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Impact of atrial fibrillation and atrial fibrillation therapies on sports performance in athletes

Heart Rhythm 2024年

作者： Khan, Ali K. Lee, Hyon Jae Hills, Mellanie True Moss, Melissa Lara, Brenna Baumann, Raymond Brandt, Cynthia Lampert, Rachel Department of Medicine Yale University School of Medicine New Haven Connecticut United States New Jersey Cardiology Associates West Orange NJ United States *** Greenwood Texas United States Department of Biomedical Informatics and Data Science VA Connecticut West Haven CT United States

Background: It has been hypothesized that both atrial fibrillation (AF) and medications for AF are poorly tolerated in athletes. Objective: The purpose of this study was to determine the impact of AF and AF treatments on sports performance. Methods: An Internet-based survey, initiated via ***, queried the impact of AF and treatment modalities on sports performance, training, and symptoms. Performance (rated in comparison to personal best), frequency of training, and competition were compared before onset of AF symptoms vs when symptoms at worst, via paired Wilcoxon, and then similarly compared in relation to participants’ AF treatments. Results: Between May 13, 2019, and February 29, 2020, 1055 athletes responded (78% male, median age 61–70 years). The sports most reported were cycling (25%) and running (20%). Development of AF was associated with declining sports performance, competition, and training frequency. Of 565 participants who detrained, 31% reported no change in symptoms, 56% reported decreased frequency, and 13% reported no further AF. Among athletes who received treatment, ablation was associated with greater improvement in sports performance than use of medications. Of 262 athletes currently taking medication, 27% reported that it was completely effective, 58% partially effective, and 15% not effective. Of 653 athletes who have taken medication currently and/or in the past, 43% reported side effects, most commonly fatigue, low energy, or decreased athletic performance. Conclusion: Athletes reported that AF was associated with worsening sports performance. Among AF treatment modalities, ablation was associated with the greater reported improvement in sports performance than use of medications, which also had a high frequency of side effects. © 2024 Heart Rhythm Society

关键词： Ablation Athlete Atrial fibrillation Detraining Exercise

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Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care

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Journal of the Society for Cardiovascular Angiography and Interventions 2025年第3期4卷 102562-102562页

作者： Aminorroaya, Arya Biswas, Dhruva Pedroso, Aline F. Khera, Rohan Section of Cardiovascular Medicine Department of Internal Medicine Yale School of Medicine New Haven Connecticut United States Cardiovascular Data Science (CarDS) Lab Yale School of Medicine New Haven Connecticut United States Center for Outcomes Research and Evaluation (CORE) Yale New Haven Hospital New Haven Connecticut United States Section of Biomedical Informatics and Data Science Yale School of Medicine New Haven Connecticut United States Section of Health Informatics Department of Biostatistics Yale School of Public Health New Haven Connecticut United States

Artificial intelligence (AI) serves as a powerful tool that can revolutionize how personalized, patient-focused care is provided within interventional cardiology. Specifically, AI can augment clinical care across the spectrum for acute coronary syndrome, coronary artery disease, and valvular heart disease, with applications in coronary and structural heart interventions. This has been enabled by the potential of AI to harness various types of health data. We review how AI-driven technologies can advance diagnosis, preprocedural planning, intraprocedural guidance, and prognostication in interventional cardiology. AI automates clinical tasks, increases efficiency, improves reliability and accuracy, and individualizes clinical care, establishing its potential to transform care. Furthermore, AI-enabled, community-based screening programs are yet to be implemented to leverage the full potential of AI to improve patient outcomes. However, to transform clinical practice, AI tools require robust and transparent development processes, consistent performance across various settings and populations, positive impact on clinical and care quality outcomes, and seamless integration into clinical workflows. Once these are established, AI can reshape interventional cardiology, improving precision, efficiency, and patient outcomes. © 2025 The Author(s)

关键词： artificial intelligence interventional cardiology percutaneous coronary intervention structural heart intervention

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Large Language Models in Healthcare

arXiv

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arXiv 2025年

作者： Al-Garadi, Mohammed Mungle, Tushar Ahmed, Abdulaziz Sarker, Abeed Miao, Zhuqi Matheny, Michael E. Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Medicine Stanford University StanfordCA United States Department of Biomedical Informatics Emory University School of Medicine AtlantaGA United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States Department of Health Services Administration UAB BirminghamAL United States Department of Biomedical Informatics and Data Science UAB BirminghamAL United States School of Business The State University of New York at New Paltz New PaltzNY United States Department of Medicine Vanderbilt University Medical Center NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Geriatric Research Education and Clinical Care Service Tennessee Valley Healthcare System VA NashvilleTN United States

