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Yang J, Mwangi AW, Kantor R, Dahabreh IJ, Nyambura M, Delong A, Hogan JW, Steingrimsson JA. Tree-based subgroup discovery using electronic health record data: heterogeneity of treatment effects for DTG-containing therapies. Biostatistics 2024; 25:323-335. [PMID: 37475638 PMCID: PMC11017113 DOI: 10.1093/biostatistics/kxad014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/01/2022] [Accepted: 11/01/2022] [Indexed: 07/22/2023] Open
Abstract
The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment effect heterogeneity. However, estimating treatment effects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covariates, treatment assignments and outcomes, and loss-to-follow-up due to dropout. Here, we develop the subgroup discovery for longitudinal data algorithm, a tree-based algorithm for discovering subgroups with heterogeneous treatment effects using longitudinal data by combining the generalized interaction tree algorithm, a general data-driven method for subgroup discovery, with longitudinal targeted maximum likelihood estimation. We apply the algorithm to EHR data to discover subgroups of people living with human immunodeficiency virus who are at higher risk of weight gain when receiving dolutegravir (DTG)-containing antiretroviral therapies (ARTs) versus when receiving non-DTG-containing ARTs.
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Affiliation(s)
- Jiabei Yang
- Department of Biostatistics, School of Public Health, Brown University, Providence, RI 02903, USA
| | - Ann W Mwangi
- Department of Mathematics, Physics and Computing, School of Science and Aerospace Studies, Moi University, Eldoret 30100, Kenya
- Academic Model Providing Access to Healthcare (AMPATH), Eldoret 30100, Kenya
| | - Rami Kantor
- Division of Infectious Diseases, Warren Alpert Medical School, Brown University, Providence, RI 02903, USA
| | - Issa J Dahabreh
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Monicah Nyambura
- Academic Model Providing Access to Healthcare (AMPATH), Eldoret 30100, Kenya
| | - Allison Delong
- Department of Biostatistics, School of Public Health, Brown University, Providence, RI 02903, USA
- Center for Statistical Sciences, School of Public Health, Brown University, Providence, RI 02903, USA
| | - Joseph W Hogan
- Department of Biostatistics, School of Public Health, Brown University, Providence, RI 02903, USA
- Center for Statistical Sciences, School of Public Health, Brown University, Providence, RI 02903, USA
| | - Jon A Steingrimsson
- Department of Biostatistics, School of Public Health, Brown University, Providence, RI 02903, USA
- Center for Statistical Sciences, School of Public Health, Brown University, Providence, RI 02903, USA
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2
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Clark-Sevilla AO, Lin YC, Saxena A, Yan Q, Wapner R, Raja A, Pe’er I, Salleb-Aouissi A. Diving into CDC pregnancy data in the United States: longitudinal study and interactive application. JAMIA Open 2024; 7:ooae024. [PMID: 38516346 PMCID: PMC10955523 DOI: 10.1093/jamiaopen/ooae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 09/20/2023] [Accepted: 03/05/2024] [Indexed: 03/23/2024] Open
Abstract
Objective Preterm birth (PTB) is a major determinant of neonatal mortality, morbidity, and childhood disability. In this article, we present a longitudinal analysis of the risk factors associated with PTB and how they have varied over the years: starting from 1968 when the CDC first started, reporting the natality data, up until 2021. Along with this article, we are also releasing an RShiny web application that will allow for easy consumption of this voluminous dataset by the research community. Further, we hope this tool can aid clinicians in the understanding and prevention of PTB. Materials and Methods This study used the CDC Natality data from 1968 to 2021 to analyze trends in PTB outcomes across the lens of various features, including race, maternal age, education, and interval length between pregnancies. Our interactive RShiny web application, CDC NatView, allows users to explore interactions between maternal risk factors and maternal morbidity conditions and the aforementioned features. Results Our study demonstrates how CDC data can be leveraged to conduct a longitudinal analysis of natality trends in the United States. Our key findings reveal an upward trend in late PTBs, which is concerning. Moreover, a significant disparity exists between African American and White populations in terms of PTB. These disparities persist in other areas, such as education, body-mass index, and access to prenatal care later in pregnancy. Discussion Another notable finding is the increase in maternal age over time. Additionally, we confirm that short interpregnancy intervals (IPIs) are a risk factor for PTBs. To facilitate the exploration of pregnancy risk factors, infections, and maternal morbidity, we developed an open-source RShiny tool called CDC NatView. This software offers a user-friendly interface to interact with and visualize the CDC natality data, which constitutes an invaluable resource. Conclusion In conclusion, our study has shed light on the rise of late PTBs and the persistent disparities in PTB rates between African American and White populations in the US. The increase in maternal age and the confirmation of a short IPI as a risk factor for PTB are noteworthy findings. Our open-source tool, CDC NatView, can be a valuable resource for further exploration of the CDC natality data to enhance our understanding of pregnancy risk factors and the interaction of PTB outcomes and maternal morbidities.
