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: The oxygen reserve index (ORi) noninvasively measures oxygen levels within the mild hyperoxia range. To evaluate whether a degree of increase in the ORi during preoxygenation for general anesthesia is associated with the occurrence of postoperative pulmonary complications (PPCs). Methods: We enrolled 154 patients who underwent preoperative pulmonary function tests and were scheduled for elective surgery under general anesthesia. We aimed to measure the increase in ORi during preoxygenation before general anesthesia and analyze its association with PPCs. Results: PPCs occurred in 76 (49%) participants. Multivariate logistic regression analysis revealed that the three-minute preoxygenation ORi was significantly associated with PPCs (Odds ratio [OR]: 0.02, 95% CI [0.00–0.16], P < 0.001). The areas under the curve (AUC [95% CI]) in the receiver operating characteristic curve analysis for the three-minute preoxygenation ORi for PPCs were 0.64 (0.55–0.73). After a subgroup analysis, multivariate logistic regression showed that the three-minute preoxygenation ORi was significantly associated with PPCs among patients who underwent thoracic surgery (OR: 0.01, 95% CI [0.00–0.19], P = 0.006). The AUC of the three-minute preoxygenation ORi for PPCs was 0.72 (0.57–0.86) in patients who underwent thoracic surgery. Conclusions A low ORi measured after 3 min of preoxygenation for general anesthesia was associated with an increased risk of PPCs, including those undergoing thoracic surgery. This study demonstrated the potential of ORi, measured after oxygen administration, as a tool for evaluating lung function that complements traditional lung function tests and scoring systems.

Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Background: and Purpose Treatments for neuromyelitis optica spectrum disorder (NMOSD) such as eculizumab, ravulizumab, satralizumab, and inebilizumab have significantly advanced relapse prevention, but they remain expensive. Rituximab is an off-label yet popular alternative that offers a cost-effective solution, but its real-world efficacy needs better quantification for guiding the application of newer approved NMOSD treatments (ANTs). This study aimed to determine real-world rituximab failure rates to anticipate the demand for ANTs and aid in resource allocation. Methods: We conducted a nationwide retrospective study involving 605 aquaporin-4-antibody-positive NMOSD patients from 22 centers in South Korea that assessed the efficacy and safety of rituximab over a median follow-up of 47 months. Results: The 605 patients treated with rituximab included 525 (87%) who received continuous therapy throughout the follow-up period (median=47 months, interquartile range=15–87 months). During this period, 117 patients (19%) experienced at least 1 relapse. Notably, 68 of these patients (11% of the total cohort) experienced multiple relapses or at least 1 severe relapse.Additionally, 2% of the patients discontinued rituximab due to adverse events, which included severe infusion reactions, neutropenia, and infections. Conclusions This study has confirmed the efficacy of rituximab in treating NMOSD, as evidenced by an 87% continuation rate among patients over a 4-year follow-up period. Nevertheless, the occurrence of at least one relapse in 19% of the cohort, including 11% who experienced multiple or severe relapses, and a 2% discontinuation rate due to adverse events highlight the urgent need for alternative therapeutic options.

Background: The oxygen reserve index (ORi) noninvasively measures oxygen levels within the mild hyperoxia range. To evaluate whether a degree of increase in the ORi during preoxygenation for general anesthesia is associated with the occurrence of postoperative pulmonary complications (PPCs). Methods: We enrolled 154 patients who underwent preoperative pulmonary function tests and were scheduled for elective surgery under general anesthesia. We aimed to measure the increase in ORi during preoxygenation before general anesthesia and analyze its association with PPCs. Results: PPCs occurred in 76 (49%) participants. Multivariate logistic regression analysis revealed that the three-minute preoxygenation ORi was significantly associated with PPCs (Odds ratio [OR]: 0.02, 95% CI [0.00–0.16], P < 0.001). The areas under the curve (AUC [95% CI]) in the receiver operating characteristic curve analysis for the three-minute preoxygenation ORi for PPCs were 0.64 (0.55–0.73). After a subgroup analysis, multivariate logistic regression showed that the three-minute preoxygenation ORi was significantly associated with PPCs among patients who underwent thoracic surgery (OR: 0.01, 95% CI [0.00–0.19], P = 0.006). The AUC of the three-minute preoxygenation ORi for PPCs was 0.72 (0.57–0.86) in patients who underwent thoracic surgery. Conclusions A low ORi measured after 3 min of preoxygenation for general anesthesia was associated with an increased risk of PPCs, including those undergoing thoracic surgery. This study demonstrated the potential of ORi, measured after oxygen administration, as a tool for evaluating lung function that complements traditional lung function tests and scoring systems.

