ArticleKidney international2022
Prediction models of treatment response in lupus nephritis.
Article in Kidney international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- The application of artificial intelligence in systemic lupus erythematosus: a bibliometric analysis of current trends and future directions.Frontiers in medicine · 2026Pooled it
- Unsupervised machine learning identifies distinct SLE patient endotypes with differential response to belimumab.Rheumatology (Oxford, England) · 2025Trial
- Combination of rituximab and short-term glucocorticoids in the treatment of anti-phospholipase AClinical and experimental medicine · 2023Trial
- Toward Precision Oral Medicine in Pemphigus Vulgaris: A Conceptual AI Framework for Rituximab Response Prediction.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Validity and applicability of machine learning models for systemic lupus erythematosus diagnosis.Lupus science & medicine · 2026Article
- Defining PI3K Signaling as a Key Regulator of Molecular Heterogeneity and Inflammation in Glomerular Lupus Nephritis.Immune network · 2026Article
- Serum uric acid in systemic lupus erythematosus with preserved renal function: a cross-sectional and longitudinal analysis.Frontiers in immunology · 2026Article
- An interpretable machine learning model for preoperative prediction of renal mass malignancy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Applications of artificial intelligence in systemic lupus erythematosus: integrating multi-omics data for precision medicine.Frontiers in immunology · 2026Review
- Lupus nephritis serum induces changes in gene expression in human glomerular endothelial cells, which is modulated by L-sepiapterin: implications for redox-mediated endothelial dysfunction.Lupus science & medicine · 2025Article
- Effectiveness and safety of finerenone in membranous nephropathy patients: a retrospective, real‑world study.International urology and nephrology · 2025Article
- Revealing VCAN as a Potential Common Diagnostic Biomarker of Renal Tubules and Glomerulus in Diabetic Kidney Disease Based on Machine Learning, Single-Cell Transcriptome Analysis and Mendelian Randomization.Diabetes & metabolism journal · 2025Article
- Artificial intelligence in autoimmune diseases: a bibliometric exploration of the past two decades.Frontiers in immunology · 2025Review
- A serum biomarker panel and miniarray detection system for tracking disease activity and flare risk in lupus nephritis.Frontiers in immunology · 2025Article
- Urinary Immune Complexes Reflect Renal Pathology in Lupus Nephritis.Diagnostics (Basel, Switzerland) · 2024Article
- Predictive modeling of co-infection in lupus nephritis using multiple machine learning algorithms.Scientific reports · 2024Article
- Non-Invasive Biomarkers for Diagnosis, Risk Prediction, and Therapy Guidance of Glomerular Kidney Diseases: A Comprehensive Review.International journal of molecular sciences · 2024Review
- Systemic lupus in the era of machine learning medicine.Lupus science & medicine · 2024Review
- Investigating the value of urinary biomarkers in relation to lupus nephritis histopathology: present insights and future prospects.Frontiers in pharmacology · 2024Review
- CXCL10 as a shared specific marker in rheumatoid arthritis and inflammatory bowel disease and a clue involved in the mechanism of intestinal flora in rheumatoid arthritis.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
In order to develop prediction models of one-year treatment response in lupus nephritis, an approach using machine learning to combine traditional clinical data and novel urine biomarkers was undertaken. Contemporary lupus nephritis biomarkers were identified through an unbiased PubMed search. Thirteen novel urine proteins contributed to the top 50% of ranked biomarkers and were selected for measurement at the time of lupus nephritis flare. These novel markers along with traditional clinical data were incorporated into a variety of machine learning algorithms to develop prediction models of one-year proteinuria and estimated glomerular filtration rate (eGFR). Models were trained on 246 individuals from four different sub-cohorts and validated on an independent cohort of 30 patients with lupus nephritis. Seven models were considered for each outcome. Three-quarters of these models demonstrated good predictive value with areas under the receiver operating characteristic curve over 0.7. Overall, prediction performance was the best for models of eGFR response to treatment. Furthermore, the best performing models contained both traditional clinical data and novel urine biomarkers, including cytokines, chemokines, and markers of kidney damage. Thus, our study provides further evidence that a machine learning approach can predict lupus nephritis outcomes at one year using a set of traditional and novel biomarkers. However, further validation of the utility of machine learning as a clinical decision aid to improve outcomes will be necessary before it can be routinely used in clinical practice to guide therapy.
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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.