Development and external validation of a diagnostic model for biopsy-proven acute interstitial nephritis using electronic health record data. Issue 11 (4th December 2021)

• Record Type: Journal Article
• Authors: Moledina, Dennis G; Eadon, Michael T; Calderon, Frida; Yamamoto, Yu; Shaw, Melissa; Perazella, Mark A; Simonov, Michael; Luciano, Randy; Schwantes-An, Tae-Hwi; Moeckel, Gilbert; Kashgarian, Michael; Kuperman, Michael; Obeid, Wassim; Cantley, Lloyd G; Parikh, Chirag R; Wilson, F Perry
• Is Part Of: Nephrology dialysis transplantation. Volume 37:Issue 11(2022)
• Page Start: 2214
• Page End: 2222
• Publication Date: 2021-12-04
• Subjects: biopsy -- creatinine -- electronic health record -- interstitial nephritis -- urinalysis; Nephrology -- Periodicals; Hemodialysis -- Periodicals; Kidneys -- Transplantation -- Periodicals; Hemodialysis; Kidneys -- Transplantation; Nephrology; Periodicals; 616.61
• DOI: 10.1093/ndt/gfab346
• Languages: English
• ISSNs: 0931-0509

Abstract

ABSTRACT: Background: Patients with acute interstitial nephritis (AIN) can present without typical clinical features, leading to a delay in diagnosis and treatment. We therefore developed and validated a diagnostic model to identify patients at risk of AIN using variables from the electronic health record. Methods: In patients who underwent a kidney biopsy at Yale University between 2013 and 2018, we tested the association of >150 variables with AIN, including demographics, comorbidities, vital signs and laboratory tests (training set 70%). We used least absolute shrinkage and selection operator methodology to select prebiopsy features associated with AIN. We performed area under the receiver operating characteristics curve (AUC) analysis with internal (held-out test set 30%) and external validation (Biopsy Biobank Cohort of Indiana). We tested the change in model performance after the addition of urine biomarkers in the Yale AIN study. Results: We included 393 patients (AIN 22%) in the training set, 158 patients (AIN 27%) in the test set, 1118 patients (AIN 11%) in the validation set and 265 patients (AIN 11%) in the Yale AIN study. Variables in the selected model included serum creatinine {adjusted odds ratio [aOR] 2.31 [95% confidence interval (CI) 1.42–3.76]}, blood urea nitrogen:creatinine ratio [aOR 0.40 (95% CI 0.20–0.78)] and urine dipstick specific gravity [aOR 0.95 (95% CI 0.91–0.99)] and protein [aOR 0.39 (95% CI 0.23–0.68)]. This model showed an AUC of 0.73 (95% CI 0.64–0.81) in the test set, which was similar to the AUC in the external validation cohort [0.74 (95% CI 0.69–0.79)]. The AUC improved to 0.84 (95% CI 0.76–0.91) upon the addition of urine interleukin-9 and tumor necrosis factor-α. Conclusions: We developed and validated a statistical model that showed a modest AUC for AIN diagnosis, which improved upon the addition of urine biomarkers. Future studies could evaluate this model and biomarkers to identify unrecognized cases of AIN. Graphical Abstract: