Search

Kristin Kostka,1,2 Talita Duarte-Salles,3 Albert Prats-Uribe,4 Anthony G Sena,5,6 Andrea Pistillo,3 Sara Khalid,4 Lana YH Lai,7 Asieh Golozar,8,9 Thamir M Alshammari,10 Dalia M Dawoud,11 Fredrik Nyberg,12 Adam B Wilcox,13,14 Alan Andryc,5 Andrew Williams,15 Anna Ostropolets,16 Carlos Areia,17 Chi Young Jung,18 Christopher A Harle,19 Christian G Reich,1,2 Clair Blacketer,5,6 Daniel R Morales,20 David A Dorr,21 Edward Burn,3,4 Elena Roel,3,22 Eng Hooi Tan,4 Evan Minty,23 Frank DeFalco,5 Gabriel de Maeztu,24 Gigi Lipori,19 Hiba Alghoul,25 Hong Zhu,26 Jason A Thomas,13 Jiang Bian,19 Jimyung Park,27 Jordi Martínez Roldán,28 Jose D Posada,29 Juan M Banda,30 Juan P Horcajada,31 Julianna Kohler,32 Karishma Shah,33 Karthik Natarajan,16,34 Kristine E Lynch,35,36 Li Liu,37 Lisa M Schilling,38 Martina Recalde,3,22 Matthew Spotnitz,14 Mengchun Gong,39 Michael E Matheny,40,41 Neus Valveny,42 Nicole G Weiskopf,21 Nigam Shah,29 Osaid Alser,43 Paula Casajust,42 Rae Woong Park,27,44 Robert Schuff,21 Sarah Seager,1 Scott L DuVall,35,36 Seng Chan You,45 Seokyoung Song,46 Sergio Fernández-Bertolín,3 Stephen Fortin,5 Tanja Magoc,19 Thomas Falconer,16 Vignesh Subbian,47 Vojtech Huser,48 Waheed-Ul-Rahman Ahmed,33,49 William Carter,38 Yin Guan,50 Yankuic Galvan,19 Xing He,19 Peter R Rijnbeek,6 George Hripcsak,16,34 Patrick B Ryan,5,16 Marc A Suchard,51 Daniel Prieto-Alhambra4 1IQVIA, Cambridge, MA, USA; 2OHDSI Center at The Roux Institute, Northeastern University, Portland, ME, USA; 3Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain; 4Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK; 5Janssen Research & Development, Titusville, NJ, USA; 6Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands; 7School of Medical Sciences, University of Manchester, Manchester, UK; 8Regeneron Pharmaceuticals, Tarrytown, NY, USA; 9Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 10College of Pharmacy, Riyadh Elm University, Riyadh, Saudi Arabia; 11National Institute for Health and Care Excellence, London, UK; 12School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; 13Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA; 14Unviersity of Washington Medicine, Seattle, WA, USA; 15Tufts Institute for Clinical Research and Health Policy Studies, Boston, MA, USA; 16Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA; 17Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; 18Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Daegu Catholic University Medical Center, Daegu, South Korea; 19University of Florida Health, Gainesville, FL, USA; 20Division of Population Health and Genomics, University of Dundee, Dundee, UK; 21Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; 22Universitat Autònoma de Barcelona, Barcelona, Spain; 23O’Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, Canada; 24IOMED, Barcelona, Spain; 25Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine; 26Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 27Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, South Korea; 28Director of Innovation and Digital Transformation, Hospital del Mar, Barcelona, Spain; 29Department of Medicine, School of Medicine, Stanford University, Redwood City, CA, USA; 30Georgia State University, Department of Computer Science, Atlanta, GA, USA; 31Department of Infectious Diseases, Hospital del Mar, Institut Hospital del Mar d’Investigació Mèdica (IMIM), Universitat Autònoma de Barcelona, Universitat Pompeu Fabra, Barcelona, Spain; 32United States Agency for International Development, Washington, DC, USA; 33Botnar Research Centre, NDORMS, University of Oxford, Oxford, UK; 34New York-Presbyterian Hospital, New York, NY, USA; 35VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; 36Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; 37Biomedical Big Data Center, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 38Data Science to Patient Value Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; 39Institute of Health Management, Southern Medical University, Guangzhou, People’s Republic of China; 40Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA; 41Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; 42Real-World Evidence, TFS, Barcelona, Spain; 43Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 44Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea; 45Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea; 46Department of Anesthesiology and Pain Medicine, Catholic University of Daegu, School of Medicine, Daegu, South Korea; 47College of Engineering, The University of Arizona, Tucson, AZ, USA; 48National Library of Medicine, National Institutes of Health, Bethesda, MD, USA; 49College of Medicine and Health, University of Exeter, St Luke’s Campus, Exeter, UK; 50DHC Technologies Co. Ltd., Beijing, People’s Republic of China; 51Departments of Biostatistics, Computational Medicine, and Human Genetics, University of California, Los Angeles, CA, USACorrespondence: Daniel Prieto-Alhambra, Botnar Research Centre, Windmill Road, Oxford, OX37LD, UK, Email daniel.prietoalhambra@ndorms.ox.ac.ukPurpose: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.Patients and Methods: We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services.Results: We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed.Conclusion: We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.Keywords: OHDSI, OMOP CDM, descriptive epidemiology, real world data, real world evidence, open science

