台灣某區醫療體系急性燒燙傷病患住院日預測模式之探討

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Title:	台灣某區醫療體系急性燒燙傷病患住院日預測模式之探討
Other Titles:	Study of predicting models of the length of hospital stay for acute burn patients at a medical system in Taiwan 臺灣某區醫療體系急性燒燙傷病患住院日預測模式之探討
Authors:	余珮瑩;Yu, Pei-Ying
Contributors:	淡江大學統計學系碩士班陳麗菁
Keywords:	燒燙傷;住院日;中位數迴歸;預後因子;burns;length of hospital stay;median regression;prognostic factors
Date:	2013
Issue Date:	2014-01-23 14:10:16 (UTC+8)
Abstract:	燒燙傷對於病患本身、病患家屬甚至對於社會經濟皆造成巨大的負擔及傷害。當燒燙傷病患需住院治療時，準確的預測住院日數不僅可以減少病患在心理以及精神上的壓力，對於病患家屬也可以有照顧病患的事先準備。除此之外，醫師在規劃治療病患的程序上能更加完善並且可以有效地分配醫療資源。本研究分析台灣某醫療體系自1997年1月13日至2010年11月1日的急性燒燙傷病患資料庫，分別以預後因子、總燒傷面積、燒傷指標和預後性燒傷指標四種模式建立中位數迴歸，並針對四種模式以AIC向後選取法選取模式，再以適合度檢定對模式進行評估。最後選出預測急性燒燙傷病患其住院日數的最佳模式為預後因子模式，該模式包含的變數為二度燒傷面積、三度燒傷面積、受傷年齡、頭頸部位是否有燒傷和是否進入加護病房。 Burn trauma could deeply affect patients and their family members, as well as the social economic burden. According to patients’ condition of burn trauma, they might need hospitalizations. Once one can predict the length of hospital stay more precise, it could lower the stress of uncertainty in the mental or spiritual health for patients and let patients’ family members be prepared for caring the patients. Besides, physicians could be more confidential to evaluate the treatment plan and allocate the medical resources. This study analyzed the data of acute burn injury patients of a medical system in Taiwan collected between January 13, 1997 and November 1, 2010. The following four predictive models are constructed: prognostic factors, total burn surface area, burn index and prognostic burn index models on the basic of median regressions. Model selection was processed by use of backward elimination procedure with AIC index and the goodness-of-fit test statistic Pseudo-R square. Eventually, prognostic factors model had better predictive ability than other models to predict the length of hospital stay. Variables included in the model were second degree of burned area, third degree of burned area, age, burn area 1(head and neck) and admission to intensive care unit.
Appears in Collections:	[Graduate Institute & Department of Statistics] Thesis

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