ArticleJournal of central nervous system disease2026
Construction and Validation of a Risk Prediction Model Incorporating Temporal Muscle Thickness for Adverse Outcome in Acute Ischemic Stroke Patients.
Article in Journal of central nervous system disease, 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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10 authors.
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Abstract
Background: Sarcopenia significantly impacts stroke prognosis. Temporal muscle thickness (TMT) is an emerging metric for sarcopenia. Objectives: To developed a TMT-incorporated model to predict 6-month adverse outcomes in acute ischemic stroke (AIS). Design: In this retrospective study, 479 AIS patients were divided into training (n=283), test (n=120), and external validation cohorts (n=76). Methods: A combined model was constructed to predict adverse outcomes in the training and test cohorts using LASSO regression analysis. Model performance was assessed via calculating accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score. Results: The proportion of patients with an adverse outcomes in the training and test sets was 18.02% vs 17.50%, respectively ( Conclusion: This study developed a combined model incorporating ischemic stroke event, admission NIHSS score, BI score, TMT and infarct volume to predict 6-month adverse outcomes in AIS patients, providing clinicians with a practical tool for treatment decisions and prognosis assessment.
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