Vijaya Sarangthem,1,2,* Aena Yi,1,* Yunjae Kim,1 Alnawaz Rehemtulla,3 Byung-Heon Lee,1 Young Hyun Jeon,4 Thoudam Debraj Singh,5 Rang-Woon Park1 1Department of Biochemistry and Cell Biology, School of Medicine, and Cell & Matrix Research Institute, Kyungpook National University, Daegu, 41944, Republic of Korea; 2Department of Pathology, All India Institute of Medical Sciences (AIIMS), New Delhi, 110029, India; 3Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA; 4Laboratory Animal Center, Daegu-Gyeongbuk Medical Innovation Foundation (DGMIF), Daegu, Republic of Korea; 5Department of Medical Oncology Laboratory, All India Institute of Medical Sciences (AIIMS), New Delhi, 110029, India*These authors contributed equally to this workCorrespondence: Rang-Woon ParkDepartment of Biochemistry and Cell Biology, School of Medicine, and Cell & Matrix Research Institute, Kyungpook National University, Daegu, 41944, Republic of KoreaTel +82 53 420 4822Fax +82 53 422 1466Email nwpark@knu.ac.krThoudam Debraj SinghDepartment of Medical Oncology Laboratory, All India Institute of Medical Sciences (AIIMS), New Delhi, 110029, IndiaEmail debraj.thoudam@gmail.comBackground: Thermal-responsive self-assembled elastin-like polypeptide (ELP)-based nanoparticles are an emerging platform for controlled delivery of therapeutic peptides, proteins and small molecular drugs. The antitumor effect of bioengineered chimeric polypeptide AP1-ELP-KLAK containing an interleukin-4 receptor (IL-4R) targeting peptide and pro-apoptotic peptide (KLAKLAK) was evaluated in glioblastoma (GBM) in vitro and in vivo.Methods and Results: Herein, the therapeutic effect of AP1-ELP-KLAK was tested in advanced, and less curable glioblastoma cells with higher expression of IL-4R. Glioblastoma cell lines stably expressing different reporter systems i.e., caspase-3 sensor (surrogate marker for cellular apoptosis) or effluc/enhanced firefly luciferase (cellular viability) were established to measure cell death non-invasively. Bioluminescence imaging (BLI) of D54/effluc and U97MG/effluc treated with AP1-ELP-KLAK exhibited higher cell death up to 2∼ 3-fold than the control. Treatment with AP1-ELP-KLAK resulted in time-dependent increase of caspase-3 sensor BLI activity in D54/C cells and D54/C tumor-bearing mice. Intravenous injection of AP1-ELP-KLAK dramatically reduced tumor growth by inducing cellular apoptosis in D54/effluc tumor-bearing mice. Further, the immuno-histological examination of the excised tumor tissue confirmed the presence of apoptotic cells as well as caspase-3 activation.Conclusion: Collectively, AP1-ELP-KLAK effectively induced cellular apoptosis of glioblastoma cells and non-invasive imaging provides a window for real-time monitoring of anti-tumor effect with the provision of improving therapeutic efficacy in a glioblastoma mice model.Keywords: tumor targeting, ELP, caspase-sensor, apoptosis, IL-4 receptor, glioblastoma

© 2015, Nature Publishing Group. All rights reserved.Prediction of new disease indications for approved drugs by computational methods has been based largely on the genomics signatures of drugs and diseases. We propose a method for drug repositioning that

Edward Chia-Cheng Lai,1–4 Patrick Ryan,5 Yinghong Zhang,4 Martijn Schuemie,5 N Chantelle Hardy,4 Yukari Kamijima,6 Shinya Kimura,7 Kiyoshi Kubota,6 Kenneth KC Man,8,9 Soo Yeon Cho,10 Rae Woong Park,10 Paul Stang,5 Chien-Chou Su,1,3 Ian CK Wong,8,9 Yea-Huei Yang Kao,1,3 Soko Setoguchi4,11 1School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, National Cheng Kung University, Tainan, Taiwan; 2Department of Pharmacy, National Cheng Kung University Hospital, Tainan, Taiwan; 3Health Outcome Research Center, National Cheng-Kung University, Tainan, Taiwan; 4Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA; 5Janssen Research & Development, LLC, Titusville, NJ, USA; 6NPO Drug Safety Research Unit Japan, Tokyo, Japan; 7Japan Medical Data Center Co.,Ltd, Tokyo, Japan; 8Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, University of Hong Kong, Hong Kong, China; 9Research Department of Practice and Policy, UCL School of Pharmacy, London, UK; 10Department of Biomedical Informatics, School of Medicine, Ajou University, Suwon, Korea; 11Institute for Health, Rutgers University and Department of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA Objective: The goal of the Asian Pharmacoepidemiology Network is to study the effectiveness and safety of medications commonly used in Asia using databases from individual Asian countries. An efficient infrastructure to support multinational pharmacoepidemiologic studies is critical to this effort. Study design and setting: We converted data from the Japan Medical Data Center database, Taiwan’s National Health Insurance Research Database, Hong Kong’s Clinical Data Analysis and Reporting System, South Korea’s Ajou University School of Medicine database, and the US Medicare 5% sample to the Observational Medical Outcome Partnership common data model (CDM). Results: We completed and documented the process for the CDM conversion. The coordinating center and participating sites reviewed the documents and refined the conversions based on the comments. The time required to convert data to the CDM varied widely across sites and included conversion to standard terminology codes and refinements of the conversion based on reviews. We mapped 97.2%, 86.7%, 92.6%, and 80.1% of domestic drug codes from the USA, Taiwan, Hong Kong, and Korea to RxNorm, respectively. The mapping rate from Japanese domestic drug codes to RxNorm (70.7%) was lower than from other countries, and we mapped remaining unmapped drugs to Anatomical Therapeutic Chemical Classification System codes. Because the native databases used international procedure coding systems for which mapping tables have been established, we were able to map >90% of diagnosis and procedure codes to standard terminology codes. Conclusion: The CDM established the foundation and reinforced collaboration for multinational pharmacoepidemiologic studies in Asia. Mapping of terminology codes was the greatest challenge, because of differences in health systems, cultures, and coding systems. Keywords: clinical coding, computer communication networks, feasibility studies, pharmacoepidemiology, pharmacovigilance

Jimin Lee,1 Yoojin Noh,1 Sooyoung Shin,1 Hong-Seok Lim,2 Rae Woong Park,3 Soo Kyung Bae,4 Euichaul Oh,4 Grace Juyun Kim,5 Ju Han Kim,5 Sukhyang Lee1 1Division of Clinical Pharmacy, College of Pharmacy, Ajou University, Suwon, South Korea; 2Department of Cardiology, School of Medicine, Ajou University, Suwon, South Korea; 3Department of Biomedical Informatics, School of Medicine, Ajou University, Suwon, South Korea; 4Division of Pharmaceutical Sciences, College of Pharmacy, The Catholic University of Korea, Bucheon, South Korea; 5Division of Biomedical Informatics, College of Medicine, Seoul National University, Seoul, South Korea Abstract: Statin therapy is beneficial in reducing cardiovascular events and mortalities in patients with atherosclerotic cardiovascular diseases. Yet, there have been concerns of increased risk of diabetes with statin use. This study was aimed to evaluate the association between statins and new onset diabetes mellitus (NODM) in patients with ischemic heart disease (IHD) utilizing the Korean Health Insurance Review and Assessment Service claims database. Among adult patients with preexisting IHD, new statin users and matched nonstatin users were identified on a 1:1 ratio using proportionate stratified random sampling by sex and age. They were subsequently propensity score matched further with age and comorbidities to reduce the selection bias. Overall incidence rates, cumulative rates and hazard ratios (HRs) between statin use and occurrence of NODM were estimated. The subgroup analyses were performed according to sex, age groups, and the individual agents and intensities of statins. A total of 156,360 patients (94,370 in the statin users and 61,990 in the nonstatin users) were included in the analysis. The incidence rates of NODM were 7.8% and 4.8% in the statin users and nonstatin users, respectively. The risk of NODM was higher among statin users (crude HR 2.01, 95% confidence interval [CI] 1.93–2.10; adjusted HR 1.84, 95% CI 1.63–2.09). Pravastatin had the lowest risk (adjusted HR 1.54, 95% CI 1.32–1.81) while those who were exposed to more than one statin were at the highest risk of NODM (adjusted HR 2.17, 95% CI 1.93–2.37). It has been concluded that all statins are associated with the risk of NODM in patients with IHD, and it is believed that our study would contribute to a better understanding of statin and NODM association by analyzing statin use in the real-world setting. Periodic screening and monitoring for diabetes are warranted during prolonged statin therapy in patients with IHD. Keywords: Atorvastatin, Fluvastatin, Lovastatin, Rosuvastatin, Pitavastatin, Pravastatin, Simvastatin, Ischemic heart disease, IHD, new onset diabetes mellitus, NODM

