{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:04:58Z","timestamp":1782846298790,"version":"3.54.5"},"reference-count":40,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Severe tubular atrophy\/interstitial fibrosis are critical pathological features associated with poor prognosis in IgA nephropathy (IgAN). The early identification of patients at high risk for severe tubular damage could guide clinical management and improve outcomes.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to construct and validate a predictive model for assessing the risk of severe tubular atrophy and interstitial fibrosis in patients diagnosed with IgAN.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A total of 3276 patients from the Hankou branch of Tongji Hospital were retrospectively enrolled for model development. A predictive model for severe tubular atrophy\/interstitial fibrosis was constructed based on independent predictors identified through univariate analysis, least absolute shrinkage and selection operator regression, and stepwise logistic regression. Furthermore, the model underwent internal and external validation using an independent dataset (n=1062), and performance evaluation using six machine learning algorithms: random forest, generalized linear model, decision tree, gradient boosting decision tree, extreme gradient boosting, and support vector machine.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n In this study, 8 variables were identified as independent predictors and used to construct a predictive model for severe tubular atrophy\/interstitial fibrosis: Logit (P)=0.011\u00d7age (years)+0.324\u00d7hypertension history\u20130.302\u00d7education+.111\u00d7coefficient of variation of red cell distribution width\u20130.152\u00d7direct bilirubin (\u03bcmol\/L)+0.003\u00d7uric acid (\u03bcmol\/L)\u20130.021\u00d7estimated glomerular filtration rate (ml\/min\/1.73m\u00b2)+1.151\u00d7ln(24 h urine microalbumin) (mg\/24h). The AUC for the predictive model was 0.860 (95% CI 0.847\u20100.873). The AUCs (95% CI) of the six machine learning algorithms ranged from 0.793 (0.765\u20100.822) to 0.880 (0.859\u20100.902) in internal validation and from 0.785 (0.756\u20100.814) to 0.862 (0.839\u20100.885) in external validation.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n We developed a concise and clinically useful model for predicting severe tubular atrophy\/interstitial fibrosis in IgA nephropathy. It offers a non-invasive tool for risk assessment when biopsy is not feasible, aiding personalized treatment decisions.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/78761","type":"journal-article","created":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T18:41:54Z","timestamp":1761676914000},"page":"e78761-e78761","source":"Crossref","is-referenced-by-count":3,"title":["Development and Validation of a Predictive Model for Severe Tubular Atrophy\/Interstitial Fibrosis in Patients with IgA Nephropathy: Multicenter Retrospective Study"],"prefix":"10.2196","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4583-9339","authenticated-orcid":false,"given":"Caizheng","family":"Yu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0384-6894","authenticated-orcid":false,"given":"Zhitong","family":"Niu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7530-9787","authenticated-orcid":false,"given":"Qin","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6679-1752","authenticated-orcid":false,"given":"Qing","family":"Lei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,10,28]]},"reference":[{"issue":"3","key":"R1","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1053\/j.ajkd.2021.01.024","article-title":"IgA nephropathy: core curriculum 2021","volume":"78","author":"Pattrapornpisut","journal-title":"Am J Kidney Dis"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.1038\/nrdp.2016.1","article-title":"IgA nephropathy","volume":"2","author":"Lai","journal-title":"Nat Rev Dis Primers"},{"issue":"1","key":"R3","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1111\/joim.20024","article-title":"Primary glomerular diseases and long-term adverse health outcomes: a nationwide cohort study","volume":"297","author":"Faucon","journal-title":"J Intern Med"},{"issue":"5","key":"R4","doi-asserted-by":"publisher","first-page":"151564","DOI":"10.1016\/j.semnephrol.2025.151564","article-title":"IgA nephropathy: epidemiology and disease risk across the world","volume":"44","author":"Ghaddar","journal-title":"Semin Nephrol"},{"issue":"5","key":"R5","doi-asserted-by":"publisher","first-page":"1014","DOI":"10.1016\/j.kint.2017.02.003","article-title":"Oxford classification of IgA nephropathy 2016: an update from the IgA nephropathy classification working group","volume":"91","author":"Trimarchi","journal-title":"Kidney Int"},{"issue":"11","key":"R6","doi-asserted-by":"publisher","first-page":"1865","DOI":"10.1093\/ndt\/gfaa079","article-title":"Macrophage interactions with collecting duct epithelial cells are capable of driving tubulointerstitial