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High-Density Lipoproteins Associate with Age-Related Macular Degeneration in the All of Us Research Program

Ophthalmology 2025年第6期132卷 684-691页

作者： Chen, Jimmy S. Esko, Jeffrey D. Walker, Evan Gordts, Philip L.S.M. Baxter, Sally L. Toomey, Christopher B. Division of Ophthalmology Informatics and Data Science Viterbi Family Department of Ophthalmology and Shiley Eye Institute University of California San Diego La Jolla California United States Glycobiology Research and Training Center University of California San Diego La Jolla California United States Division of Biomedical Informatics Department of Medicine University of California San Diego La Jolla California United States

Purpose: Extracellular lipoprotein aggregation is a critical event in age-related macular degeneration (AMD) pathogenesis. In this study, we sought to analyze associations between clinical and genetic-based factors related to lipoprotein metabolism and risk for AMD in the All of Us research program. Design: Cross-sectional retrospective data analysis. Participants: A total of 5028 healthy participants and 2328 patients with AMD from All of Us. Methods: Participants with and without AMD were age, race, and sex matched in a 1:2 ratio, respectively. Smoking status, history of hyperlipidemia, and statin use were extracted in a binary manner. Statin use was further subcategorized into hepatically versus nonhepatically metabolized statins. Laboratory values for low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides (TGs) were also extracted, and outliers were excluded from analysis. The PLINK toolkit was used to extract single nucleotide polymorphisms (SNPs) associated with LDL and HDL dysregulation, as published in prior work. Odds ratio curves were computed to assess the risk between LDL, TG, and HDL versus AMD. All clinical and genetic variables were input into a multivariable logistic regression model, and odds ratios and P values were generated. Main Outcome Measures: Statistical significance of risk factors for AMD, thresholded at P ≤ 0.05. Results: On multivariable regression analysis, statin use and low and high HDL were significantly associated with increased AMD risk (P < 0.001 for all variables). Additionally, the multivariable regression implicated HDL-associated SNP's increased risk for AMD. Last, LPA was identified (P = 0.007) as a novel SNP associated with increased AMD risk. Conclusions: There exists a U-shaped relationship between HDL and AMD risk, such that high and low HDL are significantly associated with increased AMD risk. Additionally, SNPs associated with HDL metabolism are associated with AMD risk. This analysis further e

关键词： Drusen HDL LDL Single nucleotide polymorphism TG

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Assessing the Impact of Post-COVID Clinics on 6-Month Health Care Utilization for Patients With Long COVID: A Single-Center Experience

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Mayo Clinic Proceedings: Innovations, Quality and Outcomes 2025年第3期9卷 100603-100603页

作者： Gong, Kirby D. Afshar, Ali S. Brown, Fred Alavi, Reza Ganesh, Ravindra Kharrazi, Hadi ArtiMed Inc. Baltimore MD Division of General Internal Medicine Mayo Clinic Rochester MN Division of Biomedical Informatics and Data Science Johns Hopkins School of Medicine Baltimore MD

Objective: To assess the impact of post-COVID clinics by examining the association between their early usage and downstream health care utilization. Patients and Methods: In a case-control study spanning data from March 11, 2020 to June 1, 2023, patients with Long COVID were identified from a major health system using diagnosis codes. The Fast, Large-Scale Almost Matching Exactly algorithm was used to match patients who presented early to post-COVID clinics with patients with Long COVID who did not attend such clinics. Matching was performed on demographic characteristics, acute COVID severity, comorbidities, diagnosis date, and vaccination, to reduce confounders for the comparison of the health care utilization and mortality between cohorts. Results: When exactly matching on all 46 features, the algorithm yielded 2814 matched patients, of whom 692 (24.6%;66.6% females;mean [SD] age, 48.8 [14.5] years) were seen in post-COVID clinics within the first 6 months and 2122 (75.4%;64.1% females;mean [SD] age, 49.7 [15.2] years) who were not. The average treatment effect (95% CI) of early post-COVID clinic usage was −0.60 (−0.83 to −0.39) on inpatient visits, −0.19 (−0.26 to −0.11) on emergency department visits, 7.62 (6.96-8.56) on outpatient visits, −$3467 (−$6267 to −$754) on estimated costs, and −0.006 (−0.010 to −0.003) on mortality. Conclusion: Early usage of post-COVID clinics by patients with Long COVID is associated with not only fewer downstream inpatient stays, emergency department visits, estimated costs, and reduced mortality within the first 6 months but also greater outpatient utilization. Results suggest early post-COVID clinic involvement shifts care to outpatient settings, potentially reducing costs and mortality. © 2025 The Authors