OBJECTIVE: To determine the availability of computerized physician order entry (CPOE) and electronic medical record (EMR) systems in teaching and general hospitals in the Republic of Korea. DESIGN: A combined mail and telephone survey of 283 hospitals. MEASUREMENTS: The surveys assessed the availability of CPOE and EMRs in the hospitals, as well as inducement, participation, and saturation regarding CPOE use by physicians. RESULTS: A total of 122 (43.1%) hospitals responded to the survey. The complete form of CPOE was available in 98 (80.3%) hospitals. The use of CPOE was mandatory in 92 (86.0%) of the 107 hospitals that responded to the questions regarding the requirement of CPOE use. In 85 (79.4%) of the hospitals in which CPOE was in use, more than 90% of physicians used the system. In addition, physicians entered more than 90% of their total orders through CPOE in 87 (81.3%) hospitals. In contrast, a complete EMR system was available in only 11 (9.0%) of the hospitals. CONCLUSION: Of the teaching and general hospitals in the Republic of Korea that responded to the survey, the majority (80.3%) have CPOE systems, and a complete EMR system is available in only 9%. [ABSTRACT FROM AUTHOR]

Background and Objectives: Inhaled corticosteroid (ICS) and oral corticosteroid (OCS) are often used in asthma management. This study evaluated the long-term effect of ICS/OCS on osteoporosis, osteopenia, fractures, and bone metabolism in adult asthmatics in real-world clinical practice., Methods: This is a retrospective study investigating de-identified electronic health records from Ajou University Medical Center (Korea). Adult asthmatics receiving maintenance ICS with/without OCS for at least 1 year were enrolled. They were classified into the high/low-dose of ICS or OCS groups. Primary outcomes (incidences of osteoporosis, osteopenia, and fractures) and secondary outcomes (drug prescription and laboratory values related to bone metabolism including albumin and alkaline phosphatase [ALP]) were compared after 5 years of follow-up., Results: After propensity score matching, both high- and low-dose OCS groups included 468 patients, and high/low-dose ICS groups each comprised 1,252 patients. The risk of osteoporosis/major fracture was higher (hazard ratio [95% CI]; 2.00 [1.15-3.57]/3.03 [1.04-11.11]) in the high-dose OCS group (especially in females aged ≥50 years) than in the low-dose group, although the ICS groups showed no significant differences. The high-dose ICS group showed a higher risk of osteopenia (1.92 [1.05-3.70]) than the low-dose ICS group. The linear mixed model of laboratory values showed significantly decreased serum albumin and increased ALP in the high-dose OCS group than in the low-dose OCS group., Conclusions: The results of this study suggest that long-term use of OCS can increase the risk of osteoporosis and osteoporosis-related fractures, while long-term use of ICS may increase the risk of osteopenia in adult asthmatics., (Copyright © 2024. Published by Elsevier Inc.)

Importance: Depression is a common comorbidity of adult attention-deficit/hyperactivity disorder (ADHD), and the combination of methylphenidate and selective serotonin reuptake inhibitors (SSRIs) is a frequently prescribed treatment. However, there is limited clinical evidence on the safety of this medication combination in adults with ADHD., Objective: To evaluate the safety of administering a combination of SSRI and methylphenidate in adults with ADHD and comorbid depression., Design, Setting, and Participants: This cohort study obtained data from a nationwide claims database in South Korea from January 2016 to February 2021. Participants were adults aged 18 years or older with a diagnosis of ADHD and depressive disorder who were prescribed methylphenidate. Comparisons of 4 groups who received prescriptions were conducted: (1) SSRI plus methylphenidate (hereafter, SSRI) group vs methylphenidate-only group and (2) methylphenidate plus fluoxetine (hereafter, fluoxetine) group vs methylphenidate plus escitalopram (hereafter, escitalopram) group (compared to find a preferable treatment option). Data analysis was conducted between July and December 2023., Exposures: New users of the methylphenidate and SSRI combination among adults with both ADHD and depressive disorder., Main Outcomes and Measures: A total of 17 primary and secondary outcomes, including neuropsychiatric and other events, were assessed, with respiratory tract infection used as a control outcome. Groups were matched at a 1:1 ratio using a propensity score to balance confounders. A Cox proportional hazards regression model was used to calculate hazard ratio (HRs) and 95% CIs. Subgroup analysis by sex and sensitivity analyses in varying epidemiologic settings were conducted., Results: The study included 17 234 adults with ADHD (mean [SD] age at study entry, 29.4 [10.8] years; 9079 females [52.7%]). There was no difference in the risk of outcomes between the methylphenidate-only and SSRI groups, except for a lower risk of headache in the SSRI group (HR, 0.50; 95% CI, 0.24-0.99). In sensitivity analyses of fluoxetine vs escitalopram, the risk of hypertension (HR: 1:n matching, 0.26; 95% CI, 0.08-0.67) and hyperlipidemia (HR: 1:n matching, 0.23; 95% CI, 0.04-0.81) was lower in the fluoxetine group than in the escitalopram group., Conclusions and Relevance: Results of this study revealed no significant increase in adverse event risk associated with use of SSRI plus methylphenidate vs methylphenidate alone in adults with ADHD and comorbid depression. Instead, the combination was associated with a lower risk of headache.

