A predicting tool for kidney function recovery after drug-induced acute interstitial nephritis.

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

A multicenter, retrospective, observational study was conducted in 13 nephrology departments. Patients with biopsy proven DI-AIN treated with corticosteroids between 1996 and 2023 were included. The dataset was randomly divided into training (n = 164) and validation (n = 60) sets. 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 the two sets 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 corticosteroid initiation (under/above 7 days). Based on a multivariable logistic regression model, a nomogram was developed. The area under the curve of the nomogram was 0.79 (95% confidence interval 0.71-0.88), indicating good discriminative power. Bootstrap self-sampling was performed 1000 times for validation of the model. A 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.