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Affiliation(s)
| | - Yun C Lin
- Department of Computer Science, Columbia University, New York, NY 10027, United States
| | - Arnav Saxena
- Department of Data Science, Columbia University, New York, NY 10027, United States
| | - Qi Yan
- Department of Obstetrics and Gynecology, Columbia University, New York, NY 10027, United States
| | - Ronald Wapner
- Department of Obstetrics and Gynecology, Columbia University, New York, NY 10027, United States
| | - Anita Raja
- Department of Computer Science, CUNY Hunter College, New York, NY 10065, United States
| | - Itsik Pe’er
- Department of Computer Science, Columbia University, New York, NY 10027, United States
| | - Ansaf Salleb-Aouissi
- Department of Computer Science, Columbia University, New York, NY 10027, United States
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McConeghy KW, Hur K, Dahabreh IJ, Jiang R, Pandey L, Gellad WF, Glassman P, Good CB, Miller DR, Zullo AR, Gravenstein S, Cunningham F. Early Mortality After the First Dose of COVID-19 Vaccination: A Target Trial Emulation. Clin Infect Dis 2024; 78:625-632. [PMID: 38319989 PMCID: PMC10954332 DOI: 10.1093/cid/ciad604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Vaccine hesitancy persists alongside concerns about the safety of coronavirus disease 2019 (COVID-19) vaccines. We aimed to examine the effect of COVID-19 vaccination on risk of death among US veterans. METHODS We conducted a target trial emulation to estimate and compare risk of death up to 60 days under two COVID-19 vaccination strategies: vaccination within 7 days of enrollment versus no vaccination through follow-up. The study cohort included individuals aged ≥18 years enrolled in the Veterans Health Administration system and eligible to receive a COVID-19 vaccination according to guideline recommendations from 1 March 2021 through 1 July 2021. The outcomes of interest included deaths from any cause and excluding a COVID-19 diagnosis. Observations were cloned to both treatment strategies, censored, and weighted to estimate per-protocol effects. RESULTS We included 3 158 507 veterans. Under the vaccination strategy, 364 993 received vaccine within 7 days. At 60 days, there were 156 deaths per 100 000 veterans under the vaccination strategy versus 185 deaths under the no vaccination strategy, corresponding to an absolute risk difference of -25.9 (95% confidence limit [CL], -59.5 to 2.7) and relative risk of 0.86 (95% CL, .7 to 1.0). When those with a COVID-19 infection in the first 60 days were censored, the absolute risk difference was -20.6 (95% CL, -53.4 to 16.0) with a relative risk of 0.88 (95% CL, .7 to 1.1). CONCLUSIONS Vaccination against COVID-19 was associated with a lower but not statistically significantly different risk of death in the first 60 days. These results agree with prior scientific knowledge suggesting vaccination is safe with the potential for substantial health benefits.
