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Cardiovascular risk of gonadotropin-releasing hormone antagonist versus agonist in men with prostate cancer: an observational study in Taiwan (Jun, 10.1038/s41391-022-00555-0, 2022)

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PROSTATE CANCER AND PROSTATIC DISEASES 2023年第3期26卷 631-632页

作者： Shao, Yu-Hsuan Joni Hong, Jian-Hua Chen, Chun-Kai Huang, Chao-Yuan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Department of Urology National Taiwan University Hospital College of Medicine National Taiwan University Taipei Taiwan Institute of Biomedical Engineering National Taiwan University Taipei Taiwan Division of Cardiology Department of Internal Medicine National Taiwan University Hospital Hsinchu Branch Hsinchu Taiwan

Background The impact of gonadotropin-releasing hormone (GnRH) antagonist and agonist (GnRHa) treatment on cardiovascular disease (CVD) risk in prostate cancer (PCa) remains inconclusive due to conflicting findings. We compared the effects of GnRH antagonist and GnRHa treatments on CVD risk in patients with PCa and pre-existing CVD, in a Taiwan population-based *** We assessed the risk of major adverse CV events (MACE: ischemic heart disease [IHD], stroke, congestive heart failure [CHF] or all cause deaths) and composite CV events (IHD, stroke, CHF or CV deaths) occurring ≥90 days after androgen deprivation therapy (ADT) initiation in patients with PCa after 90 days of treatment with either GnRH antagonist (degarelix; n = 499) or GnRHa (goserelin, leuprolide, triptorelin; n = 15,127). Patients identified with pre-existing CVD had received cardiac therapy for IHD, reported a stroke or CHF within a year before ADT initiation. Adjusted hazard ratios (aHR) and 95% confidence interval (CI) were obtained for MACE and composite CV events risk after adjusting for age, baseline status of diabetes, hypertension and treatments *** All GnRH antagonist-treated patients showed lower risk of composite CV events than the GnRHa-treated patients. The lower composite CV events risk associated with GnRH antagonist was also observed in patients with metastasis at diagnosis (aHR 0.16; 95% CI, 0.04–0.38; p = 0.013) and those receiving ADT for more than six months (aHR 0.30; 95% CI, 0.16–0.54; p < 0.0001). In patients with pre-existing CVD, the MACE risk was 33% lower (aHR 0.67; 95% CI, 0.46–0.96; p = 0.0299) and composite CV events risk was 84% lower (aHR 0.16; 95% CI, 0.05–0.50; p = 0.0017) in GnRH antagonist-treated than the GnRHa-treated *** In patients with PCa and pre-existing CVD, GnRH antagonist use was associated with lower risks for composite CV events and MACE compared with GnRHa.

关键词： Cancer therapy Prostate cancer

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Full-field electroretinography examinations of the human eye with the eye diagnostic device PEP-2000-First results

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Current Directions in biomedical Engineering 2022年第2期8卷 636-639页

作者： Schieding, Nikola Reuter, Thomas Grundmann, Andreas Walther, Sebastian Klee, Sascha ICM-Institut Chemnitzer Maschinen-und Anlagenbau E.V. Otto-Schmerbach-Str. 19 Chemnitz 09117 Germany Department of General Health Studies Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems an der Donau Austria Institute of Biomedical Engineering and Informatics Faculty of Computer Sciences and Automation TU Ilmenau Ilmenau Germany

Full-field electroretinography (full-field ERG) forms the diagnostic basis for numerous pathologies of the eye. For this reason, fast and accurate diagnostics in the field of ophthalmology are essential. Two examination techniques, full-field ERG and pupillometry were combined in a diagnostic device developed by ICM e.V. to reduce the examination process for both examiners and patients. In this paper, the device is examined for the quality of the full-field ERG measurements. A feasibility study with 12 healthy subjects (3 f, 9 m, 36.33 ± 11.94 years) was conducted to evaluate the device. The results showed that the peak times for both light-and dark-adapted measurements were within the range of the researched literature values. However, the amplitudes were markedly lower in both measurements compared to the averaged literature values (dark-adapted about 8.5-fold and light-adapted about 5.5-fold) and are clearly outside the range of values researched. The main reason for this is the use of cup electrodes, which were placed on the skin of the lower eyelid. Nevertheless, plausible and comparable analysis values could be obtained with the eye diagnostic device PEP-2000. Further studies with wire electrodes will be performed. © 2022 by Walter de Gruyter Berlin/Boston.

