淡江大學機構典藏:Item 987654321/87682
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    Title: 臺灣急性燒燙傷病患死亡率預測的邏吉斯迴歸分析
    Other Titles: Logistic regression analysis for predicting mortality of acute burn injury in Taiwan
    台灣急性燒燙傷病患死亡率預測的邏吉斯迴歸分析
    Authors: 劉士豪;Liu, Shih-Hao
    Contributors: 淡江大學統計學系碩士班
    陳麗菁;Chen, Li-Ching
    Keywords: 燒燙傷;燒傷指標;邏吉斯迴歸;死亡率;預後性燒傷指標;burns;burn index;Logistic Regression;mortality;prognostic burn index
    Date: 2012
    Issue Date: 2013-04-13 11:32:38 (UTC+8)
    Abstract: 近年來,由於醫療和科技的進步,人們在遭受燒燙傷後的死亡率有慢慢下降的趨勢。許多對燒燙傷病患死亡率的研究顯示,年齡、燒傷面積、吸入性傷害是急性燒傷病患對死亡率預測的重要預後因子。燒燙傷死亡率的估計,可幫助醫生決策,並提供患者和醫療團隊能有大略的治療方式和成本考量的基礎。本研究的目標是發展台灣燒燙傷的預後模型以預測死亡率。
    本研究的數據資料由中華民國兒童燒燙傷基金會提供,涵蓋1997年到2010年間44間醫院23147位入院急性燒燙傷病患之研究,研究的變數有年齡、性別、是否有吸入傷害、沖水時間、入院狀態、是否進入加護病房、二度燒傷面積、三度燒傷面積、燒傷部位和存活狀況。本研究以邏吉斯迴歸進行分析,並使用Hosmer- Lemeshow適合度檢定和接受器操作特性(ROC)曲線對模式進行評估,並討論各模式的敏感度、特異度和準確度。
    In recent years, due to medical and technological advances, the mortality of burn patients has slowly decreased. Many studies have shown that age, burn size, and inhalation injury are important predictors of mortality following an acute burn injury. Objective estimates of the probability of death from burn injuries would help clinicians to make decisions and also provide patients and others with explicit basis for medical and financial decisions about their care. The goal of the study is to develop a simple and objective model for the prediction of mortality after burn injury in Taiwan population.
    From 1997 to 2010, a prospective review of 23,147 patients admitted with acute burn injury to 44 contract hospitals of Childhood Burn Foundation of the Republic of China across Taiwan was conducted. Variables examined were age, sex, extent of burn, presence or absence of inhalation injury, flushing time, hospital admission status, referral status, admission to intensive care unit or not, and mortality. The logistic regression analyses were used for evaluation of risk factors. Model performance was evaluated by measures of the validation Hosmer-Lemeshow goodness-of-fit statistic, the receiver operator characteristic (ROC) curve, sensitivity, specificity and accuracy of the models were also discussed.
    Appears in Collections:[Graduate Institute & Department of Statistics] Thesis

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