Evidence map›Paper›PMID 42318515›Full record

ArticleTherapeutic advances in musculoskeletal disease2026

Early identification of difficult-to-manage axial spondyloarthritis using machine-learning decision trees.

Manuel Juárez-García, Chamaida Plasencia-Rodríguez, Diego Benavent, Mariana Díaz-Almirón, Marta Novella-Navarro, Carolina Tornero, Diana Peiteado, Xavier Juanola, Mireia Moreno, Eugenio de Miguel and 2 more

Abstract read
In one paragraph

Article in Therapeutic advances in musculoskeletal disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Manuel Juárez-GarcíaRheumatology Department, Hospital Universitario La Paz, Madrid 28046, Spain La Paz Research Institute, Madrid 28046, Spain.ORCID https://orcid.org/0009-0000-8228-7452
Chamaida Plasencia-RodríguezRheumatology Department, Hospital Universitario La Paz, Madrid, Spain.
Diego BenaventRheumatology Department, Hospital Universitari de Bellvitge, Barcelona, Spain.ORCID https://orcid.org/0000-0001-9119-5330
Mariana Díaz-AlmirónLIMA, Advanced Medical Informatics Lab, Madrid, Spain.
Marta Novella-NavarroRheumatology Department, Hospital Universitario La Paz, Madrid, Spain.ORCID https://orcid.org/0000-0002-2200-0859
Carolina TorneroRheumatology Department, Hospital Universitario La Paz, Madrid, Spain.
Diana PeiteadoRheumatology Department, Hospital Universitario La Paz, Madrid, Spain.
Xavier JuanolaRheumatology Department, Hospital Universitari de Bellvitge, Barcelona, Spain.
Mireia MorenoRheumatology Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona Medicine Department, Sabadell, Spain.
Eugenio de MiguelRheumatology Department, Hospital Universitario La Paz, Madrid, Spain.
Jordi GratacósRheumatology Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona Medicine Department, Sabadell, Spain.ORCID https://orcid.org/0000-0003-4007-4103
Victoria Navarro-CompánRheumatology Department, Hospital Universitario La Paz, Madrid, Spain.ORCID https://orcid.org/0000-0002-4527-852X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Axial spondyloarthritis (axSpA) is an inflammatory disease in which, despite expanding therapeutic options, a substantial proportion of patients do not achieve the desired treatment target, highlighting the emerging concept of difficult-to-manage (D2M) axSpA. Objectives: To identify characteristics and predictive factors of D2M axSpA and to develop machine learning models for early identification. Design: Longitudinal observational cohort study with external validation. Methods: Patients with axSpA from the SpA-Paz cohort initiating a first biological or targeted synthetic disease-modifying antirheumatic drug (b/tsDMARD) between 2004 and 2019 were included. D2M was defined as failure of ⩾2 b/tsDMARDs, and very good responders (GR) as retention of the first b/tsDMARD ⩾3 years or discontinuation due to improvement. Baseline clinical data and baseline/6-month disease activity measures were collected. Factors associated with D2M were assessed using descriptive, comparative, and logistic regression analyses. Classification and Regression Tree (CART) models were developed and externally validated with the REGISPONSERBIO registry. Results: Of 311 patients initiating b/tsDMARDs, 101 were included (42 D2M, 59 GR), with a D2M prevalence of 13.5%. D2M patients were more often smokers, Human Leukocyte Antigen B27 (HLA-B27) negative, and had higher rates of enthesitis and comorbidities. Baseline Axial Spondyloarthritis Disease Activity Score (ASDAS) and Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) did not differ between groups, but after 6 months D2M patients showed higher disease activity (ASDAS 2.8 vs 1.6, BASDAI 5.4 vs 3.3; both Conclusion: D2M axSpA affects approximately 1 in 8 patients initiating advanced therapy and is associated with smoking, HLA-B27 negativity, enthesitis, comorbidities, and poor 6-month response to first-line b/tsDMARDs. CART models using routine clinical data may support early identification.

Indexed as

axial spondyloarthritisdifficult-to-managemachine learningpredictors

Identifiers

PMID42318515
PMCPMC13272855

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.