A Predicting Tool for Kidney Function Recovery after Drug-Induced Acute Interstitial Nephritis.

Abstract

Background: Drug-induced acute interstitial nephritis (DI-AIN) represents a common cause of acute kidney injury. Early withdrawal of the culprit drug and corticosteroid therapy remains the mainstay of treatment.This study aimed to develop and validate a predictive nomogram to assess the probability of recovery of kidney function at 6 months after treatment.

Methods: Multicenter, retrospective, observational study in 13 nephrology departments. Patients with biopsy proven DI-AIN treated with corticosteroids between 1996-2023 were included. Dataset was randomly divided into training (n=164) and validation sets (n=60). Least absolute shrinkage and selection operator regression was used to screen the main predictors of complete (creatinine increase <25% of the last value before DI-AIN) or no recovery of kidney function (serum creatinine ≥75% or need for dialysis).

Results: The study group comprised 224 patients with DI-AIN: 51 (31%) in the training group and 19 (32%) in the validation set, achieved complete recovery at 6 months. Conversely, 33 (20%) and 8 (13%) patients in each set showed no recovery at 6 months. Clinical characteristics were well balanced between training and validation sets. The selected variables were age (under/above 65 years), gender, degree of interstitial fibrosis and time to corticosteroids initiation (under/above 7 days). Based on multivariable logistic regression model, a nomogram was developed. The area under the curve (AUC) of the nomogram was 0.79 (95% confidence interval: 0.71-0.88) indicating a good discriminative power. Bootstrap self-sampling was performed 1000 times for validation of the model. Calibration plot revealed that the predicted outcomes aligned well with the observations. Decision curve analysis suggested that the model had clinical benefit.

Conclusions: We developed and validated a nomogram to predict kidney recovery at 6 months in DI-AIN patients treated with corticosteroids. This tool helps clinicians estimate prognosis and optimize corticosteroid therapy's intensity and duration for better treatment outcomes.