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研究生:李岳蓁
研究生(外文):Li, Yueh-Chen
論文名稱:外科加護病房患者重插管之預測模式
論文名稱(外文):Prediction model for re-intubation among patients admitted to surgical intensive care units
指導教授:李中一李中一引用關係
指導教授(外文):Li, Chung-Yi
口試委員:黃敏信黃柏僩簡玉雯
口試委員(外文):Huang, Min-HsinHuang, Po-HsienChien, Yu-Wen
口試日期:2021-07-12
學位類別:碩士
校院名稱:國立成功大學
系所名稱:公共衛生研究所
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:115
中文關鍵詞:重插管預測模型羅吉斯迴歸分析決策樹隨機森林
外文關鍵詞:ReintubationPrediction modelLogistic regressionDecision treeRandom forest
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背景:過去研究已指出許多與加護病房患者重新插管有關的危險因子,整合危險因子建立預測模型,比起單一臨床指標能更準確預測患者的拔管結果。現今已發展出許多重插管預測模式,但大多應用於內科或合併多個科別的重症患者族群為主。目前探討外科重症患者拔管失敗的研究相對較少,且主要使用羅吉斯迴歸(Logistic regression)分析建立模型。近來已有研究將決策樹(Decision tree)演算法應用於內科重症患者的拔管結果,研究支持決策樹模型於預測拔管結果的可行性,而隨機森林(Random forest)演算法能集合多個決策樹模型,透過多數決方式作最終預測。選擇與傳統迴歸分析不同的決策樹與隨機森林演算法可能可以進一步找出有效的預測變項。
目的:本研究使用統計迴歸模式分析和決策樹與隨機森林演算法界定影響外科加護病房患者重插管的關鍵指標,並建立簡易計分系統和預測模型,將其應用於患者拔管前評估工作中,為臨床醫師在拔管決策時提供參考依據。
材料與方法:本研究是利用在臺灣南部某醫學中心外科重症加護病房臨床資訊資料庫已出院病人紀錄所進行之回溯性研究(Retrospective study),擷取於2013年9月1日至2016年8月31日期間住院的2783位患者。分析方法使用羅吉斯迴歸分析、決策樹和隨機森林演算法建立重插管預測模型,模型主要由外科加護病房常規測量的臨床變項與脫離呼吸器參數建立而成。由敏感度(Sensitivity)、特異度(Specificity)、準確度(Accuracy)和接收者操作特徵曲線下面積(area under the Receiver Operating Characteristic curve, AUC)評估模型的區別度(Discrimination),使用Hosmer-Lemeshow適合度檢定(Hosmer-Lemeshow goodness of fit test)評估羅吉斯迴歸模型的校準度(Calibration),而決策樹與隨機森林模型的校準度則由校準曲線圖評估。
結果:羅吉斯迴歸模型結果中經緊急手術後或是無接受手術入住加護病房、入住APACHE Ⅱ(Acute Physiology and Chronic Health Evaluation Ⅱ)分數、拔管前昏迷指數總分(Glasgow Coma Scale, GCS)和拔管前氧合指數(pO2/FiO2)為影響患者重插管的危險因素,決策樹與隨機森林模型皆顯示拔管前血小板數目為最重要特徵,第二個重要特徵分別為拔管前血氧分壓(pO2)和拔管前氧合指數。羅吉斯迴歸模型預測重插管的敏感度與特異度分別為0.74和0.66。預剪枝(Pre-pruning)與後剪枝(Post-pruning)的決策樹模型的敏感度與特異度皆分別為0.08和0.99,而隨機森林模型的敏感度與特異度分別為0.00和1.00。羅吉斯迴歸模型經Hosmer-Lemeshow適合度檢定顯示能準確評估重插管的機率,校準曲線圖顯示決策樹與隨機森林模型易高估重插管機率。與決策樹和隨機森林相比羅吉斯迴歸模型於本研究整體有較佳的預測表現。
結論:本研究結果顯示,相較於決策樹與隨機森林模型,是以羅吉斯迴歸模型有更佳的區別與校準能力,但是羅吉斯迴歸模型的敏感度與特異度仍須優化,未來研究可以考慮加入自主呼吸試驗(Spontaneous breathing trials, SBT)過程的脫離呼吸器參數值、住院期間併發症和共病情形等因素以提升模型的預測能力。
This was a retrospective study, using the records of discharged patients in a surgical intensive care unit (ICU) during the hospitalization period from September 1, 2013 to August 31, 2016. The purpose of this study was to develop a simple scoring system and prediction models for reintubation by using logistic regression analysis, decision tree and random forest algorithms. According to logistic regression, decision tree and random forest models, the risk factors related to reintubation included: type of surgical ICU admission (emergency surgery or no surgery), APACHE Ⅱ (Acute Physiology and Chronic Health Evaluation Ⅱ) score at ICU admission, Glasgow Coma Scale (GCS), pO2/FiO2, number of platelet and pO2 . The sensitivity and specificity of the pre-pruning and post-pruning decision tree models was 0.08 and 0.99, respectively; and the corresponding figures for random forest model were 0.00 and 1.00. Compared with the decision tree and the random forest models, the logistic regression model had a better predictive performance in this study sample with a sensitivity of 0.74 and a specificity of 0.66.
摘要 I
目錄 VII
表目錄 IX
圖目錄 X
壹、前言 1
第一節 研究背景 1
第二節 研究目的 3
貳、文獻回顧 4
第一節 重症加護病房呼吸器通氣過程 4
第二節 計畫性拔管失敗 7
第三節 導致拔管失敗/重插管危險因素 13
第四節 識別具有拔管失敗高風險患者 19
參、研究方法 24
第一節 資料來源與研究設計 24
第二節 研究變項 25
第三節 分析方法 27
肆、研究結果 36
第一節 描述性統計結果 36
第二節 統計迴歸模型結果 37
第三節 決策樹模型結果 39
第四節 隨機森林模型結果 43
伍、研究討論 45
第一節 本研究主要結果 45
第二節 與過去文獻比較與討論 47
第三節 研究優勢與限制 57
陸、結論 59
參考文獻 89
附錄 96
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