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Progression of Carotid Stenosis Among U.S. Veterans

Journal of vascular Surgery 2025年第6期81卷 e301-e301页

作者： Balasundaram, Naveen Yin, Ming Lee, Kyung Min Hartmann, Katherine Biagetti, Gina Shakt, Gabrielle Levin, Michael Alba, Patrick Lynch, Julie Damrauer, Scott VA Informatics and Computing Infrastructure VA Salt Lake City Healthcare System Salt Lake City Utah University of Pennsylvania Perelman School of Medicine Philadelphia PA University of Pennsylvania Perelman School of Medicine Corporal Michael J. Crescenz VA (CMCVA) Philadelphia PA

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Efficient GPU-accelerated fitting of observational health-scaled stratified and time-varying Cox models

arXiv

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arXiv 2023年

作者： Yang, Jianxiao Schuemie, Martijn J. Suchard, Marc A. Department of Computational Medicine David Geffen School of Medicine UCLA Los AngelesCA United States Department of Biostatistics Fielding School of Public Health UCLA Los AngelesCA United States Johnson & Johnson TitusvilleNJ United States Department of Human Genetics David Geffen School of Medicine UCLA Los AngelesCA United States VA Informatics and Computing Infrastructure US Department of Veterans Affairs Salt Lake CityUT United States

The Cox proportional hazards model stands as a widely-used semi-parametric approach for survival analysis in medical research and many other fields. Numerous extensions of the Cox model have further expanded its versatility. Statistical computing challenges arise, however, when applying many of these extensions with the increasing complexity and volume of modern observational health datasets. To address these challenges, we demonstrate how to employ massive parallelization through graphics processing units (GPU) to enhance the scalability of the stratified Cox model, the Cox model with time-varying covariates, and the Cox model with time-varying coefficients. First we establish how the Cox model with time-varying coefficients can be transformed into the Cox model with time-varying covariates when using discrete time-to-event data. We then demonstrate how to recast both of these into a stratified Cox model and identify their shared computational bottleneck that results when evaluating the now segmented partial likelihood and its gradient with respect to regression coefficients at scale. These computations mirror a highly transformed segmented scan operation. While this bottleneck is not an immediately obvious target for multi-core parallelization, we convert it into an un-segmented operation to leverage the efficient many-core parallel scan algorithm. Our massively parallel implementation significantly accelerates model fitting on large-scale and high-dimensional Cox models with stratification or time-varying effect, delivering an order of magnitude speedup over traditional central processing unit-based implementations. © 2023, CC BY-NC-ND.

关键词： Graphics processing unit

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Development and validation of a Computable Radiotherapy Phenotype

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International journal of radiation oncology, biology, physics 2025年

作者： Cecelia J Madison Julie A Lynch Scott L Duvall Patrick R Alba Elizabeth Hanchrow Fatai Y Agiri Kathryn M Pridgen Ryan J Burri Reid F Thompson Maria Kelly Evangelia Katsoulakis VA Informatics and Computing Infrastructure VA Salt Lake City Health Care System Salt Lake City UT USA. Electronic address: Cecilia.madison@va.gov. VA Informatics and Computing Infrastructure VA Salt Lake City Health Care System Salt Lake City UT USA Division of Epidemiology Department of Internal Medicine University of Utah School of Medicine Salt Lake City UT USA. Electronic address: julie.lynch@va.gov. Division of Epidemiology Department of Internal Medicine University of Utah School of Medicine Salt Lake City UT USA. VA Informatics and Computing Infrastructure VA Salt Lake City Health Care System Salt Lake City UT USA. Radiation Bay Pines VA Healthcare System Bay Pines Florida USA. VA National Radiation Oncology Program Richmond VA USA Division of Hospital and Specialty Medicine VA Portland Healthcare System Portland OR USA Department of Radiation Medicine Oregon Health & Science University Portland OR USA. VA National Radiation Oncology Program Richmond VA USA.

BACKGROUND:This study aims to develop a robust methodology using structured and semi-structured health data to identify patients who have undergone radiation therapy, thereby facilitating future research on treatment outcomes. METHODS:In this retrospective cohort study, we identified Veterans receiving radiation oncology care through documentation of referrals, encounters, and billing codes from 2014 through 2023. We classified administrative codes based on process of care and type of radiation received, and then analyzed utilization patterns. Unstructured data analysis was performed using keyword search of relevant clinical notes in the metadata fields and categorizing those notes by radiation oncology processes. To validate our algorithm, we compared the cohort we developed using existing data sources to a cohort that was chart reviewed. RESULTS:The final cohort included 589,318 Veterans with radiation oncology care. Among these, 355,276 had codes indicating radiotherapy delivery. The most common treatments were image guided (IGRT), three-dimensional conformal (3DCRT), intensity-modulated (IMRT), and stereotactic radiosurgery/stereotactic body (SRS/SBRT). Anatomy-specific billing codes were underutilized. Clinical note analysis identified 1,341 unique labels for radiation oncology content, with 947,928 notes found for 204,064 patients. validation against chart-reviewed datasets showed strong concordance and confirmed the accuracy of our algorithm in identifying radiation oncology care. CONCLUSION:Automated extraction of medical records can be used to identify cohorts of patients who have undergone radiotherapy. Employing this algorithm may facilitate more precise phenotyping of radiotherapy cases and thus significantly enhance our understanding of these cohorts.

