Development and validation of identification algorithms for five autoimmune diseases using electronic health records: a retrospective cohort study in China
ObjectiveThis 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...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1541203/full |
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| author | Junting Yang Junting Yang Yunxiao Wu Yunxiao Wu Jinxin Guo Jinxin Guo Xiaoxuan Wang Xiaoxuan Wang Xin Gao Xin Gao Xin Chen Xin Chen Mengdi Zhang Mengdi Zhang Jin Yang Zuojing Liu Yan Liu Zhike Liu Zhike Liu Siyan Zhan Siyan Zhan Siyan Zhan Siyan Zhan |
| author_facet | Junting Yang Junting Yang Yunxiao Wu Yunxiao Wu Jinxin Guo Jinxin Guo Xiaoxuan Wang Xiaoxuan Wang Xin Gao Xin Gao Xin Chen Xin Chen Mengdi Zhang Mengdi Zhang Jin Yang Zuojing Liu Yan Liu Zhike Liu Zhike Liu Siyan Zhan Siyan Zhan Siyan Zhan Siyan Zhan |
| author_sort | Junting Yang |
| collection | DOAJ |
| description | ObjectiveThis 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.MethodsDiagnostic 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.ResultsAmong 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%).ConclusionsThis 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. |
| format | Article |
| id | doaj-art-d4cbdbe16606455c9aa79fb4223ee573 |
| institution | DOAJ |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj-art-d4cbdbe16606455c9aa79fb4223ee5732025-08-20T03:05:21ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-04-011610.3389/fimmu.2025.15412031541203Development and validation of identification algorithms for five autoimmune diseases using electronic health records: a retrospective cohort study in ChinaJunting Yang0Junting Yang1Yunxiao Wu2Yunxiao Wu3Jinxin Guo4Jinxin Guo5Xiaoxuan Wang6Xiaoxuan Wang7Xin Gao8Xin Gao9Xin Chen10Xin Chen11Mengdi Zhang12Mengdi Zhang13Jin Yang14Zuojing Liu15Yan Liu16Zhike Liu17Zhike Liu18Siyan Zhan19Siyan Zhan20Siyan Zhan21Siyan Zhan22Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, ChinaKey Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, ChinaKey Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, ChinaKey Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, ChinaKey Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, ChinaKey Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, ChinaKey Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, ChinaKey Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, ChinaDepartment of Endocrinology and Metabolism, Peking University Third Hospital, Beijing, ChinaDepartment of Gastroenterology, Peking University Third Hospital, Beijing, ChinaDepartment of Hematology, Peking University Third Hospital, Beijing, ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, ChinaKey Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, ChinaKey Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, ChinaResearch Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, ChinaCenter for Intelligent Public Health, Institute for Artificial Intelligence, Peking University, Beijing, ChinaObjectiveThis 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.MethodsDiagnostic 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.ResultsAmong 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%).ConclusionsThis 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.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1541203/fullHashimoto's thyroiditisinflammatory bowel disease (IBD)primary immune thrombocytopeniarheumatoid arthritis (RA)type 1 diabetes (T1D)computable phenotype |
| spellingShingle | Junting Yang Junting Yang Yunxiao Wu Yunxiao Wu Jinxin Guo Jinxin Guo Xiaoxuan Wang Xiaoxuan Wang Xin Gao Xin Gao Xin Chen Xin Chen Mengdi Zhang Mengdi Zhang Jin Yang Zuojing Liu Yan Liu Zhike Liu Zhike Liu Siyan Zhan Siyan Zhan Siyan Zhan Siyan Zhan Development and validation of identification algorithms for five autoimmune diseases using electronic health records: a retrospective cohort study in China Frontiers in Immunology Hashimoto's thyroiditis inflammatory bowel disease (IBD) primary immune thrombocytopenia rheumatoid arthritis (RA) type 1 diabetes (T1D) computable phenotype |
| title | Development and validation of identification algorithms for five autoimmune diseases using electronic health records: a retrospective cohort study in China |
| title_full | Development and validation of identification algorithms for five autoimmune diseases using electronic health records: a retrospective cohort study in China |
| title_fullStr | Development and validation of identification algorithms for five autoimmune diseases using electronic health records: a retrospective cohort study in China |
| title_full_unstemmed | Development and validation of identification algorithms for five autoimmune diseases using electronic health records: a retrospective cohort study in China |
| title_short | Development and validation of identification algorithms for five autoimmune diseases using electronic health records: a retrospective cohort study in China |
| title_sort | development and validation of identification algorithms for five autoimmune diseases using electronic health records a retrospective cohort study in china |
| topic | Hashimoto's thyroiditis inflammatory bowel disease (IBD) primary immune thrombocytopenia rheumatoid arthritis (RA) type 1 diabetes (T1D) computable phenotype |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1541203/full |
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