关键词： COVID coronavirus disease COVID-19 COronaVIrus Disease 2019 ED emergency department EHR electronic health record ICD International Classification of Diseases SARS-CoV-2 severe acute respiratory syndrome coronavirus 2

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Enhanced Performance and data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Physical activity exercise behavior in individuals with a history of stroke: a retrospective study

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Discover Medicine 2025年第1期2卷 1-7页

作者： Arora, Shiv Ayyathan, Smriti Sudhir Burzynski, Emily Phillips, Vidith Department of Neurology Sardar Patel Medical College Bikaner India Department of Neurology Brihanmumbai Municipal Corporation (BMC) Mumbai India Department of Neurology Medical University of Warsaw Warsaw Poland Department of Internal Medicine Division of Biomedical Informatics and Data Science School of Medicine Johns Hopkins University Baltimore USA

Stroke remains a significant public health challenge in the United States, impacting the physical capabilities and quality of life of affected individuals. This study assesses the prevalence of self-reported physical activity among stroke patients in relation to demographic, socioeconomic, and healthcare access factors. To examine the levels of physical activity among individuals with a history of stroke and identify the variables influencing these levels. Utilizing the Behavioral Risk Factor Surveillance System (BRFSS) database for 2021, this retrospective study analyzed responses from participants identified with a stroke history. data were assessed using the BRFSS Web Enabled Analysis Tool, focusing on self-reported physical activity and including variables such as age, gender, race, education level, employment status, income, and timing of last routine checkup. Of 17,158 respondents reporting a stroke history, 58.5% engaged in physical activity. Physical activity rates were higher among younger individuals, males, and those with higher socioeconomic status. Physical activity in past one month is significantly associated with a lower prevalence of stroke in age groups 25–44 years, 45–64 years, and 65 + years. Younger individuals (18–24 years) do not show a significant association between physical activity and stroke incidence. While a considerable percentage of stroke patients reported engaging in physical activity, disparities exist influenced by age, gender, and socioeconomic factors. These findings highlight the need for targeted interventions to promote physical activities among stroke survivors, particularly those in lower socioeconomic groups or older age brackets.

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Use of Intravascular Ultrasound during Peripheral Vascular Interventions for Claudication Is Not Associated with Improved Outcomes

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Annals of Vascular Surgery 2025年 119卷 14-28页

作者： Dun, Chen Weaver, M. Libby Bose, Sanuja McDermott, Katherine M. Deery, Sarah E. Black, James H. Siracuse, Jeffrey J. Columbo, Jesse Hicks, Caitlin W. Department of Biomedical Informatics and Data Science The Johns Hopkins University School of Medicine Baltimore MD United States Department of Surgery The Johns Hopkins University School of Medicine Baltimore MD United States Division of Vascular and Endovascular Surgery University of Virginia Charlottesville VA United States Division of Vascular Surgery and Endovascular Therapy The Johns Hopkins University School of Medicine Baltimore MD United States Division of Vascular Surgery Maine Medical Center Portland ME United States Division of Acute Care Surgery Department of Surgery Beth Israel Deaconess Medical Center and Harvard Medical School Boston MA United States Section of Vascular Surgery Heart and Vascular Center Dartmouth Hitchcock Medical Center Lebanon NH United States

