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Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease

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Computers in Biology and Medicine 2024年 176卷 108588-108588页

作者： Gómez-Pascual, Alicia Naccache, Talel Xu, Jin Hooshmand, Kourosh Wretlind, Asger Gabrielli, Martina Lombardo, Marta Tiffany Shi, Liu Buckley, Noel J. Tijms, Betty M. Vos, Stephanie J.B. ten Kate, Mara Engelborghs, Sebastiaan Sleegers, Kristel Frisoni, Giovanni B. Wallin, Anders Lleó, Alberto Popp, Julius Martinez-Lage, Pablo Streffer, Johannes Barkhof, Frederik Zetterberg, Henrik Visser, Pieter Jelle Lovestone, Simon Bertram, Lars Nevado-Holgado, Alejo J. Gualerzi, Alice Picciolini, Silvia Proitsi, Petroula Verderio, Claudia Botía, Juan A. Legido-Quigley, Cristina Department of Information and Communications Engineering Faculty of Informatics University of Murcia Murcia Spain Steno Diabetes Center Copenhagen Herlev Denmark Department of Data Science City University of London United Kingdom Institute of Pharmaceutical Science King's College London London United Kingdom CNR Institute of Neuroscience Vedano al Lambro20854 Italy School of Medicine and Surgery University of Milano-Bicocca 20126 Italy Oxford United Kingdom Department of Psychiatry University of Oxford United Kingdom Kavli Institute for Nanoscience Discovery Denmark Alzheimer Center VU University Medical Center Amsterdam Netherlands Department of Psychiatry and Neuropsychology Alzheimer Centrum Limburg Maastricht University Maastricht Netherlands Department of Biomedical Sciences University of Antwerp Antwerp Belgium Vrije Universiteit Brussel Brussels Belgium Complex Genetics Group VIB Center for Molecular Neurology VIB Antwerp Belgium Institute Born-Bunge Department of Biomedical Sciences University of Antwerp Antwerp Belgium University of Geneva Geneva Switzerland IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia Italy Institute of Neuroscience and Physiology Sahlgrenska Academy at University of Gothenburg Gothenburg Sweden Barcelona Spain Old age psychiatry University Hospital of Lausanne University of Lausanne Switzerland Department of Geriatric Psychiatry University Hospital of Psychiatry Zürich University of Zürich Switzerland CITA-Alzheimer Foundation San Sebastian Spain AC Immune SA formerly Janssen R&D LLC. Beerse Belgium at the time of study conduct Lausanne Switzerland Department of Radiology and Nuclear Medicine Amsterdam UMC Vrije Universiteit Netherlands Queen Square Institute of Neurology and Centre for Medical Image Computing University College London United Kingdom Clinical Neurochemistry Laboratory Sahlgrenska University Hospital Mölndal Sweden Department of Psychiatry and Neurochemistry Institute of

Background: Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed. Method: Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis. Results: Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others. Conclusions: This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways. © 2024 The Authors

关键词： Metabolites

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PL02.2.A RAPID, COMPREHENSIVE REPORTING OF MOLECULAR DIAGNOSTICS WITH RAPID-CNS2: A PROSPECTIVE VALIDATION COHORT

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Neuro-Oncology 2023年第SUPPLEMENT_2期25卷 ii2-ii2页

作者： Patel, A Hinz, F Dogan, H Payne, A Göbel, K Schrimpf, D Ritter, M Krause, E Pfeifer, L Krech, M Blume, C Stichel, D Goidts, V Schweizer, L Unterberg, A Wick, W Pfister, S Sill, M Loose, M von Deimling, A Jones, D T W Schlesner, M Sahm, F Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) German Cancer Research Center Heidelberg Germany and Dept. of Neuropathology University Hospital Heidelberg Heidelberg Germany Heidelberg Germany Hopp Children's Cancer Center (KiTZ) Heidelberg Germany University of Nottingham Nottingham United Kingdom Charité – Universitätsmedizin Berlin Berlin Germany Neurological Institute (Edinger Institute) University Hospital Frankfurt Frankfurt Germany Department of Neurosurgery University Hospital Heidelberg Heidelberg Germany Clinical Cooperation Unit Neurooncology German Consortium for Translational Cancer Research (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany and Department of Neurology and Neurooncology Program NCT University Hospital Heidelberg Heidelberg Germany Division of Pediatric Neurooncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Pediatric Glioma Research Group German Cancer Research Center (DKFZ) Heidelberg Germany Biomedical Informatics Data Mining and Data Analytics University of Augsburg Augsburg Germany

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Diffusion mapping of drug targets on disease signaling network elements reveals drug combination strategies 23rd

Diffusion mapping of drug targets on disease signaling netwo...

