Construction of a risk prediction model of vancomycin-associated nephrotoxicity to be used at the time of initial therapeutic drug monitoring: A data mining analysis using a decision tree model.

OBJECTIVES: In our previous study, we built a risk prediction model of vancomycin (VCM)-associated nephrotoxicity using decision tree (DT) analysis. However, this has several limitations in clinical applications. Our objective here was to construct a clinically applicable risk prediction model to be used at the time of initial therapeutic drug monitoring (TDM), in patients with uncomplicated infections.

METHOD: A retrospective study was conducted at Hokkaido University Hospital. Subjects that had received VCM were extracted between November 2011 and April 2017. Nephrotoxicity was defined as an increase in serum creatinine of 0.5 mg/dL or 50% or higher from baseline. The additional inclusion criteria in this study were as follows: (1) the target trough level of VCM was set to 10 to 15 mg/L, and (2) the duration of therapy was 7 to 14 days. Patients were assumed to have uncomplicated infections. Risk factors for nephrotoxicity were evaluated, which could be extracted at the initial TDM. In the DT analysis, a chi-squared automatic interaction detection algorithm was constructed.

RESULTS: A total of 402 patients were enrolled, and 56 (13.9%) patients developed nephrotoxicity. In the DT analysis, concomitant medications (furosemide, piperacillin-tazobactam, and vasopressor drugs) and an initial VCM trough concentration ≥ 15.0 mg/L were extracted as predictive variables by which patients were divided into six subgroups. The incidence of nephrotoxicity was 5.2% to 70.0%, with subgroups classified as low to high risk of nephrotoxicity. The accuracy of DT model was favourable (87.1%).

CONCLUSION: We propose that the DT model built in this study is applicable to clinical practice.