Background: People with mental health conditions were potentially more vulnerable than others to the neuropsychiatric effects of the COVID-19 pandemic and the global efforts taken to contain it. The aim of this multinational study was to examine the changes in psychotropic drug prescribing during the pandemic among people with depressive and anxiety disorders., Methods: This study included electronic medical records and claims data from nine databases in six countries (France, Germany, Italy, the UK, South Korea, and the USA) of patients with a diagnosis of depressive or anxiety disorders between 2016 and 2021. The outcomes were monthly prevalence rates of antidepressant, antipsychotic, and anxiolytic drug prescribing. The associations between the pandemic and psychotropic drug prescribing were examined with interrupted time series analyses for the total sample and stratified by sex and age group. People with lived experience were not involved in the research and writing process., Findings: Between Jan 1, 2016 and Dec 31, 2020, an average of 16 567 914 patients with depressive disorders (10 820 956 females [65·31%] and 5 746 958 males [34·69%]) and 15 988 451 patients with anxiety disorders (10 688 788 females [66·85%] and 5 299 663 males [33·15%]) were identified annually. Most patients with depressive disorders and anxiety disorders were aged 45-64 years. Ethnicity data were not available. Two distinct trends in prescribing rates were identified. The first pattern shows an initial surge at the start of the pandemic (eg, antipsychotics among patients with depressive disorders in MDCD_US (rate ratio [RR] 1·077, 95% CI 1·055-1·100), followed by a gradual decline towards the counterfactual level (RR 0·990, 95% CI 0·988-0·992). The second pattern, observed in four databases for anxiolytics among patients with depressive disorders and two for antipsychotics among patients with anxiety disorders, shows an immediate increase (eg, antipsychotics among patients with anxiety disorders in IQVIA_UK: RR 1·467, 95% CI 1·282-1·675) without a subsequent change in slope (RR 0·985, 95% CI 0·969-1·003). In MDCD_US and IQVIA_US, the anxiolytic prescribing rate continued to increase among patients younger than 25 years for both disorders., Interpretation: The study reveals persistently elevated rates of psychotropic drug prescriptions beyond the initial phase of the pandemic. These findings underscore the importance of enhanced mental health support and emphasise the need for regular review of psychotropic drug use among this patient group in the post-pandemic era., Funding: University Grants Committee, Research Grants Council, The Government of the Hong Kong Special Administrative Region., Competing Interests: Declaration of interests HL reports grants from Research Grants Council of Hong Kong, outside the submitted work. WCYL reports research grants from AIR@InnoHK administered by Innovation and Technology Commission, outside the submitted work. JH reports grants from UKRI, Consultancy Fees from Wellcome Trust and Juli Health, and patents pending and stock from Juli Health, outside the submitted work. COT reports stock or stock options and other financial or non-financial interests from Roche Pharmaceutical as an employee, outside the submitted work. XL reports grants from IQVIA, outside the submitted work. SF and DMK report stock or stock options and other financial or non-financial interests from Johnson and Johnson as employees, outside the submitted work. JL reports stock or stock options and other financial or non-financial interests from IQVIA as an employee, outside the submitted work. KKCM reports grants from the CW Maplethorpe Fellowship, National Institute of Health Research, UK, Hong Kong Research Grant Council, and the European Commission Horizon 2020 Framework, outside the submitted work. ICKW reports grants from Amgen, Bristol-Myers Squibb, Pfizer, Janssen, Bayer, GSK and Novartis, the Hong Kong RGC, and the Hong Kong Health and Medical Research Fund, National Institute for Health Research in England, European Commission, and National Health and Medical Research Council in Australia, consulting fees from IQVIA, payment for expert testimony and a role as an independent non-executive director of Jacobson Medical, Hong Kong, outside of the submitted work. All other authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)

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., Objective: This study aimed to develop prediction models for 30-day readmissions using home healthcare notes and structured data. In addition, it explored the development of 14- and 180-day prediction models using variables in the 30-day model., Design: A retrospective observational cohort study., Setting(s): This study was conducted at Ajou University School of Medicine in South Korea., Participants: Data from electronic health records, encompassing demographic characteristics of 1819 participants, along with information on conditions, drug, and home healthcare, were utilized., Methods: Two distinct models were developed for each prediction window (30-, 14-, 180-day): the traditional model, which utilized structured variables alone, and the common data model (CDM)-NLP model, which incorporated structured and topic variables extracted from home healthcare notes. BERTopic facilitated topic generation and risk probability, representing the likelihood of documents being assigned to specific topics. Feature selection involved experimenting with various algorithms. The best-performing algorithm, determined using the area under the receiver operating characteristic curve (AUROC), was used for model development. Model performance was assessed using various learning metrics including AUROC., Results: Among 1819 patients, 251 (13.80 %) experienced 30-day readmission. The least absolute shrinkage and selection operator was used for feature extraction and model development. The 15 structured features were used in the traditional model. Moreover, five additional topic variables from the home healthcare notes were applied in the CDM-NLP model. The AUROC of the traditional model was 0.739 (95 % CI: 0.672-0.807). The AUROC of the CDM-NLP model was high at 0.824 (95 % CI: 0.768-0.880), which indicated an outstanding performance. The topics in the CDM-NLP model included emotional distress, daily living functions, nutrition, postoperative status, and cardiorespiratory issues. In extended prediction model development for 14- and 180-day readmissions, the CDM-NLP consistently outperformed the traditional model., Conclusions: This study developed effective prediction models using both structured and unstructured data, thereby emphasizing the significance of postdischarge information from home healthcare notes in readmission prediction., Competing Interests: Declaration of Competing Interest None., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: The association between delirium and dementia has been suggested, but mostly in the postoperative setting. This study aims to explore this relationship in a broader inpatient population, leveraging extensive real-world data to provide a more generalized understanding., Methods: In this retrospective cohort study, electronic health records of 11,970,475 hospitalized patients aged over 60 from nine institutions in South Korea were analyzed. Patients with and without delirium were identified, and propensity score matching (PSM) was used to create comparable groups. A 10-year longitudinal analysis was conducted using the Cox proportional hazards model, which calculated the hazard ratio (HR) and 95% confidence interval (CI). Additionally, a meta-analysis was performed, aggregating results from all nine medical institutions. Lastly, we conducted various subgroup and sensitivity analyses to demonstrate the consistency of our study results across diverse conditions., Results: After 1:1 PSM, a total of 47,306 patients were matched in both the delirium and nondelirium groups. Both groups had a median age group of 75-79 years, with 43.1% being female. The delirium group showed a significantly higher risk of all dementia compared with the nondelirium group (HR: 2.70 [95% CI: 2.27-3.20]). The incidence risk for different types of dementia was also notably higher in the delirium group (all dementia or mild cognitive impairment, HR: 2.46 [95% CI: 2.10-2.88]; Alzheimer's disease, HR: 2.74 [95% CI: 2.40-3.13]; vascular dementia, HR: 2.55 [95% CI: 2.07-3.13]). This pattern was consistent across all subgroup and sensitivity analyses., Conclusions: Delirium significantly increases the risk of onset for all types of dementia. These findings highlight the importance of early detection of delirium and prompt intervention. Further research studies are warranted to investigate the mechanisms linking delirium and dementia., Competing Interests: DISCLOSURES The authors declare no conflicts of interest., (Copyright © 2024 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.)

