| Title: |
Development and validation of a model to predict the disease activity score:towards a remote treat-to-target approach for rheumatoid arthritis |
| Authors: |
Looijen,Agnes E M; Welsing, Paco M J; Bergstra,Sytske Anne; van der Helm-van Mil,Annette H M; de Jong,Pascal H P; MS Reumatologie/Immunologie/Infectie; Infection & Immunity; Regenerative Medicine and Stem Cells; JC onderzoeksprogramma Methodology |
| Publication Year: |
2026 |
| Subject Terms: |
Immunology and Allergy; Rheumatology; Immunology; General Biochemistry,Genetics and Molecular Biology; Journal Article |
| Description: |
Objectives: Remote monitoring of disease activity in patients with rheumatoid arthritis (RA) offers a promising solution to increasing healthcare demands. This study aimed to develop and validate a model using selected clinical and patient-reported outcome measure (PROM) items that efficiently and accurately reflect the original disease activity score (DAS). Methods: Data from 5802 visits of 612 patients with RA from the treatment in the Rotterdam Early Arthritis Cohort and TApering strategies in RA trials were randomly split (1:1) into derivation and internal validation sets. An external validation was performed using 4404 visits from 1554 patients with RA from the Early Arthritis Cohort. A model was developed using Least Absolute Shrinkage and Selection Operator (LASSO) regression that incorporated age, sex, disease duration, autoantibody status, and individual PROM items, including visual analogue scale (VAS) general health, all Health Assessment Questionnaire-Disability Index (HAQ-DI) items, VAS pain, and VAS fatigue, to predict the DAS. The model’s ability to detect active disease (DAS >2.4) and remission (DAS |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| ISSN: |
0003-4967 |
| Relation: |
https://dspace.library.uu.nl/handle/1874/483784 |
| Availability: |
https://dspace.library.uu.nl/handle/1874/483784 |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.D177D0D9 |
| Database: |
BASE |