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Affiliation(s)
- Kevin W McConeghy
- Center of Innovation Long-Term Services and Supports, Veterans Administration Medical Center, Providence, Rhode Island, USA
- Department of Health Services, Policy and Practice, School of Public Health, Brown University, Providence, Rhode Island, USA
| | - Kwan Hur
- VA Center for Medication Safety, Department of Veterans Affairs, Chicago, Illinois, USA
| | - Issa J Dahabreh
- CAUSALab, Harvard T. H. Chan School of Public Health, Boston, Massachusett, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Rong Jiang
- VA Center for Medication Safety, Department of Veterans Affairs, Chicago, Illinois, USA
| | - Lucy Pandey
- VA Center for Medication Safety, Department of Veterans Affairs, Chicago, Illinois, USA
| | - Walid F Gellad
- Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, USA
- University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Centers for High Value Health Care, and Value-Based Pharmacy Initiatives, UPMC Health Plan, Pittsburgh, Pennsylvania, USA
| | - Peter Glassman
- Pharmacy Benefits Management Services, Department of Veterans Affairs, Washington, DC, USA
- Department of Medicine, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Chester B Good
- VA Center for Medication Safety, Department of Veterans Affairs, Chicago, Illinois, USA
- University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Centers for High Value Health Care, and Value-Based Pharmacy Initiatives, UPMC Health Plan, Pittsburgh, Pennsylvania, USA
| | - Donald R Miller
- VA Center for Medication Safety, Department of Veterans Affairs, Chicago, Illinois, USA
- Center for Population Health, Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, Massachusetts, USA
| | - Andrew R Zullo
- Center of Innovation Long-Term Services and Supports, Veterans Administration Medical Center, Providence, Rhode Island, USA
- Department of Epidemiology, School of Public Health, Brown University, Providence, Rhode Island, USA
| | - Stefan Gravenstein
- Center of Innovation Long-Term Services and Supports, Veterans Administration Medical Center, Providence, Rhode Island, USA
- Department of Health Services, Policy and Practice, School of Public Health, Brown University, Providence, Rhode Island, USA
- Division of Geriatrics and Palliative Medicine, Department of Medicine, Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Francesca Cunningham
- VA Center for Medication Safety, Department of Veterans Affairs, Chicago, Illinois, USA
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4
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Gargano MA, Matentzoglu N, Coleman B, Addo-Lartey EB, Anagnostopoulos A, Anderton J, Avillach P, Bagley AM, Bakštein E, Balhoff JP, Baynam G, Bello SM, Berk M, Bertram H, Bishop S, Blau H, Bodenstein DF, Botas P, Boztug K, Čady J, Callahan TJ, Cameron R, Carbon S, Castellanos F, Caufield JH, Chan LE, Chute C, Cruz-Rojo J, Dahan-Oliel N, Davids JR, de Dieuleveult M, de Souza V, de Vries BBA, de Vries E, DePaulo JR, Derfalvi B, Dhombres F, Diaz-Byrd C, Dingemans AJM, Donadille B, Duyzend M, Elfeky R, Essaid S, Fabrizzi C, Fico G, Firth HV, Freudenberg-Hua Y, Fullerton JM, Gabriel DL, Gilmour K, Giordano J, Goes FS, Moses RG, Green I, Griese M, Groza T, Gu W, Guthrie J, Gyori B, Hamosh A, Hanauer M, Hanušová K, He Y(O, Hegde H, Helbig I, Holasová K, Hoyt CT, Huang S, Hurwitz E, Jacobsen JOB, Jiang X, Joseph L, Keramatian K, King B, Knoflach K, Koolen DA, Kraus M, Kroll C, Kusters M, Ladewig MS, Lagorce D, Lai MC, Lapunzina P, Laraway B, Lewis-Smith D, Li X, Lucano C, Majd M, Marazita ML, Martinez-Glez V, McHenry TH, McInnis MG, McMurry JA, Mihulová M, Millett CE, Mitchell PB, Moslerová V, Narutomi K, Nematollahi S, Nevado J, Nierenberg AA, Čajbiková NN, Nurnberger JI, Ogishima S, Olson D, Ortiz A, Pachajoa H, Perez de Nanclares G, Peters A, Putman T, Rapp CK, Rath A, Reese J, Rekerle L, Roberts A, Roy S, Sanders SJ, Schuetz C, Schulte EC, Schulze TG, Schwarz M, Scott K, Seelow D, Seitz B, Shen Y, Similuk MN, Simon ES, Singh B, Smedley D, Smith CL, Smolinsky JT, Sperry S, Stafford E, Stefancsik R, Steinhaus R, Strawbridge R, Sundaramurthi JC, Talapova P, Tenorio Castano JA, Tesner P, Thomas RH, Thurm A, Turnovec M, van Gijn ME, Vasilevsky NA, Vlčková M, Walden A, Wang K, Wapner R, Ware JS, Wiafe AA, Wiafe SA, Wiggins LD, Williams AE, Wu C, Wyrwoll MJ, Xiong H, Yalin N, Yamamoto Y, Yatham LN, Yocum AK, Young AH, Yüksel Z, Zandi PP, Zankl A, Zarante I, Zvolský M, Toro S, Carmody LC, Harris NL, Munoz-Torres MC, Danis D, Mungall CJ, Köhler S, Haendel MA, Robinson PN. The Human Phenotype Ontology in 2024: phenotypes around the world. Nucleic Acids Res 2024; 52:D1333-D1346. [PMID: 37953324 PMCID: PMC10767975 DOI: 10.1093/nar/gkad1005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 10/12/2023] [Accepted: 10/19/2023] [Indexed: 11/14/2023] Open
Abstract
The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.
