Publications by authors named "Chungsoo Kim"

Background: The World Health Organisation (WHO) has identified a range of symptomatic manifestations to aid in the clinical diagnosis of post-COVID conditions, herein referred to as post-acute COVID-19 symptoms. We conducted an international network cohort study to estimate the burden of these symptoms in North American, European, and Asian populations.

Methods: A federated analysis was conducted including 10 databases from the United Kingdom, Netherlands, Norway, Estonia, Spain, France, South Korea, and the United States, between September 1st 2020 and latest data availability (which varied from December 31st 2021 to February 28th 2023), covering primary and secondary care, nationwide registries, and claims data, all mapped to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM).

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  • The study investigates the long-term effects of inhaled corticosteroids (ICS) and oral corticosteroids (OCS) on bone health in adults with asthma, specifically looking at osteoporosis, osteopenia, and fractures.
  • Data was collected from electronic health records of patients at Ajou University Medical Center in Korea, with a focus on outcomes over 5 years for those on maintenance therapy involving ICS and/or OCS.
  • Findings indicate that high-dose OCS is linked to a significantly higher risk of osteoporosis and fractures, especially in women over 50, while high-dose ICS is associated with an increased risk of osteopenia.
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  • Depression frequently co-occurs with adult ADHD, and combining methylphenidate with SSRIs is a common treatment, but safety data for this combo in adults is limited.
  • The study analyzed data from a nationwide claims database in South Korea, focusing on adults diagnosed with both ADHD and depression who were prescribed methylphenidate.
  • A total of 17,234 adults were studied, and results showed no significant safety differences between those taking the combination of SSRIs with methylphenidate compared to those on methylphenidate alone.
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  • Acute kidney injury (AKI) is a serious condition indicating renal toxicity, and current studies on predicting AKI using distributed research networks (DRN) with time series data are limited.
  • The study aimed to identify early AKI occurrences in patients taking nephrotoxic medications by employing an interpretable long short-term memory (LSTM) model using hospital electronic health records from six different institutions.
  • Results showed a significant analysis of 39,655 patients, revealing that vancomycin led to earlier AKI onset compared to other drugs, with the predictive model achieving high accuracy, particularly for acyclovir, which produced an impressive average score of 0.94 in predicting AKI risk.
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Background: Hospital readmission is an important indicator of inpatient care quality and a significant driver of increasing medical costs. Therefore, it is important to explore the effects of postdischarge information, particularly from home healthcare notes, on enhancing readmission prediction models. Despite the use of Natural Language Processing (NLP) and machine learning in prediction model development, current studies often overlook insights from home healthcare notes.

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Both bisphosphonates and denosumab are the mainstays of treatment for osteoporosis to prevent fractures. However, there are still few trials directly comparing the prevention of fractures and the safety of 2 drugs in the treatment of osteoporosis. We aimed to compare the efficacy and safety between denosumab and bisphosphonates using a nationwide claims database.

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Background: There is a lack of knowledge on how patients with asthma or chronic obstructive pulmonary disease (COPD) are globally treated in the real world, especially with regard to the initial pharmacological treatment of newly diagnosed patients and the different treatment trajectories. This knowledge is important to monitor and improve clinical practice.

Methods: This retrospective cohort study aims to characterise treatments using data from four claims (drug dispensing) and four electronic health record (EHR; drug prescriptions) databases across six countries and three continents, encompassing 1.

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Hydrophobic surfaces have a wide range of applications, such as water harvesting, self-cleaning, and anti-biofouling. However, traditional methods of achieving hydrophobicity often involve the use of toxic materials such as fluoropolymers. This study aims to create controllable wettability surfaces with a three-dimensional geometry using a laser base powder bed fusion (PBF) process with commercially pure titanium (CP-Ti) and silicone oil as non-toxic materials.

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Background: The association between antihypertensive medication and schizophrenia has received increasing attention; however, evidence of the impact of antihypertensive medication on subsequent schizophrenia based on large-scale observational studies is limited. We aimed to compare the schizophrenia risk in large claims-based US and Korea cohort of patients with hypertension using angiotensin-converting enzyme (ACE) inhibitors versus those using angiotensin receptor blockers (ARBs) or thiazide diuretics.

Methods: Adults aged 18 years who were newly diagnosed with hypertension and received ACE inhibitors, ARBs, or thiazide diuretics as first-line antihypertensive medications were included.

