基於多變量分析之亞急性期中風後失語命名恢復預測模型建構：準確性與可靠性評估

// Construction of a Prediction Model for Naming Recovery in Subacute Poststroke Aphasia Based on Multivariate Analysis: Evaluation of Accuracy and Reliability

本研究建立邏輯回歸模型，利用病灶位置、布洛卡區損傷、教育程度、失語嚴重度及經顱直流電刺激（tDCS）預測亞急性期中風後失語症患者命名恢復｜模型準確率高，優於單一變數預測，且指出tDCS透過神經可塑性促進復原的重要調控因子｜但tDCS效果受布洛卡區完整性影響，建議對該區損傷患者探索其他治療目標，助臨床早期介入提升復健成效。

// This study developed a logistic regression model to predict naming rehabilitation outcomes in patients with subacute poststroke aphasia using clinical variables such as lesion site, Broca’s area damage, education, aphasia severity, and transcranial direct current stimulation (tDCS). The model showed high accuracy and clinical utility, outperforming single-predictor models, with tDCS identified as a key modifiable factor aiding recovery via neuroplasticity. However, the effectiveness of tDCS depends on Broca’s area integrity, suggesting alternative targets for patients with damage there. The model can help clinicians identify patients for early intervention to improve rehabilitation results.

Published 2026年7月8日