{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T22:53:19Z","timestamp":1783119199041,"version":"3.54.6"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2021,12,4]],"date-time":"2021-12-04T00:00:00Z","timestamp":1638576000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,19]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>In patients who underwent a kidney biopsy at Yale University between 2013 and 2018, we tested the association of &amp;gt;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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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\u20133.76]}, blood urea nitrogen:creatinine ratio [aOR 0.40 (95% CI 0.20\u20130.78)] and urine dipstick specific gravity [aOR 0.95 (95% CI 0.91\u20130.99)] and protein [aOR 0.39 (95% CI 0.23\u20130.68)]. This model showed an AUC of 0.73 (95% CI 0.64\u20130.81) in the test set, which was similar to the AUC in the external validation cohort [0.74 (95% CI 0.69\u20130.79)]. The AUC improved to 0.84 (95% CI 0.76\u20130.91) upon the addition of urine interleukin-9 and tumor necrosis factor-\u03b1.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ndt\/gfab346","type":"journal-article","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T20:13:46Z","timestamp":1638476026000},"page":"2214-2222","source":"Crossref","is-referenced-by-count":21,"title":["Development and external validation of a diagnostic model for biopsy-proven acute interstitial nephritis using electronic health record data"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9537-9038","authenticated-orcid":false,"given":"Dennis G","family":"Moledina","sequence":"first","affiliation":[{"name":"Section of Nephrology and Clinical and Translational Research Accelerator, Department of Internal Medicine, Yale School of Medicine , New Haven, CT , 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