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Kidney Week

Abstract: FR-OR02

A Parsimonious Model for Diagnosis of Biopsy-Proven Acute Interstitial Nephritis Using Electronic Health Record Data

Session Information

Category: Acute Kidney Injury

  • 102 AKI: Clinical, Outcomes, and Trials


  • Moledina, Dennis G., Yale University School of Medicine, New Haven, Connecticut, United States
  • Yamamoto, Yu, Yale University School of Medicine, New Haven, Connecticut, United States
  • Parikh, Chirag R., Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Wilson, Francis Perry, Yale University School of Medicine, New Haven, Connecticut, United States

Due to its atypical clinical features and difficulty in establishing diagnosis without a biopsy, acute interstitial nephritis (AIN) diagnosis is delayed or missed. We developed a predictive model for AIN using clinical data from all patients who underwent a kidney biopsy available through the electronic health record.


We obtained data on all patients who underwent a native kidney biopsy at two centers between 2013-18 and obtained corresponding information of demographics, comorbidities, and all laboratory tests collected up to one year before biopsy. We used least absolute shrinkage and selection operator (LASSO) method to select features associated with AIN and performed area under received operating characteristics curve (AUC) analysis in temporally-split training (70%) and test (30%) sets. We also applied this model to a separate cohort of kidney biopsies with AIN diagnosis adjudicated by 3 pathologists and compared it to the clinician’s prebiopsy impression of AIN obtained through chart review.


Among 551 patients who underwent native kidney biopsies, 60 (11%) had AIN on clinical pathology diagnosis. We evaluated 163 potential features for their association with AIN. The five features with the highest AUC were last creatinine at the time of biopsy (AUC, 0.73), BUN to creatinine ratio (0.70), urine specific gravity before biopsy (0.67), serum bicarbonate (0.62), and urine protein (0.62). The top 4 variables picked using LASSO had an AUC of 0.76 in the test set (table). Applying this model to a separate cohort of participants with adjudicated AIN, we noted an AUC of 0.80 (0.73, 0.87), which was higher than the clinician’s pre-biopsy impression of AIN (0.61 (0.52, 0.70), P<0.001).


We noted four variables associated with AIN and the model containing these showed a modest AUC but was an improvement on clinician’s pre-biopsy impression of AIN.

Features associated with AIN
Risk factor for AINOR (95% CI)
Higher creatinine before biopsy (per 1 mg/dl increase)1.2 (1.0, 1.3)
Lower BUN: Cr ratio (per unit increase)1.1 (1.0, 1.2)
Lower proteinuria (0 and 1+ vs. 2+ or higher)2.5 (1.4, 4.4)
Lower specific gravity (per 0.001 increase)1.1 (1.0, 1.1)
AUC test0.76 (0.70, 0.82)

AUC, area under receiver operating characteristic curve; OR, odds ratio; CI, confidence interval AUC training set=0.79


  • NIDDK Support