(B-221) Development and Validation of Identification Algorithms for Five Autoimmune Diseases Using Electronic Health Records: A Retrospective Cohort Study in China

Monday, August 25, 2025

11:30 AM - 1:00 PM ET

Presenting Author(s)

YW

Yunxiao Wu

Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China, China (People's Republic)

Co-Author(s)

JY

Junting Yang

Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China, China (People's Republic)
XW

Xiaoxuan Wang

Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China, China (People's Republic)
XG

Xin Gao

Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China, China (People's Republic)
XC

Xin Chen

Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China, China (People's Republic)
MZ

Mengdi Zhang

Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China, China (People's Republic)
ZL

Zhike Liu

Key Laboratory of Epidemiology of Major Diseases, Ministry of Education/Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China, China (People's Republic)
SZ

Siyan Zhan, PhD, MD

Dean
School of Public Health, Peking University, China (People's Republic)
FS

Feng Sun

Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China, China (People's Republic)

Background: Autoimmune diseases affect 3–10% of the global population, necessitating high-quality real-world data. This study evaluates the performance of phenotype algorithms in the Yinzhou database in China, investigates diagnostic delays, and supports vaccine safety monitoring and disease research.

Objectives: 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), integrating 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 integration 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 incorporating 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.