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23rd Pacific Symposium on Biocomputing, PSB 2018

作者： Xu, Jielin Regan-Fendt, Kelly Deng, Siyuan Carson, William E. Payne, Philip R. O. Li, Fuhai Department of Biomedical Informatics The Ohio State University ColumbusOH43210 United States Comprehensive Cancer Center The Ohio State University ColumbusOH43210 United States Institute for Informatics Washington University in St. Louis St. LouisMO63110 United States Department of Biomedical Informatics Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

The emergence of drug resistance to traditional chemotherapy and newer targeted therapies in cancer patients is a major clinical challenge. Reactivation of the same or compensatory signaling pathways is a common class of drug resistance mechanisms. Employing drug combinations that inhibit multiple modules of reactivated signaling pathways is a promising strategy to overcome and prevent the onset of drug resistance. However, with thousands of available FDA-approved and investigational compounds, it is infeasible to experimentally screen millions of possible drug combinations with limited resources. Therefore, computational approaches are needed to constrain the search space and prioritize synergistic drug combinations for preclinical studies. In this study, we propose a novel approach for predicting drug combinations through investigating potential effects of drug targets on disease signaling network. We first construct a disease signaling network by integrating gene expression data with disease-associated driver genes. Individual drugs that can partially perturb the disease signaling network are then selected based on a drug-disease network "impact matrix", which is calculated using network diffusion distance from drug targets to signaling network elements. The selected drugs are subsequently clustered into communities (subgroups), which are proposed to share similar mechanisms of action. Finally, drug combinations are ranked according to maximal impact on signaling sub-networks from distinct mechanism-based communities. Our method is advantageous compared to other approaches in that it does not require large amounts drug dose response data, drug-induced "omics" profiles or clinical efficacy data, which are not often readily available. We validate our approach using a BRAF-mutant melanoma signaling network and combinatorial in vitro drug screening data, and report drug combinations with diverse mechanisms of action and opportunities for drug repositioning. © 2017 Th

关键词： Chemotherapy

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TMOD-04. IMAGE-BASED DRUG RESPONSE PROFILING FROM PEDIATRIC TUMOR CELL SPHEROIDS USING PATIENT-BY-PATIENT DEEP TRANSFER LEARNING

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Neuro-Oncology 2021年第SUPPLEMENT_1期23卷 i36-i36页

作者： Berker, Yannick ElHarouni, Dina Peterziel, Heike Oehme, Ina Schlesner, Matthias Witt, Olaf Oppermann, Sina Hopp Children’s Cancer Center Heidelberg (KiTZ) Heidelberg Germany Translational Pediatric Pharmacology Clinical Cooperation Unit Pediatric Oncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Clinical Bioinformatics Pediatric Neurooncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Clinical Cooperation Unit Pediatric Oncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Biomedical Informatics Data Mining and Data Analytics University of Augsburg Augsburg Germany

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JS08.6.A A connectivity signature for glioblastoma

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Neuro-Oncology 2022年第SUPPLEMENT_2期24卷 ii8-ii9页

作者： Hoffmann, D C Hai, L Wagener, R Mandelbaum, H Xie, R Hausmann, D Dominguez Azorín, D Weil, S Sievers, P Cebulla, G Ito, J Warnken, U Venkataramani, V Ernst, K Reibold, D Will, R Suvà, M L Herold-Mende, C Sahm, F Winkler, F Schlesner, M Wick, W Kessler, T Clinical Cooperation Unit Neurooncology German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Department of Neurology and Neurooncology Program National Center for Tumor Diseases Heidelberg University Hospital Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Bioinformatics and Omics Data Analytics DKFZ Heidelberg Germany Department of Neuropathology Institute of Pathology University Hospital Heidelberg Heidelberg Germany Clinical Cooperation Unit Neuropathology DKTK DKFZ Heidelberg Germany Department of Neuroanatomy Institute for Anatomy and Cell Biology Heidelberg University Heidelberg Germany Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ) Heidelberg Germany Division of Pediatric Neurooncology DKTK DKFZ Heidelberg Germany Genomics and Proteomics Core Facility DKTK DKFZ Heidelberg Germany Broad Institute of Harvard and MIT Cambridge MA United States Department of Pathology and Center for Cancer Research Massachusetts General Hospital and Harvard Medical School Boston MA United States Department of Neurosurgery Heidelberg University Hospital Heidelberg Germany Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany