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We developed a standardized framework named RHEA to represent longitudinal status of patient with cancer. RHEA generates a dashboard to visualize patients' data in the Observational Medical Outcomes Partnership-Common Data Model format. The generated dashboard consists of three main parts for providing the macroscopic characteristics of the patient: 1) cohort-level visualization, 2) individual-level visualization and 3) cohort generation.

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  • To extract information from clinical records while protecting patient privacy, a process called de-identification is necessary due to the presence of protected health information (PHI).
  • The researchers identified a list of PHI and fine-tuned a deep learning model, specifically BERT, to create a de-identification model.
  • The fine-tuned model achieved a high F1 score of 0.924, indicating its effectiveness for developing a reliable de-identification tool.
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This study developed readmission prediction models using Home Healthcare (HHC) documents via natural language processing (NLP). An electronic health record of Ajou University Hospital was used to develop prediction models (A reference model using only structured data, and an NLP-enriched model with structured and unstructured data). Among 573 patients, 63 were readmitted to the hospital.

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Introduction: Given the similar efficacies across antipsychotic medications for schizophrenia, understanding their safety profiles, particularly concerning receptor-binding differences, is crucial for optimal drug selection, especially for patients with first episode schizophrenia. We aimed to compare the safety outcomes of second-generation antipsychotics.

Methods: We conducted a retrospective cohort study with new user active comparator design using a nationwide claims database in South Korea.

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Background: Blood lipids affect airway inflammation in asthma. Although several studies have suggested anti-inflammatory effects of statins on asthmatic airways, further studies are needed to clarify the long-term effectiveness of statins on asthma control and whether they are an effective treatment option.

Objective: To evaluate the long-term effectiveness of statins in the chronic management of adult asthma in real-world practice.

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  • The paper emphasizes the importance of transparent and FAIR (Findable, Accessible, Interoperable, Reusable) disclosure of healthcare data and infrastructure, which has been lacking in public awareness.
  • The authors developed a common data model (OMOP) using national claims data from South Korea's Health Insurance Review and Assessment Service, converting over 10 billion claims and data from 56 million patients.
  • They built an analytics environment for distributed research and made the data metadata publicly accessible, aiming to reduce information inequality among researchers and enhance the generation of high-quality medical evidence.
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Background: Prediction of antibiotic non-susceptibility based on patient characteristics and clinical status may support selection of empiric antibiotics for suspected hospital-acquired urinary tract infections (HA-UTIs).

Methods: Prediction models were developed to predict non-susceptible results of eight antibiotic susceptibility tests ordered for suspected HA-UTI. Eligible patients were those with urine culture and susceptibility test results after 48 hours of admission between 2010-2021.

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  • This study aimed to create a deep learning model to predict drug-induced liver injury (DILI) in patients taking angiotensin receptor blockers (ARBs) using data from six hospitals in Korea.
  • A retrospective analysis of 10,852 patient records revealed a 1.09% incidence rate of DILI, varying by drug, with valsartan having the highest rate (1.24%) and olmesartan the lowest (0.83%).
  • The model's prediction performance was strong, particularly for telmisartan, losartan, and irbesartan, highlighting useful variables like hematocrit and albumin for better clinical decision-making.
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Background: Mood disorder has emerged as a serious concern for public health; in particular, bipolar disorder has a less favorable prognosis than depression. Although prompt recognition of depression conversion to bipolar disorder is needed, early prediction is challenging due to overlapping symptoms. Recently, there have been attempts to develop a prediction model by using federated learning.

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Background: Aspirin-exacerbated respiratory disease (AERD) is a phenotype of severe asthma, but its disease course has not been well documented compared with that of aspirin-tolerant asthma (ATA).

Objectives: This study aimed to investigate the long-term clinical outcomes between AERD and ATA.

Methods: AERD patients were identified by the diagnostic code and positive bronchoprovocation test in a real-world database.

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Importance: The antiandrogenic effect of the 5α-reductase inhibitor (5-ARI) has been investigated for its role in preventing male-predominant cancers. Although 5-ARI has been widely associated with prostate cancer, its association with urothelial bladder cancer (BC), another cancer experienced predominantly by males, has been less explored.

Objective: To assess the association between 5-ARI prescription prior to BC diagnosis and reduced risk of BC progression.

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