Derivation and Validation of a Risk Prediction Model for Vancomycin-Associated Acute Kidney Injury in Chinese Population

• Published in: Therapeutics and clinical risk management 2020-01, Vol.16, p.539-550
• Main Authors: Xu, Nana, Zhang, Qiao, Wu, Guolan, Lv, Duo, Zheng, Yunliang
• Format: Article
• Language: English
• Subjects: acute kidney injury; Antibiotics; Classification; Creatinine; Diabetes; Diabetic retinopathy; Drug dosages; Hospitals; Information systems; Kidney diseases; Laboratories; Medical research; Nomograms; Original Research; Population; prediction model; Staphylococcus infections; Statistical analysis; vancomycin; Variables
• ISSN: 1176-6336; 1178-203X; 1178-203X
• DOI: 10.2147/TCRM.S253587

Vancomycin is the standard therapy for methicillin-resistant (MRSA) infection; however, nephrotoxicity happened with a high incidence of 15%~40%. Weighting the risk before receiving vancomycin treatment facilitates timely prevention of nephrotoxicity, but no standardized strategy exists for this purpose. A retrospective cohort study was performed. A total of 524 hospitalized patients treated with vancomycin were included in this study. They were divided into derivation cohort (n=341) and externally validation cohort (n=183) according to their admission time. Using univariate and multivariable logistic regression, we identified potential predictors of vancomycin-associated acute kidney injury (AKI) and developed a risk score by plotting nomogram. The predictive performance of this novel risk score was assessed and validated by discrimination and calibration. Besides, the risk score was also compared with existing prediction models according to integrated discrimination index (IDI) and net reclassification index (NRI). The incidence of AKI was 16.1% (55/341) in the derivation cohort and 16.4% (30/183) in the validation cohort. Three factors (vancomycin serum trough concentration, piperacillin/tazobactam and furosemide) were determined as predictors for vancomycin-associated AKI. The established three-item risk score showed a comparable discrimination in both derivation cohort (AUC=0.793, 95% CI: 0.732-0.855) and validation cohort (AUC=0.788, 95% CI: 0.698-0.877). The risk score also demonstrated a good calibration in the derivation cohort ( =6.079, =0.638>0.05) and validation cohort (χ =5.665, =0.686>0.05). Compared with prediction by C alone, this risk score significantly improved reclassification accuracy (IDI=0.050, 95% CI: 0.024-0.076,