Large language models (LLMs) hold promise for transforming healthcare, from streamlining administrative and clinical workflows to enriching patient engagement and advancing clinical decision-making. However, their successful integration requires rigorous development, adaptation, and evaluation strategies tailored to clinical needs. In this Review, we highlight recent advancements, explore emerging opportunities for LLM-driven innovation, and propose a framework for their responsible implementation in healthcare settings. We examine strategies for adapting LLMs to domain-specific healthcare tasks, such as fine-tuning, prompt engineering, and multimodal integration with electronic health records. We also summarize various evaluation metrics tailored to healthcare, addressing clinical accuracy, fairness, robustness, and patient outcomes. Furthermore, we discuss the challenges associated with deploying LLMs in healthcare—including data privacy, bias mitigation, regulatory compliance, and computational sustainability—and underscore the need for interdisciplinary collaboration. Finally, these challenges present promising future research directions for advancing LLM implementation in clinical settings and healthcare. © 2025, CC BY-NC-ND.

关键词： Patient treatment

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Modeling Path Importance for Effective Alzheimer’s Disease Drug Repurposing

arXiv

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arXiv 2023年

作者： Xiang, Shunian Lawrence, Patrick J. Peng, Bo Chiang, ChienWei Kim, Dokyoon Shen, Li Ning, Xia Biomedical Informatics Department The Ohio State University ColumbusOH43210 United States Computer Science and Engineering Department The Ohio State University ColumbusOH43210 United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA19104 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network’s rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies. © 2023, CC BY.

关键词： Embeddings

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How to develop, externally validate, and update multinomial prediction models

arXiv

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arXiv 2023年

作者： Gehringer, Celina K. Martin, Glen P. Van Calster, Ben Hyrich, Kimme L. Verstappen, Suzanne M.M. Sergeant, Jamie C. Centre for Epidemiology Versus Arthritis Centre for Musculoskeletal Research Division of Musculoskeletal and Dermatological Sciences University of Manchester Manchester United Kingdom Centre for Biostatistics Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Centre for Health Informatics Division of Informatics Imaging and Data Sciences University of Manchester Manchester United Kingdom Department of Biomedical Data Sciences Leiden University Medical Centre Leiden Netherlands Department of Development & Regeneration KU Leuven Leuven Belgium NIHR Manchester Biomedical Research Centre Manchester University NHS Foundation Trust Manchester Academic Health Science Centre Manchester United Kingdom

Objectives: Multinomial prediction models (MPMs) have a range of potential applications across healthcare where the primary outcome of interest has multiple nominal or ordinal categories. However, the application of MPMs is scarce, which may be due to the added methodological complexities that they bring. This article provides a guide of how to develop, externally validate, and update MPMs. Study Design and Setting: Using a previously developed and validated MPM for treatment outcomes in rheumatoid arthritis as an example, we outline guidance and recommendations for producing a clinical prediction model using multinomial logistic regression. This article is intended to supplement existing general guidance on prediction model research. Results: This guide is split into three parts: 1) Outcome definition and variable selection, 2) Model development, and 3) Model evaluation (including performance assessment, internal and external validation, and model recalibration). We outline how to evaluate and interpret the predictive performance of MPMs. R code is provided. Conclusions: We recommend the application of MPMs in clinical settings where the prediction of a nominal polytomous outcome is of interest. Future methodological research could focus on MPM-specific considerations for variable selection and sample size criteria for external validation. © 2023, CC BY.

关键词： Forecasting

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Towards Optimal Use of Surrogate Markers to Improve Power

arXiv

引用

arXiv 2022年

作者： Wang, Xuan Parast, Layla Tian, Lu Cai, Tianxi Department of Biostatistics Harvard University BostonMA02115 United States Department of Statistics and Data Sciences University of Texas at Austin AustinTX78712 United States Department of Biomedical Data Science Stanford University StanfordCA94305 United States Department of Biomedical Informatics Harvard University BostonMA02115 United States

