Literature DB >> 32475078

Latent Class Trajectory Modeling of 2-Component Disease Activity Score in 28 Joints Identifies Multiple Rheumatoid Arthritis Phenotypes of Response to Biologic Disease-Modifying Antirheumatic Drugs.

Arianna Dagliati1, Darren Plant2, Nisha Nair3, Meghna Jani4, Beatrice Amico5, Niels Peek6, Ann W Morgan7, John Isaacs8, Anthony G Wilson9, Kimme L Hyrich10, Nophar Geifman1, Anne Barton2.   

Abstract

OBJECTIVE: To determine whether using a reweighted disease activity score that better reflects joint synovitis, i.e., the 2-component Disease Activity Score in 28 joints (DAS28) (based on swollen joint count and C-reactive protein level), produces more clinically relevant treatment outcome trajectories compared to the standard 4-component DAS28.
METHODS: Latent class mixed modeling of response to biologic treatment was applied to 2,991 rheumatoid arthritis (RA) patients in whom treatment with a biologic disease-modifying antirheumatic drug was being initiated within the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate cohort, using both 4-component and 2-component DAS28 scores as outcome measures. Patient groups with similar trajectories were compared in terms of pretreatment baseline characteristics (including disability and comorbidities) and follow-up characteristics (including antidrug antibody events, adherence to treatments, and blood drug levels). We compared the trajectories obtained using the 4- and 2-component scores to determine which characteristics were better captured by each.
RESULTS: Using the 4-component DAS28, we identified 3 trajectory groups, which is consistent with previous findings. We showed that the 4-component DAS28 captures information relating to depression. Using the 2-component DAS28, 7 trajectory groups were identified; among them, distinct groups of nonresponders had a higher incidence of respiratory comorbidities and a higher proportion of antidrug antibody events. We also identified a group of patients for whom the 2-component DAS28 scores remained relatively low; this group included a high percentage of patients who were nonadherent to treatment. This highlights the utility of both the 4- and 2-component DAS28 for monitoring different components of disease activity.
CONCLUSION: Here we show that the 2-component modified DAS28 defines important biologic and clinical phenotypes associated with treatment outcome in RA and characterizes important underlying response mechanisms to biologic drugs.
© 2020 The Authors. Arthritis & Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.

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Year:  2020        PMID: 32475078     DOI: 10.1002/art.41379

Source DB:  PubMed          Journal:  Arthritis Rheumatol        ISSN: 2326-5191            Impact factor:   10.995


  5 in total

1.  Factors associated with drug survival on first biologic therapy in patients with rheumatoid arthritis: a population-based cohort study.

Authors:  Mohammad E Naffaa; Fadi Hassan; Avivit Golan-Cohen; Eugene Merzon; Ilan Green; Amir Saab; Ziv Paz
Journal:  Rheumatol Int       Date:  2021-09-16       Impact factor: 2.631

2.  Longitudinal Trajectories of Hair Cortisol: Hypothalamic-Pituitary-Adrenal Axis Dysfunction in Early Childhood.

Authors:  Cynthia R Rovnaghi; Joseph Rigdon; Jean-Michel Roué; Monica O Ruiz; Victor G Carrion; Kanwaljeet J S Anand
Journal:  Front Pediatr       Date:  2021-10-11       Impact factor: 3.418

3.  Disease activity trajectories for early and established rheumatoid arthritis: Real-world data from a rheumatoid arthritis cohort.

Authors:  Mohammad Movahedi; Angela Cesta; Xiuying Li; Claire Bombardier
Journal:  PLoS One       Date:  2022-09-07       Impact factor: 3.752

4.  Distinct patterns of disease activity over time in patients with active SLE revealed using latent class trajectory models.

Authors:  John A Reynolds; Jennifer Prattley; Nophar Geifman; Mark Lunt; Caroline Gordon; Ian N Bruce
Journal:  Arthritis Res Ther       Date:  2021-07-29       Impact factor: 5.156

5.  Nothing about us without us: involving patient collaborators for machine learning applications in rheumatology.

Authors:  Stephanie J W Shoop-Worrall; Katherine Cresswell; Imogen Bolger; Beth Dillon; Kimme L Hyrich; Nophar Geifman
Journal:  Ann Rheum Dis       Date:  2021-07-05       Impact factor: 19.103

  5 in total

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