A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy
A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy
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DOI：
10.1016/j.ejon.2015.03.006
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发表时间：
2015-12-01
影响因子：
2.8
通讯作者：
Yun, Eun Kyoung
中科院分区：
文献类型：
--
作者：
Park, Ji Hyun;Kim, Hyeon-Young;Yun, Eun Kyoung
目的：比较logistic回归和决策树分析方法在评估肿瘤化疗患者感染危险因素中的应用效果。方法：选取韩国首尔K大学医院接受化疗的732例癌症患者为研究对象。数据收集于2011年3月至2013年2月，使用IBM SPSS Statistics 19和Modeler 15.1程序进行描述性分析、逻辑回归和决策树分析。结果：肿瘤化疗患者最常见的感染危险因素为烷基化剂、长春花生物碱和潜在的糖尿病。逻辑回归解释了66.7%的敏感性变异和88.9%的特异性变异。决策树分析在敏感性方面占数据变异的55.0%，在特异性方面占89.0%。在整体分类准确率方面，逻辑回归解释了88.0%，决策树分析解释了87.2%。结论：logistic回归分析具有较高的敏感性和分类准确率。因此，logistic回归分析是建立化疗患者感染预测模型更为有效和实用的方法。(C) 2015 Elsevier Ltd.版权所有。
Purpose: This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy.Method: The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs.Results: The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%.Conclusions: The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy. (C) 2015 Elsevier Ltd. All rights reserved.