ArticleArthritis & rheumatology (Hoboken, N.J.)2016
Development of Biomarker Models to Predict Outcomes in Lupus Nephritis.
Article in Arthritis & rheumatology (Hoboken, N.J.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Predictive biological markers of systemic lupus erythematosus flares: a systematic literature review.Arthritis research & therapy · 2017Pooled it
- Development and validation of nomograms for predicting activity index and tubulointerstitial lesions in lupus nephritis: a multicentre study.Lupus science & medicine · 2025Article
- Machine learning in lupus nephritis: bridging prediction models and clinical decision-making towards personalized nephrology.Frontiers in medicine · 2025Review
- Clinical characteristics, prognosis, and predictive modeling in class IV ± V lupus nephritis.Frontiers in immunology · 2025Article
- A serum biomarker panel and miniarray detection system for tracking disease activity and flare risk in lupus nephritis.Frontiers in immunology · 2025Article
- How artificial intelligence can enable personalized mesenchymal stem cell-based therapeutic strategies in systemic lupus erythematosus.Frontiers in immunology · 2025Review
- Gene-Based Predictive Modelling for Enhanced Detection of Systemic Lupus Erythematosus Using CNN-Based DL Algorithm.Diagnostics (Basel, Switzerland) · 2024Article
- Advancing Point-of-Care Diagnosis: Digitalizing Combinatorial Biomarker Signals for Lupus Nephritis.Biosensors · 2024Article
- Systemic lupus in the era of machine learning medicine.Lupus science & medicine · 2024Review
- Lupus Nephritis Biomarkers: A Critical Review.International journal of molecular sciences · 2024Review
- Investigating the value of urinary biomarkers in relation to lupus nephritis histopathology: present insights and future prospects.Frontiers in pharmacology · 2024Review
- Article
- Four trajectories of 24-hour urine protein levels in real-world lupus nephritis cohorts.RMD open · 2023Article
- Endothelial cell activation and glycocalyx shedding - potential as biomarkers in patients with lupus nephritis.Frontiers in immunology · 2023Review
- Current Insights on Biomarkers in Lupus Nephritis: A Systematic Review of the Literature.Journal of clinical medicine · 2022Review
- Prediction models of treatment response in lupus nephritis.Kidney international · 2022Article
- An introduction to machine learning and analysis of its use in rheumatic diseases.Nature reviews. Rheumatology · 2021Review
- Development of a lupus nephritis suboptimal response prediction tool using renal histopathological and clinical laboratory variables at the time of diagnosis.Lupus science & medicine · 2021Article
- Machine Learning in Rheumatic Diseases.Clinical reviews in allergy & immunology · 2021Review
- Variable selection methods for identifying predictor interactions in data with repeatedly measured binary outcomes.Journal of clinical and translational science · 2020Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
objectiveThe American College of Rheumatology guidelines for the treatment of lupus nephritis recommend change in induction therapy when response to therapy has not occurred within 6 months. Response is not defined, and renal fibrosis can occur while waiting for this end point. Therefore, a decision support tool to better define response is needed to guide clinicians when starting patients on therapy. This study was undertaken to identify biomarker models with sufficient predictive power to develop such a tool.
methodsUrine samples from 140 patients with biopsy-proven lupus nephritis who had not yet started induction therapy were analyzed for a panel of urinary biomarkers. Univariate receiver operating characteristic (ROC) curves were generated for each individual biomarker and compared to the ROC area under the curve values from machine learning models developed using random forest algorithms. Biomarker models of outcome developed with novel markers in addition to clinical markers were compared to those developed with traditional clinical markers alone.
resultsModels developed with the combined traditional and novel biomarker panels demonstrated clinically meaningful predictive power. Markers most predictive of response were chemokines, cytokines, and markers of cellular damage.
conclusionThis is the first study to demonstrate the power of low-abundance biomarker panels and machine learning algorithms for predicting lupus nephritis outcomes. This is a critical first step in research to develop clinically meaningful decision support tools.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.