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.

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.

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.

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.

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.

Background: Islet transplantation holds promise for treating selected type 1 diabetes mellitus patients, yet the scarcity of human donor organs impedes widespread adoption. Porcine islets, deemed a viable alternative, recently demonstrated successful longterm survival without zoonotic risks in a clinically relevant pig-to-non-human primate islet transplantation model. This success prompted the development of a clinical trial protocol for porcine islet xenotransplantation in humans. Methods: A single-center, open-label clinical trial initiated by the sponsor will assess the safety and efficacy of porcine islet transplantation for diabetes patients at Gachon Hospital. The protocol received approval from the Gachon Hospital Institutional Review Board (IRB) and the Korean Ministry of Food and Drug Safety (MFDS) under the Investigational New Drug (IND) process. Two diabetic patients, experiencing inadequate glycemic control despite intensive insulin treatment and frequent hypoglycemic unawareness, will be enrolled. Participants and their family members will engage in deliberation before xenotransplantation during the screening period. Each patient will receive islets isolated from designated pathogen-free pigs. Immunosuppressants and systemic infection prophylaxis will follow the program schedule. The primary endpoint is to confirm the safety of porcine islets in patients, and the secondary endpoint is to assess whether porcine islets can reduce insulin dose and the frequency of hypoglycemic unawareness. Conclusion A clinical trial protocol adhering to global consensus guidelines for porcine islet xenotransplantation is presented, facilitating streamlined implementation of comparable human trials worldwide.

Background: Islet transplantation holds promise for treating selected type 1 diabetes mellitus patients, yet the scarcity of human donor organs impedes widespread adoption. Porcine islets, deemed a viable alternative, recently demonstrated successful longterm survival without zoonotic risks in a clinically relevant pig-to-non-human primate islet transplantation model. This success prompted the development of a clinical trial protocol for porcine islet xenotransplantation in humans. Methods: A single-center, open-label clinical trial initiated by the sponsor will assess the safety and efficacy of porcine islet transplantation for diabetes patients at Gachon Hospital. The protocol received approval from the Gachon Hospital Institutional Review Board (IRB) and the Korean Ministry of Food and Drug Safety (MFDS) under the Investigational New Drug (IND) process. Two diabetic patients, experiencing inadequate glycemic control despite intensive insulin treatment and frequent hypoglycemic unawareness, will be enrolled. Participants and their family members will engage in deliberation before xenotransplantation during the screening period. Each patient will receive islets isolated from designated pathogen-free pigs. Immunosuppressants and systemic infection prophylaxis will follow the program schedule. The primary endpoint is to confirm the safety of porcine islets in patients, and the secondary endpoint is to assess whether porcine islets can reduce insulin dose and the frequency of hypoglycemic unawareness. Conclusion A clinical trial protocol adhering to global consensus guidelines for porcine islet xenotransplantation is presented, facilitating streamlined implementation of comparable human trials worldwide.

Background: Islet transplantation holds promise for treating selected type 1 diabetes mellitus patients, yet the scarcity of human donor organs impedes widespread adoption. Porcine islets, deemed a viable alternative, recently demonstrated successful longterm survival without zoonotic risks in a clinically relevant pig-to-non-human primate islet transplantation model. This success prompted the development of a clinical trial protocol for porcine islet xenotransplantation in humans. Methods: A single-center, open-label clinical trial initiated by the sponsor will assess the safety and efficacy of porcine islet transplantation for diabetes patients at Gachon Hospital. The protocol received approval from the Gachon Hospital Institutional Review Board (IRB) and the Korean Ministry of Food and Drug Safety (MFDS) under the Investigational New Drug (IND) process. Two diabetic patients, experiencing inadequate glycemic control despite intensive insulin treatment and frequent hypoglycemic unawareness, will be enrolled. Participants and their family members will engage in deliberation before xenotransplantation during the screening period. Each patient will receive islets isolated from designated pathogen-free pigs. Immunosuppressants and systemic infection prophylaxis will follow the program schedule. The primary endpoint is to confirm the safety of porcine islets in patients, and the secondary endpoint is to assess whether porcine islets can reduce insulin dose and the frequency of hypoglycemic unawareness. Conclusion A clinical trial protocol adhering to global consensus guidelines for porcine islet xenotransplantation is presented, facilitating streamlined implementation of comparable human trials worldwide.

Background: Islet transplantation holds promise for treating selected type 1 diabetes mellitus patients, yet the scarcity of human donor organs impedes widespread adoption. Porcine islets, deemed a viable alternative, recently demonstrated successful longterm survival without zoonotic risks in a clinically relevant pig-to-non-human primate islet transplantation model. This success prompted the development of a clinical trial protocol for porcine islet xenotransplantation in humans. Methods: A single-center, open-label clinical trial initiated by the sponsor will assess the safety and efficacy of porcine islet transplantation for diabetes patients at Gachon Hospital. The protocol received approval from the Gachon Hospital Institutional Review Board (IRB) and the Korean Ministry of Food and Drug Safety (MFDS) under the Investigational New Drug (IND) process. Two diabetic patients, experiencing inadequate glycemic control despite intensive insulin treatment and frequent hypoglycemic unawareness, will be enrolled. Participants and their family members will engage in deliberation before xenotransplantation during the screening period. Each patient will receive islets isolated from designated pathogen-free pigs. Immunosuppressants and systemic infection prophylaxis will follow the program schedule. The primary endpoint is to confirm the safety of porcine islets in patients, and the secondary endpoint is to assess whether porcine islets can reduce insulin dose and the frequency of hypoglycemic unawareness. Conclusion A clinical trial protocol adhering to global consensus guidelines for porcine islet xenotransplantation is presented, facilitating streamlined implementation of comparable human trials worldwide.

Purpose: There have been few reports on comparison between sunitinib and pazopanib as first-line targeted therapy in Korean metastatic clear cell renal cell carcinoma (ccRCC). We sought to analyze the treatment trends of metastatic ccRCC by comparing the effects and adverse events of sunitinib and pazopanib. Materials and Methods: Data of 357 metastatic RCC patients who received the sunitinib or pazopanib as the first-line targeted therapy from the Daegyeong Oncology Study Group database was obtained and analyzed. Among these patients, patients who only clear cell type was confirmed after needle biopsy or nephrectomy were included, and patients who underwent target therapy for less than 3 months were excluded. Results: Of 251 patients who met the inclusion criteria, sunitinib and pazopanib group were identified in 156 (62%) and 95 patients (38%), respectively. Pazopanib group was older (66 years vs. 61 years, p=0.001) and more symptomatic (65% vs. 52%, p=0.037) and had more patients with Karnofsky performance status <80 (20% vs. 11%, p=0.048) and fewer number of organ metastases (p=0.004) compared to sunitinib group. There was no significant difference in disease control rate (88.5% vs. 87.3%, p=0.744), the median progression-free survival (19 months vs. 15 months, p=0.444) and overall survival (25 months vs. 19 months, p=0.721) between sunitinib and pazopanib. The most common grade 3/4 adverse events with sunitinib and pazopanib were anemia (5%) and hand-foot syndrome (3%), respectively. There was no significant difference between sunitinib and pazopanib in number of patients who experienced grade 3/4 adverse events (15% vs. 11%, p=0.275). However, there were more patients who discontinued treatment due to only adverse events in sunitinib group compared to pazopanib group (12% vs. 3%, p=0.020). Conclusions In Korean metastatic ccRCC, pazopanib tended to be used in patients with poorer health status compared to sunitinib. Sunitinib and pazopanib had no significant difference in treatment effect and survival, but pazopanib had more tolerable adverse events.