Yonsei Med J.
2025 Mar;66(3):187-194. 10.3349/ymj.2023.0628.
Digital Phenotyping of Rare Endocrine Diseases Across International Data Networks and the Effect of Granularity of Original Vocabulary
- Affiliations
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- 1Department of Internal Medicine, Endocrine Research Institute, Yonsei University College of Medicine, Seoul, Korea
- 2Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
- 3Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, Korea
- 4Real-World Solutions, IQVIA, Durham, USA
- 5Department of Internal Medicine, Korea University College of Medicine, Seoul, Korea
- 6Department of Internal Medicine, Korea University Anam Hospital, Seoul, Korea
- 7Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- 8Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Korea
- KMID: 2565536
- DOI: http://doi.org/10.3349/ymj.2023.0628
Abstract
- Purpose
Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model.
Materials and Methods
Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea.
Results
The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK).
Conclusion
Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.
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