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.
15 affiliations
- 1Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- 2Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- 3The University of Hong Kong - Shenzhen Hospital, ShenZhen, China.
- 4School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
- 5Population Health Data Center, National Cheng Kung University, Tainan, Taiwan.
- 6Musketeers Foundation Institute of Data Science, The University of Hong Kong, Hong Kong SAR, China.
- 7Shanghai Intelligent Computing Research Center, The University of Hong Kong, Shanghai, China.
- 8Shenzhen Loop Area Institute, Shenzhen, China.
- 9Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, Adelaide University, Adelaide, SA, Australia.
- 10School of Pharmacy, Aston University, Birmingham, UK.
- 11Advanced Data Analytics for Medical Science (ADAMS) Limited, Hong Kong SAR, China.
- 12James L. Winkle College of Pharmacy, University of Cincinnati, Cincinnati, OH, USA.
- 13Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China. [email protected].
- 14Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China. [email protected].
- 15The University of Hong Kong - Shenzhen Hospital, ShenZhen, China. [email protected].
Abstract
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.
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BibTeX
@article{peng2026predicting,
title = {{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.}},
author = {Kuan Peng and Deliang Yang and Jiaqi Wang and Chin-Yao Shen and Michael Chun-Yuan Cheng and Shirley C W Chan and Iris Y K Tang and Qingpeng Zhang and Edward Chia-Cheng Lai and Nicole L Pratt and Ian Chi Kei Wong and Chak-Sing Lau and Jeff Jianfei Guo and Xue Li},
journal = {BMC medical informatics and decision making},
year = {2026},
month = aug,
doi = {10.1186/s12911-026-03739-z},
url = {https://europepmc.org/article/MED/42968045},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - 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.
AU - Kuan Peng
AU - Deliang Yang
AU - Jiaqi Wang
AU - Chin-Yao Shen
AU - Michael Chun-Yuan Cheng
AU - Shirley C W Chan
AU - Iris Y K Tang
AU - Qingpeng Zhang
AU - Edward Chia-Cheng Lai
AU - Nicole L Pratt
AU - Ian Chi Kei Wong
AU - Chak-Sing Lau
AU - Jeff Jianfei Guo
AU - Xue Li
PY - 2026
DA - 2026/08/05
JO - BMC medical informatics and decision making
DO - 10.1186/s12911-026-03739-z
UR - https://europepmc.org/article/MED/42968045
AB - 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.
SN - 1472-6947
ER -