{ "abstract": "Background\n\nPatients 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\n\nPrimary 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\n\nWe 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\n\nA 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\n\nThis 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.", "abstract_shown": true, "affiliations": [ "Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.", "Centre 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.", "The University of Hong Kong - Shenzhen Hospital, ShenZhen, China.", "School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.", "Population Health Data Center, National Cheng Kung University, Tainan, Taiwan.", "Musketeers Foundation Institute of Data Science, The University of Hong Kong, Hong Kong SAR, China.", "Shanghai Intelligent Computing Research Center, The University of Hong Kong, Shanghai, China.", "Shenzhen Loop Area Institute, Shenzhen, China.", "Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, Adelaide University, Adelaide, SA, Australia.", "School of Pharmacy, Aston University, Birmingham, UK.", "Advanced Data Analytics for Medical Science (ADAMS) Limited, Hong Kong SAR, China.", "James L. Winkle College of Pharmacy, University of Cincinnati, Cincinnati, OH, USA.", "Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China. sxueli@hku.hk.", "Centre 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. sxueli@hku.hk.", "The University of Hong Kong - Shenzhen Hospital, ShenZhen, China. sxueli@hku.hk." ], "article_type": "research article", "arxiv": null, "author_total": 14, "authors": [ { "affs": [ 0, 1 ], "name": "Kuan Peng" }, { "affs": [ 0, 1, 2 ], "name": "Deliang Yang" }, { "affs": [ 0, 1 ], "name": "Jiaqi Wang" }, { "affs": [ 3, 4 ], "name": "Chin-Yao Shen" }, { "affs": [ 3, 4 ], "name": "Michael Chun-Yuan Cheng" }, { "affs": [ 0 ], "name": "Shirley C W Chan" }, { "affs": [ 0 ], "name": "Iris Y K Tang" }, { "affs": [ 1, 5, 6, 7, 2 ], "name": "Qingpeng Zhang" }, { "affs": [ 3, 4 ], "name": "Edward Chia-Cheng Lai" }, { "affs": [ 8 ], "name": "Nicole L Pratt" }, { "affs": [ 1, 9, 10 ], "name": "Ian Chi Kei Wong" }, { "affs": [ 0 ], "name": "Chak-Sing Lau" }, { "affs": [ 11 ], "name": "Jeff Jianfei Guo" }, { "affs": [ 12, 13, 14 ], "name": "Xue Li", "orcid": "0000-0003-4836-7808" } ], "bibtex": "https://research.draconitic.com/paper/0rjjmdmz7tmd5.bib", "cited_by": 0, "doi": "10.1186/s12911-026-03739-z", "fulltext_url": "https://europepmc.org/articles/PMC13726225", "id": "0rjjmdmz7tmd5", "issns": [ "1472-6947" ], "landing_url": "https://europepmc.org/article/MED/42968045", "language": "eng", "licence": "CC-BY-NC-ND", "licence_label": "CC BY-NC-ND", "licence_source": "article", "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/", "metadata_licence": null, "native_subjects": [ [ "heading", "Research" ], [ "openalex", "2745|Rheumatoid Arthritis Research and Therapies" ] ], "openalex": "W7172488102", "pdf_url": "https://europepmc.org/articles/PMC13726225?pdf=render", "pmcid": "PMC13726225", "pmid": "42968045", "published": "2026-08-05", "publisher": "BMC", "ris": "https://research.draconitic.com/paper/0rjjmdmz7tmd5.ris", "sources": [ { "first_seen": "2026-11-03T15:20:54Z", "native_id": "MED:42968045", "source": "europepmc", "url": "https://europepmc.org/article/MED/42968045" }, { "first_seen": "2026-11-05T12:22:37Z", "native_id": "PMC13726225", "source": "pmc", "url": "https://doi.org/10.1186/s12911-026-03739-z" } ], "subjects": [ { "field": "medicine", "origin": "openalex:2745|Rheumatoid Arthritis Research and Therapies", "subfield": "general" } ], "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.", "title_text": "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.", "type": "peer-reviewed", "updated_at": "2026-11-06T12:22:14Z", "url": "https://research.draconitic.com/paper/0rjjmdmz7tmd5", "venue": "BMC medical informatics and decision making", "versions": [] }