Aims: Several factors related to vancomycin-induced nephrotoxicity (VIN) have not yet been clarified. In the present study, we used Japanese big data to investigate novel factors and their high-risk combinations that influence VIN. Methods: We employed a large Japanese electronic medical record database and included patients who had been administered intravenous vancomycin between June 2000 and December 2020. VIN was defined as an increase in serum creatinine ≥0.5 mg/dL or 1.5-fold higher than the baseline. The outcomes were: (1) factors affecting VIN that were identified using multiple logistic regression analysis, and (2) combinations of factors that affect the risk of VIN according to a decision tree analysis, which is a typical machine learning method. Results: Of the 7306 patients that were enrolled, VIN occurred in 14.2% of them (1035). A multivariate analysis extracted 22 variables as independent factors. Concomitant ramelteon use (odds ratio 0.701, 95% confidence interval 0.512-0.959), ward pharmacy service (0.741, 0.638-0.861), duration of VCM < 7 days (0.748, 0.623-0.899) and trough concentrations 10-15 mg/L (0.668, 0.556-0.802) reduce the risk of VIN. Meanwhile, concomitant piperacillin-tazobactam use (2.056, 1.754-2.409) and piperacillin use (2.868, 1.298-6.338) increase the risk. The decision tree analysis showed that a combination of vancomycin trough concentrations ≥20 mg/L and concomitant piperacillin-tazobactam use was associated with the highest risk. Conclusions: We revealed that the concomitant ramelteon use and ward pharmacy service may decrease the risk of VIN, while the concomitant use of not only piperacillin-tazobactam but also piperacillin may increase the risk.
- electronic medical record database
- machine learning
- ward pharmacy service
ASJC Scopus subject areas
- Pharmacology (medical)