急性缺血性腦中風的診療成效高度依賴於治療時效，若能於黃金時間窗內迅速辨識中風亞型與病灶範圍，即可提升血管再灌流治療成功率並改善患者預後。
然而，現行流程仍高度仰賴第一線醫護人員之臨床判斷與即時診斷工具，衍生假警報率偏高、醫療資源過度使用與效率失衡等問題。因此，如何兼顧診斷準確性與資源配置效益，已成為急診中風管理的重要挑戰。本研究旨在建立一套具臨床實用性的多變項預測模型，以協助急診檢傷階段快速辨識大血管阻塞型中風（Large Vessel Occlusion, LVO）高風險患者，期望提升中風啟動策略精準度並優化資源使用。研究目標包括：（1）分析急診常用臨床指標（如 G-FAST、格拉斯哥昏迷指數〔Glasgow Coma Scale, GCS〕、生命徵象與檢傷分級）對 LVO 的預測效度；（2）建構臨床決策支持模型，提供急診早期辨識 LVO 之依據。
本研究採回溯性設計，蒐集某醫學中心自 2016 至 2023 年間急診缺血性腦中風個案，最終納入 1,086 例具完整影像與檢傷紀錄之患者。以 LVO 診斷為主要依變項，分析人口學特徵、共病史、神經功能量表與生命徵象等變項，並以多變項邏輯斯迴歸建立預測模型。
分析結果顯示，G-FAST 總分 ≥2（OR = 1.76, p < .001）、GCS <12（OR = 4.09, p < .001）、以及慢性腎臟病（OR = 1.99, p = .006）為顯著獨立預測因子。整體模型判別力達中度水準（AUC = 0.72），具一定臨床應用價值。
本研究主要貢獻包括：（1）以急診檢傷即可取得之臨床資料建構 LVO 預測模型，降低對高成本影像檢查之依賴；（2）首次於台灣急診中風情境下，結合資源分配理論、風險分層概念與精實醫療理念驗證其應用價值；（3）提出具操作性與解釋力之模型，兼具臨床決策支持與政策參考意涵。未來可透過多中心驗證與人工智慧導入，進一步強化即時判讀能力，並推動智慧中風照護系統之發展。

The outcomes of acute ischemic stroke are closely tied to timely treatment. Early recognition of stroke subtypes and lesion extent within the therapeutic window enhances the success of reperfusion therapy and improves prognosis. Yet, current emergency workflows rely heavily on clinicians’ judgment and real-time diagnostic tools, leading to high false alarm rates, inefficient imaging use, and imbalanced resource allocation. Balancing diagnostic accuracy with efficiency remains a critical challenge in stroke management.
This study developed a multivariable prediction model to identify large vessel occlusion (LVO) during triage. A retrospective analysis included 1,086 ischemic stroke cases from 2016–2023. Logistic regression identified independent predictors: G-FAST ≥2 (OR = 1.76, p < .001), GCS <12 (OR = 4.09, p < .001), and chronic kidney disease (OR = 1.99, p = .006). The model showed moderate discriminatory power (AUC = 0.72).
The findings highlight a practical, interpretable tool that reduces imaging reliance, supports decision-making, and informs resource allocation in emergency stroke care.

目錄
論文審定書 i
致謝 ii
中文摘要 iii
Abstract iv
目錄 viii
圖次 x
表次 xi
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 4
第二章 文獻探討及研究假設 6
第一節中風急診醫療管理相關的支持理論 6
第二節 急性腦中風的現況與流程 10
第三節 急診急性腦中風患者的識別與啟動 12
第四節 大血管阻塞型(LVO)中風的啟動預測變數及建立研究假設 16
第三章 研究方法 32
第一節 研究設計與倫理 32
第二節 研究資料 34
第三節 研究變項操作型定義 35
第四節 統計分析方法 35
第四章 研究結果 41
第五章 研究討論與結論 71
第一節 研究討論 71
第二節 研究結論 75
第三節 研究限制 77
第四節 管理意涵與建議 78
參考文獻 79
附錄 96

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