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Affiliation(s)
| | | | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | | | | | - Joel Anderton
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Anita M Bagley
- Shriners Children's Northern California, Sacramento, CA, USA
| | - Eduard Bakštein
- National Institute of Mental Health, Klecany, Czech Republic
| | - James P Balhoff
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27517, USA
| | - Gareth Baynam
- Rare Care Centre, Perth Children's Hospital, Perth, Australia
| | | | - Michael Berk
- Deakin University, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia
| | - Holli Bertram
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Somer Bishop
- Department of Psychiatry and Behavioral Sciences, UCSF Weil Institute for Neuroscience, San Francisco, CA, USA
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - David F Bodenstein
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | | | - Kaan Boztug
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Jolana Čady
- Institute of Health Information and Statistics of the Czech Republic, Prague, Czech Republic
| | - Tiffany J Callahan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, NY, NY, USA
| | | | - Seth J Carbon
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | | | - J Harry Caufield
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Lauren E Chan
- College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Jaime Cruz-Rojo
- UDISGEN (Dysmorphology and Genetics Unit), 12 de Octubre Hospital, Madrid, Spain
| | - Noémi Dahan-Oliel
- Department of Clinical Research, Shriners Hospitals for Children, Montreal, Quebec, Canada
| | - Jon R Davids
- Shriners Children's Northern California, Sacramento, CA, USA
| | - Maud de Dieuleveult
- Département I&D, AP-HP, Banque Nationale de Données Maladies Rares, Paris, France
| | - Vinicius de Souza
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Bert B A de Vries
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | | | - J Raymond DePaulo
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Beata Derfalvi
- Department of Pediatrics, Dalhousie University, Halifax, NS, Canada
| | - Ferdinand Dhombres
- Fetal Medicine Department, Armand Trousseau Hospital, Sorbonne University, GRC26, INSERM, Limics, Paris, France
| | - Claudia Diaz-Byrd
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Alexander J M Dingemans
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Bruno Donadille
- St Antoine Hospital, Reference Center for Rare Growth Endocrine Disorders, Sorbonne University, AP-HP, INSERM, US14 - Orphanet, Plateforme Maladies Rares, Paris, France
| | | | - Reem Elfeky
- Department of Immunology, GOS Hospital for Children NHS Foundation Trust, University College London, London, UK
| | - Shahim Essaid
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | | | - Giovanna Fico
- Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
| | - Helen V Firth
- Addenbrooke's Hospital, Cambridge University Hospitals, Cambridge, UK
| | - Yun Freudenberg-Hua
- Department of Psychiatry, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | | | - Davera L Gabriel
- School of Medicine, Johns Hopkins University, Baltimore, MD 21287, USA
| | | | - Jessica Giordano
- Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, USA
| | - Fernando S Goes
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Rachel Gore Moses
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ian Green
- SNOMED International, London W2 6BD, UK
| | - Matthias Griese
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, LMU Munich, German center for Lung research (DZL), Munich, Germany
| | - Tudor Groza
- Rare Care Centre, Perth Children's Hospital, Perth, Australia
| | | | - Julia Guthrie
- Department of Structural and Computational Biology, University of Vienna; Max Perutz Labs, Vienna, Austria
| | - Benjamin Gyori
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Ada Hamosh
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Marc Hanauer
- INSERM, US14 - Orphanet, Plateforme Maladies Rares, Paris, France
| | - Kateřina Hanušová
- Institute of Health Information and Statistics of the Czech Republic, Prague, Czech Republic
| | | | - Harshad Hegde
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Ingo Helbig
- Neurology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Kateřina Holasová
- Institute of Health Information and Statistics of the Czech Republic, Prague, Czech Republic
| | - Charles Tapley Hoyt
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | | | - Eric Hurwitz
- University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Julius O B Jacobsen
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | | | - Lisa Joseph
- Neurodevelopmental and Behavioral Phenotyping Service, National Institute of Mental Health, Bethesda, MD, USA
| | - Kamyar Keramatian
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Bryan King
- Department of Psychiatry and Behavioral Sciences, UCSF Weil Institute for Neuroscience, San Francisco, CA, USA
| | - Katrin Knoflach
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, LMU Munich, German center for Lung research (DZL), Munich, Germany
| | - David A Koolen
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Megan L Kraus
- University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Carlo Kroll
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Maaike Kusters