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. The database was covered with 10 million, 20% of the whole Korean population sampled by age and sex stratification of the Health Insurance Review and Assessment Service in South Korea. Among 228 367 subjects who were over 50 yr of age and taking denosumab or bisphosphonate from January 2018 to April 2022, the analysis was performed on 91 460 subjects after 1:1 propensity score matching. The primary outcome was treatment effectiveness; total fracture, major osteoporotic fracture, femur fracture, pelvic fracture, vertebral fracture, adverse drug reactions; acute kidney injury, chronic kidney disease, and atypical femoral fracture. Total fracture and osteoporotic major fracture, as the main outcomes of efficacy, were comparable in the denosumab and bisphosphonate group (HR 1.06, 95% CI, 0.98-1.15, P = .14; HR 1.13, 95% CI, 0.97-1.32, P = .12, respectively). Safety for acute kidney injury, chronic kidney disease, and atypical femoral fracture also did not show any differences between the 2 groups. In subgroup analysis according to ages, the denosumab group under 70 yr of age had a significantly lower risk for occurrences of acute kidney injury compared to the bisphosphonate group under 70 yr of age (HR 0.53, 95% CI, 0.29-0.93, P = .03). In real-world data reflecting clinical practice, denosumab and bisphosphonate showed comparable effectiveness for total fractures and major osteoporosis fractures, as well as safety regarding acute kidney injury, chronic kidney disease, and atypical femoral fracture., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society for Bone and Mineral Research. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Acute kidney injury (AKI) is a marker of clinical deterioration and renal toxicity. While there are many studies offering prediction models for the early detection of AKI, those predicting AKI occurrence using distributed research network (DRN)-based time series data are rare., Objective: In this study, we aimed to detect the early occurrence of AKI by applying an interpretable long short-term memory (LSTM)-based model to hospital electronic health record (EHR)-based time series data in patients who took nephrotoxic drugs using a DRN., Methods: We conducted a multi-institutional retrospective cohort study of data from 6 hospitals using a DRN. For each institution, a patient-based data set was constructed using 5 drugs for AKI, and an interpretable multivariable LSTM (IMV-LSTM) model was used for training. This study used propensity score matching to mitigate differences in demographics and clinical characteristics. Additionally, the temporal attention values of the AKI prediction model's contribution variables were demonstrated for each institution and drug, with differences in highly important feature distributions between the case and control data confirmed using 1-way ANOVA., Results: This study analyzed 8643 and 31,012 patients with and without AKI, respectively, across 6 hospitals. When analyzing the distribution of AKI onset, vancomycin showed an earlier onset (median 12, IQR 5-25 days), and acyclovir was the slowest compared to the other drugs (median 23, IQR 10-41 days). Our temporal deep learning model for AKI prediction performed well for most drugs. Acyclovir had the highest average area under the receiver operating characteristic curve score per drug (0.94), followed by acetaminophen (0.93), vancomycin (0.92), naproxen (0.90), and celecoxib (0.89). Based on the temporal attention values of the variables in the AKI prediction model, verified lymphocytes and calcvancomycin ium had the highest attention, whereas lymphocytes, albumin, and hemoglobin tended to decrease over time, and urine pH and prothrombin time tended to increase., Conclusions: Early surveillance of AKI outbreaks can be achieved by applying an IMV-LSTM based on time series data through an EHR-based DRN. This approach can help identify risk factors and enable early detection of adverse drug reactions when prescribing drugs that cause renal toxicity before AKI occurs., (© Suncheol Heo, Eun-Ae Kang, Jae Yong Yu, Hae Reong Kim, Suehyun Lee, Kwangsoo Kim, Yul Hwangbo, Rae Woong Park, Hyunah Shin, Kyeongmin Ryu, Chungsoo Kim, Hyojung Jung, Yebin Chegal, Jae-Hyun Lee, Yu Rang Park. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

Suicide is a growing public health problem around the world. The most important risk factor for suicide is underlying psychiatric illness, especially depression. Detailed classification of suicide in patients with depression can greatly enhance personalized suicide control efforts. This study used unstructured psychiatric charts and brain magnetic resonance imaging (MRI) records from a psychiatric outpatient clinic to develop a machine learning-based suicidal thought classification model. The study included 152 patients with new depressive episodes for development and 58 patients from a geographically different hospital for validation. We developed an eXtreme Gradient Boosting (XGBoost)-based classification models according to the combined types of data: independent components-map weightings from brain T1-weighted MRI and topic probabilities from clinical notes. Specifically, we used 5 psychiatric symptom topics and 5 brain networks for models. Anxiety and somatic symptoms topics were significantly more common in the suicidal group, and there were group differences in the default mode and cortical midline networks. The clinical symptoms plus structural brain patterns model had the highest area under the receiver operating characteristic curve (0.794) versus the clinical notes only and brain MRI only models (0.748 and 0.738, respectively). The results were consistent across performance metrics and external validation. Our findings suggest that focusing on personalized neuroimaging and natural language processing variables improves evaluation of suicidal thoughts., (© 2024. The Author(s).)

Background: The exogenous delivery of miRNA to mimic and restore miRNA-34a activity in various cancer models holds significant promise in cancer treatment. Nevertheless, its effectiveness is often impeded by challenges, including a short half-life, propensity for off-target accumulation, susceptibility to inactivation by blood-based enzymes, concerns regarding patient safety, and the substantial cost associated with scaling up. As a means of overcoming these barriers, we propose the development of miRNA-loaded Tat-A86 nanoparticles by virtue of Tat-A86's ability to shield the loaded agent from external environmental factors, reducing degradation and inactivation, while enhancing circulation time and targeted accumulation., Results: Genetically engineered Tat-A86, featuring 16 copies of the interleukin-4 receptor (IL-4R)-binding peptide (AP1), Tat for tumor penetration, and an elastin-like polypeptide (ELP) for presenting target ligands and ensuring stability, served as the basis for this delivery system. Comparative groups, including Tat-E60 and A86, were employed to discern differences in binding and penetration. The designed ELP-based nanoparticle Tat-A86 effectively condensed miRNA, forming stable nanocomplexes under physiological conditions. The miRNA/Tat-A86 formulation bound specifically to tumor cells and facilitated stable miRNA delivery into them, effectively inhibiting tumor growth. The efficacy of miRNA/Tat-A86 was further evaluated using three-dimensional spheroids of lewis lung carcinoma (LLC) as in vitro model and LLC tumor-bearing mice as an in vivo model. It was found that miRNA/Tat-A86 facilitates effective cell killing by markedly improving miRNA penetration, leading to a substantial reduction in the size of LLC spheroids. Compared to other controls, Tat-A86 demonstrated superior efficacy in suppressing the growth of 3D cellular aggregates. Moreover, at equivalent doses, miRNA-34a delivered by Tat-A86 inhibited the growth of LLC cells in allograft mice., Conclusions: Overall, these studies demonstrate that Tat-A86 nanoparticles can deliver miRNA systemically, overcoming the basic hurdles impeding miRNA delivery by facilitating both miRNA uptake and stability, ultimately leading to improved therapeutic effects., (© 2024. The Author(s).)

