Predicting the progression of difficult-to-treat rheumatoid arthritis by a machine learning scoring system, from the FIRST registry.

ABSTRACT:

Objectives

This study aimed to develop and validate a prediction model for the future progression of difficult-to-treat rheumatoid arthritis (D2T RA) and support the precise use of biologic and targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs).

Methods

Data were analysed from 1221 patients with rheumatoid arthritis in the FIRST registry, a multicentre collaborative database, who initiated their first b/tsDMARD. 25 prediction models for D2T RA were generated using 43 baseline characteristics prior to initiating the first b/tsDMARD. Model performances were tested. A scoring system based on the selected model was designed and internally validated for use in routine clinical practice.

Results

Among the 1221 patients, 193 (15.8%) progressed to D2T RA af

SUBMITTER: Ueno M 

PROVIDER: S-EPMC12815114 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature