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Background <p>Patients with rheumatoid arthritis (RA) initiating biologic or targeted synthetic disease-modifying antirheumatic drugs (b/ts DMARDs) face elevated risk of serious infections, necessitating tools for individualized risk stratification.</p> Objectives <p>Primary objective was to develop and validate a clinically interpretable machine learning (ML) model to predict 1-year risk of serious infection after b/ts DMARD initiation; secondary objectives were to estimate infection incidence and identify key predictors associated with risk.</p> Methods <p>We performed a retrospective cohort study using territory-wide EHR from Hong Kong’s Clinical Data Analysis and Reporting System (CDARS) for model development and internal validation, with external validation in the U.S. All of Us database. The outcome was first serious infection requiring hospitalization within 1 year. Candidate predictors included demographics, comorbidities, prior infections and medications, laboratory markers. Multiple ML algorithms were trained; model selection was based on AUROC, and interpretability was assessed using SHAP.</p> Results <p>A total of 3,159 patients from CDARS (8.8% with serious infections) and 1,845 from All of Us (2.8% with serious infections) were included. The model demonstrated the highest AUROC in internal validation (0.840, 95% CI: 0.793–0.888) and maintained robust performance in external validation (AUROC: 0.729, 95% CI: 0.665–0.793). Key predictors included prior infections, diabetes, b/ts DMARD type, and inflammatory markers. Rituximab was linked to the highest infection risk, while tofacitinib and upadacitinib had the lowest.</p> Conclusion <p>This study developed and validated an ML model using routine clinical data to predict serious infection risk in RA patients, supporting personalised treatment and proactive infection management.</p>

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Predicting infection risk in rheumatoid arthritis patients receiving biological or targeted synthetic disease-modifying anti-rheumatic drugs: an application of machine learning and healthcare big data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月5日

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  • Kuan Peng,
  • Deliang Yang,
  • Jiaqi Wang,
  • Chin-Yao Shen,
  • Michael Chun-Yuan Cheng,
  • Shirley C. W. Chan,
  • Iris Y. K. Tang,
  • Qingpeng Zhang,
  • Edward Chia‑Cheng Lai,
  • Nicole L. Pratt,
  • Ian Chi Kei Wong,
  • Chak-sing Lau,
  • Jeff Jianfei Guo,
  • Xue Li

摘要

Background

Patients with rheumatoid arthritis (RA) initiating biologic or targeted synthetic disease-modifying antirheumatic drugs (b/ts DMARDs) face elevated risk of serious infections, necessitating tools for individualized risk stratification.

Objectives

Primary objective was to develop and validate a clinically interpretable machine learning (ML) model to predict 1-year risk of serious infection after b/ts DMARD initiation; secondary objectives were to estimate infection incidence and identify key predictors associated with risk.

Methods

We performed a retrospective cohort study using territory-wide EHR from Hong Kong’s Clinical Data Analysis and Reporting System (CDARS) for model development and internal validation, with external validation in the U.S. All of Us database. The outcome was first serious infection requiring hospitalization within 1 year. Candidate predictors included demographics, comorbidities, prior infections and medications, laboratory markers. Multiple ML algorithms were trained; model selection was based on AUROC, and interpretability was assessed using SHAP.

Results

A total of 3,159 patients from CDARS (8.8% with serious infections) and 1,845 from All of Us (2.8% with serious infections) were included. The model demonstrated the highest AUROC in internal validation (0.840, 95% CI: 0.793–0.888) and maintained robust performance in external validation (AUROC: 0.729, 95% CI: 0.665–0.793). Key predictors included prior infections, diabetes, b/ts DMARD type, and inflammatory markers. Rituximab was linked to the highest infection risk, while tofacitinib and upadacitinib had the lowest.

Conclusion

This study developed and validated an ML model using routine clinical data to predict serious infection risk in RA patients, supporting personalised treatment and proactive infection management.