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Predicting Infection Risk in Rheumatoid Arthritis Patients Treated with Biological and Targeted Synthetic

Kuan Peng1,2, Deliang Yang1,2, Jiaqi Wang2

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Patients with rheumatoid arthritis (RA) on targeted therapies face higher infection risks. A new machine learning model predicts this one-year risk, aiding personalized treatment and prevention strategies for better patient outcomes.

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biological and targeted synthetic disease-modifying anti-rheumatic drugsinfection riskmachine learningrheumatoid arthritis

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Area of Science:

  • Rheumatology
  • Artificial Intelligence
  • Epidemiology

Background:

  • Patients with rheumatoid arthritis (RA) undergoing treatment with biologic or targeted synthetic disease-modifying antirheumatic drugs (b/ts DMARDs) have an elevated risk of serious infections.
  • Identifying patients at high risk is crucial for implementing timely preventive measures and optimizing treatment strategies.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting the one-year risk of serious infections in RA patients treated with b/ts DMARDs.
  • To identify key predictors of serious infection risk in this patient population.

Main Methods:

  • A machine learning model was developed and validated using retrospective data from two large cohorts: Hong Kong's Corporate Data Analysis and Reporting System (CDARS) and the U.S. All of Us research program.
  • The model utilized demographic, clinical, and laboratory data, including prior infection history, comorbidities (e.g., diabetes), specific b/ts DMARD used, and inflammatory markers.

Main Results:

  • The model demonstrated good predictive performance, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.840 in the CDARS cohort (n=3,159) and 0.729 in the All of Us cohort (n=1,845).
  • Significant predictors of infection risk included a history of prior infections, presence of diabetes, type of b/ts DMARD used (with Rituximab identified as highest risk), and elevated inflammatory markers.
  • The model's findings were consistent across both independent datasets, indicating generalizability.

Conclusions:

  • The developed machine learning model effectively predicts one-year serious infection risk in RA patients on b/ts DMARDs.
  • This predictive tool can support personalized treatment decisions and proactive infection prevention strategies, potentially improving patient safety and outcomes.
  • Identifying high-risk patients, such as those on Rituximab or with specific comorbidities, allows for targeted interventions.