关键词： Ophthalmology

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Advancing the Use of Longitudinal Electronic Health Records: Tutorial for Uncovering Real-World Evidence in Chronic Disease Outcomes

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Journal of Medical Internet Research 2025年第1期27卷 e71873页

作者： Huang, Feiqing Hou, Jue Zhou, Ningxuan Greco, Kimberly Lin, Chenyu Sweet, Sara Morini Wen, Jun Shen, Lechen Gonzalez, Nicolas Zhang, Sinian Liao, Katherine P. Cai, Tianrun Xia, Zongqi Bourgeois, Florence T. Cai, Tianxi Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA United States Harvard-MIT Center for Regulatory Science Harvard Medical School Boston MA United States Division of Biostatistics and Health Data Science School of Public Health University of Minnesota Minneapolis MN United States Department of Biomedical Informatics Harvard Medical School Boston MA United States Department of Engineering University of Toronto Toronto ON Canada Division of Rheumatology Inflammation and Immunity Brigham and Women's Hospital Boston MA United States Department of Neurology University of Pittsburgh Pittsburgh PA United States Computational Health Informatics Program (CHIP) Boston Children's Hospital Boston MA United States Department of Pediatrics Harvard Medical School Boston MA United States

Managing chronic diseases requires ongoing monitoring of disease activity and therapeutic responses to optimize treatment plans. With the growing availability of disease-modifying therapies, it is crucial to investigate comparative effectiveness and long-term outcomes beyond those available from randomized clinical trials. We introduce a comprehensive pipeline for generating reproducible and generalizable real-world evidence on disease outcomes by leveraging electronic health record data. The pipeline first generates scalable disease outcomes by linking electronic health record data with registry data containing a small sample of labeled outcomes. It then applies causal analysis using these scalable outcomes to evaluate therapies for chronic diseases. The implementation of the pipeline is illustrated in a case study based on multiple sclerosis. Our approach addresses challenges in real-world evidence generation for disease activity of chronic conditions, specifically the lack of direct observations on key outcomes and biases arising from imperfect or incomplete data. We present advanced machine learning techniques such as semisupervised and ensemble methods to impute missing outcome data, further incorporating steps for calibrated causal analyses and bias correction. ©Feiqing Huang, Jue Hou, Ningxuan Zhou, Kimberly Greco, Chenyu Lin, Sara Morini Sweet, Jun Wen, Lechen Shen, Nicolas Gonzalez, Sinian Zhang, Katherine P Liao, Tianrun Cai, Zongqi Xia, Florence T Bourgeois, Tianxi Cai.

关键词： calibration causal inference chronic disease outcomes data imputation electronic health records longitudinal disease activity machine learning real-world evidence

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InfoKids+: A Validation Study of a Pediatric Acuity Risk Stratification Algorithm

Mayo Clinic Proceedings: Digital Health

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Mayo Clinic Proceedings: Digital Health 2025年第2期3卷 100220-100220页

作者： Starvaggi, Carl A. Affentranger, Sophie Lengeler, Noelie Siebert, Johan N. Galetto-Lacour, Annick Tan, Rainer Jaboyedoff, Manon Kuehni, Claudia E. Hartley, Mary-Anne Keitel, Kristina Division of Pediatric Emergency Medicine Department of Pediatrics Inselspital Bern University Hospital Bern Switzerland Division of Pediatric Respiratory Medicine and Allergology Department of Pediatrics Inselspital Bern University Hospital Bern Switzerland Department of Pediatrics Inselspital Bern University Hospital Bern Switzerland Department of Orthopedic Surgery and Traumatology Spitalzentrum Oberwallis Brig Switzerland for Health Sciences University of Bern Bern Switzerland Institute of Social and Preventive Medicine University of Bern Bern Switzerland Department of Pediatrics Children's Hospital Lucerne Lucerne Cantonal Hospital Lucerne Switzerland Department of Pediatric Emergency Medicine Geneva Children's Hospital Geneva University Hospitals Geneva Switzerland Unisanté Center for Primary Care and Public Health University of Lausanne Lausanne Switzerland Swiss Tropical and Public Health Institute Allschwil Switzerland Pediatric Infectious Diseases and Vaccinology Unit Service of Pediatrics Department Women-Mother-Child Lausanne University Hospital and University of Lausanne Lausanne Switzerland Laboratory for intelligent Global Health and Humanitarian Response Technologies (LiGHT Yale School of Medicine Department of Biomedical Informatics and Data Science New Haven CT Laboratory for intelligent Global Health and Humanitarian Response Technologies (LiGHT) EPFL School of Computer Science Lausanne Switzerland