Purpose: To investigate the outcomes of brolucizumab reinjection after intraocular inflammation (IOI) development. Methods: This retrospective study analyzed patients with brolucizumab injections from April 2021 to January 2024. Patients who developed IOI after brolucizumab were included and categorized into subgroups depending on reinjection, discontinuation, and further IOI development. Results: A total of 472 eyes of 432 patients received brolucizumab injections. Thirty-eight cases developed IOI at least once, and 25 continued brolucizumab. Sixteen cases had no more IOI events, and nine experienced a second or more IOI events. Among the nine cases, three maintained brolucizumab injections despite IOI recurrence. The incidence of IOI was 8.1% based on the number of eyes (38 of 472 eyes) and 2.0% based on the number of brolucizumab injections (50 of 2,468 injections). The incidence of occlusive retinal vasculitis was 0.2% (1 of 472 eyes). The recurrence rate was 23.7% (9 of 38 eyes). The average number of injections between the first brolucizumab injection and the injection date on which IOI first developed was 2.15 times in the no-reinjection group, 3.44 times in the no-IOI-recurrence group, and 2.0 times in the second-IOI-episode group. Time to IOI occurrence in cases with first IOI episode was 18.60 ± 16.73 days, with 15 cases developing IOI within 1 week. Conclusions This study elucidates the real-world incidence of brolucizumab associated IOIs, with a description of information related to reinjections after the IOI episodes. A comprehensive understanding of brolucizumab reinjection is essential for its optimal utilization.

Objective: In South Korea, there is a significant gap in systematic, evidence-based research on intensive case management (ICM) for individuals with severe mental illness (SMI). This study aims to evaluate the effectiveness of ICM through a randomized controlled trial (RCT) comparing ICM with standard case management (non-ICM). Methods: An RCT was conducted to assess the effectiveness of Seoul-intensive case management (S-ICM) vs. non-ICM in individuals with SMI in Seoul. A total of 78 participants were randomly assigned to either the S-ICM group (n=41) or the control group (n=37). Various clinical assessments, including the Brief Psychiatric Rating Scale (BPRS), Montgomery–Åsberg Depression Rating Scale, Health of the Nation Outcome Scale, and Clinical Global Impression-Improvement (CGI-I), along with quality-of-life measures such as the WHO Disability Assessment Schedule, WHO Quality of Life scale, and Multidimensional Scale of Perceived Social Support (MSPSS) were evaluated over a 3-month period. Statistical analyses, including analysis of covariance and logistic regression, were used to determine the effectiveness of S-ICM. Results: The S-ICM group had significantly lower odds of self-harm or suicidal attempts compared to the control group (adjusted odds ratio [aOR]=0.30, 95% confidence interval [CI]: 0.21–1.38). Psychiatric symptoms measured by the BPRS and perceived social support measured by the MSPSS significantly improved in the S-ICM group. The S-ICM group also had significantly higher odds of CGI-I compared to the control group (aOR=8.20, 95% CI: 2.66–25.32). Conclusion This study provides inaugural evidence on the effectiveness of S-ICM services, supporting their standardization and potential nationwide expansion.

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: Given the association between nonalcoholic fatty liver disease and metabolic risks, a new term, metabolic dysfunction- associated steatotic liver disease (MASLD) has been proposed. We aimed to explore the association between MASLD and all-cause, cause-specific mortalities. Methods: We included individuals with steatotic liver disease (SLD) from the Korean National Health Insurance Service. Moreover, SLD was defined as a fatty liver index ≥30. Furthermore, MASLD, metabolic alcohol-associated liver disease (MetALD), and alcoholic liver disease (ALD) with metabolic dysfunction (MD) were categorized based on alcohol consumption and MD. We also analyzed all-cause, liver-, cancer-, hepatocellular carcinoma (HCC)- and cardiovascular (CV)-related mortalities. Results: This retrospective nationwide cohort study included 1,298,993 individuals aged 40 to 79 years for a mean follow-up duration of 9.04 years. The prevalence of MASLD, MetALD, and ALD with MD was 33.11%, 3.93%, and 1.00%, respectively. Relative to the “no SLD” group, multivariable analysis identified that MASLD (adjusted hazard ratio [aHR], 1.28; 95% confidence interval [CI], 1.26 to 1.31), MetALD (aHR, 1.38; 95% CI, 1.32 to 1.44), and ALD with MD group (aHR, 1.80; 95% CI, 1.68 to 1.93) have a significantly higher risk of all-cause mortality. Furthermore, MASLD, MetALD, ALD with MD groups showed higher liver-, cancer- and HCC-related mortality than “no SLD” group. While all-cause specific mortalities increase from MASLD to MetALD to ALD with MD, the MetALD group shows a lower risk of CV-related mortality compared to MASLD. However, ALD with MD group still have a higher risk of CV-related mortality compared to MASLD. Conclusion SLD is associated with an increased risk of all-cause, liver-, cancer-, HCC-, and CV-related mortalities.