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: Primary cutaneous CD30+ lymphoproliferative disorders (pcCD30-LPDs) are a diseases with various clinical and prognostic characteristics. Objective: Increasing our knowledge of the clinical characteristics of pcCD30-LPDs and identifying potential prognostic variables in an Asian population. Methods: Clinicopathological features and survival data of pcCD30-LPD cases obtained from 22 hospitals in South Korea were examined. Results: A total of 413 cases of pcCD30-LPDs (lymphomatoid papulosis [LYP], n=237; primary cutaneous anaplastic large cell lymphoma [C-ALCL], n=176) were included. Ninety percent of LYP patients and roughly 50% of C-ALCL patients presented with multiple skin lesions. Both LYP and C-ALCL affected the lower limbs most frequently. Multiplicity and advanced T stage of LYP lesions were associated with a chronic course longer than 6 months. Clinical morphology with patch lesions and elevated serum lactate dehydrogenase were significantly associated with LPDs during follow-up in LYP patients. Extracutaneous involvement of C-ALCL occurred in 13.2% of patients. Lesions larger than 5 cm and increased serum lactate dehydrogenase were associated with a poor prognosis in C-ALCL. The survival of patients with C-ALCL was unaffected by the anatomical locations of skin lesions or other pathological factors. Conclusion The multiplicity or size of skin lesions was associated with a chronic course of LYP and survival among patients with C-ALCL.

Incidence of depression increases in patients with end-stage kidney disease (ESKD). We evaluated the association between depression and mortality among older patients with ESKD, which has not been studied previously. Methods: This nationwide prospective cohort study included 487 patients with ESKD aged >65 years, who were categorized into minimal, mild-to-moderate, and severe depression groups based on their Beck Depression Inventory-II (BDI-II) scores. Predisposing factors for high BDI-II scores and the association between the scores and survival were analyzed. Results: The severe depression group showed a higher modified Charlson comorbidity index value and lower serum albumin, phosphate, and uric acid levels than the other depression groups. The Kaplan-Meier curve revealed a significantly lower survival in the severe depression group than in the minimal and mild-to-moderate depression groups (p = 0.011). Multivariate Cox regression analysis confirmed that severe depression was an independent risk factor for mortality in the study cohort (hazard ratio, 1.39; 95% confidence interval, 1.01–1.91; p = 0.041). Additionally, BDI-II scores were associated with modified Charlson comorbidity index (p = 0.009) and serum albumin level (p = 0.004) in multivariate linear regression. Among the three depressive symptoms, higher somatic symptom scores were associated with increased mortality. Conclusion: Severe depression among older patients with ESKD increases mortality compared with minimal or mild-to-moderate depression, and patients with concomitant somatic symptoms require careful management of their comorbidities and nutritional status.

Incidence of depression increases in patients with end-stage kidney disease (ESKD). We evaluated the association between depression and mortality among older patients with ESKD, which has not been studied previously. Methods: This nationwide prospective cohort study included 487 patients with ESKD aged >65 years, who were categorized into minimal, mild-to-moderate, and severe depression groups based on their Beck Depression Inventory-II (BDI-II) scores. Predisposing factors for high BDI-II scores and the association between the scores and survival were analyzed. Results: The severe depression group showed a higher modified Charlson comorbidity index value and lower serum albumin, phosphate, and uric acid levels than the other depression groups. The Kaplan-Meier curve revealed a significantly lower survival in the severe depression group than in the minimal and mild-to-moderate depression groups (p = 0.011). Multivariate Cox regression analysis confirmed that severe depression was an independent risk factor for mortality in the study cohort (hazard ratio, 1.39; 95% confidence interval, 1.01–1.91; p = 0.041). Additionally, BDI-II scores were associated with modified Charlson comorbidity index (p = 0.009) and serum albumin level (p = 0.004) in multivariate linear regression. Among the three depressive symptoms, higher somatic symptom scores were associated with increased mortality. Conclusion: Severe depression among older patients with ESKD increases mortality compared with minimal or mild-to-moderate depression, and patients with concomitant somatic symptoms require careful management of their comorbidities and nutritional status.

Incidence of depression increases in patients with end-stage kidney disease (ESKD). We evaluated the association between depression and mortality among older patients with ESKD, which has not been studied previously. Methods: This nationwide prospective cohort study included 487 patients with ESKD aged >65 years, who were categorized into minimal, mild-to-moderate, and severe depression groups based on their Beck Depression Inventory-II (BDI-II) scores. Predisposing factors for high BDI-II scores and the association between the scores and survival were analyzed. Results: The severe depression group showed a higher modified Charlson comorbidity index value and lower serum albumin, phosphate, and uric acid levels than the other depression groups. The Kaplan-Meier curve revealed a significantly lower survival in the severe depression group than in the minimal and mild-to-moderate depression groups (p = 0.011). Multivariate Cox regression analysis confirmed that severe depression was an independent risk factor for mortality in the study cohort (hazard ratio, 1.39; 95% confidence interval, 1.01–1.91; p = 0.041). Additionally, BDI-II scores were associated with modified Charlson comorbidity index (p = 0.009) and serum albumin level (p = 0.004) in multivariate linear regression. Among the three depressive symptoms, higher somatic symptom scores were associated with increased mortality. Conclusion: Severe depression among older patients with ESKD increases mortality compared with minimal or mild-to-moderate depression, and patients with concomitant somatic symptoms require careful management of their comorbidities and nutritional status.

Incidence of depression increases in patients with end-stage kidney disease (ESKD). We evaluated the association between depression and mortality among older patients with ESKD, which has not been studied previously. Methods: This nationwide prospective cohort study included 487 patients with ESKD aged >65 years, who were categorized into minimal, mild-to-moderate, and severe depression groups based on their Beck Depression Inventory-II (BDI-II) scores. Predisposing factors for high BDI-II scores and the association between the scores and survival were analyzed. Results: The severe depression group showed a higher modified Charlson comorbidity index value and lower serum albumin, phosphate, and uric acid levels than the other depression groups. The Kaplan-Meier curve revealed a significantly lower survival in the severe depression group than in the minimal and mild-to-moderate depression groups (p = 0.011). Multivariate Cox regression analysis confirmed that severe depression was an independent risk factor for mortality in the study cohort (hazard ratio, 1.39; 95% confidence interval, 1.01–1.91; p = 0.041). Additionally, BDI-II scores were associated with modified Charlson comorbidity index (p = 0.009) and serum albumin level (p = 0.004) in multivariate linear regression. Among the three depressive symptoms, higher somatic symptom scores were associated with increased mortality. Conclusion: Severe depression among older patients with ESKD increases mortality compared with minimal or mild-to-moderate depression, and patients with concomitant somatic symptoms require careful management of their comorbidities and nutritional status.