Objective: To prospectively validate InfoKids+, a pediatric acuity electronic risk stratification algorithm (eRSA), against a nurse-based triage standard (nbTS). Participants and Methods: We conducted a prospective validation study in a Swiss university hospital pediatric emergency department to assess the performance of a pediatric acuity eRSA, InfoKids+, on the basis of a well-established parental guidance application, InfoKids. Participants completed the eRSA once seated in a consultation booth. We compared the acuity levels from InfoKids+ (urgent, <4 hours;nonurgent, <24 hours;and no emergency, ≥24 hours) against an nbTS. The primary outcome was the level of agreement and rate of alignment between InfoKids+ and the reference standard. Results: We included 1990 participants from June 3, 2020, through January 31, 2022. InfoKids+ showed a slight level of agreement with the nbTS (κ_lw=0.08;95% CI, 0.06-0.10). InfoKids+ triaged 1762 (89%) cases as urgent (<4 hours), 106 (5%) as nonurgent (≤24 hours), and 122 (6%) as no emergency (≥24 hours), compared with 810 (41%), 843 (42%), and 337 (17%) triages by the nbTS, respectively (P<.001). InfoKids+ acuity level aligned with the reference standard in 888 (45%) cases, whereas it overreferred and underreferred in 999 (50%) and 103 (5%) cases, respectively (P<.001). Conclusion: In summary, our study uncovered notable discrepancies between the InfoKids+ algorithmic triage and conventional nurse-based triage. Our results highlight the critical need for rigorous validation of such tools for accuracy and safety before public release to ensure these tools are beneficial and do not inadvertently cause harm or misallocation of resources. © 2025 The Authors

关键词： ATS Australasian Triage Scale eCRF electronic case report form eRSA electronic risk stratification algorithm nbTS nurse-based triage standard PED pediatric emergency department

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A general sample size framework for developing or updating a clinical prediction model

arXiv

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arXiv 2025年

作者： Riley, Richard D. Whittle, Rebecca Sadatsafavi, Mohsen Martin, Glen P. Pate, Alexander Collins, Gary S. Ensor, Joie Department of Applied Health Sciences School of Health Sciences College of Medicine and Health University of Birmingham Birmingham United Kingdom Birmingham Biomedical Research Centre United Kingdom Respiratory Evaluation Sciences Program Faculty of Pharmaceutical Sciences The University of British Columbia Vancouver Canada Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford OxfordOX3 7LD United Kingdom

Background Existing sample size calculations for developing clinical prediction models rely on standard (unpenalised) regression-based formula. A general approach is needed for developing or updating a CPM using any statistical or machine learning method. Objectives To propose a general framework for sample size calculations applicable to any model development approach, based on drawing samples from anticipated sampling/posterior distributions and targeting minimal degradation in predictive performance compared to reference model. Methods Researchers must provide candidate predictors, their reference model (e.g., a regression equation matching outcome incidence, predictor weights and c-statistic of previous models), and a (synthetic) dataset reflecting the joint distribution of candidate predictors in the target population. Then, a fully simulation-based approach allows the impact of a chosen development sample size and modelling strategy to be examined. This generates thousands of models and, by applying each to the entire target population, leads to sampling/posterior distributions of individual predictions and model performance (degradation) metrics, to inform required sample sizes. To improve computational speed for penalised regression, we also propose a one-sample Bayesian analysis that combines shrinkage priors with a likelihood decomposed into sample size and Fisher’s unit information. Results The framework is illustrated when developing pre-eclampsia prediction models using logistic regression (unpenalised, uniform shrinkage, lasso or ridge) and random forests. It encompasses existing sample size calculation criteria whilst also providing model assurance probabilities, instability metrics, and degradation statistics about calibration, discrimination, clinical utility, prediction error and fairness. Crucially, the required sample size depends on the users’ key estimands of interest and planned model development or updating approach. Conclusions The new, flex

关键词： Size distribution

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BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text

arXiv

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arXiv 2025年

作者： Wu, Jiageng Gu, Bowen Zhou, Ren Xie, Kevin Snyder, Doug Jiang, Yixing Carducci, Valentina Wyss, Richard Desai, Rishi J. Alsentzer, Emily Celi, Leo Anthony Rodman, Adam Schneeweiss, Sebastian Chen, Jonathan H. Romero-Brufau, Santiago Lin, Kueiyu Joshua Yang, Jie Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Siebel School of Computing and Data Science The Grainger College of Engineering University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Department of Otorhinolaryngology – Head & Neck Surgery Mayo Clinic RochesterMN United States Department of Biostatistics Harvard T.H. Chan School of Public Health Harvard University BostonMA United States Department of Biomedical Data Science Stanford University Palo AltoCA United States Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA United States Division of General Internal Medicine Department of Medicine Beth Israel Deaconess Medical Center BostonMA United States Stanford Center for Biomedical Informatics Research Stanford University StanfordCA United States Division of Hospital Medicine Stanford University StanfordCA United States Stanford Clinical Excellence Research Center Stanford University StanfordCA United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University MA United States Broad Institute of MIT and Harvard CambridgeMA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