Purpose: Finasteride and dutasteride are used to treat benign prostatic hyperplasia (BPH) and reduce the risk of developing prostate cancer. Finasteride blocks only the type 2 form of 5-alpha-reductase, whereas dutasteride blocks both type 1 and 2 forms of the enzyme. Previous studies suggest the possibility that dutasteride may be superior to finasteride in preventing prostate cancer. We directly compared the effects of finasteride and dutasteride on the risk of prostate cancer in patients with BPH using a pooled analysis of 15 real-world databases., Materials and Methods: We conducted a multicenter, cohort study of new-users of finasteride and dutasteride. We include patients who were prescribed 5 mg finasteride or dutasteride for the first time to treat BPH and had at least 180 days of prescription. We excluded patients with a history of prostate cancer or a prostate-specific antigen level ≥ 4 ng/mL before the study drug prescription. Cox regression analysis was performed to examine the hazard ratio (HR) for prostate cancer after propensity score (PS) matching., Results: A total of 8,284 patients of new-users of finasteride and 8,670 patients of new-users of dutasteride were included across the 15 databases. In the overall population, compared to dutasteride, finasteride was associated with a lower risk of prostate cancer in both on-treatment and intent-to-treat time-at-risk periods. After 1:1 PS matching, 4,897 patients using finasteride and 4,897 patients using dutasteride were enrolled in the present study. No significant differences were observed for risk of prostate cancer between finasteride and dutasteride both on-treatment (HR=0.66, 95% confidence interval [CI]: 0.44-1.00; p=0.051) and intent-to-treat time-at-risk periods (HR=0.87, 95% CI: 0.67-1.14; p=0.310)., Conclusions: Using real-world databases, the present study demonstrated that dutasteride was not associated with a lower risk of prostate cancer than finasteride in patients with BPH., Competing Interests: The authors have nothing to disclose., (Copyright © 2024 Korean Society for Sexual Medicine and Andrology.)

Purpose: Drug-resistant epilepsy (DRE) poses a significant challenge in epilepsy management, and reliable biomarkers for identifying patients at risk of DRE are lacking. This study aimed to investigate the association between serum uric acid (UA) levels and the conversion rate to DRE., Methods: A retrospective cohort study was conducted using a common data model database. The study included patients newly diagnosed with epilepsy, with prediagnostic serum UA levels within a six-month window. Patients were categorized into hyperUA (≥7.0 mg/dL), normoUA (<7.0 and >2.0 mg/dL), and hypoUA (≤2.0 mg/dL) groups based on their prediagnostic UA levels. The outcome was the conversion rate to DRE within five years of epilepsy diagnosis., Results: The study included 5,672 patients with epilepsy and overall conversion rate to DRE was 19.4%. The hyperUA group had a lower DRE conversion rate compared to the normoUA group (HR: 0.81 [95% CI: 0.69-0.96]), while the hypoUA group had a higher conversion rate (HR: 1.88 [95% CI: 1.38-2.55])., Conclusions: Serum UA levels have the potential to serve as a biomarker for identifying patients at risk of DRE, indicating a potential avenue for novel therapeutic strategies aimed at preventing DRE conversion., Competing Interests: Declaration of competing interest None of the authors has any conflict of interest to disclose., (Copyright © 2024. Published by Elsevier Ltd.)

Background: Predictive models show promise in healthcare, but their successful deployment is challenging due to limited generalizability. Current external validation often focuses on model performance with restricted feature use from the original training data, lacking insights into their suitability at external sites. Our study introduces an innovative methodology for evaluating features during both the development phase and the validation, focusing on creating and validating predictive models for post-surgery patient outcomes with improved generalizability., Methods: Electronic health records (EHRs) from 4 countries (United States, United Kingdom, Finland, and Korea) were mapped to the OMOP Common Data Model (CDM), 2008-2019. Machine learning (ML) models were developed to predict post-surgery prolonged opioid use (POU) risks using data collected 6 months before surgery. Both local and cross-site feature selection methods were applied in the development and external validation datasets. Models were developed using Observational Health Data Sciences and Informatics (OHDSI) tools and validated on separate patient cohorts., Results: Model development included 41 929 patients, 14.6% with POU. The external validation included 31 932 (UK), 23 100 (US), 7295 (Korea), and 3934 (Finland) patients with POU of 44.2%, 22.0%, 15.8%, and 21.8%, respectively. The top-performing model, Lasso logistic regression, achieved an area under the receiver operating characteristic curve (AUROC) of 0.75 during local validation and 0.69 (SD = 0.02) (averaged) in external validation. Models trained with cross-site feature selection significantly outperformed those using only features from the development site through external validation (P < .05)., Conclusions: Using EHRs across four countries mapped to the OMOP CDM, we developed generalizable predictive models for POU. Our approach demonstrates the significant impact of cross-site feature selection in improving model performance, underscoring the importance of incorporating diverse feature sets from various clinical settings to enhance the generalizability and utility of predictive healthcare models., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Although 20 % of patients with depression receiving treatment do not achieve remission, predicting treatment-resistant depression (TRD) remains challenging. In this study, we aimed to develop an explainable multimodal prediction model for TRD using structured electronic medical record data, brain morphometry, and natural language processing. In total, 247 patients with a new depressive episode were included. TRD-predictive models were developed based on the combination of following parameters: selected tabular dataset features, independent components-map weightings from brain T1-weighted magnetic resonance imaging (MRI), and topic probabilities from clinical notes. All models applied the extreme gradient boosting (XGBoost) algorithm via five-fold cross-validation. The model using all data sources showed the highest area under the receiver operating characteristic of 0.794, followed by models that used combined brain MRI and structured data, brain MRI and clinical notes, clinical notes and structured data, brain MRI only, structured data only, and clinical notes only (0.770, 0.762, 0.728, 0.703, 0.684, and 0.569, respectively). Classifications of TRD were driven by several predictors, such as previous exposure to antidepressants and antihypertensive medications, sensorimotor network, default mode network, and somatic symptoms. Our findings suggest that a combination of clinical data with neuroimaging and natural language processing variables improves the prediction of TRD., Competing Interests: Declaration of competing interest All authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Emergency departments (ED) are complex, triage is a main task in the ED to prioritize patient with limited medical resources who need them most. Machine learning (ML) based ED triage tool, Score for Emergency Risk Prediction (SERP), was previously developed using an interpretable ML framework with single center. We aimed to develop SERP with 3 Korean multicenter cohorts based on common data model (CDM) without data sharing and compare performance with inter-hospital validation design. This retrospective cohort study included all adult emergency visit patients of 3 hospitals in Korea from 2016 to 2017. We adopted CDM for the standardized multicenter research. The outcome of interest was 2-day mortality after the patients' ED visit. We developed each hospital SERP using interpretable ML framework and validated inter-hospital wisely. We accessed the performance of each hospital's score based on some metrics considering data imbalance strategy. The study population for each hospital included 87,670, 83,363 and 54,423 ED visits from 2016 to 2017. The 2-day mortality rate were 0.51%, 0.56% and 0.65%. Validation results showed accurate for inter hospital validation which has at least AUROC of 0.899 (0.858-0.940). We developed multicenter based Interpretable ML model using CDM for 2-day mortality prediction and executed Inter-hospital external validation which showed enough high accuracy., (© 2024. The Author(s).)