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Self?reported physical functioning, cardiometabolic Million Veteran Program enrollees with Traumatic Brain Injury Screening and Evaluation Program data

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Military Medical Research 2023年第5期 587-598页

作者： Alexandra L.Clark Makenna B.McGill Erin D.Ozturk David M.Schnyer Catherine Chanfreau-Coffinier Victoria C.Merritt Research Service VA San Diego Healthcare System (VASDHS) Department of Psychology the University of Texas at Austin San Diego State University/University of California San Diego Joint Doctoral Program VA Informatics and Computing Infrastructure (VINCI) VA Salt Lake City Health Care System Department of Psychiatry School of Medicine University of California San Diego La Jolla CA 29093 USA.6Center of Excellence for Stress and Mental Health

Background:Examining the health outcomes of veterans who have completed the United States Veterans Health Administration’s （VHA’s） Traumatic Brain Injury （TBI） Screening and Evaluation Program may aid in the refinement and improvement of clinical care initiatives within the *** study compared self-reported physical functioning,cardiometabolic health conditions,and health care utilization patterns in Million Veteran Program enrollees with TBI Screening and Evaluation Program data （collected between 2007 and 2019）,with the goal of enhancing understanding of potentially modifiable health conditions in this ***:In this observational cohort study,veterans （n=16,452） were grouped based on the diagnostic outcome of the TBI Screening and Evaluation Program:1) negative TBI screen （Screen–）;2) positive TBI screen but no confirmed TBI diagnosis[Screen+/Comprehensive TBI Evaluation （CTBIE）–];or 3) positive TBI screen and confirmed TBI diagnosis（Screen+/CTBIE+）.Chi-square tests and analysis of covariance were used to explore group differences in physical functioning,cardiometabolic health conditions,and health care utilization patterns,and logistic regressions were used to examine predictors of Screen+/–and CTBIE+/–group ***:The results showed that veterans in the Screen+/CTBIE–and Screen+/CTBIE+groups generally reported poorer levels of physical functioning （P’s<0.001,np2=0.02 to 0.03）,higher rates of cardiometabolic health conditions （P’s<0.001,φ=0.14 to 0.52）,and increased health care utilization （P’s<0.001,φ=0.14 to>0.5） compared with the Screen–group;however,health outcomes were generally comparable between the Screen+/CTBIE–and Screen+/CTBIE+*** analyses confirmed that while physical functioning,hypertension,stroke,healthcare utilization,and prescription medication use reliably distinguished between the Screen–and Screen+groups （P’s<0.02,OR’s=0.78 to 3.38）,only physical functioning distinguished between the Screen+/CTBIE–and Scree

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50563 Anatomical Locations of Keratinocyte Carcinomas Among United States Veterans Affairs Healthcare System Patients in Minnesota

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Journal of the American Academy of Dermatology 2024年第3期91卷 AB124-AB124页

作者： Usovich, Mikhail Minneapolis VA Health Care System Katelyn Rypka Minneapolis VA Medical Center Department of Dermatology Anders Westanmo Minneapolis VA Medical Center Department of Pharmacy Benefits Services Amy Gravely Minneapolis VA Medical Center Department of Research Julie Lynch VA Informatics and Computing Infrastructure VA Salt Lake City Health Care System Noah Goldfarb Minneapolis VA Medical Center Departments of Dermatology and Internal Medicine

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Adjusting for both sequential testing and systematic error in safety surveillance using observational data: Empirical calibration and MaxSPRT

arXiv

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arXiv 2022年

作者： Schuemie, Martijn J. Bu, Fan Nishimura, Akihiko Suchard, Marc A. Observational Health Data Analytics Janssen Research & Development TitusvilleNJ United States Department of Biostatistics University of California Los Angeles Los AngelesCA United States Department of Biostatistics Johns Hopkins University BaltimoreMD United States Department of Human Genetics University of California Los Angeles Los AngelesCA United States VA Informatics and Computing Infrastructure US Department of Veterans Affairs Salt Lake CityUT United States

