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ORIGINAL RESEARCH article
Front. Immunol.
Sec. Systems Immunology
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1541203
This article is part of the Research Topic Advancements in Diagnostic Technologies for Early Detection of Autoimmune Diseases View all 4 articles
Development and Validation of Identification Algorithms for Five Autoimmune Diseases Using Electronic Health Records: A Retrospective Cohort Study in China
Provisionally accepted
Junting Yang 1
Yunxiao Wu 1 Jinxin Guo 1 Xiaoxuan Wang 1 Xin Gao 1 Xin Chen 1 Mengdi Zhang 1
Jin Yang 2
Zuojing Liu 3 Yan Liu 4
Zhike Liu 1*
Siyan Zhan 1*- 1 School of Public Health, Health Science Centre, Peking University, Beijing, Beijing Municipality, China
- 2 Department of Endocrinology and Metabolism, Peking University Third Hospital, Beijing, China
- 3 Department of Gastroenterology, Peking University Third Hospital, Beijing, China
- 4 Department of Hematology, Peking University Third Hospital, Beijing, China
Objective: This study aims to assess the identification algorithms for five autoimmune diseases -Hashimoto's thyroiditis, inflammatory bowel disease (IBD), primary immune thrombocytopenia (ITP), rheumatoid arthritis (RA) , and type 1 diabetes (T1D)-using the Yinzhou Regional Health Information Platform (YRHIP) in China.Methods: Diagnostic data was extracted from YRHIP's population registry (2010-2021), combining ICD-10 codes and Chinese medical terminology from outpatient, inpatient, and discharge records. Algorithms were validated through chart reviews, adhering to global clinical guidelines. Cases were adjudicated using electronic case report forms. We evaluated algorithm performance based on sensitivity and positive predictive value (PPV), with a 70% PPV threshold for optimization.Results: Among all reviewed cases, we identified 136 cases for Hashimoto's thyroiditis, 65 for IBD, 76 for ITP, 130 for RA, and 43 for T1D. Algorithm performance varied across diseases: the final algorithm for Hashimoto's thyroiditis achieved optimal accuracy (sensitivity 97.44%, PPV 98.28%), followed by RA (sensitivity 100.00%, PPV 76.92%). Algorithms for IBD and ITP required synthesis of multiple data sources to achieve acceptable performance (IBD: sensitivity 79.66%, PPV 70.15%; ITP: sensitivity 62.50%, PPV 70.00%). For T1D, the final algorithm utilizing both admission and outpatient records yielded satisfactory results (sensitivity 84.09%, PPV 74.00%).Conclusions: This study presents the first validated algorithms for identifying autoimmune diseases using EHR data in China, demonstrating satisfactory performance (PPV >70%) across all diseases. Our findings demonstrate that a combination of data sources is crucial for accurate case identification in complex autoimmune conditions, providing an important methodological foundation for future real-world studies in Chinese populations.
Keywords: Case validation, Autoimmune Diseases, computable phenotype, cohort, Algorithms
Received: 07 Dec 2024; Accepted: 25 Mar 2025.
Copyright: © 2025 Yang, Wu, Guo, Wang, Gao, Chen, Zhang, Yang, Liu, Liu, Liu and Zhan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhike Liu, School of Public Health, Health Science Centre, Peking University, Beijing, 100191, Beijing Municipality, China
Siyan Zhan, School of Public Health, Health Science Centre, Peking University, Beijing, 100191, Beijing Municipality, China
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