- Immunology, NIHR Great Ormond Street Hospital BRC, London, UK
| | - Markus S Ladewig
- Department of Ophthalmology, University Clinic Marburg - Campus Fulda, Fulda, Germany
| | - David Lagorce
- INSERM, US14 - Orphanet, Plateforme Maladies Rares, Paris, France
| | - Meng-Chuan Lai
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Pablo Lapunzina
- Institute of Medical and Molecular Genetics, Hospital Univ. La Paz, Madrid, Spain
| | - Bryan Laraway
- University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - David Lewis-Smith
- Translational and Clinical Research Institute, Henry Wellcome Building, Framlington Place, Newcastle University, Newcastle-Upon-Tyne NE14LP, UK
| | | | - Caterina Lucano
- INSERM, US14 - Orphanet, Plateforme Maladies Rares, Paris, France
| | - Marzieh Majd
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Mary L Marazita
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Victor Martinez-Glez
- Center for Genomic Medicine, Parc Taulí Hospital Universitari, Institut d’Investigació i Innovació Parc Taulí (I3PT-CERCA), Sabadell, Spain
| | - Toby H McHenry
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Melvin G McInnis
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Julie A McMurry
- University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Michaela Mihulová
- Department of Biology and Medical Genetics, 2nd Medical Faculty of Charles University and University Hospital Motol, Prague, Czech Republic
| | - Caitlin E Millett
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Philip B Mitchell
- Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine & Health, University of New South Wales, Sydney, NSW, Australia
| | - Veronika Moslerová
- Department of Biology and Medical Genetics, 2nd Medical Faculty of Charles University and University Hospital Motol, Prague, Czech Republic
| | - Kenji Narutomi
- Okinawa Prefectural Nanbu Medical Center & Children's Medical Center
| | - Shahrzad Nematollahi
- School of Physical and Occupational Therapy, McGill University, Montreal, Quebec, Canada
| | - Julian Nevado
- Institute of Medical and Molecular Genetics, Hospital Univ. La Paz, Madrid, Spain
| | - Andrew A Nierenberg
- Dauten Family Center for Bipolar Treatment Innovation, Massachusetts General Hospital, Boston, MA, USA
| | - Nikola Novák Čajbiková
- Department of Biology and Medical Genetics, 2nd Medical Faculty of Charles University and University Hospital Motol, Prague, Czech Republic
| | - John I Nurnberger
- Stark Neurosciences Research Institute, Departments of Psychiatry and Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Daniel Olson
- Data Collaboration Center, Data Science, Critical Path Institute, Tucson, AZ, USA
| | - Abigail Ortiz
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Harry Pachajoa
- Centro de Investigaciones en Anomalías Congénitas y Enfermedades Raras (CIACER), Universidad Icesi, Cali, Colombia
| | - Guiomar Perez de Nanclares
- Molecular (epi) genetics lab, Bioaraba Health Research Institute, Araba University Hospital, Vitoria-Gasteiz, Spain
| | - Amy Peters
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Tim Putman
- University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Christina K Rapp
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, LMU Munich, German center for Lung research (DZL), Munich, Germany
| | - Ana Rath
- INSERM, US14 - Orphanet, Plateforme Maladies Rares, Paris, France
| | - Justin Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Lauren Rekerle
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Angharad M Roberts
- National Heart & Lung Institute & MRC London Institute of Medical Sciences, Imperial College London, London W12 0HS, UK
| | - Suzy Roy
- SNOMED International, London W2 6BD, UK
| | - Stephan J Sanders
- Department of Paediatrics, Institute of Developmental and Regenerative Medicine, University of Oxford, Oxford, UK
| | - Catharina Schuetz
- Universitätsklinikum Carl Gustav Carus, Medizinische Fakultät, TU, Dresden, Germany
| | - Eva C Schulte
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, Germany
| | - Thomas G Schulze
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Martin Schwarz
- Department of Biology and Medical Genetics, 2nd Medical Faculty of Charles University and University Hospital Motol, Prague, Czech Republic
| | - Katie Scott
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Dominik Seelow
- Exploratory Diagnostic Sciences, Berliner Institut für Gesundheitsforschung - Charité, Berlin, Germany
| | - Berthold Seitz
- Department of Ophthalmology, Saarland University Medical Center UKS, Homburg/Saar, Germany
| | | | - Morgan N Similuk
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Eric S Simon
- Eisenberg Family Depression Center, University of Michigan, Ann Arbor, MI, USA
| | - Balwinder Singh
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Damian Smedley
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | | | - Jake T Smolinsky
- Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ, USA
| | - Sarah Sperry
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | | | - Ray Stefancsik
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Robin Steinhaus
- Exploratory Diagnostic Sciences, Berliner Institut für Gesundheitsforschung - Charité, Berlin, Germany
| | - Rebecca Strawbridge
- Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | | | - Polina Talapova
