Digital Phenotyping of Rare Endocrine Diseases Across International Data Networks and the Effect of Granularity of Original Vocabulary

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.

Keywords: Common data model; digital phenotyping; hypoparathyroidism; medullary thyroid cancer; pheochromocytoma; rare diseases.

Fig. 1. Studyflow. In ICD-10-originated OMOP concepts-level 1, we used the operational definitions reported in previous Korean studies based on ICD-10. In ICD-10-originated OMOP concepts-level 2, we aimed to improve sensitivity or positive predictive value through expert consensus. The changes made from ICD-10-originated OMOP concepts-level 1 to ICD-10-originated OMOP concepts-level 2 were as follows: 1) for medullary thyroid cancer, we removed the exclusion criterion of “thyroglobulin test”; 2) for nonsurgical hypoparathyroidism, we added “patients taking levothyroxine” to the inclusion criteria; 3) for pheochromocytoma/paraganglioma, we revised the inclusion criterion from “measuring catecholamine at least twice during the entire period” to “measuring catecholamine at least once before and once after surgery”. In SNOMED CT-originated OMOP concepts, the vocabulary was changed from ICD-10 to SNOMED CT. The performance of these three digital phenotyping was tested using the RED–CDM database in South Korea, and subsequently applied to the IQVIA database (United Kingdom and United States). ICD-10, International Classification of Diseases-10; OMOP, observational medical outcome partnership; SNOMED CT, systematized nomenclature of medicine clinical terms; RED–CDM, rare endocrine disease–common data model.