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DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images

arXiv

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arXiv 2023年

作者： Qian, Bo Chen, Hao Wang, Xiangning Che, Haoxuan Kwon, Gitaek Kim, Jaeyoung Choi, Sungjin Shin, Seoyoung Krause, Felix Unterdechler, Markus Hou, Junlin Feng, Rui Li, Yihao Daho, Mostafa El Habib Wu, Qiang Zhang, Ping Yang, Xiaokang Cai, Yiyu Li, Huating Jia, Weiping Sheng, Bin Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Department of Ophthalmology Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China VUNO Inc. Seoul Korea Republic of AI/DX Convergence Business Group KT Korea Republic of Johannes Kepler University Linz Linz Austria School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Academy for Engineering and Technology Fudan University China LaTIM UMR 1101 Inserm Brest France University of Western Brittany Brest France Department of Computer Science and Engineering The Ohio State University Ohio United States Department of Biomedical Informatics The Ohio State University Ohio United States Translational Data Analytics Institute The Ohio State University Ohio United States MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Shanghai China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Department of Endocrinology and Metabolism Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China

Computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness. Ultra-wide optical coherence tomography angiography (UW-OCTA) is a non-invasive and safe imaging modality in DR diagnosis system, but there is a lack of publicly available benchmarks for model development and evaluation. To promote further research and scientific benchmarking for diabetic retinopathy analysis using UW-OCTA images, we organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). The challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams from geographically diverse institutes submitting different solutions in these three tasks, respectively. This paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge. The obtained results from top algorithms indicate the importance of data augmentation, model architecture and ensemble of networks in improving the performance of deep learning models. These findings have the potential to enable new developments in diabetic retinopathy analysis. The challenge remains open for post-challenge registrations and submissions for benchmarking future methodology developments. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical tomography

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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HEALing Communities Study: data measures for supporting a community-based intervention to reduce opioid overdose deaths

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Drug and Alcohol Dependence 2025年 274卷

作者： Svetla Slavova Jennifer Villani Daniel J. Feaster Austin Booth JaNae L. Holloway Peter J. Rock Lindsey R. Hammerslag Aimee Mack Charles E. Knott John V. McCarthy Jeffery Talbert Marc R. LaRochelle Bridget Freisthler Brent J. Gibbons Gregory Patts Matthew J. Bullard Sharon L. Walsh Department of Biostatistics University of Kentucky Healthy Kentucky Research Building Suite 260 760 Press Avenue Lexington KY 40536 USA National Institutes of Health National Institute on Drug Abuse 3WFN MSC 6025 301 North Stonestreet Avenue Bethesda MD 20892 USA Department of Public Health Sciences University of Miami Miller School of Medicine 1425 NW 10th Avenue Room 1059 Miami FL 33136 USA Social Statistical and Environment Sciences Health Practice Area RTI International 6110 Executive Blvd Suite 900 Rockville MD 20852 USA Center for Clinical Research Analytics Practice Area RTI International 3040 E Cornwallis Rd Research Triangle Park NC 27709 USA Institute for Biomedical Informatics University of Kentucky Healthy Kentucky Research Building Suite 260 760 Press Avenue Lexington KY 40536 USA Division for Biomedical Informatics College of Medicine University of Kentucky Healthy Kentucky Research Building Suite 260 760 Press Avenue Lexington KY 40536 USA The Ohio Colleges of Medicine Government Resource Center The Ohio State University Wexner Medical Center 150 Pressey Hall 1070 Carmack Road Columbus OH 43210 USA Social Statistical and Environment Sciences Data Practice Area RTI International 3040 E. Cornwallis Road Research Triangle Park NC 27709 USA Social Statistical and Environment Sciences Analytics Practice Area RTI International 3040 E. Cornwallis Road Research Triangle Park NC 27709 USA Division of Biomedical Informatics University of Kentucky College of Medicine 267 Healthy Kentucky Research Building 760 Press Avenue Lexington KY 40536 USA Clinical Addiction Research and Education Unit Section of General Internal Medicine Department of Medicine Boston University School of Medicine and Boston Medical Center 801 Massachusetts Avenue 2nd Floor Boston MA 02118 USA Ohio State University College of Social Work 1947 N. College Road Columbus OH 43210 USA Social Statistical and Environment Sciences Health Practice Area RTI I

Introduction The HEALing Communities Study (HCS) tested a community-based intervention in 67 communities across Kentucky, Massachusetts, New York, and Ohio to reduce opioid overdose deaths. This paper introduces the HCS measures for monitoring the intervention uptake, reports crude rates for benchmarking, and highlights the importance of interpreting jurisdictional trends in the context of state policies. Methods We present technical specifications for the HCS measures and the common data model. Crude rates for the evaluation period (July 2021- June 2022) are reported by state and study arm (intervention/Wave 1 or wait-listed/Wave 2 communities), along with longitudinal trends from 2017 to 2023. Year 2023 serves as a post-intervention period for Wave 1 communities and an intervention year for Wave 2 communities. Results After unprecedented increases in 2020–2021, the HCS crude opioid overdose death rates declined in 2023, but remained higher than the 2019 pre-pandemic rates. Opioid overdose death rates exceeded 100/100,000 adults among Non-Hispanic Black individuals in several states. In response to the rapid increase in opioid overdose deaths in Kentucky, the HCS team expanded the naloxone distribution in Kentucky intervention communities, reaching a 10-fold increase in Quarter 3 of 2021 (1498.2 units/100,000 residents). The methadone medication for opioid use disorder (MOUD) treatment rate for Medicaid enrollees with opioid use disorder during the evaluation period was highest in Massachusetts intervention communities (274/1000), while the buprenorphine MOUD treatment rate was highest in Kentucky (441/1000). Conclusions The HCS measures support comprehensive planning and evaluation of population-level opioid overdose prevention interventions and policies.

