Background: and Purpose Previous research on patients with acute ischemic stroke (AIS) has shown a 0.5% incidence of major gastrointestinal bleeding (GIB) requiring blood transfusion during hospitalization. The existing literature has insufficiently explored the long-term incidence in this population despite the decremental impact of GIB on stroke outcomes. Methods: We analyzed the data from a cohort of patients with AIS admitted to 14 hospitals as part of a nationwide multicenter prospective stroke registry between 2011 and 2013. These patients were followed up for up to 6 years. The occurrence of major GIB events, defined as GIB necessitating at least two units of blood transfusion, was tracked using the National Health Insurance Service claims data. Results: Among 10,818 patients with AIS (male, 59%; mean age, 68±13 years), 947 (8.8%) experienced 1,224 episodes of major GIB over a median follow-up duration of 3.1 years. Remarkably, 20% of 947 patients experienced multiple episodes of major GIB. The incidence peaked in the first month after AIS, reaching 19.2 per 100 person-years, and gradually decreased to approximately one-sixth of this rate by the 2nd year with subsequent stabilization. Multivariable analysis identified the following predictors of major GIB: anemia, estimated glomerular filtration rate <60 mL/min/1.73 m2 , and a 3-month modified Rankin Scale score of ≥4. Conclusion Patients with AIS are susceptible to major GIB, particularly in the first month after the onset of AIS, with the risk decreasing thereafter. Implementing preventive strategies may be important, especially for patients with anemia and impaired renal function at stroke onset and those with a disabling stroke.

The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea’s cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.

Background: and Purpose To determine the clinical phenotypes, relapse timing, treatment responses, and outcomes of children with relapsing myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). Methods: We collected the demographic, clinical, laboratory, and radiological data of patients aged <18 years who had been diagnosed with MOGAD at Seoul National University Children’s Hospital between January 2010 and January 2022; 100 were identified as positive for MOG antibodies, 43 of whom experienced relapse. Results: The median age at onset was 7 years (range 2–16 years). The median number of relapses was 2 (range 1–8), and patients were followed up for a median of 65 months (range 5–214 months). The first relapse was experienced before 3 months from onset by 15 patients (34.9%). The most-common initial phenotypes were acute disseminated encephalomyelitis (n=17, 39.5%) and optic neuritis (ON; n=11, 25.6%). The most-common relapse phenotypes were neuromyelitis optica spectrum disorder (n=9, 20.9%), relapsing ON (n=6, 14.0%), and multiphasic disseminated encephalomyelitis (n=6, 14.0%). Many of the patients (n=18, 41.9%) were not specifically categorized. A high proportion of these patients had non-acute disseminated encephalomyelitis encephalitis. Atypical phenotypes such as prolonged fever or hemiplegic migraine-like episodes were also noted. Mycophenolate mofetil and cyclic immunoglobulin treatment significantly reduced the annual relapse rates. Conclusions Our 43 pediatric patients with relapsing MOGAD showed a tendency toward early relapse and various relapse phenotypes. The overall prognoses of these patients were good regardless of phenotype or response to second-line immunosuppressant treatment.

The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea’s cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.

Background: and Purpose To determine the clinical phenotypes, relapse timing, treatment responses, and outcomes of children with relapsing myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). Methods: We collected the demographic, clinical, laboratory, and radiological data of patients aged <18 years who had been diagnosed with MOGAD at Seoul National University Children’s Hospital between January 2010 and January 2022; 100 were identified as positive for MOG antibodies, 43 of whom experienced relapse. Results: The median age at onset was 7 years (range 2–16 years). The median number of relapses was 2 (range 1–8), and patients were followed up for a median of 65 months (range 5–214 months). The first relapse was experienced before 3 months from onset by 15 patients (34.9%). The most-common initial phenotypes were acute disseminated encephalomyelitis (n=17, 39.5%) and optic neuritis (ON; n=11, 25.6%). The most-common relapse phenotypes were neuromyelitis optica spectrum disorder (n=9, 20.9%), relapsing ON (n=6, 14.0%), and multiphasic disseminated encephalomyelitis (n=6, 14.0%). Many of the patients (n=18, 41.9%) were not specifically categorized. A high proportion of these patients had non-acute disseminated encephalomyelitis encephalitis. Atypical phenotypes such as prolonged fever or hemiplegic migraine-like episodes were also noted. Mycophenolate mofetil and cyclic immunoglobulin treatment significantly reduced the annual relapse rates. Conclusions Our 43 pediatric patients with relapsing MOGAD showed a tendency toward early relapse and various relapse phenotypes. The overall prognoses of these patients were good regardless of phenotype or response to second-line immunosuppressant treatment.

Background: and Purpose Previous research on patients with acute ischemic stroke (AIS) has shown a 0.5% incidence of major gastrointestinal bleeding (GIB) requiring blood transfusion during hospitalization. The existing literature has insufficiently explored the long-term incidence in this population despite the decremental impact of GIB on stroke outcomes. Methods: We analyzed the data from a cohort of patients with AIS admitted to 14 hospitals as part of a nationwide multicenter prospective stroke registry between 2011 and 2013. These patients were followed up for up to 6 years. The occurrence of major GIB events, defined as GIB necessitating at least two units of blood transfusion, was tracked using the National Health Insurance Service claims data. Results: Among 10,818 patients with AIS (male, 59%; mean age, 68±13 years), 947 (8.8%) experienced 1,224 episodes of major GIB over a median follow-up duration of 3.1 years. Remarkably, 20% of 947 patients experienced multiple episodes of major GIB. The incidence peaked in the first month after AIS, reaching 19.2 per 100 person-years, and gradually decreased to approximately one-sixth of this rate by the 2nd year with subsequent stabilization. Multivariable analysis identified the following predictors of major GIB: anemia, estimated glomerular filtration rate <60 mL/min/1.73 m2 , and a 3-month modified Rankin Scale score of ≥4. Conclusion Patients with AIS are susceptible to major GIB, particularly in the first month after the onset of AIS, with the risk decreasing thereafter. Implementing preventive strategies may be important, especially for patients with anemia and impaired renal function at stroke onset and those with a disabling stroke.

