Usefulness of a decision tree model for the analysis of adverse drug reactions: Evaluation of a risk prediction model of vancomycin-associated nephrotoxicity constructed using a data mining procedure

Shungo Imai, Takehiro Yamada, Kumiko Kasashi, Masaki Kobayashi, Ken Iseki

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

Objectives: 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. Method: 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. Results: 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. Conclusion: This study suggests the usefulness of DT models for the evaluation of adverse drug reactions.

Original languageEnglish
Pages (from-to)1240-1246
Number of pages7
JournalJournal of Evaluation in Clinical Practice
Volume23
Issue number6
DOIs
Publication statusPublished - 2017 Dec
Externally publishedYes

Keywords

  • data mining
  • decision tree analysis
  • nephrotoxicity
  • vancomycin

ASJC Scopus subject areas

  • Health Policy
  • Public Health, Environmental and Occupational Health

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