Trial reportRheumatology (Oxford, England)2025
Predictors of rituximab efficacy in systemic sclerosis-associated interstitial lung disease: machine-learning analysis of the DESIRES trial.
Trial report in Rheumatology (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Autoantibody landscape and functional role of anti-C-C motif chemokine receptor 8 autoantibodies in systemic sclerosis: post-hoc analysis of a B-cell depletion trial.Nature communications · 2025Trial
- Isolated Anti-SS-A Antibody Seropositivity as a Poor Prognostic Factor in Systemic Sclerosis: Insights From a Cohort of 307 Cases.The Journal of dermatology · 2026Article
- Harnessing artificial intelligence to advance insights in systemic sclerosis skin and lung disease.Current opinion in rheumatology · 2025Review
- Machine Learning-Assisted Analysis of the Oral Cancer Immune Microenvironment: From Single-Cell Level to Prognostic Model Construction.Journal of cellular and molecular medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
objectivesRituximab is emerging as a promising therapeutic option for systemic sclerosis-associated interstitial lung disease (SSc-ILD). However, little is known about factors that predict the efficacy of rituximab in SSc-ILD.
methodsA post-hoc analysis was performed on prospective data from 48 patients with SSc-ILD in the double-blind, randomized, placebo-controlled DESIRES trial. A total of 28 baseline factors were selected as candidates to predict the efficacy of rituximab on the percentage of predicted forced vital capacity (ppFVC) at 24 weeks. A machine learning causal tree algorithm was used to explore the combination of predictors to identify subpopulations with a good response to rituximab.
resultsSerum levels of C-reactive protein (CRP) and Krebs von den Lungen-6 (KL-6) were selected as branches of the decision tree to stratify patients into three subpopulations. In the subpopulation with serum CRP levels ≥0.055 mg/dl, ΔppFVC was significantly higher in the rituximab group than in the placebo group [difference 8.01% (95% CI: 4.40%, 11.62%)]. In the subpopulation with serum CRP levels <0.055 mg/dl and serum KL-6 levels ≥364 U/ml, ΔppFVC was comparable between the two groups [difference 2.47% (95% CI: -1.99%, 6.92%)]. In the subpopulation with serum CRP levels <0.055 mg/dl and serum KL-6 levels <364 U/ml, ΔppFVC was significantly lower in rituximab than in placebo [difference -6.85% (95% CI: -10.80%, -2.91%)].
conclusionEven slight elevations in serum CRP levels are associated with the improvement in ppFVC and may serve as predictors of rituximab efficacy in SSc-ILD.
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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 Socratic graph.