發(fā)布時間：2018-03-26 02:41

  本文選題：肝膽手術　切入點：手術部位感染　出處：《石河子大學》2017年碩士論文

【摘要】：目的:通過構建肝膽術后手術部位感染風險預測模型,為篩選手術部位感染高危人群提供技術支持。方法:回顧性收集2013年1月—2015年12月國內6家醫(yī)院1145例肝膽手術病例資料,通過逐份查閱電子病例收集包括患者人口學特征,合并基礎疾病情況,手術相關信息,抗菌藥物使用情況及術后感染情況五個方面信息。將所有病例按照7:3的比例隨機分為建模組和驗證組,建模組用來建立模型,驗證組用來驗證模型。在建模組,所有變量先進行單因素分析,單因素分析中有統(tǒng)計學意義的變量納入多元Logistic回歸分析,采用Back—LR法建立風險預測模型。利用驗證組數據對模型進行驗證,以Hosmer-Lemeshow反映模型的符合程度,以受試者工作特征曲線(ROC)及其曲線下的面積(AUC)反應模型的判別能力。ROC曲線中取Youden指數最大值為最佳閾值,對模型進行回代驗證。根據Logistic模型中各風險因素的權重β系數建立評分表,按各評分感染率將患者進行風險分層,≥5分為高風險組,3-4分為中風險組,0-2分為低風險組。結果:本研究共納入肝膽手術病例1145例,術后發(fā)生手術部位感染143例,總感染率為12.49%,其中表淺切口感染89例(7.77%)、深部切口感染17例(1.48%)、器官腔隙感染37例(3.23%)。通過單因素與多因素分析,最終納入風險預測模型的變量包括低蛋白血癥、高血壓、術前合并膽道感染、術前腹部手術史、手術切口分類、手術持續(xù)時間、ASA分級和吻合口瘺8個指標。驗證結果顯示:所建立模型的ROC曲線下面積(AUC)為0.851,優(yōu)于NNIS風險指數預測效力(AUC為0.731);ROC曲線中最佳閾值為0.139,驗證組回代結果:模型靈敏度68.29%,特異度86.01%,陽性預測值41.18%,陰性預測值94.98%,總準確率為83.79%,說明模型的判別能力很好。將模型轉化為風險評分,手術切口分類賦值2分,吻合口瘺賦值3分,其余六個變量賦值1分。將患者進行危險分層,高中低風險組手術患者所占的比例分別為3.42%,13.45%,83.13%,感染率分別為85.71%,30.91%,6.47%。結論:低蛋白血癥、高血壓、術前合并膽道感染、術前腹部手術史、手術切口分類、手術持續(xù)時間、ASA分級和吻合口瘺是肝膽手術手術部位感染的獨立風險因素。利用肝膽手術患者病例資料建立的手術部位感染風險預測模型效力較高,對于肝膽手術發(fā)生感染的術前評估及群體監(jiān)測均有一定的應用價值。
[Abstract]:Objective: to provide technical support for screening high risk population of postoperative hepatobiliary infection by constructing a prediction model of postoperative infection in hepatobiliary surgery. Methods: 1145 cases of hepatobiliary surgery were collected retrospectively from January 2013 to December 2015 in 6 hospitals in China. The electronic case collection includes demographic features of patients, combined with underlying diseases, and operation-related information. All cases were randomly divided into modeling group and verification group according to the proportion of 7:3. The modeling group was used to build the model, and the validation group was used to verify the model. All variables were analyzed by single factor analysis, the variables with statistical significance in single factor analysis were included in multivariate Logistic regression analysis, and the risk prediction model was established by Back-LR method. The model was validated by validation group data. The maximum value of Youden exponent was chosen as the best threshold in the discriminant ability of the response model of Hosmer-Lemeshow and its area under the curve, according to the degree of coincidence of the model reflected by Hosmer-Lemeshow, and the maximum value of Youden exponent was taken as the optimum threshold in the response model of the test subjects' operating characteristic curve and its area. According to the weight 尾 coefficient of each risk factor in Logistic model, the scoring table was established, and the risk was stratified according to the infection rate of each score. 鈮，

本文編號：1665971