- Institute for Research and Health Policy Studies, Tufts Medicine, Boston, MA 2111, USA
| | | | - Pavel Tesner
- Department of Biology and Medical Genetics, 2nd Medical Faculty of Charles University and University Hospital Motol, Prague, Czech Republic
| | - Rhys H Thomas
- Translational and Clinical Research Institute, Henry Wellcome Building, Framlington Place, Newcastle University, Newcastle-Upon-Tyne NE14LP, UK
| | - Audrey Thurm
- Neurodevelopmental and Behavioral Phenotyping Service, National Institute of Mental Health, Bethesda, MD, USA
| | - Marek Turnovec
- Department of Biology and Medical Genetics, 2nd Medical Faculty of Charles University and University Hospital Motol, Prague, Czech Republic
| | - Marielle E van Gijn
- Department of Genetics, University Medical Center Groningen, Groningen, Netherlands
| | | | - Markéta Vlčková
- Department of Biology and Medical Genetics, 2nd Medical Faculty of Charles University and University Hospital Motol, Prague, Czech Republic
| | - Anita Walden
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Kai Wang
- Chinese HPO Consortium, Beijing, China
| | - Ron Wapner
- Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, USA
| | - James S Ware
- National Heart & Lung Institute & MRC London Institute of Medical Sciences, Imperial College London, London W12 0HS, UK
| | | | | | - Lisa D Wiggins
- National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Andrew E Williams
- Institute for Research and Health Policy Studies, Tufts Medicine, Boston, MA 2111, USA
| | - Chen Wu
- Chinese HPO Consortium, Beijing, China
| | - Margot J Wyrwoll
- Centre for Regenerative Medicine, Institute for Regeneration and Repair, Institute for Stem Cell Research, University of Edinburgh, Edinburgh, UK
| | - Hui Xiong
- Chinese HPO Consortium, Beijing, China
| | - Nefize Yalin
- Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Yasunori Yamamoto
- Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Japan
| | - Lakshmi N Yatham
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Anastasia K Yocum
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Allan H Young
- Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London & South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Monks Orchard Road, Beckenham, Kent, London SE5 8AF, UK
| | - Zafer Yüksel
- Department of Human Genetics, Bioscientia Healthcare GmbH, Ingelheim, Germany
| | - Peter P Zandi
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Andreas Zankl
- Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia
| | - Ignacio Zarante
- Institute of Human Genetics, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Miroslav Zvolský
- Institute of Health Information and Statistics of the Czech Republic, Prague, Czech Republic
| | - Sabrina Toro
- University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Leigh C Carmody
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Nomi L Harris
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Monica C Munoz-Torres
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Daniel Danis
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Christopher J Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | | | - Melissa A Haendel
- University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
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Newbury A, Liu H, Idnay B, Weng C. The suitability of UMLS and SNOMED-CT for encoding outcome concepts. J Am Med Inform Assoc 2023; 30:1895-1903. [PMID: 37615994 PMCID: PMC10654851 DOI: 10.1093/jamia/ocad161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/14/2023] [Accepted: 08/02/2023] [Indexed: 08/25/2023] Open
Abstract
OBJECTIVE Outcomes are important clinical study information. Despite progress in automated extraction of PICO (Population, Intervention, Comparison, and Outcome) entities from PubMed, rarely are these entities encoded by standard terminology to achieve semantic interoperability. This study aims to evaluate the suitability of the Unified Medical Language System (UMLS) and SNOMED-CT in encoding outcome concepts in randomized controlled trial (RCT) abstracts. MATERIALS AND METHODS We iteratively developed and validated an outcome annotation guideline and manually annotated clinically significant outcome entities in the Results and Conclusions sections of 500 randomly selected RCT abstracts on PubMed. The extracted outcomes were fully, partially, or not mapped to the UMLS via MetaMap based on established heuristics. Manual UMLS browser search was performed for select unmapped outcome entities to further differentiate between UMLS and MetaMap errors. RESULTS Only 44% of 2617 outcome concepts were fully covered in the UMLS, among which 67% were complex concepts that required the combination of 2 or more UMLS concepts to represent them. SNOMED-CT was present as a source in 61% of the fully mapped outcomes. DISCUSSION Domains such as Metabolism and Nutrition, and Infections and Infectious Diseases need expanded outcome concept coverage in the UMLS and MetaMap. Future work is warranted to similarly assess the terminology coverage for P, I, C entities. CONCLUSION Computational representation of clinical outcomes is important for clinical evidence extraction and appraisal and yet faces challenges from the inherent complexity and lack of coverage of these concepts in UMLS and SNOMED-CT, as demonstrated in this study.