Objective Contrast agents used for radiological examinations are an important cause of acute kidney injury (AKI). We developed and validated a machine learning and clinical scoring prediction model to stratify the risk of contrast-induced nephropathy, considering the limitations of current classical and machine learning models. Methods This retrospective study included 38,481 percutaneous coronary intervention cases from 23,703 patients in a tertiary hospital. We divided the cases into development and internal test sets (8:2). Using the development set, we trained a gradient boosting machine prediction model (complex model). We then developed a simple model using seven variables based on variable importance. We validated the performance of the models using an internal test set and tested them externally in two other hospitals. Results The complex model had the best area under the receiver operating characteristic (AUROC) curve at 0.885 [95% confidence interval (CI) 0.876-0.894] in the internal test set and 0.837 (95% CI 0.819-0.854) and 0.850 (95% CI 0.781-0.918) in two different external validation sets. The simple model showed an AUROC of 0.795 (95% CI 0.781-0.808) in the internal test set and 0.766 (95% CI 0.744-0.789) and 0.782 (95% CI 0.687-0.877) in the two different external validation sets. This was higher than the value in the well-known scoring system (Mehran criteria, AUROC=0.67). The seven precatheterization variables selected for the simple model were age, known chronic kidney disease, hematocrit, troponin I, blood urea nitrogen, base excess, and N-terminal pro-brain natriuretic peptide. The simple model is available at http://52.78.230.235:8081/Conclusions We developed an AKI prediction machine learning model with reliable performance. This can aid in bedside clinical decision making.

Aims: Population-wide restrictions during the COVID-19 pandemic may create barriers to mental health diagnosis. This study aims to examine changes in the number of incident cases and the incidence rates of mental health diagnoses during the COVID-19 pandemic., Methods: By using electronic health records from France, Germany, Italy, South Korea and the UK and claims data from the US, this study conducted interrupted time-series analyses to compare the monthly incident cases and the incidence of depressive disorders, anxiety disorders, alcohol misuse or dependence, substance misuse or dependence, bipolar disorders, personality disorders and psychoses diagnoses before (January 2017 to February 2020) and after (April 2020 to the latest available date of each database [up to November 2021]) the introduction of COVID-related restrictions., Results: A total of 629,712,954 individuals were enrolled across nine databases. Following the introduction of restrictions, an immediate decline was observed in the number of incident cases of all mental health diagnoses in the US (rate ratios (RRs) ranged from 0.005 to 0.677) and in the incidence of all conditions in France, Germany, Italy and the US (RRs ranged from 0.002 to 0.422). In the UK, significant reductions were only observed in common mental illnesses. The number of incident cases and the incidence began to return to or exceed pre-pandemic levels in most countries from mid-2020 through 2021., Conclusions: Healthcare providers should be prepared to deliver service adaptations to mitigate burdens directly or indirectly caused by delays in the diagnosis and treatment of mental health conditions.

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 1.3 million patients with asthma or COPD. We analysed treatment trajectories at drug class level from first diagnosis and visualised these in sunburst plots., Results: In four countries (USA, UK, Spain and the Netherlands), most adults with asthma initiate treatment with short-acting ß2 agonists monotherapy (20.8%-47.4% of first-line treatments). For COPD, the most frequent first-line treatment varies by country. The largest percentages of untreated patients (for asthma and COPD) were found in claims databases (14.5%-33.2% for asthma and 27.0%-52.2% for COPD) from the USA as compared with EHR databases (6.9%-15.2% for asthma and 4.4%-17.5% for COPD) from European countries. The treatment trajectories showed step-up as well as step-down in treatments., Conclusion: Real-world data from claims and EHRs indicate that first-line treatments of asthma and COPD vary widely across countries. We found evidence of a stepwise approach in the pharmacological treatment of asthma and COPD, suggesting that treatments may be tailored to patients' needs., Competing Interests: Competing interests: AFM, PRR, JAK and KMV work for a department that receives/received unconditional research grants from Amgen, Chiesi, Johnson and Johnson, UCB Biopharma, the European Medicines Agency and the Innovative Medicines Initiative. DP-A’s department has received grants from Amgen, Chiesi-Taylor, Lilly, Johnson and Johnson, Novartis, UCB Biopharma, the European Medicines Agency, and the Innovative Medicines Initiative. DP-A’s research group has received consultancy fees from Astra Zeneca and UCB Biopharma. DP-A’s department has organised training programmes funded or supported by Amgen, Astellas, Johnson and Johnson, Synapse Management Partners, and UCB Biopharma. JK’s institute has received grants from the American Lung Association, COPD Foundation, National Heart Lung and Blood institute, PCORI, Regeneron, Sergey Brin Family Foundation and US National Institutes of Health. JK has received consultancy fees from AstraZeneca, BData and GlaxoSmithKline. JK has received honoraria from University of Chicago, University of Washington/VA Puget Sound and travel support from the Global Initiative for Asthma, American Thoracic Society. GBB’s institute has received grants from Merck Sharp & Dohme. GBB has received honoraria from AstraZeneca, Boehringer-Ingelheim, Chiesi, GlaxoSmithKline, Novartis and Sanofi. All other authors declare no competing interests., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

