Department of Pharmacy, Hokkaido University Hospital, Sapporo, Japan.
Infection Control Team, Hokkaido University Hospital, Sapporo, Japan.
J Clin Pharm Ther. 2019 Oct;44(5):726-734. doi: 10.1111/jcpt.12852. Epub 2019 May 30.
Haematological toxicities such as neutropaenia are a common side effect of ganciclovir (GCV); however, risk factors for GCV-induced neutropaenia have not been well established. Decision tree (DT) analysis is a typical technique of data mining consisting of a flow chart-like framework that shows various outcomes from a series of decisions. By following the flow chart, users can estimate combinations of risk factors that may increase the probability of certain events. In our previous study, we demonstrated the usefulness of this approach in the evaluation of adverse drug reactions. Therefore, we aimed to construct a risk prediction model of GCV-induced neutropaenia including severity grade.
We performed a retrospective study at the Hokkaido University Hospital and enrolled patients who received GCV between April 2008 and March 2018. Neutropaenia was defined as an absolute neutrophil count (ANC) <1500 cells/mm and a decrease to <75% relative to baseline. We classified the patients who developed neutropaenia in three groups (Grades 2-4) based on the National Cancer Institute-Common Terminology Criteria for Adverse Events. Data collection was achieved through the retrieval of medical records. We employed a chi-squared automatic interaction detection algorithm to construct the DT model and compared the accuracies to the logistic regression model (a conventional statistical method) to evaluate the established model.
In total, 396 adult patients were included in the study; 61 (15.4%) developed neutropaenia. Three predictive factors (hematopoietic stem cell transplantation, baseline ANC <3854 cells/mm and duration of therapy ≥15 days) were extracted using the DT analysis to produce five subgroups, the incidence of neutropaenia ranged between 1.7% and 52.8%. In each subgroup, patients who developed neutropaenia were categorized based on the severity. The accuracies of each model were the same (84.6%), which indicated precision.
We successfully built a risk prediction model of GCV-induced neutropaenia including severity grade. This model is expected to assist decision-making in the clinical setting.
中性粒细胞减少等血液学毒性是更昔洛韦（GCV）的常见副作用；然而，GCV 引起的中性粒细胞减少的危险因素尚未得到很好的确定。决策树（DT）分析是一种数据挖掘的典型技术，由一个类似于流程图的框架组成，该框架显示了一系列决策的各种结果。通过遵循流程图，用户可以估计可能增加某些事件概率的风险因素组合。在我们之前的研究中，我们证明了这种方法在评估药物不良反应方面的有用性。因此，我们旨在构建一个包括严重程度在内的 GCV 诱导中性粒细胞减少的风险预测模型。
我们在北海道大学医院进行了一项回顾性研究，纳入了 2008 年 4 月至 2018 年 3 月期间接受 GCV 的患者。中性粒细胞减少定义为绝对中性粒细胞计数（ANC）<1500 个细胞/mm3，与基线相比下降至<75%。我们根据国家癌症研究所常见不良事件术语标准将发生中性粒细胞减少的患者分为三组（等级 2-4）。数据收集是通过检索病历实现的。我们采用卡方自动交互检测算法构建 DT 模型，并将其准确性与逻辑回归模型（一种传统的统计方法）进行比较，以评估所建立的模型。
共有 396 名成年患者纳入研究；61 名（15.4%）发生中性粒细胞减少。使用 DT 分析提取了三个预测因素（造血干细胞移植、基线 ANC<3854 个细胞/mm3 和治疗持续时间≥15 天），产生了五个亚组，中性粒细胞减少的发生率在 1.7%至 52.8%之间。在每个亚组中，根据严重程度对发生中性粒细胞减少的患者进行分类。每个模型的准确性相同（84.6%），表明具有精度。
我们成功地构建了一个包括严重程度在内的 GCV 诱导中性粒细胞减少的风险预测模型。该模型有望在临床环境中辅助决策。