Motivated by increasing pressure for decision makers to shorten the time required to evaluate the efficacy of a treatment such that treatments deemed safe and effective can be made publicly available, there has been substantial recent interest in using an earlier or easier to measure surrogate marker, S, in place of the primary outcome, Y. To validate the utility of a surrogate marker in these settings, a commonly advocated measure is the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker (PTE). Model based and model free estimators for PTE have also been developed. While this measure is very intuitive, it does not directly address the important questions of how S can be used to make inference of the unavailable Y in the next phase clinical trials. In this paper, to optimally use the information of surrogate S, we provide a framework for deriving an optimal transformation of S, gopt(S), such that the treatment effect on gopt(S) maximally approximates the treatment effect on Y in a certain sense. Based on the optimally transformed surrogate, gopt(S), we propose a new measure to quantify surrogacy, the relative power (RP), and demonstrate how RP can be used to make decisions with S instead of Y for next phase trials. We propose nonparametric estimation procedures, derive asymptotic properties, and compare the RP measure with the PTE measure. Finite sample performance of our estimators is assessed via a simulation study. We illustrate our proposed procedures using an application to the Diabetes Prevention Program (DPP) clinical trial to evaluate the utility of hemoglobin A1c and fasting plasma glucose as surrogate markers for diabetes. Copyright © 2022, The Authors. All rights reserved.

关键词： Decision making

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Software package for regression algorithms based on Gaussian Conditional Random Fields

Software package for regression algorithms based on Gaussian...

引用

International Conference on Machine Learning and Applications (ICMLA)

作者： Tijana Markovic Vladan Devedzic Fang Zhou Zoran Obradovic School of Innovation Design and Engineering Malardalen University Vasteras Sweden Faculty of Organizational Sciences University of Belgrade Belgrade Serbia School of Data Science and Engineering East China Normal University Shanghai China Center for Data Analytics and Biomedical Informatics Temple University Philadelphia USA

The Gaussian Conditional Random Fields (GCRF) algorithm and its extensions are used for machine learning regression problems in which the attributes of objects and the correlation between objects should be considered when making predictions. These algorithms can be applied in different domains where problems can be seen as graphs, but their implementation requires complex calculations and good programming skills. This paper presents an open source software package that includes a tool with graphical user interface (GCRFs tool) and Java library (GCRFs library). GCRFs tool is software that integrates various GCRF-based algorithms and supports training and testing of those algorithms on real-world datasets. The main goal of GCRFs tool is to provide a straightforward and user-friendly graphical user interface that will simplify the use of GCRF-based algorithms. GCRFs Java library contains basic classes for GCRF concepts and can be used by researchers who have experience in Java programming. Also, this paper presents the results of a pilot usability evaluation of the GCRFs tool, where the software was evaluated with expert and non-expert users. This evaluation gave us detailed insight into the experiences and opinions of the users and helped us outline priorities for future development.

关键词： Training Java Machine learning algorithms Software packages Software algorithms Programming Prediction algorithms

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AB1435 CLINICAL PREDICTION MODELS FOR METHOTREXATE OUTCOMES IN PATIENTS WITH RHEUMATOID ARTHRITIS: SYSTEMATIC REVIEW AND CRITICAL APPRAISAL

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Annals of the Rheumatic Diseases 2022年 81卷 1823-1824页

作者： C. Gehringer G. Martin K. Hyrich S. Verstappen J. Sergeant University of Manchester Centre for Epidemiology Versus Arthritis Manchester United Kingdom University of Manchester Centre for Health Informatics Division of Informatics Imaging and Data Science Manchester United Kingdom Manchester University NHS Foundation Trust NIHR Manchester Biomedical Research Centre Manchester United Kingdom University of Manchester Centre for Biostatistics Manchester Academic Health Science Centre Manchester United Kingdom

Background Methotrexate (MTX) is the preferred first line therapy for rheumatoid arthritis (RA). MTX has several advantages over other treatments including effectiveness and low cost; however, around 40% of patients are classed as non-responders after 6 months (1). Therefore, there is a clinical need to identify patients at high-risk of poor outcomes, such that patients could potentially be fast tracked onto alternative therapies to improve their clinical outcomes and quality of life. Such risk stratification is possible through prognostic prediction models, although models which have previously been developed appear to have had little impact on practice. This may be in part due to methodological features of their development and validation but, to date, no review has collated the evidence in this field. Objectives This systematic review aimed to (i) identify and summarise multivariable prediction models of MTX treatment outcomes in biologic-naïve adult RA patients, and (ii) critically appraise their methodological properties. Methods The electronic databases Medline and Embase were searched to identify studies developing or validating prediction models of MTX outcomes in the population of interest, including demographic, disease-specific or treatment-related covariates, published after 2005. Models were stratified by outcome definition, and information on participants, predictors, model performance, handling of missing data and model validation were extracted. A risk of bias (ROB) assessment using PROBAST (prediction model risk of bias assessment tool) was carried out. Two reviewers were independently involved in screening, data extraction, and ROB stages. Results The included studies used three main outcome definitions: a state of disease activity, such as low disease activity or remission; the EULAR response criteria; or discontinuation due to adverse events (AEs). Some studies incorporated AEs into a composite outcome with disease activity and few accounted for