Large language models (LLMs) hold great promise for medical applications and are evolving rapidly, with new models being released at an accelerated pace. However, current evaluations of LLMs in clinical contexts remain limited. Most existing benchmarks rely on medical exam-style questions or PubMed-derived text, failing to capture the complexity of real-world electronic health record (EHR) data. Others focus narrowly on specific application scenarios, limiting their generalizability across broader clinical use. To address this gap, we present BRIDGE, a comprehensive multilingual benchmark comprising 87 tasks sourced from real-world clinical data sources across nine languages. We systematically evaluated 52 state-of-the-art LLMs (including DeepSeek-R1, GPT-4o, Gemini, and Llama 4) under various inference strategies. With a total of 13,572 experiments, our results reveal substantial performance variation across model sizes, languages, natural language processing tasks, and clinical specialties. Notably, we demonstrate that open-source LLMs can achieve performance comparable to proprietary models, while medically fine-tuned LLMs based on older architectures often underperform versus updated general-purpose models. The BRIDGE and its corresponding leaderboard serve as a foundational resource and a unique reference for the development and evaluation of new LLMs in real-world clinical text understanding. © 2025, CC BY-NC-SA.

关键词： Electronic health record

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Using annotation for computerized support for fast skimming of cardiology electronic health record notes

Using annotation for computerized support for fast skimming ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Dehkordi, Mahshad Koohi H. Einstein, Andrew J. Zhou, Shuxin Elhanan, Gai Perl, Yehoshua Keloth, Vipina K. Geller, James Liu, Hao Njit Department of Computer Science NewarkNJ United States Irving Medical Center Cardiology Division Dept. of Medicine New YorkNY United States Desert Research Institute Renown Institute for Health Innovation RenoNV United States Yale School of Medicine Biomedical Informatics and Data Science New HavenCT United States Njit Department of Data Science NewarkNJ United States Montclair State University Department of Computer Science MontclairNJ United States

ISBN: (纸本)9798350337488

Under the circumstances prevalent in current healthcare, medical professionals such as physicians and nurses need to read large numbers of Electronic Health Record (EHR) notes. This demand fosters a situation in which providers typically do not read a whole note but quickly skim it, to capture its essential content. Consequently, by fast skimming, one may miss a critical medical fact. Annotation highlights important content in EHR notes, and enables healthcare professionals to perform fast skimming, thereby minimizing the risk of missing critical information, which is detrimental to patient care. We designed the Cardiology Interface Terminology (CIT) for the purpose of annotation of cardiology EHRs. We emphasize that by annotation we refer to highlighting important information in the EHR notes for enabling fast skimming rather than just recognizing names of diseases, drugs, etc. from Reference Terminologies as is usually done by existing Named Entity Recognition (NER) systems. The CIT design starts with the cardiology components of SNOMED CT. It is enhanced by mining phrases from cardiology EHRs, as potential CIT concepts, which are of higher granularity than SNOMED concepts. Machine learning (ML), the state of the art technique for mining concepts from EHRs, requires training data. However, there is no training data for designing CIT. In the first stage, we introduce an innovative semi-automatic method for mining concepts from EHRs, to replace costly manual mining. The only manual portion is the review of the automatically mined phrases, before their insertion as CIT concepts. The effectiveness of annotation of cardiology EHRs with CIT was evaluated utilizing proper metrics, and compared to annotation with SNOMED CT. In a future second stage, ML mining techniques will be used for enhancing CIT with extra concepts from EHRs, utilizing the concepts added in the first stage as training data. This work focuses on a novel semi-automated method to design the Cardiology I

关键词： annotation cardiology EHR fast skimming

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MRI detects blood-brain barrier alterations in a rat model of Alzheimer's disease and lung infection

