Lee Seunghyun, Hong Namki, Kim Gyu Seop, Li Jing, Lin Xiaoyu, Seager Sarah, Shin Sungjae, Kim Kyoung Jin, Bae Jae Hyun, You Seng Chan, Rhee Yumie, Kim Sin Gon
Department of Internal Medicine, Endocrine Research Institute, Yonsei University College of Medicine, Seoul, Korea.
Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea.
Yonsei Med J. 2025 Mar;66(3):187-194. doi: 10.3349/ymj.2023.0628.
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.
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.
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).
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.
罕见病的发病率低于十万分之五十，需要终身管理。然而，此类疾病的基本流行病学数据匮乏，跨时间和地区的连续监测系统仍有待建立。需要标准化的数字表型来利用国际数据网络开展罕见内分泌疾病研究。我们使用观察性医疗结局合作组织通用数据模型开发了罕见内分泌疾病的数字表型。
在使用不同词汇表的三个数据库中验证了三种罕见内分泌疾病（甲状腺髓样癌、甲状旁腺功能减退症、嗜铬细胞瘤/副神经节瘤）的数字表型：韩国Severance医院的电子健康记录；IQVIA公司的英国全科医生数据库；以及IQVIA公司的美国综合医院数据库。我们根据英国和美国的《国际疾病分类》（ICD）-10或韩国的医学系统命名法临床术语（SNOMED CT）评估了不同数字表型方法的性能。
在韩国，对于所有三种疾病，基于SNOMED CT的表型分析的数字表型阳性预测值高于基于ICD-10的表型分析（例如，嗜铬细胞瘤/副神经节瘤：ICD-10为58%-62%；SNOMED CT为89%）。通过数字表型分析估计的发病率如下：甲状腺髓样癌，0.34-2.07（韩国），0.13-0.30（美国）；甲状旁腺功能减退症，0.40-1.20（韩国），0.59-1.01（美国），0.00-1.78（英国）；嗜铬细胞瘤/副神经节瘤，0.95-1.67（韩国），0.35-0.77（美国），0.00-0.49（英国）。
我们的研究结果证明了开发罕见内分泌疾病数字表型的可行性，并强调了在日常临床实践中实施SNOMED CT以提供研究粒度的重要性。
