Machine learning-assisted screening of clinical features for predicting difficult-to-treat rheumatoid arthritis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023728%3A_____%2F25%3AN0000049" target="_blank" >RIV/00023728:_____/25:N0000049 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11110/25:10504369
Result on the web
<a href="https://doi.org/10.1038/s41598-025-18298-y" target="_blank" >https://doi.org/10.1038/s41598-025-18298-y</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41598-025-18298-y" target="_blank" >10.1038/s41598-025-18298-y</a>
Alternative languages
Result language
angličtina
Original language name
Machine learning-assisted screening of clinical features for predicting difficult-to-treat rheumatoid arthritis
Original language description
To identify clinical features that predict the risk of meeting difficult-to-treat (D2T) rheumatoid arthritis (RA) definition in advance. This retrospective analysis included RA patients from the ATTRA registry who initiated biologic (b-) or targeted synthetic (ts-) disease-modifying anti-rheumatic drugs (DMARDs) between 2002 and 2023. Patients with D2T RA met the EULAR criteria, while controls achieved sustained remission, defined as a Simple Disease Activity Index (SDAI) < 3.3 and a Swollen Joint Count (SJC) ≤ 1, maintained across two consecutive visits 12 weeks apart. Patients were assessed at baseline and at one and two years before fulfilling the D2T RA definition. Predictive models were developed using machine learning techniques (lasso and ridge logistic regression, support vector machines, random forests, and XGBoost). Shapley additive explanation (SHAP) values were used to assess the contribution of individual variables to model predictions. Among 8,543 RA patients, 641 met the criteria for D2T RA, while 1,825 achieved remission. The machine learning models demonstrated an accuracy range of 0.606-0.747, with an area under the receiver operating characteristic curve (AUC) of 0.656-0.832 for predicting D2T RA. SHAP analysis highlighted key predictive variables, including disease activity measures (DAS28-ESR, CDAI, CRP), patient-reported outcomes (HAQ), and the duration of b/tsDMARD treatment. We identified clinical features predictive of D2T RA at baseline and up to one year before meeting the formal criteria. These findings provide valuable insights into early indicators of D2T RA progression and support the importance of earlier recognition and timely therapeutic intervention to improve long-term patient outcomes.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
30226 - Rheumatology
Result continuities
Project
<a href="/en/project/NU23-10-00434" target="_blank" >NU23-10-00434: Screening for biomarkers characterizing patients with difficult-to-treat rheumatoid arthritis in the Czech population</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
SCIENTIFIC REPORTS
ISSN
2045-2322
e-ISSN
2045-2322
Volume of the periodical
15
Issue of the periodical within the volume
Art. Nr. 34747
Country of publishing house
GB - UNITED KINGDOM
Number of pages
10
Pages from-to
1-10
UT code for WoS article
001589233300007
EID of the result in the Scopus database
2-s2.0-105017931697