Background: The efficacy of intravascular ultrasound (IVUS) for improving outcomes of peripheral vascular interventions (PVIs) has not been well studied. We aimed to evaluate the association of IVUS with long-term outcomes in patients undergoing PVI for claudication. Methods: We conducted a two-cohort study using data from 100% of Medicare fee-for-service claims (2018–2022) and the Vascular Quality Initiative Vascular Implant Surveillance and Interventional Outcomes Network (VISION;2016–2019). For both cohorts, we identified all patients who underwent an index (first-time) femoropopliteal PVI for claudication, excluding patients with chronic limb-threatening ischemia (CLTI) and acute limb ischemia. We compared IVUS use over time and by procedure type and setting. We used multivariable Cox proportional hazards models to assess the associations of IVUS with repeat PVI, conversion to CLTI, and amputation. For the Medicare cohort, adjustments were made for baseline patient characteristics, while for the VISION cohort, additional adjustments were made for detailed anatomic factors. All models were clustered by physician. Results: In the Medicare dataset, 69,092 patients (median age 74 years;40.5% female;12.1% non-Hispanic Black) underwent an index femoropopliteal PVI for claudication, of whom 22.1% (N = 15,253) received IVUS. In the VISION dataset, 6,722 patients (median age 72 years;38.7% female;11.6% non-Hispanic Black) underwent an index femoropopliteal PVI for claudication, of whom 3.8% (N = 254) received IVUS. The mean follow-up time for both cohorts was 2.7 years. For both the Medicare and VISION cohorts, IVUS use significantly increased over time, particularly in ambulatory surgery center/office-based laboratory settings and in conjunction with atherectomy procedures (P < 0.001). In the Medicare cohort, IVUS use was associated with a higher hazard of repeat PVI (adjusted hazard ratio [aHR] 1.07, 95% CI 1.02–1.12) and progression to CLTI (aHR 1.11, 95% CI 1.03–1.20

关键词： amputation chronic limb-threatening ischemia endovascular reintervention ultrasound

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powerROC: An Interactive Web Tool for Sample Size Calculation in Assessing Models' Discriminative Abilities

arXiv

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arXiv 2025年

作者： Grolleau, François Tibshirani, Robert Chen, Jonathan H. Stanford Center for Biomedical Informatics Research Stanford University United States Department of Statistics Stanford University United States Department of Biomedical Data Science Stanford University United States Stanford Clinical Excellence Research Center Stanford University United States Division of Hospital Medicine Stanford University United States

Rigorous external validation is crucial for assessing the generalizability of prediction models, particularly by evaluating their discrimination (AUROC) on new data. This often involves comparing a new model's AUROC to that of an established reference model. However, many studies rely on arbitrary rules of thumb for sample size calculations, often resulting in underpowered analyses and unreliable conclusions. This paper reviews crucial concepts for accurate sample size determination in AUROC-based external validation studies, making the theory and practice more accessible to researchers and clinicians. We introduce powerROC, an open-source web tool designed to simplify these calculations, enabling both the evaluation of a single model and the comparison of two models. The tool offers guidance on selecting target precision levels and employs flexible approaches, leveraging either pilot data or user-defined probability distributions. We illustrate powerROC's utility through a case study on hospital mortality prediction using the MIMIC database. Copyright © 2025, The Authors. All rights reserved.

关键词： Prediction models

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Predicting positive Clostridioides difficile test results using large-scale longitudinal data of demographics and medication history

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Heliyon 2025年第1期11卷 e41350页

作者： Pham, Anh El-Kareh, Robert Myers, Frank Ohno-Machado, Lucila Kuo, Tsung-Ting Division of Biomedical Informatics Department of Medicine University of California San Diego La Jolla CA United States UCSD Health System San Diego CA United States Department of Biomedical Informatics and Data Science Yale School of Medicine New Haven CT United States Department of Surgery Yale School of Medicine New Haven CT United States

Background: Clostridioides difficile infection is a major health threat. Healthcare institutions have strong medical and financial incentives to keep infections under control. Blanket testing at admission is in general not recommended, and current predictive models either used moderate sample sizes, over-inflated the number of covariates, or chose non-interpretable algorithms. We aim to develop models using patient data to predict positive Clostridioides difficile test results with discrimination performance, interpretable results, and a reasonable number of covariates that reflect health over a long-time span. Materials and methods: We processed records from 157,493 University of California San Diego Health patients seen between January 01, 2016–July 03, 2019 with at least 6 months of medication history, excluding pregnant women, patients under 18, and prisoners. Three models (Logistic Regression, Random Forest, and Ensemble) were constructed using hyper-parameters selected through 10-fold cross-validation. Model performance was measured by the Area Under the Receiver Operating Characteristic Curve (AUROC). The model coefficients’ odds ratios and p-values were calculated for the Logistic Regression model, as were Gini indices for Random Forest. Decision boundary analysis was conducted using pair-wise false positive and false negative cases each model would predict at a specific threshold. Results: Logistic Regression, Random Forest, and Ensemble models yielded test AUROCs of 0.839, 0.851, and 0.866, respectively. Significant covariates that may affect risk include age, immuno-compromised treatments, past antibiotic uses, and some medications for the gastrointestinal tract. Conclusions: The models achieve high discrimination performance (AUROC >0.83). There is a general consensus among different analysis approaches regarding predictors that impact patients' chances of having a positive test, which may influence Clostridioides difficile risk, including features clini

