Evidence map›Paper›PMID 39718799›Full record

Trial reportRheumatology (Oxford, England)2025

Predictors of rituximab efficacy in systemic sclerosis-associated interstitial lung disease: machine-learning analysis of the DESIRES trial.

Ai Kuzumi, Koji Oba, Satoshi Ebata, Kosuke Kashiwabara, Keiko Ueda, Yukari Uemura, Takeyuki Watadani, Takemichi Fukasawa, Shunsuke Miura, Asako Yoshizaki-Ogawa and 3 more

Abstract readRandomized Controlled Trial
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Ai KuzumiDepartment of Dermatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Koji ObaDepartment of Biostatistics, School of Public Health, The University of Tokyo, Tokyo, Japan.
Satoshi EbataDepartment of Dermatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Kosuke KashiwabaraClinical Research Support Center, Tokyo University Hospital, Tokyo, Japan.
Keiko UedaClinical Research Support Center, Tokyo University Hospital, Tokyo, Japan.
Yukari UemuraClinical Research Support Center, Tokyo University Hospital, Tokyo, Japan.
Takeyuki WatadaniDepartment of Diagnostic Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Takemichi FukasawaDepartment of Dermatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID 0000-0002-6093-1881
Shunsuke MiuraDepartment of Dermatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Asako Yoshizaki-OgawaDepartment of Dermatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Hidenori KageDepartment of Respiratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Shinichi SatoDepartment of Dermatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Ayumi YoshizakiDepartment of Dermatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID 0000-0002-8194-9140

Funding

Japan Cosmetic AssociationJapan Federation of Medium & Small Enterprise Organizations
6 · The paper itself

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.

Indexed as

Antirheumatic AgentsLung Diseases, InterstitialMachine LearningRituximabScleroderma, SystemicAdultAgedC-Reactive ProteinDouble-Blind MethodFemaleHumansMaleMiddle AgedMucin-1Prospective StudiesTreatment OutcomeAntirheumatic AgentsC-Reactive ProteinMUC1 protein, humanMucin-1Rituximabinterstitial lung diseasemachine learningrituximabsystemic sclerosis

Identifiers

PMID39718799
PMCPMC12695043

What Socratic holds

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LicenceCC BY
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Registered trials

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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.