Researchers from both Johns Hopkins Medicine and Yale University collaborated on the development and application of a diagnostic model to detect acute interstitial nephritis (AIN) in patients, which could have a lasting impact on getting patients diagnosed earlier.
The findings, which build on the researchers’ prior development of the diagnostic model, is published in the November 5th issue of Journal of the American Society of Nephrology. The model was developed using a panel of lab tests documented in electronic medical records.
In this study, researchers developed a diagnostic model to predict AIN in patients by using a machine-learning selection technique called least absolute shrinkage and selection operator (LASSO). The laboratory tests using the LASSO feature included serum creatinine, blood urea nitrogen (BUN), urine protein, and the density of urine compared to water, which is known as a specific gravity test.
The study consisted of two patient cohorts. Both cohorts had previously undergone kidney biopsy procedures at either The Johns Hopkins Hospital (JHH) or Yale University. The JHH cohort identified 1,454 patients who underwent a native kidney biopsy between January 2019 and December 2022, while the Yale cohort examined 528 patients who were scheduled to undergo a clinical kidney biopsy between July 2020 and June 2023. Both cohorts excluded patients who did not have a serum creatinine value within one year before the biopsy; were undergoing kidney allograft biopsy or being evaluated for kidney mass; and with known vasculitis or lupus nephritis before the biopsy.
In total, 1,982 patients were examined and a combined 22% of them were found to have been diagnosed with AIN. In both cohorts, those with AIN were more likely to be hospitalized, and had higher serum creatinine and blood urea nitrogen to creatinine ratio. While the diagnostic model was able to improve the accuracy of AIN diagnosis for clinicians to 77%, there were noted differences in the prevalence of AIN in both patient cohorts. After accounting for prevalence at the center, the calibration of the diagnostic model improved significantly, which led to more accurate AIN diagnoses.
The formula for predicting AIN is now available in MDCalc.
Researchers hope that with these study findings, the AIN diagnostic model could guide the decision of whether to perform a kidney biopsy in patients with AKI. This model could also be incorporated into clinicians’ decisions on choosing the most appropriate treatment for patients with AIN.
