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.

The applicability of the Phoenix criteria and Phoenix Sepsis Score in higher-resource pediatric intensive care units (PICUs) outside the United States requires further validation. A retrospective cohort study analyzed electronic health records of 1,304 PICU admissions under 18 years old with suspected infection between February 2017 and December 2023. The score was calculated using two methods: 24-hour assessment, based on worst sub-scores within 24 hours of admission, and prompt assessment, using values closest to admission within 6 hours before or after. Based on the 24-hour assessment, in-hospital mortality was 8.3% for sepsis and 10.3% for septic shock. The score demonstrated an area under the precision-recall curve of 0.42 (95% confidence interval, 0.31–0.55) for in-hospital mortality. Results were consistent across both assessment methods. The Phoenix criteria and the Phoenix Sepsis Score are reliable predictors of mortality outcomes. Further investigation in diverse clinical settings is warranted.

While advances in cancer treatment have led to improved survival rates, cancer survivors are at a significant risk of developing atherosclerotic cardiovascular disease (ASCVD).This review examines the risk, diagnosis, and prevention of ASCVD in this population.Cancer survivors, especially those diagnosed with certain types, face a significantly higher risk of developing ASCVD than the general population. We introduce the “triad model” to explain this increased risk of ASCVD among cancer patients. This model includes three interconnected components: common catalysts, cancer influence, and treatment impact.The factors contributing to this model are the shared risk factors between cancer and ASCVD, such as smoking, obesity, and systemic inflammation; the direct effects of cancer on cardiovascular health through chronic systemic inflammation and endothelial damage;and the significant effects of anticancer treatments, including chemotherapy and radiation, which can worsen cardiovascular complications and hasten the progression of ASCVD.Furthermore, cancer survivors are at a higher risk of developing and dying from ASCVD, highlighting the necessity for tailored guidelines and strategies for ASCVD prevention and management in this population. The review explores the utility of diagnostic tools, such as coronary artery calcium scoring, in predicting and managing ASCVD risk. It also emphasizes the importance of prevention strategies that include regular cardiovascular monitoring and lifestyle modifications. Finally, the relationship between cancer survival and cardiovascular health highlights the importance of integrated and comprehensive care approaches.Continued research, the development of prediction models, and specific preventative strategies are essential to improve cancer survivors’ overall health outcomes.

The applicability of the Phoenix criteria and Phoenix Sepsis Score in higher-resource pediatric intensive care units (PICUs) outside the United States requires further validation. A retrospective cohort study analyzed electronic health records of 1,304 PICU admissions under 18 years old with suspected infection between February 2017 and December 2023. The score was calculated using two methods: 24-hour assessment, based on worst sub-scores within 24 hours of admission, and prompt assessment, using values closest to admission within 6 hours before or after. Based on the 24-hour assessment, in-hospital mortality was 8.3% for sepsis and 10.3% for septic shock. The score demonstrated an area under the precision-recall curve of 0.42 (95% confidence interval, 0.31–0.55) for in-hospital mortality. Results were consistent across both assessment methods. The Phoenix criteria and the Phoenix Sepsis Score are reliable predictors of mortality outcomes. Further investigation in diverse clinical settings is warranted.

While advances in cancer treatment have led to improved survival rates, cancer survivors are at a significant risk of developing atherosclerotic cardiovascular disease (ASCVD).This review examines the risk, diagnosis, and prevention of ASCVD in this population.Cancer survivors, especially those diagnosed with certain types, face a significantly higher risk of developing ASCVD than the general population. We introduce the “triad model” to explain this increased risk of ASCVD among cancer patients. This model includes three interconnected components: common catalysts, cancer influence, and treatment impact.The factors contributing to this model are the shared risk factors between cancer and ASCVD, such as smoking, obesity, and systemic inflammation; the direct effects of cancer on cardiovascular health through chronic systemic inflammation and endothelial damage;and the significant effects of anticancer treatments, including chemotherapy and radiation, which can worsen cardiovascular complications and hasten the progression of ASCVD.Furthermore, cancer survivors are at a higher risk of developing and dying from ASCVD, highlighting the necessity for tailored guidelines and strategies for ASCVD prevention and management in this population. The review explores the utility of diagnostic tools, such as coronary artery calcium scoring, in predicting and managing ASCVD risk. It also emphasizes the importance of prevention strategies that include regular cardiovascular monitoring and lifestyle modifications. Finally, the relationship between cancer survival and cardiovascular health highlights the importance of integrated and comprehensive care approaches.Continued research, the development of prediction models, and specific preventative strategies are essential to improve cancer survivors’ overall health outcomes.

The applicability of the Phoenix criteria and Phoenix Sepsis Score in higher-resource pediatric intensive care units (PICUs) outside the United States requires further validation. A retrospective cohort study analyzed electronic health records of 1,304 PICU admissions under 18 years old with suspected infection between February 2017 and December 2023. The score was calculated using two methods: 24-hour assessment, based on worst sub-scores within 24 hours of admission, and prompt assessment, using values closest to admission within 6 hours before or after. Based on the 24-hour assessment, in-hospital mortality was 8.3% for sepsis and 10.3% for septic shock. The score demonstrated an area under the precision-recall curve of 0.42 (95% confidence interval, 0.31–0.55) for in-hospital mortality. Results were consistent across both assessment methods. The Phoenix criteria and the Phoenix Sepsis Score are reliable predictors of mortality outcomes. Further investigation in diverse clinical settings is warranted.

While advances in cancer treatment have led to improved survival rates, cancer survivors are at a significant risk of developing atherosclerotic cardiovascular disease (ASCVD).This review examines the risk, diagnosis, and prevention of ASCVD in this population.Cancer survivors, especially those diagnosed with certain types, face a significantly higher risk of developing ASCVD than the general population. We introduce the “triad model” to explain this increased risk of ASCVD among cancer patients. This model includes three interconnected components: common catalysts, cancer influence, and treatment impact.The factors contributing to this model are the shared risk factors between cancer and ASCVD, such as smoking, obesity, and systemic inflammation; the direct effects of cancer on cardiovascular health through chronic systemic inflammation and endothelial damage;and the significant effects of anticancer treatments, including chemotherapy and radiation, which can worsen cardiovascular complications and hasten the progression of ASCVD.Furthermore, cancer survivors are at a higher risk of developing and dying from ASCVD, highlighting the necessity for tailored guidelines and strategies for ASCVD prevention and management in this population. The review explores the utility of diagnostic tools, such as coronary artery calcium scoring, in predicting and managing ASCVD risk. It also emphasizes the importance of prevention strategies that include regular cardiovascular monitoring and lifestyle modifications. Finally, the relationship between cancer survival and cardiovascular health highlights the importance of integrated and comprehensive care approaches.Continued research, the development of prediction models, and specific preventative strategies are essential to improve cancer survivors’ overall health outcomes.

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.