Npj imaging

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Npj imaging 2025年第1期3卷 8页

作者： Yolanda Ohene William J Morrey Elizabeth Powell Katherine F Smethers Nadim Luka Kieron South Michael Berks Catherine B Lawrence Geoff J M Parker Laura M Parkes Hervé Boutin Ben R Dickie Division of Psychology Communication and Human Neuroscience School of Health Sciences Faculty of Biology Medicine and Health University of Manchester Manchester UK. Geoffrey Jefferson Brain Research Centre Manchester Academic Health Science Centre University of Manchester Manchester UK. Division of Neuroscience School of Biological Sciences Faculty of Biology Medicine and Health University of Manchester Manchester UK. Department of Medical Physics and Biomedical Engineering and Department of Neuroinflammation Centre for Medical Image Computing UCL London UK. Division of Informatics Imaging and Data Sciences Faculty of Biology Medicine and Health University of Manchester Manchester UK. Bioxydyn Limited Manchester UK. Imaging Brain & Neuropsychiatry iBraiN Université de Tours INSERM Tours France.

Pneumonia is a common infection in people suffering with Alzheimer's disease, leading to delirium, critical illness or severe neurological decline, which may be due to an amplified response of the blood-brain barrier (BBB) to peripheral insult. We assess the response of the BBB to repeated lung infection in rat model of Alzheimer's disease (TgF344-AD), at 13- and 18-months old, using dynamic contrast-enhanced (DCE) MRI and filter exchange imaging. Higher BBB water exchange rate is initially detected in infected TgF344-AD rats. BBB water exchange rates correlated with hippocampus aquaporin-4 water channel expression in infected animals. We detected no differences in BBB permeability to gadolinium contrast agent measured by DCE-MRI, confirmed by staining for tight junction proteins, occludin and claudin-5. These findings provide insight into the mechanisms of how peripheral inflammation impacts the BBB.

关键词： Diseases Imaging the immune system

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Multicenter target trial emulation to evaluate corticosteroids for sepsis stratified by predicted organ dysfunction trajectory

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Nature Communications 2025年第1期16卷 1-10页

作者： Rajendran, Suraj Xu, Zhenxing Pan, Weishen Zang, Chengxi Siempos, Ilias Torres, Lisa Xu, Jie Bian, Jiang Schenck, Edward J. Wang, Fei Tri-Institutional Computational Biology & Medicine Program Cornell University Cornell NY United States Division of Health Informatics Department of Population Health Sciences Weill Cornell Medicine New York NY United States Division of Pulmonary and Critical Care Medicine Department of Medicine New York-Presbyterian Hospital-Weill Cornell Medical Center Weill Cornell Medicine New York NY United States First Department of Critical Care Medicine and Pulmonary Services Evangelismos Hospital National and Kapodistrian University of Athens Medical School Athens Greece Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Gainesville FL United States Department of Biostatistics and Health Data Science School of Medicine Indiana University Indianapolis IN United States Regenstrief Institute Indianapolis IN United States

Corticosteroids decrease the duration of organ dysfunction in sepsis and a range of overlapping and complementary infectious critical illnesses, including septic shock, pneumonia and the acute respiratory distress syndrome (ARDS). The risk and benefit of corticosteroids are not fully defined using the construct of organ dysfunction duration. This retrospective multicenter, proof-of-concept study aimed to evaluate the association between usage of corticosteroids and mortality of patients with sepsis, pneumonia and ARDS by emulating a target trial framework stratified by predicted organ dysfunction trajectory. The study employed a two staged machine learning (ML) methodology to first subphenotype based on organ dysfunction trajectory then predict this defined trajectory. Once patients were classified by predicted trajectory we conducted a target trial emulation. Our analysis revealed that the association between corticosteroid use and 28-day mortality varied by predicted trajectory and between *** findings suggest that matching treatment strategies to empirically observed pathobiology may offer a more nuanced understanding of corticosteroid utility. © The Author(s) 2025.

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Single Cell Analysis of Adult Human Hematopoietic Stem and Progenitor Cells Identifies a Novel Lymphoid Primed Multipotent Progenitor That Expands in Relapsed Acute Myeloid Leukemia

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BLOOD 2021年 138卷 3259-3259页

作者： Ediriwickrema, Asiri Luca, Bogdan A. Newman, Aaron M. Gentles, Andrew J. Majeti, Ravindra Department of Medicine Division of Hematology Stanford University Stanford CA Stanford Center for Biomedical Informatics Research Department of Medicine Stanford University Stanford CA Department of Biomedical Data Science Stanford University Stanford CA Departments of Medicine (Biomedical Informatics Research) and Biomedical Data Science Stanford University School of Medicine Stanford CA Department of Medicine Division of Hematology Cancer Institute and Institute for Stem Cell Biology and Regenerative Medicine Stanford University Stanford CA

3259

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