-month functional outcomes using machine learning. Methods: This retrospective analysis included AIS patients without atrial fibrillation (AF) who received IVT at Dongguan People's Hospital between January 2017 and December 2022. Hourly HR metrics (mean HR, SD, coefficient of variation [CV]) were analyzed. Primary outcomes were END (≥4-point NIHSS increase within 72 h) and poor 3-month outcomes (mRS ≥ 3). Machine learning models were developed and validated via receiver operating characteristic (ROC) analysis. Results: Among 381 patients, logistic regression identified NIHSS on admission (OR = 1.287, Conclusion: In AIS patients without AF, early HR dynamics-particularly maximum HR, minimum HR, SD, and CV-strongly correlate with 3-month functional outcomes after IVT. The machine learning model demonstrated high predictive accuracy, highlighting the potential of real-time HR monitoring for risk stratification and personalized management in thrombolysis-treated AIS patients.
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