Predictive model to identify multiple failure to biological therapy in patients with rheumatoid arthritis

Novella-Navarro, Marta; Benavent, D.; Ruíz-Esquide, Virginia; Tornero, C.; Díaz-Almirón, Mariana; Chacur, Chafik Alejandro; Peiteado, Diana; Villalba, A.; Sanmarti, Raimón; Plasencia-Rodríguez, Chamaida; Balsa, Alejandro

doi:10.1177/1759720x221124028

Cited by 6 publications

(5 citation statements)

References 41 publications

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“…of disease activity through the measurement of cytokines (36) and multirefractory and non-refractory patients by using simple measurements recorded in routine clinical practice for individual treatment protocols in an early stage of RA (37). Studies have shown that irregular dental attendance at a late stage of primary school and high levels of salivary S.mutans and male sex at an early stage of primary school are associated with a higher risk of dental caries (38).…”

Section: Discussionmentioning

confidence: 99%

“…It chooses the "best" characteristics to partition the data into specific classes (34,35). This method has been used to predict disease activity (36) and treatment response in rheumatoid arthritis (RA) (37) as well as to identify a high risk group for dental caries in school children (38). The Classification and Regression Tree (CART) algorithm, a type of DTA, has been used to develop a diagnostic algorithm for BS uveitis (39) and to predict relapse in intestinal BS (40).…”

Section: Introductionmentioning

confidence: 99%

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Does decision tree analysis predict oral ulcer activity-related factors in patients with Behçet's syndrome?

Çandereli,

Arslan,

Özdamar

et al. 2023

Clinical and Experimental Rheumatology

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The study aimed to identify the interactions among treatment protocols and oral ulcer activity related factors in patients withBehçet's syndrome (BS) using the Classification and Regression Tree (CART) algorithm. MethodsIn this cross-sectional study, 979 patients with BS were included from16 centres in Turkey, Jordan, Brazil and the United Kingdom. In the CART algorithm, activities of oral ulcer (active vs. inactive), genital ulcer (active vs. inactive), cutaneous involvement (active vs. inactive), musculoskeletal involvement (active vs. inactive), gender (male vs. female), disease severity (mucocutaneous and musculoskeletal involvement vs. major organ involvement), smoking habits (current smoker vs. non-smoker), tooth brushing habits (irregular vs. regular), were input variables. The treatment protocols regarding immunosuppressive (IS) or non-IS medications were the target variable used to split from parent nodes to purer child nodes in the study. ResultsIn mucocutaneous and musculoskeletal involvement (n=538), the ratio of IS use was higher in patients with irregular toothbrushing (ITB) habits (27.1%) than in patients with regular toothbrushing (RTB) habits (14.2%) in oral ulcer activity. In major organ involvement (n=441), male patients with ITB habits were more likely treated with IS medications compared to those with RTB habits (91.6% vs. 77.6%, respectively). ConclusionMale BS patients on IS who have major organ involvement and oral ulcer activity with mucocutaneous and musculoskeletal involvement have irregular toothbrushing habits. Improved oral hygiene practices should be considered to be an integral part for implementing patient empowerment strategies for BS.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Does decision tree analysis predict oral ulcer activity-related factors in patients with Behçet's syndrome?

Çandereli,

Arslan,

Özdamar

et al. 2023

Clinical and Experimental Rheumatology

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Section: Discussionmentioning

confidence: 99%

“…The use of this composite index during the early stages of treatment with b/tsDMARDs provides an objective measurement that is able to not only predict the outcome of multidrug failure, but also to discriminate between D2TRA-inefficacy patients vs good responders. 15 With regard to our previous data, 7 15 in this study we have modified the inclusion criteria for the NoD2TRA group, including those patients who responded well to the first b/tsDMARD and those who failed a line of treatment, but who did not meet the D2TRA definition. Likewise, by using a different statistical model, we corroborated that DAS28 is a robust classification measure for assessing multiple drug failure, taking into account these subgroups of patients.…”

Section: Rmd Open Rmd Open Rmd Openmentioning

confidence: 99%

Patient and physician assessment in difficult-to-treat rheumatoid arthritis: patterns of subjective perception at early stages of b/tsDMARD treatment

Novella -Navarro,

Cabrera-Alarcón,

López-Juanes

et al. 2023

RMD Open

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ObjectivesTo analyse the trajectories of Disease Activity Score 28 (DAS28), patient global assessment (PGA) and physician global assessment (PhGA) and to assess their predictive capabilities on difficult-to-treat rheumatoid arthritis (D2TRA) classification.MethodsLongitudinal study of patients with rheumatoid arthritis (RA) from 2020 to 2022. Based on the D2TRA EULAR (European Alliance of Associations for Rheumatology) definition, patients were classified as D2TRA according to biological or targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) failure due to inefficacy (D2TRA-inefficacy) or other reasons (D2TRA-other). Patients who did not fulfil the D2TRA criteria were classified as NoD2TRA. DAS28, PGA and PhGA scores collected every 6 months during the first 24 months of b/tsDMARD treatment were used to identify different trajectories using latent class mixed models (LCMM).ResultsThe study population comprised 255 patients with RA, of whom 167 were NoD2TRA, 58 D2TRA-inefficacy and 30 D2TRA-other. LCMM stratified patients into two different trajectories for DAS28 and PhGA and three for PGA according to the most stable model. The most notable variation occurred during the first 6 months of treatment, thereafter remaining stable during the follow-up period. Most D2TRA-inefficacy patients fitted the trajectory, showing higher values of the studied parameters. NoD2TRA followed the trajectory with lower values, and D2TRA-other were distributed more homogeneously across all trajectories.ConclusionsThe assessment of disease activity, together with patients’ and physicians’ perceptions, form a key element in the correct discrimination of patients who are going to develop D2TRA-inefficacy. However, identifying those patients who will be D2TRA-other remains challenging, whether by subjective or objective parameters.

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“…ML algorithms have demonstrated efficacy in enhancing the precision of response prediction for TNF inhibitors and MTX. Furthermore, ML methodologies are being increasingly utilized in forecasting treatment responses to a range of other biologic therapies ( 61 – 64 ). Clinical data may be limited by trial design, including inclusion and exclusion criteria.Using deep learning technology for cluster analysis on RA patients has revealed the connection between patient characteristics and treatment response ( 105 ).…”

Section: Models In Precision Diagnosis and Therapeutics For Ramentioning

confidence: 99%

Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management

Shi,

Zhou,

Chang

et al. 2024

Front. Immunol.

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Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA.

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Predictive model to identify multiple failure to biological therapy in patients with rheumatoid arthritis

Cited by 6 publications

References 41 publications

Does decision tree analysis predict oral ulcer activity-related factors in patients with Behçet's syndrome?

Does decision tree analysis predict oral ulcer activity-related factors in patients with Behçet's syndrome?

Patient and physician assessment in difficult-to-treat rheumatoid arthritis: patterns of subjective perception at early stages of b/tsDMARD treatment

Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management

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