inflammation and fibrosis in immunoglobulin A nephropathy","volume":"35","author":"Pawluczyk","journal-title":"Nephrol Dial Transplant"},{"issue":"1","key":"R7","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1038\/ki.2015.322","article-title":"The MEST score provides earlier risk prediction in lgA nephropathy","volume":"89","author":"Barbour","journal-title":"Kidney Int"},{"issue":"3","key":"R8","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1053\/j.ajkd.2019.02.016","article-title":"Prediction and risk stratification of kidney outcomes in IgA nephropathy","volume":"74","author":"Chen","journal-title":"Am J Kidney Dis"},{"issue":"1","key":"R9","doi-asserted-by":"publisher","DOI":"10.1186\/s12882-025-04049-8","article-title":"Development of a multiple urinary biomarker model to predict the tubulointerstitial fibrosis area in patients with primary IgA nephropathy","volume":"26","author":"Gonz\u00e1lez Rodr\u00edguez","journal-title":"BMC Nephrol"},{"issue":"1","key":"R10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5414\/CN111270","article-title":"Plasma neutrophil gelatinase-associated lipocalin predicts renal tubulointerstitial injury in patients with IgA nephropathy","volume":"101","author":"Zhao","journal-title":"Clin Nephrol"},{"key":"R11","doi-asserted-by":"publisher","DOI":"10.1093\/qjmed\/hcaf111","article-title":"Age-related physiological and molecular biomarkers associated with kidney function: a systematic review and meta-analysis","author":"McGarvey","journal-title":"QJM"},{"key":"R12","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2024.1397160","article-title":"Biomarkers of chronic kidney disease in older individuals: navigating complexity in diagnosis","volume":"11","author":"Muglia","journal-title":"Front Med (Lausanne)"},{"issue":"10","key":"R13","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3390\/cells14100729","article-title":"G-Protein-coupled receptors in chronic kidney disease induced by hypertension and diabetes","volume":"14","author":"Tang","journal-title":"Cells"},{"issue":"11","key":"R14","doi-asserted-by":"publisher","first-page":"1833","DOI":"10.1093\/ndt\/gfu366","article-title":"Nephrosclerosis: update on a centenarian","volume":"30","author":"Meyrier","journal-title":"Nephrol Dial Transplant"},{"issue":"12","key":"R15","doi-asserted-by":"publisher","first-page":"3383","DOI":"10.1038\/s41440-024-01916-z","article-title":"Heterogeneous afferent arteriolopathy: a key concept for understanding blood pressure-dependent renal damage","volume":"47","author":"Kohagura","journal-title":"Hypertens Res"},{"issue":"7","key":"R16","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1093\/ndt\/gfy361","article-title":"Educational level and risk of chronic kidney disease: longitudinal data from the PREVEND study","volume":"35","author":"Thio","journal-title":"Nephrol Dial Transplant"},{"issue":"5","key":"R17","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1093\/aje\/kwaa237","article-title":"Associations of genetic factors, educational attainment, and their interaction with kidney function outcomes","volume":"190","author":"Thio","journal-title":"Am J Epidemiol"},{"key":"R18","doi-asserted-by":"publisher","DOI":"10.3389\/fnut.2024.1400577","article-title":"Cardiometabolic traits mediating the effect of education on the risk of DKD and CKD: a Mendelian randomization study","volume":"11","author":"Wang","journal-title":"Front Nutr"},{"issue":"6","key":"R19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0324632","article-title":"Machine learning-based prediction model for cognitive impairment risk in patients with chronic kidney disease","volume":"20","author":"Cao","journal-title":"PLoS ONE"},{"key":"R20","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2022.1041356","article-title":"Association of mean platelet volume\/lymphocyte ratio with inflammation in non-dialysis patients with chronic kidney disease stages 1\u20134: A retrospective study","volume":"13","author":"Xu","journal-title":"Front Immunol"},{"issue":"4","key":"R21","doi-asserted-by":"publisher","first-page":"628","DOI":"10.5858\/133.4.628","article-title":"Relation between red blood cell distribution width and inflammatory biomarkers in a large cohort of unselected outpatients","volume":"133","author":"Lippi","journal-title":"Arch Pathol Lab Med"},{"issue":"5","key":"R22","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1016\/j.clnu.2010.03.001","article-title":"Serum antioxidants and inflammation predict red cell distribution width in older women: the women\u2019s health and aging study I","volume":"29","author":"Semba","journal-title":"Clin Nutr"},{"issue":"2","key":"R23","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1172\/JCI11918","article-title":"Hostile takeovers: viral appropriation of the NF-kappaB pathway","volume":"107","author":"Hiscott","journal-title":"J Clin Invest"},{"issue":"1","key":"R24","doi-asserted-by":"publisher","first-page":"41","DOI":"10.2217\/bmm-2021-0364","article-title":"Red blood cell distribution