Post-approval safety surveillance of medical products using observational healthcare data can help identify safety issues beyond those found in pre-approval trials. When testing sequentially as data accrue, maximum sequential probability ratio testing (MaxSPRT) is a common approach to maintaining nominal type 1 error. However, the true type 1 error may still deviate from the specified one because of systematic error due to the observational nature of the analysis. This systematic error may persist even after controlling for known confounders. Here we propose to address this issue by combing MaxSPRT with empirical calibration. In empirical calibration, we assume uncertainty about the systematic error in our analysis, the source of uncertainty commonly overlooked in practice. We infer a probability distribution of systematic error by relying on a large set of negative controls: exposure-outcome where no causal effect is believed to exist. Integrating this distribution into our test statistics has previously been shown to restore type 1 error to nominal. Here we show how we can calibrate the critical value central to MaxSPRT. We evaluate this novel approach using simulations and real electronic health records, using H1N1 vaccinations during the 2009-2010 season as an example. Results show that combining empirical calibration with MaxSPRT restores nominal type 1 error. In our real-world example, adjusting for systematic error using empirical calibration has a larger impact than, and hence is just as essential as, adjusting for sequential testing using MaxSPRT. We recommend performing both, using the method described here. Copyright © 2022, The Authors. All rights reserved.

关键词： Statistical tests

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44541 Assessment of Biologic Use in the Treatment of Patients with Hidradenitis Suppurativa in the va Health Care System

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Journal of the American Academy of Dermatology 2023年第3期89卷 AB124-AB124页

作者： Wendland, Zachary, MD, MPH Rypka, Katelyn, BS Herzog, Claire, BS Greenlund, Lindsey, BS Agiri, Fatai, BS Lynch, Julie, PhD, MBA, RN Goldfarb, Noah, MD Departments of Medicine and Dermatology Minneapolis Veterans Affairs Health Care System Minneapolis MN USA University of Minnesota Medical School Minn University of Minnesota Medical School Minneapolis MN USA VA Informatics and Computing Infrastructure VA Salt Lake City Healthcare System Salt Lake City UT

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51444 Potential Underutilization of Biologic Therapies and Description of Healthcare Resource Utilization (HCRU) in US Veterans With Chronic Spontaneous Urticaria (CSU)

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Journal of the American Academy of Dermatology 2024年第3期91卷 AB283-AB283页

作者： Lillian Sun Fatai Y. Agiri Peter N. Danilov Gailen Marshall Jonathan Rodrigues Ashley Post Dhaval Patil Wing Chow Amit Garg Julie A. Lynch Elma D. Baron Department of Dermatology Louis Stokes Cleveland VA Medical Center Cleveland OH USA VA Informatics and Computing Infrastructure (VINCI) VA Salt Lake City Health Care System Salt Lake City UT USA University of Mississippi Medical Center Jackson MS USA Novartis Pharmaceuticals Corporation East Hanover NJ USA Northwell Health New Hyde Park NY USA Division of Epidemiology Department of Internal Medicine University of Utah School of Medicine Salt Lake City UT USA

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RWD172 Optimizing RWE Design and Conduct by Leveraging Computable Operational Definitions Indication Libraries: Development, Verification, and value

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value in Health 2024年第12期27卷 S607-S607页

作者： Kamauu, J. Buck, M. Thompson, D. Shields, A. Somerday, M. Kamauu, A.G. Parker, C.G. Duvall, S. Kamauu, A. Navidence LLC Draper UT USA Navidence Cedar Hills UT USA Rubidoux Research LLC Manchester MA USA Navidence LLC Chapel Hill NC USA Navidence LLC Salt Lake City UT USA Navidence LLC Sandy UT USA VA Informatics and Computing Infrastructure VA Salt Lake City Health Care System Salt Lake City UT USA Navidence LLC Bountiful UT USA

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Massive Parallelization of Massive Sample-size Survival Analysis

arXiv

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arXiv 2022年

作者： Yang, Jianxiao Schuemie, Martijn J. Ji, Xiang Suchard, Marc A. Department of Computational Medicine David Geffen School of Medicine at UCLA Los AngelesCA United States Department of Biostatistics Fielding School of Public Health at UCLA Los AngelesCA United States Janssen Research and Development TitusvilleNJ United States Department of Human Genetics David Geffen School of Medicine at UCLA Los AngelesCA United States VA Informatics and Computing Infrastructure US Department of Veterans Affairs Salt Lake CityUT United States Department of Mathematics Tulane University New OrleansLA United States

Large-scale observational health databases are increasingly popular for conducting comparative effectiveness and safety studies of medical products. However, increasing number of patients poses computational challenges when fitting survival regression models in such studies. In this paper, we use graphics processing units (GPUs) to parallelize the computational bottlenecks of massive sample-size survival analyses. Specifically, we develop and apply time- and memory-efficient single-pass parallel scan algorithms for Cox proportional hazards models and forward-backward parallel scan algorithms for Fine-Gray models for analysis with and without a competing risk using a cyclic coordinate descent optimization approach. We demonstrate that GPUs accelerate the computation of fitting these complex models in large databases by orders of magnitude as compared to traditional multi-core CPU parallelism. Our implementation enables efficient large-scale observational studies involving millions of patients and thousands of patient characteristics. The above implementation is available in the open-source R package Cyclops (Suchard et al., 2013). © 2022, CC BY-NC-ND.

关键词： Graphics processing unit

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