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Development and External Validation of a Multivariable Predictive Model for Progression to Difficult-to-Treat

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Area of Science:

  • Rheumatology
  • Clinical Prediction Modeling
  • Biostatistics

Background:

  • Rheumatoid arthritis (RA) affects millions globally, with a subset experiencing treatment resistance.
  • Difficult-to-treat RA (D2T-RA) impacts approximately 20% of patients, necessitating better risk stratification tools.
  • Current clinical practice lacks validated tools to identify RA patients at high risk for D2T-RA.

Purpose of the Study:

  • To develop and validate a multivariable prediction model for identifying patients at risk of progressing to D2T-RA.
  • To define D2T-RA using the European Alliance of Associations for Rheumatology (EULAR) 2021 criteria.

Main Methods:

  • Utilized data from two independent observational RA cohorts for model development and external validation.
  • Employed random survival forests on participants initiating their first biologic and/or targeted synthetic disease-modifying antirheumatic drug (b/tsDMARD).
  • Validated the model in a separate cohort of patients initiating or switching b/tsDMARD therapies.

Main Results:

  • The derivation cohort included 700 patients (16% D2T-RA), and the validation cohort included 2,070 patients (28% D2T-RA).
  • The model achieved moderate predictive ability with C-index values of 0.643 (derivation) and 0.620 (validation).
  • Key predictors for D2T-RA included worsened functional status, pain, fatigue, and global disease activity.

Conclusions:

  • The developed model shows moderate discrimination and calibration for predicting D2T-RA.
  • Accurately predicting D2T-RA remains challenging, indicating a need for improved predictive models.
  • Future research should explore incorporating additional biomarkers to enhance prediction accuracy.