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Can Researchers Assess the Suitability of datasets to Answer Their Research Questions, with Access to Metadata Only? 18

Can Researchers Assess the Suitability of Datasets to Answer...

引用

18th World Congress on Medical and Health informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021

作者： Tilston, George Williams, Richard Griffiths, Emily Al-Adely, Sarah Lawson-Tovey, Saskia Hulme, Will Short, Andrea Davies, Jim Welch, James Peek, Niels Centre for Health Informatics Division of Informatics Imaging and Data Sciences School of Health Sciences The University of Manchester ManchesterM13 9GB United Kingdom Nihr Manchester Biomedical Research Centre The University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Nihr Greater Manchester Patient Safety Translational Research Centre The University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Centre for Epidemiology Versus Arthritis The University of Manchester Manchester United Kingdom Centre for Genetics and Genomics Versus Arthritis Centre for Musculoskeletal Research The University of Manchester Manchester United Kingdom Division of Human Communication and Hearing School of Health Sciences The University of Manchester Manchester United Kingdom Nihr Oxford Biomedical Research Centre Oxford Big Data Institute Li Ka Shing Centre for Health Information and Discovery Old Road Campus Oxford United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom

ISBN: (纸本)9781643682648

Health research increasingly requires effective ways to identify existing datasets and assess their suitability for research. We sought to test whether researchers could use an existing metadata catalogue to assess the suitability of datasets for addressing specified research questions. Five datasets were described in the National Institute for Health Research Health informatics Collaborative metadata catalogue, and for each dataset five associated research questions were formulated, some of which were answerable with the dataset while others were not. Thirteen researchers each assessed whether the ten questions associated with two randomly selected datasets were answerable with the described datasets. After removing instances where participants misunderstood the question or lacked subject matter knowledge to make the assessment, we found that 87 out of 109 assessments (80%) were correct. Participants particularly struggled with one dataset which consisted of EHR data. The most common reason for incorrect assessments was the inability to find the relevant information in the metadata catalogue. © 2022 International Medical informatics Association (IMIA) and IOS Press.

关键词： Metadata

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***: AN OPEN-SOURCE RETRIEVAL-AUGMENTED LARGE LANGUAGE MODEL SYSTEM FOR ANSWERING MEDICAL QUESTIONS USING SCIENTIFIC LITERATURE

arXiv

引用

arXiv 2023年

作者： Lozano, Alejandro Chiang, Chia-Chun Fleming, Scott L. Shah, Nigam Department of Biomedical Data Science Stanford University StanfordCA United States Department of Neurology Mayo Clinic Rochester MN United States Human Centered Artificial-Intelligence Institute Stanford University StanfordCA United States Center for Biomedical Informatics Research Clinical Excellence Research Center Stanford University Technology and Digital Solutions Stanford Healthcare United States

The quickly-expanding nature of published medical literature makes it challenging for clinicians and researchers to keep up with and summarize recent, relevant findings in a timely manner. While several closed-source summarization tools based on large language models (LLMs) now exist, rigorous and systematic evaluations of their outputs are lacking. Furthermore, there is a paucity of high-quality datasets and appropriate benchmark tasks with which to evaluate these tools. We address these issues with four contributions: we release ***, an open-source WebApp that answers clinical questions based on dynamically retrieved scientific literature;we specify an information retrieval and abstractive summarization task to evaluate the performance of such retrieval-augmented LLM systems;we release a dataset of 200 questions and corresponding answers derived from published systematic reviews, which we name PubMed Retrieval and Synthesis (PubMedRS-200);and report benchmark results for *** and other publicly available OpenQA systems on PubMedRS-200. © 2023, CC BY.

关键词： Search engines

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