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. The study population was sub-grouped based on age (> 45 years). The comparison groups were matched using a large-scale propensity score (PS)-matching algorithm. The primary endpoint was incidence of schizophrenia., Results: 5,907,522; 2,923,423; and 1,971,549 patients used ACE inhibitors, ARBs, and thiazide diuretics, respectively. After PS matching, the risk of schizophrenia was not significantly different among the groups (ACE inhibitor vs. ARB: summary hazard ratio [HR] 1.15 [95% confidence interval, CI, 0.99-1.33]; ACE inhibitor vs. thiazide diuretics: summary HR 0.91 [95% CI, 0.78-1.07]). In the older subgroup, there was no significant difference between ACE inhibitors and thiazide diuretics (summary HR, 0.91 [95% CI, 0.71-1.16]). The risk for schizophrenia was significantly higher in the ACE inhibitor group than in the ARB group (summary HR, 1.23 [95% CI, 1.05-1.43])., Conclusions: The risk of schizophrenia was not significantly different between the ACE inhibitor vs. ARB and ACE inhibitor vs. thiazide diuretic groups. Further investigations are needed to determine the risk of schizophrenia associated with antihypertensive drugs, especially in people aged > 45 years., (© 2024. The Author(s).)

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., Methods: Electronic medical record data spanning 28 years, collected from the Ajou University Medical Center in Korea, were used to conduct a retrospective study. Clinical outcomes were compared between patients with asthma who had maintained statin use (the statin group) and those not taking statins, whose blood lipid tests were always normal (the non-statin group). We performed propensity score matching and calculated hazard ratios with 95% CIs using the Cox proportional hazards model. Severe asthma exacerbation was the primary outcome; asthma exacerbation, asthma-related hospitalization, and new-onset type 2 diabetes mellitus and hypertension were secondary outcomes., Results: After 1:1 propensity score matching, the statin and non-statin groups each included 545 adult patients with asthma. The risk of severe asthma exacerbations and asthma exacerbations was significantly lower in the statin group than in the non-statin group (hazard ratios [95% CI] = 0.57 [0.35-0.90] and 0.71 [0.52-0.96], respectively). There were no significant differences in the risk of asthma-related hospitalization or new-onset type 2 diabetes mellitus or hypertension between groups (0.76 [0.53-1.09], 2.33 [0.94-6.59], and 1.71 [0.95-3.17], respectively)., Conclusion: Statin use is associated with a lower risk of asthma exacerbation, with better clinical outcomes in adult asthma., (Copyright © 2023 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

Background: Perioperative adverse cardiac events (PACE), a composite of myocardial infarction, coronary revascularization, congestive heart failure, arrhythmic attack, acute pulmonary embolism, cardiac arrest, and stroke during 30-day postoperative period, is associated with long-term mortality, but with limited clinical evidence. We compared long-term mortality with PACE using data from nationwide multicenter electronic health records., Methods: Data from 7 hospitals, converted to Observational Medical Outcomes Partnership Common Data Model, were used. We extracted records of 277,787 adult patients over 18 years old undergoing non-cardiac surgery for the first time at the hospital and had medical records for more than 180 days before surgery. We performed propensity score matching and then an aggregated meta‑analysis., Results: After 1:4 propensity score matching, 7,970 patients with PACE and 28,807 patients without PACE were matched. The meta‑analysis showed that PACE was associated with higher one-year mortality risk (hazard ratio [HR]: 1.33, 95% CI [1.10, 1.60], P = 0.005) and higher three-year mortality (HR: 1.18, 95% CI [1.01, 1.38], P = 0.038). In subgroup analysis, the risk of one-year mortality by PACE became greater with higher-risk surgical procedures (HR: 1.20, 95% CI [1.04, 1.39], P = 0.020 for low-risk surgery; HR: 1.69, 95% CI [1.45, 1.96], P < 0.001 for intermediate-risk; and HR: 2.38, 95% CI [1.47, 3.86], P = 0.034 for high-risk)., Conclusions: A nationwide multicenter study showed that PACE was significantly associated with increased one-year mortality. This association was stronger in high-risk surgery, older, male, and chronic kidney disease subgroups. Further studies to improve mortality associated with PACE are needed.

To extract information from free-text in clinical records due to the patient's protected health information PHI in the records pre-processing of de-identification is required. Therefore we aimed to identify PHI list and fine-tune the deep learning BERT model for developing de-identification model. The result of fine-tuning the model is strict F1 score of 0.924. Due to the convinced score the model can be used for the development of a de-identification model.

Observational Medical Outcome Partners - Common Data Model (OMOP-CDM) is an international standard model for standardizing electronic medical record data. However, unstructured data such as medical image data which is beyond the scope of standardization by the current OMOP-CDM is difficult to be used in multi-institutional collaborative research. Therefore, we developed the Radiology-CDM (R-CDM) which standardizes medical imaging data. As a proof of concept, 737,500 Optical Coherence Tomography (OCT) data from two tertiary hospitals in South Korea is standardized in the form of R-CDM. The relationship between chronic disease and retinal thickness was analyzed by using the R-CDM. Central macular thickness and retinal nerve fiber layer (RNFL) thickness were significantly thinner in the patients with hypertension compared to the control cohort. It is meaningful in that multi-institutional collaborative research using medical image data and clinical data simultaneously can be conducted very efficiently.

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. Five topics were extracted from HHC documents and improved the model performance (AUROC 0.740).

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.

Glioblastoma (GBM) is the deadliest tumor of the central nervous system, with a median survival of less than 15 months. Despite many trials, immune checkpoint-blocking (ICB) therapies using monoclonal antibodies against the PD-1/PD-L1 axis have demonstrated only limited benefits for GBM patients. Currently, the main hurdles in brain tumor therapy include limited drug delivery across the blood-brain barrier (BBB) and the profoundly immune-suppressive microenvironment of GBM. Thus, there is an urgent need for new therapeutics that can cross the BBB and target brain tumors to modulate the immune microenvironment. To this end, we developed an ICB strategy based on the BBB-permeable, 24-subunit human ferritin heavy chain, modifying the ferritin surface with 24 copies of PD-L1-blocking peptides to create ferritin-based ICB nanocages. The PD-L1pep ferritin nanocages first demonstrated their tumor-targeting and antitumor activities in an allograft colon cancer model. Next, we found that these PD-L1pep ferritin nanocages efficiently penetrated the BBB and targeted brain tumors through specific interactions with PD-L1, significantly inhibiting tumor growth in an orthotopic intracranial tumor model. The addition of PD-L1pep ferritin nanocages to triple in vitro cocultures of T cells, GBM cells, and glial cells significantly inhibited PD-1/PD-L1 interactions and restored T-cell activity. Collectively, these findings indicate that ferritin nanocages displaying PD-L1-blocking peptides can overcome the primary hurdle of brain tumor therapy and are, therefore, promising candidates for treating GBM.