{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,12]],"date-time":"2026-11-12T22:39:45Z","timestamp":1794523185707,"version":"4.9.1"},"reference-count":22,"publisher":"BMJ","issue":"1","license":[{"start":{"date-parts":[[2021,8,24]],"date-time":"2021-08-24T00:00:00Z","timestamp":1629763200000},"content-version":"unspecified","delay-in-days":23,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000069","name":"National Institute of Arthritis and Musculoskeletal and Skin Diseases","doi-asserted-by":"publisher","award":["K08 AR002193"],"award-info":[{"award-number":["K08 AR002193"]}],"id":[{"id":"10.13039\/100000069","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000069","name":"National Institute of Arthritis and Musculoskeletal and Skin Diseases","doi-asserted-by":"publisher","award":["P30 AR072582"],"award-info":[{"award-number":["P30 AR072582"]}],"id":[{"id":"10.13039\/100000069","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000069","name":"National Institute of Arthritis and Musculoskeletal and Skin Diseases","doi-asserted-by":"publisher","award":["P60 AR062755"],"award-info":[{"award-number":["P60 AR062755"]}],"id":[{"id":"10.13039\/100000069","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000069","name":"National Institute of Arthritis and Musculoskeletal and Skin Diseases","doi-asserted-by":"publisher","award":["R01 AR045476"],"award-info":[{"award-number":["R01 AR045476"]}],"id":[{"id":"10.13039\/100000069","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000069","name":"National Institute of Arthritis and Musculoskeletal and Skin Diseases","doi-asserted-by":"publisher","award":["R01 AR071947"],"award-info":[{"award-number":["R01 AR071947"]}],"id":[{"id":"10.13039\/100000069","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000738","name":"U.S. Department of Veterans Affairs","doi-asserted-by":"publisher","award":["I01 CX000218"],"award-info":[{"award-number":["I01 CX000218"]}],"id":[{"id":"10.13039\/100000738","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["UL1 RR029882"],"award-info":[{"award-number":["UL1 RR029882"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["UL1 TR001450"],"award-info":[{"award-number":["UL1 TR001450"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Lupus Sci Med"],"accepted":{"date-parts":[[2021,8,2]]},"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Lupus nephritis (LN) is an immune complex-mediated glomerular and tubulointerstitial disease in patients with SLE. Prediction of outcomes at the onset of LN diagnosis can guide decisions regarding intensity of monitoring and therapy for treatment success. Currently, no machine learning model of outcomes exists. Several outcomes modelling works have used univariate or linear modelling but were limited by the disease heterogeneity. We hypothesised that a combination of renal pathology results and routine clinical laboratory data could be used to develop and to cross-validate a clinically meaningful machine learning early decision support tool that predicts LN outcomes at approximately 1 year.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>To address this hypothesis, patients with LN from a prospective longitudinal registry at the Medical University of South Carolina enrolled between 2003 and 2017 were identified if they had renal biopsies with International Society of Nephrology\/Renal Pathology Society pathological classification. Clinical laboratory values at the time of diagnosis and outcome variables at approximately 1 year were recorded. Machine learning models were developed and cross-validated to predict suboptimal response.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Five machine learning models predicted suboptimal response status in 10 times cross-validation with receiver operating characteristics area under the curve values &gt;0.78. The most predictive variables were interstitial inflammation, interstitial fibrosis, activity score and chronicity score from renal pathology and urine protein-to-creatinine ratio, white blood cell count and haemoglobin from the clinical laboratories. A web-based tool was created for clinicians to enter these baseline clinical laboratory and histopathology variables to produce a probability score of suboptimal response.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>Given the heterogeneity of disease presentation in LN, it is important that risk prediction models incorporate several data elements. This report provides for the first time a clinical proof-of-concept tool that uses the five most predictive models and simplifies understanding of them through a web-based application.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1136\/lupus-2021-000489","type":"journal-article","created":{"date-parts":[[2021,8,24]],"date-time":"2021-08-24T12:39:13Z","timestamp":1629808753000},"page":"e000489","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":23,"title":["Development of a lupus nephritis suboptimal response prediction tool using renal histopathological and clinical laboratory variables at the time of diagnosis"],"prefix":"10.1136","volume":"8","author":[{"given":"Lindsay N","family":"Helget","sequence":"first","affiliation":[{"name":"Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA"},{"name":"Department of Medicine, University of Nebraska Medical Center, Omaha, Nebraska, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David J","family":"Dillon","sequence":"additional","affiliation":[{"name":"Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bethany","family":"Wolf","sequence":"additional","affiliation":[{"name":"Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura P","family":"Parks","sequence":"additional","affiliation":[{"name":"Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sally E","family":"Self","sequence":"additional","affiliation":[{"name":"Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, South Carolina, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evelyn T","family":"Bruner","sequence":"additional","affiliation":[{"name":"Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, South Carolina, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evan E","family":"Oates","sequence":"additional","affiliation":[{"name":"Vanderbilt University, Nashville, Tennessee, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5426-1359","authenticated-orcid":false,"given":"Jim C","family":"Oates","sequence":"additional","affiliation":[{"name":"Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA"},{"name":"Medical Service, Ralph H Johnson VA Medical Center, Charleston, South Carolina, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2021,8,24]]},"reference":[{"key":"2025091514103232000_8.1.e000489.1","doi-asserted-by":"publisher","DOI":"10.1038\/ki.1997.162"},{"key":"2025091514103232000_8.1.e000489.2","doi-asserted-by":"publisher","DOI":"10.1002\/acr.21664"},{"key":"2025091514103232000_8.1.e000489.3","doi-asserted-by":"publisher","DOI":"10.1002\/art.1780400928"},{"key":"2025091514103232000_8.1.e000489.4","doi-asserted-by":"crossref","unstructured":"Isenberg D , Wallace DJ , Nived O . Derivation and validation of systemic lupus international collaborating clinics classification criteria for systemic lupus erythematosus. Arthritis Rheum 2012;64.doi:10.1002\/art.34473","DOI":"10.1002\/art.34473"},{"key":"2025091514103232000_8.1.e000489.5","doi-asserted-by":"publisher","DOI":"10.1016\/j.kint.2017.11.023"},{"key":"2025091514103232000_8.1.e000489.6","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0057852","article-title":"Using mathematical algorithms to modify glomerular filtration rate estimation equations","volume":"8","author":"Pei","year":"2013","journal-title":"PLoS One"},{"key":"2025091514103232000_8.1.e000489.7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2008.08.010"},{"key":"2025091514103232000_8.1.e000489.8","doi-asserted-by":"publisher","DOI":"10.1136\/lupus-2017-000213"},{"key":"2025091514103232000_8.1.e000489.9","doi-asserted-by":"publisher","DOI":"10.1002\/art.21625"},{"key":"2025091514103232000_8.1.e000489.10","doi-asserted-by":"publisher","DOI":"10.1002\/art.37940"},{"key":"2025091514103232000_8.1.e000489.11","unstructured":"Team RC . A language and environment for statistical computing, 2020. Available: https:\/\/www.R-project.org"},{"key":"2025091514103232000_8.1.e000489.12","doi-asserted-by":"publisher","DOI":"10.1159\/000323281"},{"key":"2025091514103232000_8.1.e000489.13","unstructured":"Hastie T , Tibshirani R , Friedman JH . The elements of statistical learning : data mining, inference, and prediction : with 200 full-color illustrations. 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