Construction of a flow chart–like risk prediction model of ganciclovir-induced neutropaenia including severity grade: A data mining approach using decision tree

What is known and objective: 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. Methods: 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/mm³ 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. Results and discussion: 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/mm³ 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. What is new and conclusion: 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.