Predictive model to identify multiple failure to biological therapy in patients with rheumatoid arthritis
© The Author(s), 2022..
Background: Despite advances in the treatment of rheumatoid arthritis (RA) and the wide range of therapies available, there is a percentage of patients whose treatment presents a challenge for clinicians due to lack of response to multiple biologic and target-specific disease-modifying antirheumatic drugs (b/tsDMARDs).
Objective: To develop and validate an algorithm to predict multiple failure to biological therapy in patients with RA.
Design: Observational retrospective study involving subjects from a cohort of patients with RA receiving b/tsDMARDs.
Methods: Based on the number of prior failures to b/tsDMARDs, patients were classified as either multi-refractory (MR) or non-refractory (NR). Patient characteristics were considered in the statistical analysis to design the predictive model, selecting those variables with a predictive capability. A decision algorithm known as 'classification and regression tree' (CART) was developed to create a prediction model of multi-drug resistance. Performance of the prediction algorithm was evaluated in an external independent cohort using area under the curve (AUC).
Results: A total of 136 patients were included: 51 MR and 85 NR. The CART model was able to predict multiple failures to b/tsDMARDs using disease activity score-28 (DAS-28) values at 6 months after the start time of the initial b/tsDMARD, as well as DAS-28 improvement in the first 6 months and baseline DAS-28. The CART model showed a capability to correctly classify 94.1% NR and 87.5% MR patients with a sensitivity = 0.88, a specificity = 0.94, and an AUC = 0.89 (95% CI: 0.74-1.00). In the external validation cohort, 35 MR and 47 NR patients were included. The AUC value for the CART model in this cohort was 0.82 (95% CI: 0.73-0.9).
Conclusion: Our model correctly classified NR and MR patients based on simple measurements available in routine clinical practice, which provides the possibility to characterize and individualize patient treatments during early stages.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:14 |
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| Enthalten in: |
Therapeutic advances in musculoskeletal disease - 14(2022) vom: 19., Seite 1759720X221124028 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Novella-Navarro, Marta [VerfasserIn] |
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| Links: |
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| Themen: |
B/tsDMARDs |
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| Anmerkungen: |
Date Revised 14.10.2022 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1177/1759720X221124028 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
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| 245 | 1 | 0 | |a Predictive model to identify multiple failure to biological therapy in patients with rheumatoid arthritis |
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| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © The Author(s), 2022. | ||
| 520 | |a Background: Despite advances in the treatment of rheumatoid arthritis (RA) and the wide range of therapies available, there is a percentage of patients whose treatment presents a challenge for clinicians due to lack of response to multiple biologic and target-specific disease-modifying antirheumatic drugs (b/tsDMARDs) | ||
| 520 | |a Objective: To develop and validate an algorithm to predict multiple failure to biological therapy in patients with RA | ||
| 520 | |a Design: Observational retrospective study involving subjects from a cohort of patients with RA receiving b/tsDMARDs | ||
| 520 | |a Methods: Based on the number of prior failures to b/tsDMARDs, patients were classified as either multi-refractory (MR) or non-refractory (NR). Patient characteristics were considered in the statistical analysis to design the predictive model, selecting those variables with a predictive capability. A decision algorithm known as 'classification and regression tree' (CART) was developed to create a prediction model of multi-drug resistance. Performance of the prediction algorithm was evaluated in an external independent cohort using area under the curve (AUC) | ||
| 520 | |a Results: A total of 136 patients were included: 51 MR and 85 NR. The CART model was able to predict multiple failures to b/tsDMARDs using disease activity score-28 (DAS-28) values at 6 months after the start time of the initial b/tsDMARD, as well as DAS-28 improvement in the first 6 months and baseline DAS-28. The CART model showed a capability to correctly classify 94.1% NR and 87.5% MR patients with a sensitivity = 0.88, a specificity = 0.94, and an AUC = 0.89 (95% CI: 0.74-1.00). In the external validation cohort, 35 MR and 47 NR patients were included. The AUC value for the CART model in this cohort was 0.82 (95% CI: 0.73-0.9) | ||
| 520 | |a Conclusion: Our model correctly classified NR and MR patients based on simple measurements available in routine clinical practice, which provides the possibility to characterize and individualize patient treatments during early stages | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a b/tsDMARDs | |
| 650 | 4 | |a difficult-to-treat rheumatoid arthritis | |
| 650 | 4 | |a refractory rheumatoid arthritis | |
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| 700 | 1 | |a Ruiz-Esquide, Virginia |e verfasserin |4 aut | |
| 700 | 1 | |a Tornero, Carolina |e verfasserin |4 aut | |
| 700 | 1 | |a Díaz-Almirón, Mariana |e verfasserin |4 aut | |
| 700 | 1 | |a Chacur, Chafik Alejandro |e verfasserin |4 aut | |
| 700 | 1 | |a Peiteado, Diana |e verfasserin |4 aut | |
| 700 | 1 | |a Villalba, Alejandro |e verfasserin |4 aut | |
| 700 | 1 | |a Sanmartí, Raimon |e verfasserin |4 aut | |
| 700 | 1 | |a Plasencia-Rodríguez, Chamaida |e verfasserin |4 aut | |
| 700 | 1 | |a Balsa, Alejandro |e verfasserin |4 aut | |
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