Development and Validation of a Nomogram For Predicting Drug-Induced Acute Kidney Injury in Hospitalized Patients

Abstract Background: Drug-induced acute kidney injury (D-AKI) is associated with increased mortality and longer hospital stays. This study aims to establish a nomogram to predict the occurrence of D-AKI in hospitalized patients in a multi-drug environment. Methods: A retrospective study among adult hospitalized patients was conducted from July 2019 to September 2019 based on the Adverse Drug Events Active Surveillance and Assessment System-2 developed by our hospital. According to the propensity score matching algorithm, four controls per case were matched to eliminate the confounding bias caused by individual baseline variables. The predictors for D-AKI were obtained by logistic regression equation and used to establish the nomogram. Results: Among 51,772 hospitalized patients, 332 were diagnosed with D-AKI. After matching, 288 pairs and 1,440 patients were included in the study, including 1,005 cases in the development group and 435 cases in the validation group. The top three drugs in terms of incidence were amphotericin B liposome (4/52, 7.69%), amphotericin B (2/39, 5.13%) and ceftazidime (2/52, 3.85%). Six variables were independent predictors for D-AKI: alcohol abuse, the concurrent use of nonsteroidal anti-inflammatory drugs or diuretics, chronic kidney disease, lower baseline red blood cell count and neutrophil count ≥7×109/L. The area under the curve (AUC) of the prediction model in the development group and validation group were 0.787 (95%CI, 0.752 ~ 0.823) and 0.788 (95%CI, 0.736 ~ 0.840), respectively. The GiViTI calibration belts showed that the model had a good prediction accuracy for the occurrence of D-AKI (P>0.05). Conclusions: This nomogram can help identify patients at high risk of D-AKI, which was useful in preventing the progression of-D-AKI and treating it in the early stages.