Imai Shungo, Yamada Takehiro, Kasashi Kumiko, Kobayashi Masaki, Iseki Ken
Department of Pharmacy, Hokkaido University Hospital, Sapporo, Japan.
Division of Pharmasciences, Faculty of Pharmaceutical Sciences, Hokkaido University, Sapporo, Japan.
J Eval Clin Pract. 2017 Dec;23(6):1240-1246. doi: 10.1111/jep.12767. Epub 2017 May 23.
Several publications concerning decision tree (DT) analysis in medical fields have recently demonstrated its usefulness for defining prognostic factors in various diseases. However, there are minimal reports on the predictors of adverse drug reactions. We attempted to use DT analysis to discover combinations of multiple risk factors that would increase the risk of nephrotoxicity associated with vancomycin (VCM). To demonstrate the usefulness of DT analysis, we compared its predictive performance with that of multiple logistic regression analysis.
A single-centre, retrospective study was conducted at Hokkaido University Hospital. A total of 592 patients, who received intravenous administrations of VCM between November 2011 and April 2016, were enrolled. Nephrotoxicity was defined as an increase in serum creatinine of ≥0.5 mg/dL or a ≥50% increase in serum creatinine from the baseline. Risk factors for VCM nephrotoxicity were extracted from previous reports. In the DT analysis, a chi-squared automatic interaction detection algorithm was constructed. For evaluating the established algorithms, a 10-fold cross validation method was adopted to calculate the misclassification risk of the model. Moreover, to compare the accuracy of the DT analysis, multiple logistic regression analysis was conducted.
Eighty-seven (14.7%) patients developed nephrotoxicity. A VCM trough concentration of ≥15.0 mg/L, concomitant medication (vasopressor drugs and furosemide), and a duration of therapy ≥14 days were extracted to build the DT model, in which the patients were divided into 6 subgroups based on variable rates of nephrotoxicity, ranging from 4.6 to 69.6%. The predictive accuracies of the DT and logistic regression models were similar (87.3%, respectively), indicating that they were accurate.
This study suggests the usefulness of DT models for the evaluation of adverse drug reactions.
近期，医学领域中几篇关于决策树（DT）分析的文献证明了其在定义各种疾病预后因素方面的作用。然而，关于药物不良反应预测因素的报道却很少。我们试图运用DT分析来发现多种风险因素的组合，这些因素会增加万古霉素（VCM）相关肾毒性的风险。为证明DT分析的作用，我们将其预测性能与多重逻辑回归分析的预测性能进行了比较。
在北海道大学医院进行了一项单中心回顾性研究。纳入了2011年11月至2016年4月期间接受静脉注射VCM的592例患者。肾毒性定义为血清肌酐升高≥0.5mg/dL或血清肌酐较基线水平升高≥50%。从既往报道中提取VCM肾毒性的风险因素。在DT分析中，构建了卡方自动交互检测算法。为评估所建立的算法，采用10倍交叉验证法计算模型的误分类风险。此外，为比较DT分析的准确性，进行了多重逻辑回归分析。
87例（14.7%）患者发生了肾毒性。提取VCM谷浓度≥15.0mg/L、合并用药（血管升压药和呋塞米）以及治疗持续时间≥14天以建立DT模型，其中患者根据肾毒性发生率的不同分为6个亚组，范围为4.6%至69.6%。DT模型和逻辑回归模型的预测准确性相似（分别为87.3%），表明它们是准确的。
本研究表明DT模型在评估药物不良反应方面是有用的。