关键词： HEALing Communities Study (HCS) Opioid overdose Medication for opioid use disorder (MOUD) Naloxone Methadone Buprenorphine

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One cutoff is not enough: Assessing different area deprivation index cutoffs for insurance types on surgical Desirability of Outcome Ranking (DOOR)

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Healthcare 2025年第1期13卷 100762-100762页

作者： Schmidt, Susanne Jacobs, Michael A. Hall, Daniel E. Stitzenberg, Karyn B. Kao, Lillian S. Brimhall, Bradley B. Wang, Chen-Pin Manuel, Laura S. Su, Hoah-Der Silverstein, Jonathan C. Shireman, Paula K. Department of Population Health Sciences University of Texas Health San Antonio San Antonio TX United States Department of Surgery University of Texas Health San Antonio San Antonio TX United States Center for Health Equity Research and Promotion and Geriatric Research Education and Clinical Center Veterans Affairs Pittsburgh Healthcare System Pittsburgh PA United States Department of Surgery University of Pittsburgh Pittsburgh PA United States Wolff Center University of Pittsburgh Medical Center Pittsburgh PA United States Department of Surgery University of North Carolina Chapel Hill NC United States Department of Surgery McGovern Medical School The University of Texas Health Science Center at Houston Houston TX United States Department of Pathology and Laboratory Medicine University of Texas Health San Antonio San Antonio TX United States University Health San Antonio TX United States UT Health Physicians Business Intelligence and Data Analytics University of Texas Health San Antonio San Antonio TX United States Department of Biomedical Informatics University of Pittsburgh School of Medicine Pittsburgh PA United States Departments of Medical Physiology and Primary Care & Rural Medicine College of Medicine Texas A&M University Bryan TX United States

Background: Social Determinants of Health impact health outcomes. Area Deprivation Index (ADI) is used to risk-adjust for neighborhood affluence/deprivation but guidance on choosing deprivation cutoffs is lacking. We hypothesize that different ADI cutoffs are required for different insurance types. Methods: National Surgical Quality Improvement Program data 2013–2019 merged with electronic health records from three academic healthcare systems. Desirability of Outcome Ranking (DOOR) assessed the association of ADI cutoffs for different insurance types, adjusted for operative stress, frailty, and case status (elective, urgent, emergent). Secondary analyses assessed the association of ADI with case status. Results: Patients with Private insurance living in areas with ADI>85 had higher/worse DOOR outcomes, which lost significance after adjusting for case status. Medicare cases with ADI>75 exhibited higher/worse DOOR outcomes even after adjusting for case status. ADI was not associated with outcomes in the Medicaid and Uninsured groups. High ADI was associated with increased odds of urgent and emergent cases for the Private and Medicare but not Medicaid or Uninsured groups. Conclusions: ADI is a useful metric to identify at-risk patients and can be used for risk adjustment. Health systems must understand their population demographics and use their data to determine ADI cutoffs. Patients in deprived neighborhoods have higher odds of urgent and emergent surgeries, despite having Private insurance or Medicare, suggesting that delays/barriers to primary and preventive care may be a major driver of worse outcomes. While insurance coverage is important, healthcare policies supporting reductions in urgent/emergent cases could have the largest impact on improving outcomes. © 2025 Elsevier Inc.

关键词： Area Deprivation Index Health disparities Preoperative acuity Safety-net hospitals Surgical Desirability of Outcome Ranking

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0571 Repolarization Variability Predicts Cardiovascular Death in Obstructive Sleep Apnea

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Sleep 2020年第SUPPLEMENT_1期43卷 A219–A219页

作者： Patel, S I Zareba, W Couderc, J Xia, X LaFleur, B Torabzadeh, E Woosley, R Parthasarathy, S UAHS Center for Sleep and Circadian Sciences University of Arizona Tucson AZ Division of Cardiology and Heart Research University of Rochester Medical Center Rochester NY UAHS Center for Biomedical Informatics and Biostatistics University of Arizona Tucson AZ Division of Data Analytics and Decision Support University of Arizona College of Medicine-Phoenix Phoenix AZ

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