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.

Purpose: Ejection fraction, measured as the fraction of blood ejected from the ventricle in each heartbeat using M-mode echocardiography, serves as a primary indicator of left ventricular systolic function. This study explores the correlation between blood pressure and left ventricular systolic function in neonates using M-mode echocardiography. Methods: Neonates who underwent echocardiography in the neonatal intensive care unit between January 2011 and December 2020 were retrospectively studied. Results: Our analyses showed a significant association between ejection fraction and systolic blood pressure, but not with diastolic or mean blood pressure—both of which are more sensitive to hypotension. Ejection fraction was also not significantly associated with heart rate, urine output, or inotropic support in this study, suggesting that factors influencing urine output may not directly relate to ejection fraction. Additionally, we found that higher systolic blood pressure was correlated with advanced gestational age, the absence of patent ductus arteriosus, and no need for fentanyl administration. Notably, lower gestational age and lack of mechanical ventilation were both associated with increased hourly urine output, suggesting that developmental maturity and respiratory stability may influence renal function. Conclusion Neonatal hypotension occurred secondary to decreased systolic cardiac function and peripheral vascular resistance. Neonatologists should carefully monitor the individual components of blood pressure and prescribe medications accordingly, considering that systolic blood pressure is correlated with ejection function.

Purpose: Ejection fraction, measured as the fraction of blood ejected from the ventricle in each heartbeat using M-mode echocardiography, serves as a primary indicator of left ventricular systolic function. This study explores the correlation between blood pressure and left ventricular systolic function in neonates using M-mode echocardiography. Methods: Neonates who underwent echocardiography in the neonatal intensive care unit between January 2011 and December 2020 were retrospectively studied. Results: Our analyses showed a significant association between ejection fraction and systolic blood pressure, but not with diastolic or mean blood pressure—both of which are more sensitive to hypotension. Ejection fraction was also not significantly associated with heart rate, urine output, or inotropic support in this study, suggesting that factors influencing urine output may not directly relate to ejection fraction. Additionally, we found that higher systolic blood pressure was correlated with advanced gestational age, the absence of patent ductus arteriosus, and no need for fentanyl administration. Notably, lower gestational age and lack of mechanical ventilation were both associated with increased hourly urine output, suggesting that developmental maturity and respiratory stability may influence renal function. Conclusion Neonatal hypotension occurred secondary to decreased systolic cardiac function and peripheral vascular resistance. Neonatologists should carefully monitor the individual components of blood pressure and prescribe medications accordingly, considering that systolic blood pressure is correlated with ejection function.

Purpose: Ejection fraction, measured as the fraction of blood ejected from the ventricle in each heartbeat using M-mode echocardiography, serves as a primary indicator of left ventricular systolic function. This study explores the correlation between blood pressure and left ventricular systolic function in neonates using M-mode echocardiography. Methods: Neonates who underwent echocardiography in the neonatal intensive care unit between January 2011 and December 2020 were retrospectively studied. Results: Our analyses showed a significant association between ejection fraction and systolic blood pressure, but not with diastolic or mean blood pressure—both of which are more sensitive to hypotension. Ejection fraction was also not significantly associated with heart rate, urine output, or inotropic support in this study, suggesting that factors influencing urine output may not directly relate to ejection fraction. Additionally, we found that higher systolic blood pressure was correlated with advanced gestational age, the absence of patent ductus arteriosus, and no need for fentanyl administration. Notably, lower gestational age and lack of mechanical ventilation were both associated with increased hourly urine output, suggesting that developmental maturity and respiratory stability may influence renal function. Conclusion Neonatal hypotension occurred secondary to decreased systolic cardiac function and peripheral vascular resistance. Neonatologists should carefully monitor the individual components of blood pressure and prescribe medications accordingly, considering that systolic blood pressure is correlated with ejection function.

Purpose: Ejection fraction, measured as the fraction of blood ejected from the ventricle in each heartbeat using M-mode echocardiography, serves as a primary indicator of left ventricular systolic function. This study explores the correlation between blood pressure and left ventricular systolic function in neonates using M-mode echocardiography. Methods: Neonates who underwent echocardiography in the neonatal intensive care unit between January 2011 and December 2020 were retrospectively studied. Results: Our analyses showed a significant association between ejection fraction and systolic blood pressure, but not with diastolic or mean blood pressure—both of which are more sensitive to hypotension. Ejection fraction was also not significantly associated with heart rate, urine output, or inotropic support in this study, suggesting that factors influencing urine output may not directly relate to ejection fraction. Additionally, we found that higher systolic blood pressure was correlated with advanced gestational age, the absence of patent ductus arteriosus, and no need for fentanyl administration. Notably, lower gestational age and lack of mechanical ventilation were both associated with increased hourly urine output, suggesting that developmental maturity and respiratory stability may influence renal function. Conclusion Neonatal hypotension occurred secondary to decreased systolic cardiac function and peripheral vascular resistance. Neonatologists should carefully monitor the individual components of blood pressure and prescribe medications accordingly, considering that systolic blood pressure is correlated with ejection function.

Purpose: Ejection fraction, measured as the fraction of blood ejected from the ventricle in each heartbeat using M-mode echocardiography, serves as a primary indicator of left ventricular systolic function. This study explores the correlation between blood pressure and left ventricular systolic function in neonates using M-mode echocardiography. Methods: Neonates who underwent echocardiography in the neonatal intensive care unit between January 2011 and December 2020 were retrospectively studied. Results: Our analyses showed a significant association between ejection fraction and systolic blood pressure, but not with diastolic or mean blood pressure—both of which are more sensitive to hypotension. Ejection fraction was also not significantly associated with heart rate, urine output, or inotropic support in this study, suggesting that factors influencing urine output may not directly relate to ejection fraction. Additionally, we found that higher systolic blood pressure was correlated with advanced gestational age, the absence of patent ductus arteriosus, and no need for fentanyl administration. Notably, lower gestational age and lack of mechanical ventilation were both associated with increased hourly urine output, suggesting that developmental maturity and respiratory stability may influence renal function. Conclusion Neonatal hypotension occurred secondary to decreased systolic cardiac function and peripheral vascular resistance. Neonatologists should carefully monitor the individual components of blood pressure and prescribe medications accordingly, considering that systolic blood pressure is correlated with ejection function.