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Affiliation(s)
- Abigail Newbury
- Department of Biomedical Informatics, Columbia University, New York City, NY 10032, United States
| | - Hao Liu
- Department of Biomedical Informatics, Columbia University, New York City, NY 10032, United States
| | - Betina Idnay
- Department of Biomedical Informatics, Columbia University, New York City, NY 10032, United States
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York City, NY 10032, United States
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Ye Z, Mayer J, Leary EJ, Kitchner T, Dart RA, Brilliant MH, Hebbring SJ. Estimating the efficacy of pharmacogenomics over a lifetime. Front Med (Lausanne) 2023; 10:1006743. [PMID: 38020121 PMCID: PMC10645151 DOI: 10.3389/fmed.2023.1006743] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 07/10/2023] [Indexed: 12/01/2023] Open
Abstract
It is well known that common variants in specific genes influence drug metabolism and response, but it is currently unknown what fraction of patients are given prescriptions over a lifetime that could be contraindicated by their pharmacogenomic profiles. To determine the clinical utility of pharmacogenomics over a lifetime in a general patient population, we sequenced the genomes of 300 deceased Marshfield Clinic patients linked to lifelong medical records. Genetic variants in 33 pharmacogenes were evaluated for their lifetime impact on drug prescribing using extensive electronic health records. Results show that 93% of the 300 deceased patients carried clinically relevant variants. Nearly 80% were prescribed approximately three medications on average that may have been impacted by these variants. Longitudinal data suggested that the optimal age for pharmacogenomic testing was prior to age 50, but the optimal age is greatly influenced by the stability of the population in the healthcare system. This study emphasizes the broad clinical impact of pharmacogenomic testing over a lifetime and demonstrates the potential application of genomic medicine in a general patient population for the advancement of precision medicine.
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Merle JL, Li D, Keiser B, Zamantakis A, Queiroz A, Gallo CG, Villamar JA, McKay V, Zapata JP, Mustanski B, Benbow N, Smith JD. Categorising implementation determinants and strategies within the US HIV implementation literature: a systematic review protocol. BMJ Open 2023; 13:e070216. [PMID: 36927593 PMCID: PMC10030793 DOI: 10.1136/bmjopen-2022-070216] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 03/06/2023] [Indexed: 03/18/2023] Open
Abstract
INTRODUCTION Despite decreased rates of new infections, HIV/AIDS continues to impact certain US populations. In order to achieve the goals laid out in the Ending the HIV Epidemic (EHE) in the US initiative, implementation science is needed to expand the sustained use of effective prevention and treatment interventions, particularly among priority populations at risk for and living with HIV/AIDS. Over 200 HIV-related implementation studies have been funded by the US National Institutes of Health. Therefore, a comprehensive review of the literature identifying implementation determinants (barriers and facilitators) and categorising implementation strategies across the continuum of HIV prevention and care in the USA is appropriate and needed to enhance current knowledge and help achieve the goals laid out in the EHE national strategic plan. METHODS AND ANALYSIS This systematic review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Between November 2020 and January 2022, a broad database search strategy of Ovid MEDLINE, PsycINFO and Web of Science was conducted to capture implementation-related studies along the HIV prevention and care continuum. Articles were eligible for inclusion if they were: conducted in the USA, published after the year 2000, written in English, related to HIV/AIDS, focused on outcomes related to dissemination and implementation (ie, did not test/evaluate/explore implementation determinants or strategies) and were behavioural studies (ie, not basic science). We plan to conduct three systematic reviews to identify and categorise determinants and strategies associated with three HIV focus areas: pre-exposure prophylaxis, testing/diagnosing and linkage to care, and treatment. Determinants will be coded according to an adapted Consolidated Framework for Implementation Research 2.0. Implementation strategies and outcomes will be categorised in accordance with existing taxonomies and frameworks. ETHICS AND DISSEMINATION Ethics approval is not applicable. No original data will be collected. Results will be disseminated through peer-reviewed publications, conference presentations and via online tools. PROSPERO REGISTRATION NUMBER CRD42021233089.
