{"doi":"10.1093/ndt/gfab346","title":"Development and external validation of a diagnostic model for biopsy-proven acute interstitial nephritis using electronic health record data","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.","journal":"Nephrology Dialysis Transplantation","year":2021,"id":172141,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":26,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.6218,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":307583,"name":"Michael T. Eadon","orcid":"0000-0003-3066-2876","position":1,"is_corresponding":false},{"id":708297,"name":"Frida Calderon","orcid":null,"position":2,"is_corresponding":false},{"id":551783,"name":"Yu Yamamoto","orcid":"0000-0002-8323-8676","position":3,"is_corresponding":false},{"id":694387,"name":"Melissa Shaw","orcid":null,"position":4,"is_corresponding":false},{"id":108988,"name":"Mark A. Perazella","orcid":"0000-0002-6994-2659","position":5,"is_corresponding":false},{"id":217909,"name":"Michael Simonov","orcid":"0000-0001-8032-1119","position":6,"is_corresponding":false},{"id":457084,"name":"Randy L. Luciano","orcid":"0000-0002-3038-4231","position":7,"is_corresponding":false},{"id":388337,"name":"Tae‐Hwi Schwantes‐An","orcid":"0000-0001-6387-0095","position":8,"is_corresponding":false},{"id":274505,"name":"Gilbert Moeckel","orcid":"0000-0002-6059-0837","position":9,"is_corresponding":false},{"id":284016,"name":"Michael Kashgarian","orcid":"0000-0003-0362-7688","position":10,"is_corresponding":false},{"id":248441,"name":"Michael Kuperman","orcid":null,"position":11,"is_corresponding":false},{"id":406754,"name":"Wassim Obeid","orcid":"0000-0002-6054-411X","position":12,"is_corresponding":false},{"id":249672,"name":"Lloyd G. Cantley","orcid":"0000-0002-8444-6469","position":13,"is_corresponding":false},{"id":241229,"name":"Chirag R. Parikh","orcid":"0000-0001-9051-7385","position":14,"is_corresponding":false},{"id":234941,"name":"F. Perry Wilson","orcid":"0000-0002-2633-2412","position":15,"is_corresponding":false},{"id":258520,"name":"Dennis G. Moledina","orcid":"0000-0002-9537-9038","position":0,"is_corresponding":true}],"reference_count":43,"raw_metadata":null,"created_at":"2026-07-18T23:46:37.150824Z","pmid":"34865148","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}