width as a prognostic biomarker for viral infections: prospects and challenges","volume":"16","author":"Owoicho","journal-title":"Biomark Med"},{"issue":"3","key":"R25","doi-asserted-by":"publisher","DOI":"10.3390\/biomedicines10030696","article-title":"Translational approach to the protective effect of bilirubin in diabetic kidney disease","volume":"10","author":"Bianco","journal-title":"Biomedicines"},{"issue":"1","key":"R26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1042\/CS20140566","article-title":"Looking to the horizon: the role of bilirubin in the development and prevention of age-related chronic diseases","volume":"129","author":"Wagner","journal-title":"Clin Sci (Lond)"},{"issue":"9","key":"R27","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1016\/j.molmed.2016.07.004","article-title":"A novel perspective on the biology of bilirubin in health and disease","volume":"22","author":"Gazzin","journal-title":"Trends Mol Med"},{"issue":"Suppl 1","key":"R28","doi-asserted-by":"publisher","first-page":"S11","DOI":"10.33549\/physiolres.933581","article-title":"Bilirubin and atherosclerotic diseases","volume":"66","author":"V\u00edtek","journal-title":"Physiol Res"},{"issue":"2","key":"R29","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/j.aohep.2019.02.001","article-title":"Association between plasma bilirubin and mortality","volume":"18","author":"Vitek","journal-title":"Ann Hepatol"},{"issue":"6","key":"R30","doi-asserted-by":"publisher","first-page":"2372","DOI":"10.2741\/3860","article-title":"Protective role of heme oxygenase-1 against inflammation in atherosclerosis","volume":"16","author":"Durante","journal-title":"Front Biosci (Landmark Ed)"},{"issue":"4","key":"R31","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1002\/med.21660","article-title":"Bilirubin as a signaling molecule","volume":"40","author":"V\u00edtek","journal-title":"Med Res Rev"},{"issue":"7","key":"R32","doi-asserted-by":"publisher","DOI":"10.1096\/fj.202300393R","article-title":"Hyperuricemia: a key contributor to endothelial dysfunction in cardiovascular diseases","volume":"37","author":"Wei","journal-title":"FASEB J"},{"issue":"3","key":"R33","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0320482","article-title":"Serum uric acid level is associated with renal arteriolar hyalinosis and predicts post-donation renal function in living kidney donors","volume":"20","author":"Kano","journal-title":"PLoS ONE"},{"issue":"5","key":"R34","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1007\/s00467-018-3979-2","article-title":"Uric acid and progression of chronic kidney disease","volume":"34","author":"Weaver","journal-title":"Pediatr Nephrol"},{"key":"R35","doi-asserted-by":"publisher","DOI":"10.1016\/j.tox.2022.153387","article-title":"Uric acid accumulation in the kidney triggers mast cell degranulation and aggravates renal oxidative stress","volume":"483","author":"Zhang","journal-title":"Toxicology"},{"issue":"1","key":"R36","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1159\/000545492","article-title":"Roles of serum NLRP3 inflammasome and associated cytokines in gout-induced kidney injury","volume":"50","author":"Xu","journal-title":"Kidney Blood Press Res"},{"issue":"1","key":"R37","doi-asserted-by":"publisher","DOI":"10.1186\/s12882-019-1506-8","article-title":"Uric acid regulates NLRP3\/IL-1\u03b2 signaling pathway and further induces vascular endothelial cells injury in early CKD through ROS activation and K efflux","volume":"20","author":"Yin","journal-title":"BMC Nephrol"},{"issue":"12","key":"R38","doi-asserted-by":"publisher","first-page":"2343","DOI":"10.1016\/j.numecd.2020.07.038","article-title":"Increased serum uric acid levels are associated to renal arteriolopathy and predict poor outcome in IgA nephropathy","volume":"30","author":"Russo","journal-title":"Nutr Metab Cardiovasc Dis"},{"issue":"1","key":"R39","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-03561-z","article-title":"Individualized calibration of estimated glomerular filtration rate for serial renal function determination: a pilot study","volume":"15","author":"Mekonnen","journal-title":"Sci Rep"},{"issue":"4","key":"R40","doi-asserted-by":"publisher","first-page":"S117","DOI":"10.1016\/j.kint.2023.10.018","article-title":"KDIGO 2024 clinical practice guideline for the evaluation and management of chronic kidney disease","volume":"105","author":"Stevens","journal-title":"Kidney Int"}],"container-title":["JMIR Medical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/medinform.jmir.org\/2025\/1\/e78761","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:11:29Z","timestamp":1782843089000},"score":1,"resource":{"primary":{"URL":"https:\/\/medinform.jmir.org\/2025\/1\/e78761"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,28]]},"references-count":40,"URL":"https:\/\/doi.org\/10.2196\/78761","relation":{},"ISSN":["2291-9694"],"issn-type":[{"value":"2291-9694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,28]]},"article-number":"v13i3e78761"}}