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Evaluation by Data Mining Techniques of Fluconazole Breakpoints Established by the Clinical and Laboratory Standards Institute (CLSI) and Comparison with Those of the European Committee on Antimicrobial Susceptibility Testing (EUCAST)

机译:由临床和实验室标准协会(CLSI)建立的氟康唑断点数据挖掘技术评估并与欧洲抗菌药物敏感性试验委员会(EUCAST)进行比较

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摘要

The EUCAST and the CLSI have established different breakpoints for fluconazole and Candida spp. However, the reference methodologies employed to obtain the MICs provide similar results. The aim of this work was to apply supervised classification algorithms to analyze the clinical data used by the CLSI to establish fluconazole breakpoints for Candida infections and to compare these data with the results obtained with the data set used to set up EUCAST fluconazole breakpoints, where the MIC for detecting failures was >4 mg/liter, with a sensitivity of 87%, a false-positive rate of 8%, and an area under the receiver operating characteristic (ROC) curve of 0.89. Five supervised classifiers (J48 and CART decision trees, the OneR decision rule, the naïve Bayes classifier, and simple logistic regression) were used to analyze the original cohort of patients (Rex's data set), which was used to establish CLSI breakpoints, and a later cohort of candidemia (Clancy's data set), with which CLSI breakpoints were validated. The target variable was the outcome of the infections, and the predictor variable was the MIC or dose/MIC ratio. For Rex's data set, the MIC detecting failures was >8 mg/liter, and for Clancy's data set, the MIC detecting failures was >4 mg/liter, in close agreement with the EUCAST breakpoint (MIC > 4 mg/liter). The sensitivities, false-positive rates, and areas under the ROC curve obtained by means of CART, the algorithm with the best statistical results, were 52%, 18%, and 0.7, respectively, for Rex's data set and 65%, 6%, and 0.72, respectively, for Clancy's data set. In addition, the correlation between outcome and dose/MIC ratio was analyzed for Clancy's data set, where a dose/MIC ratio of >75 was associated with successes, with a sensitivity of 93%, a false-positive rate of 29%, and an area under the ROC curve of 0.83. This dose/MIC ratio of >75 was identical to that found for the cohorts used by EUCAST to establish their breakpoints (a dose/MIC ratio of >75, with a sensitivity of 91%, a false-positive rate of 10%, and an area under the ROC curve of 0.90).
机译:EUCAST和CLSI为氟康唑和假丝酵母菌建立了不同的断点。但是,用于获取MIC的参考方法提供了相似的结果。这项工作的目的是应用监督分类算法来分析CLSI用于建立念珠菌感染的氟康唑断点的临床数据,并将这些数据与用于建立EUCAST氟康唑断点的数据集进行比较,其中用于检测故障的MIC大于4 mg / L,灵敏度为87%,假阳性率为8%,接收器工作特性(ROC)曲线下的面积为0.89。五个监督分类器(J48和CART决策树,OneR决策规则,朴素的贝叶斯分类器和简单logistic回归)用于分析患者的原始队列(雷克斯的数据集),用于建立CLSI断点,以及后来的念珠菌队列(Clancy的数据集),通过该队列验证了CLSI的断点。目标变量是感染的结果,预测变量是MIC或剂量/ MIC比。对于Rex的数据集,MIC检测失败率> 8 mg / L,对于Clancy的数据集,MIC检测失败率> 4 mg / L,与EUCAST断点(MIC> 4 mg / L)非常一致。对于Rex的数据集,具有最佳统计结果的算法CART获得的灵敏度,假阳性率和ROC曲线下面积分别为Rex数据集的52%,18%和0.7。 ,分别为Clancy的数据集和0.72。此外,针对Clancy的数据集分析了结局与剂量/ MIC比率之间的相关性,其中剂量/ MIC比率> 75与成功相关,敏感性为93%,假阳性率为29%,并且ROC曲线下的面积为0.83。大于75的剂量/ MIC比率与EUCAST用于确定其断点的同类人群的剂量/ MIC比率相同(剂量/ MIC比率大于75,敏感性为91%,假阳性率为10%,并且ROC曲线下的面积为0.90)。

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