The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea’s cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.

Background: and Purpose To determine the clinical phenotypes, relapse timing, treatment responses, and outcomes of children with relapsing myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). Methods: We collected the demographic, clinical, laboratory, and radiological data of patients aged <18 years who had been diagnosed with MOGAD at Seoul National University Children’s Hospital between January 2010 and January 2022; 100 were identified as positive for MOG antibodies, 43 of whom experienced relapse. Results: The median age at onset was 7 years (range 2–16 years). The median number of relapses was 2 (range 1–8), and patients were followed up for a median of 65 months (range 5–214 months). The first relapse was experienced before 3 months from onset by 15 patients (34.9%). The most-common initial phenotypes were acute disseminated encephalomyelitis (n=17, 39.5%) and optic neuritis (ON; n=11, 25.6%). The most-common relapse phenotypes were neuromyelitis optica spectrum disorder (n=9, 20.9%), relapsing ON (n=6, 14.0%), and multiphasic disseminated encephalomyelitis (n=6, 14.0%). Many of the patients (n=18, 41.9%) were not specifically categorized. A high proportion of these patients had non-acute disseminated encephalomyelitis encephalitis. Atypical phenotypes such as prolonged fever or hemiplegic migraine-like episodes were also noted. Mycophenolate mofetil and cyclic immunoglobulin treatment significantly reduced the annual relapse rates. Conclusions Our 43 pediatric patients with relapsing MOGAD showed a tendency toward early relapse and various relapse phenotypes. The overall prognoses of these patients were good regardless of phenotype or response to second-line immunosuppressant treatment.

Background: and Purpose Previous research on patients with acute ischemic stroke (AIS) has shown a 0.5% incidence of major gastrointestinal bleeding (GIB) requiring blood transfusion during hospitalization. The existing literature has insufficiently explored the long-term incidence in this population despite the decremental impact of GIB on stroke outcomes. Methods: We analyzed the data from a cohort of patients with AIS admitted to 14 hospitals as part of a nationwide multicenter prospective stroke registry between 2011 and 2013. These patients were followed up for up to 6 years. The occurrence of major GIB events, defined as GIB necessitating at least two units of blood transfusion, was tracked using the National Health Insurance Service claims data. Results: Among 10,818 patients with AIS (male, 59%; mean age, 68±13 years), 947 (8.8%) experienced 1,224 episodes of major GIB over a median follow-up duration of 3.1 years. Remarkably, 20% of 947 patients experienced multiple episodes of major GIB. The incidence peaked in the first month after AIS, reaching 19.2 per 100 person-years, and gradually decreased to approximately one-sixth of this rate by the 2nd year with subsequent stabilization. Multivariable analysis identified the following predictors of major GIB: anemia, estimated glomerular filtration rate <60 mL/min/1.73 m2 , and a 3-month modified Rankin Scale score of ≥4. Conclusion Patients with AIS are susceptible to major GIB, particularly in the first month after the onset of AIS, with the risk decreasing thereafter. Implementing preventive strategies may be important, especially for patients with anemia and impaired renal function at stroke onset and those with a disabling stroke.

Purpose: This study aimed to evaluate the quality of artificial intelligence (AI)/machine learning (ML) studies on hemorrhagic stroke using the Minimum Information for Medical AI Reporting (MINIMAR) and Minimum Information About Clinical Artificial Intelligence Modeling (MI-CLAIM) frameworks to promote clinical application. Materials and Methods: PubMed, MEDLINE, and Embase were searched for AI/ML studies on hemorrhagic stroke. Out of the 531 articles found, 29 relevant original research articles were included. MINIMAR and MI-CLAIM scores were assigned by two experienced radiologists to assess the quality of the studies. Results: We analyzed 29 investigations that utilized AI/ML in the field of hemorrhagic stroke, involving a median of 224.5 patients. The majority of studies focused on diagnostic outcomes using computed tomography scans (89.7%) and were published in computer science journals (48.3%). The overall adherence rates to reporting guidelines, as assessed through the MINIMAR and MI-CLAIM frameworks, were 47.6% and 46.0%, respectively. In MINIMAR, none of the studies reported the socioeconomic status of the patients or how missing values had been addressed. In MI-CLAIM, only two studies applied model-examination techniques to improve model interpretability. Transparency and reproducibility were limited, as only 10.3% of the studies had publicly shared their code. Cohen’s kappa between the two radiologists was 0.811 and 0.779 for MINIMAR and MI-CLAIM, respectively. Conclusion The overall reporting quality of published AI/ML studies on hemorrhagic stroke is suboptimal. It is necessary to incorporate model examination techniques for interpretability and promote code openness to enhance transparency and increase the clinical applicability of AI/ML studies.