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: Prenatal exposure to ambient air pollution is linked to a higher risk of unfavorable pregnancy outcomes. However, the association between pregnancy complications and exposure to indoor air pollution remains unclear. The Air Pollution on Pregnancy Outcomes research is a hospital-based prospective cohort research created to look into the effects of aerodynamically exposed particulate matter (PM) 10 and PM 2.5 on pregnancy outcomes. Methods: This prospective multicenter observational cohort study was conducted from January 2021 to June 2023. A total of 662 women with singleton pregnancies enrolled in this study. An AirguardK ® air sensor was installed inside the homes of the participants to measure the individual PM 10 and PM 2.5 levels in the living environment. The time–activity patterns and PM 100 and PM 2.5 , determined as concentrations from the time-weighted average model, were applied to determine the anticipated exposure levels to air pollution of each pregnant woman. The relationship between air pollution exposure and pregnancy outcomes was assessed using logistic and linear regression analyses. Results: Exposure to elevated levels of PM 10 throughout the first, second, and third trimesters as well as throughout pregnancy was strongly correlated with the risk of pregnancy problems according to multiple logistic regression models adjusted for variables. Except for in the third trimester of pregnancy, women exposed to high levels of PM 2.5 had a high risk of pregnancy complications. During the second trimester and entire pregnancy, the risk of preterm birth (PTB) increased by 24% and 27%, respectively, for each 10 μg/m 3 increase in PM 10. Exposure to high PM 10 levels during the second trimester increased the risk of gestational diabetes mellitus (GDM) by 30%. The risk of GDM increased by 15% for each 5 μg/m 3 increase in PM2.5 during the second trimester and overall pregnancy, respectively. Exposure to high PM 10 and PM 2.5 during the first trimester of pregnancy increased the risk of delivering small for gestational age (SGA) infants by 96% and 26%, respectively. Conclusion Exposure to high concentrations of PM 10 and PM 2.5 is strongly correlated with the risk of adverse pregnancy outcomes. Exposure to high levels of PM10 and PM2.5 during the second trimester and entire pregnancy, respectively, significantly increased the risk of PTB and GDM. Exposure to high levels of PM 10 and PM2.5 during the first trimester of pregnancy considerably increased the risk of having SGA infants. Our findings highlight the need to measure individual particulate levels during pregnancy and the importance of managing air quality in residential environment.

Background/Aims: Immunocompromised patients with acute colonic diverticulitis are at high risk for complications and failure of non-surgical treatment. However, evidence on the comparative outcomes of immunocompromised and immunocompetent patients with diverticulitis is lacking. This systematic review and meta-analysis investigated the outcomes of medical treatment in immunocompromised and immunocompetent patients with diverticulitis. Methods: A comprehensive literature search was conducted in PubMed, Embase, and the Cochrane Library. Studies comparing the clinical outcomes of immunocompromised and immunocompetent patients with diverticulitis were included. Results: A total of 10 studies with 1,946,461 subjects were included in the quantitative synthesis. The risk of emergency surgery and postoperative mortality after emergency surgery was significantly higher in immunocompromised patients than in immunocompetent patients with diverticulitis (risk ratio [RR], 1.76; 95% confidence interval [CI], 1.31–2.38 and RR, 3.05; 95% CI, 1.70–5.45, respectively). Overall risk of complications associated with diverticulitis was non-significantly higher in immunocompromised than in immunocompetent patients (RR, 1.24; 95% CI, 0.95–1.63). Overall mortality irrespective of surgery was significantly higher in immunocompromised than in immunocompetent patients with diverticulitis (RR, 3.65; 95% CI, 1.73–7.69). By contrast, postoperative mortality after elective surgery was not significantly different between immunocompromised and immunocompetent patients with diverticulitis. In subgroup analysis, the risk of emergency surgery and recurrence was significantly higher in immunocompromised patients with complicated diverticulitis, whereas no significant difference was shown in mild disease. Conclusions Immunocompromised patients with diverticulitis should be given the best medical treatment with multidisciplinary approach because they had increased risks of surgery, postoperative morbidity, and mortality than immunocompetent patients.

Background/Aims: Some management strategies for acute colonic diverticulitis remain controversial in Korean real-world practice because their clinical features differ from those in the West. This study aimed to investigate the opinions of Korean physicians regarding the diagnosis and treatment of acute diverticulitis. Methods: A web-based survey was conducted among gastroenterologists specializing on treating lower gastrointestinal disorders. The questionnaires concerned overall management strategies for colonic diverticulitis, including diagnosis, treatment, and follow-up. Results: In total, 209 gastroenterologists responded to the survey. Less than one-fourth of the respondents (23.6%) answered that left-sided colonic diverticulitis is more likely to be complicated than right-sided colonic diverticulitis. Most respondents agreed that immunocompromised patients with diverticulitis have worse clinical outcomes than immunocompetent patients (71.3%). Computed tomography was the most preferred tool for diagnosing diverticulitis (93.9%). Approximately 89% of the respondents answered that they believed antibiotic treatment is necessary to treat acute uncomplicated diverticulitis. Most respondents (92.6%) agreed that emergency surgery is not required for diverticulitis with an abscess or microperforation without panperitonitis. Further, 94.7% of the respondents agreed that colon cancer screening is necessary in patients aged ≥ 50 years with diverticulitis after they have recovered from acute illness. Many respondents (71.4%) agreed that surgery for recurrent diverticulitis should be individualized. Conclusions Opinions regarding management strategies for colonic diverticulitis among Korean gastroenterologists were well agreed upon in some areas but did not agree well in other areas. Evidence-based guidelines that meet the practical needs of the Korean population should be developed.

Objective: The air pollution on pregnancy outcome (APPO) study is a prospective hospital-based cohort study designed to investigate the maternal and fetal effects of a particulate matter with an aerodynamic below 10 μm (PM10) and PM2.5 (below 2.5 μm) exposure. This study aims to analyze a relationship between particulate matter and adverse pregnancy outcomes and to find related biomarkers and develop management guidelines. Methods: About 1,200 pregnant women are recruited for 3 years (from January 2021 to December 2023) from seven university hospitals to investigate the effects of particulate matter on pregnancy complications and adverse pregnancy outcomes. We collect biological samples by 5 mL of maternal venous blood and 15 mL of urine in each trimester of pregnancy, and 5 mL of umbilical cord blood and 2×2×2 cm of placental tissue are collected after delivery. In addition, by applying PM10 and PM2.5 concentration values and time-activity patterns from the time weighted average model, the individual predicted exposure of air pollution for the pregnant women are obtained. Results: The average exposure of PM10 and PM2.5 of the participants in the entire period of pregnancy, was exceeded the World Health Organization air quality guidelines (an annual level, PM10 >15 μg/m3, PM2.5 >5 μg/m3). Moreover, it was revealed that the PM concentration was increasing toward the 3rd trimester of pregnancy. Conclusion The APPO study will be able to identify the degree of exposure to air pollution in pregnant women and use it as basic data for estimating individual exposure to particulate matter. And the results of the APPO study will facilitate in the development of health management for pregnant women against air pollution.