关键词： Clostridioides difficile infection Decision support systems Electronic health record Machine learning

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An Overview and Recent Developments in the Analysis of Multistate Processes

arXiv

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arXiv 2025年

作者： Gorfine, Malka Cook, Richard J. Andersen, Per Kragh Therneau, Terry M. Joly, Pierre Putter, Hein Perme, Maja Pohar Abrahamowicz, Michal Department of Statistics and Operations Research Tel Aviv University Israel Department of Statistics and Actuarial Science University of Waterloo Canada Section of Biostatistics University of Copenhagen Denmark Division of Biomedical Statistics and Informatics Mayo Clinic New York United States Inserm ISPED Bordeaux Populations Health Research Center University of Bordeaux France Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Department of Biostatistics and Medical Informatics Medical Faculty University of Ljubljana Slovenia Department of Epidemiology Biostatistics and Occupational Health McGill University MontrealQC Canada

Multistate models offer a powerful framework for studying disease processes and can be used to formulate intensity-based and more descriptive marginal regression models. They also represent a natural foundation for the construction of joint models for disease processes and dynamic marker processes, as well as joint models incorporating random censoring and intermittent observation times. This article reviews the ways multistate models can be formed and fitted to life history data. Recent work on pseudo-values and the incorporation of random effects to model dependence on the process history and between-process heterogeneity are also discussed. The software available to facilitate such analyses is listed. © 2025, CC BY.

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ARDS Subphenotypes Exhibit Different Right Ventricular-Pulmonary Arterial Coupling Profiles

CHEST Critical Care

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CHEST Critical Care 2025年第1期3卷 100119-100119页

作者： Siuba, Matthew T. Hockstein, Maxwell A. Rey, Diego Ariel Duggal, Abhijit Deliberato, Rodrigo Octavio Department of Critical Care Medicine Integrated Hospital Care Institute Cleveland Clinic Cleveland OH Department of Biostatistics Health Informatics and Data Science University of Cincinnati College of Medicine Cincinnati OH Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center Cincinnati OH Departments of Emergency Medicine and Critical Care MedStar Washington Hospital Center Washington DC Research Department Endpoint Health Inc. Palo Alto CA

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Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound

arXiv

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arXiv 2025年

作者： Huang, Yuhao Chang, Ao Dou, Haoran Tao, Xing Zhou, Xinrui Cao, Yan Huang, Ruobing Frangi, Alejandro F. Bao, Lingyun Yang, Xin Ni, Dong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Lab Shenzhen University Shenzhen China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen China School of Computing University of Leeds Leeds United Kingdom Shenzhen RayShape Medical Technology Co. Ltd Shenzhen China Division of Informatics Imaging and Data Science School of Health Sciences University of Manchester Manchester United Kingdom Department of Computer Science School of Engineering University of Manchester Manchester United Kingdom Department of Electrical Engineering Department of Cardiovascular Sciences KU Leuven Belgium Alan Turing Institute London United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom Department of Ultrasound Affiliated Hangzhou First People’s Hospital School of Medicine Westlake University China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China

Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation (WSS) can streamline the laborious and intricate annotation process. However, current WSS methods face challenges in achieving precise nodule segmentation, as many of them depend on inaccurate activation maps or inefficient pseudo-mask generation algorithms. In this study, we introduce a novel multi-agent reinforcement learning-based WSS framework called Flip Learning, which relies solely on 2D/3D boxes for accurate segmentation. Specifically, multiple agents are employed to erase the target from the box to facilitate classification tag flipping, with the erased region serving as the predicted segmentation mask. The key contributions of this research are as follows: 1) Adoption of a superpixel/supervoxel-based approach to encode the standardized environment, capturing boundary priors and expediting the learning process. 2) Introduction of three meticulously designed rewards, comprising a classification score reward and two intensity distribution rewards, to steer the agents’ erasing process precisely, thereby avoiding both under- and over-segmentation. 3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency. Extensively validated on the large in-house BUS and ABUS datasets, our Flip Learning method outperforms state-of-the-art WSS methods and foundation models, and achieves comparable performance as fully-supervised learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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