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| 題 名 | 利用空間分析方法初探外傷創傷登錄資料:以南部某醫院創傷性腦傷登錄資料為例=Using Spatial Data Analysis Approaches to Explore Trauma Registry Database: An Example of Traumatic Brain Injury Cases in Southern Taiwan |
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| 作 者 | 張育通; 周廷郁; 王韻嫻; 宋佳臻; 林天愛; 李佩玲; 吳亦軒; 陳昭文; | 書刊名 | 醫療救護與緊急應變 |
| 卷 期 | 6 2025.02[民114.02] |
| 頁 次 | 頁1-7 |
| 分類號 | 419.53 |
| 關鍵詞 | 創傷性腦部外傷; 空間資料分析; 到院前救護; 創傷登錄; Traumatic brain injury; Spatial data analysis; Prehospital care; Trauma registry; |
| 語 文 | 中文(Chinese) |
| DOI | 10.30216/JPER.202502_(6).0001 |
| 中文摘要 | 研究背景:創傷性腦部外傷(TBI)是導致死亡和失能的主要原因之一。根據到院前創傷救命術(PHTLS)指引,急救人員應縮短現場處置時間,維持病人的呼吸道通暢與穩定的血壓,以減少次發性損傷風險。創傷登錄系統可提供完整的病人資料,作為評估創傷照護成效與推動預防政策的依據。本研究運用空間資料分析方法,探討TBI事件的地理分布及其與環境因素的關聯。研究方法:本研究為回溯性研究,分析2023年南部某醫院創傷登錄資料與到院前救護紀錄。從1,047筆資料中篩選出375筆符合條件的TBI案例。資料分析涵蓋受傷地點、發生機轉、到院距離、昏迷指數、創傷嚴重度分數(ISS)、血壓等變項,並採用熱區圖、聚類分析算法(Density-based spatial clustering of applications with noise, 簡稱DBSCAN)、分層設色圖和區域莫蘭指數(Local Moran's I)等空間分析方法。研究結果:結果顯示,TBI病例占所有創傷案例的35.8%,其中69.9%因車禍受傷。高風險區域集中於車流密集路段,如民族一路。Local Moran's I分析確定三民區等八個行政區為高風險群聚區。線性回歸結果顯示,ISS分數與住院天數呈正相關;而羅吉斯迴歸分析指出,重度昏迷與危急個案與啟動外傷小組的概率相關。研究結論:空間資料分析提供了多維度的視角,揭示TBI案件的地理分布特徵。本研究建議在高風險區域加強車速管理與道路設計,並提升急救人員對TBI的處置能力,優化急診與救護系統的聯繫。未來研究應進一步整合救護與醫療系統數據,以完善創傷照護體系。 |
| 英文摘要 | Background: Traumatic brain injury (TBI) is a leading cause of death and disability. According to Prehospital Trauma Life Support (PHTLS) guidelines, emergency medical personnel should minimize on-scene time and maintain airway patency, stable respiration, and systolic blood pressure above 110 mmHg to reduce the risk of secondary brain injury. Trauma registries, which contain comprehensive patient data, are crucial for assessing trauma care effectiveness and informing injury prevention policies. This study employs spatial data analysis to explore the geographic distribution of TBI incidents and their relationship with environmental factors. Methods: This retrospective study analyzed trauma registry data from a hospital in southern Taiwan from January to December 2023, linking it with prehospital emergency records. Of 1,047 cases, 375 TBI cases met the inclusion criteria. Variables analyzed include injury location, mechanism, transport distance, Glasgow Coma Scale (GCS) score, Injury Severity Score (ISS), and blood pressure. Spatial analysis methods such as heatmaps, DBSCAN clustering, choropleth maps, and Local Moran's I were applied. Results: TBI cases accounted for 35.8% of all trauma cases, with traffic accidents being the leading cause (69.9%). High-risk areas were concentrated in high-traffic zones, notably along Minzu 1st Road. Local Moran's I identified eight administrative districts, including Sanmin District, as significant high-risk clusters. Linear regression analysis showed a positive correlation between ISS and hospital stay duration, while logistic regression indicated that severe cases were more likely to trigger trauma team activation. Conclusions: Spatial data analysis offers a multidimensional perspective on TBI case distribution. The findings suggest enhancing traffic management and road design in high-risk areas and improving emergency personnel's TBI handling skills. Future research should focus on integrating prehospital and hospital data to optimize the trauma care system. |
本系統中英文摘要資訊取自各篇刊載內容。