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Affiliation(s)
- James Lorenz Merle
- Department of Population Health Sciences, University of Utah Health, Salt Lake City, Utah, USA
| | - Dennis Li
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Evanston, Illinois, USA
| | - Brennan Keiser
- Department of Medical Social Sciences, Northwestern University, Chicago, Illinois, USA
| | - Alithia Zamantakis
- Department of Medical Social Sciences, Northwestern University, Chicago, Illinois, USA
| | - Artur Queiroz
- Department of Medical Social Sciences, Northwestern University, Chicago, Illinois, USA
| | - Carlos G Gallo
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Evanston, Illinois, USA
| | - Juan A Villamar
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Evanston, Illinois, USA
| | - Virginia McKay
- Center for Public Health Systems Science, Brown School, Washington University in St. Louis, St Louis, Missouri, USA
| | - Juan Pablo Zapata
- Department of Medical Social Sciences, Northwestern University, Chicago, Illinois, USA
| | - Brian Mustanski
- Department of Medical Social Sciences, Northwestern University, Chicago, Illinois, USA
| | - Nanette Benbow
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Evanston, Illinois, USA
| | - Justin D Smith
- Department of Population Health Sciences, University of Utah Health, Salt Lake City, Utah, USA
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Richman I, Tessier-Sherman B, Galusha D, Oladele CR, Wang K. Breast cancer screening during the COVID-19 pandemic: moving from disparities to health equity. J Natl Cancer Inst 2023; 115:139-145. [PMID: 36069622 PMCID: PMC9494402 DOI: 10.1093/jnci/djac172] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 08/02/2022] [Accepted: 08/29/2022] [Indexed: 11/19/2022] Open
Abstract
The COVID-19 pandemic created unprecedented disruptions to routine health care in the United States. Screening mammography, a cornerstone of breast cancer control and prevention, was completely halted in the spring of 2020, and screening programs have continued to face challenges with subsequent COVID-19 waves. Although screening mammography rates decreased for all women during the pandemic, a number of studies have now clearly documented that reductions in screening have been greater for some populations than others. Specifically, minoritized women have been screened at lower rates than White women across studies, although the specific patterns of disparity vary depending on the populations and communities studied. We posit that these disparities are likely due to a variety of structural and contextual factors, including the differential impact of COVID-19 on communities. We also outline key considerations for closing gaps in screening mammography. First, practices, health systems, and communities must measure screening mammography use to identify whether gaps exist and which populations are most affected. Second, we propose that strategies to close disparities in breast cancer screening must be multifaceted, targeting the health system or practice, but also structural factors at the policy level. Health disparities arise from a complex set of conditions, and multimodal solutions that address the complex, multifactorial conditions that lead to disparities may be more likely to succeed and are necessary for promoting health equity.
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Affiliation(s)
- Ilana Richman
- Correspondence to: Ilana Richman, MD, MHS, Department of Medicine, Yale School of Medicine, 367 Cedar St, Harkness Hall A, Room 301a, New Haven, CT 06510, USA (e-mail: )
| | - Baylah Tessier-Sherman
- Department of Medicine, Equity Research and Innovation Center, Yale School of Medicine, New Haven, CT, USA
| | - Deron Galusha
- Department of Medicine, Equity Research and Innovation Center, Yale School of Medicine, New Haven, CT, USA
| | - Carol R Oladele
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Medicine, Equity Research and Innovation Center, Yale School of Medicine, New Haven, CT, USA
| | - Karen Wang
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Medicine, Equity Research and Innovation Center, Yale School of Medicine, New Haven, CT, USA
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Kravchenko OV, Boyce RD, Gomez-Lumbreras A, Kocis PT, Villa Zapata L, Tan M, Leonard CE, Andersen KM, Mehta H, Alexander GC, Malone DC. Drug-drug interaction between dexamethasone and direct-acting oral anticoagulants: a nested case-control study in the National COVID Cohort Collaborative (N3C). BMJ Open 2022; 12:e066846. [PMID: 36581417 PMCID: PMC9806069 DOI: 10.1136/bmjopen-2022-066846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
OBJECTIVE The goal of this work is to evaluate if there is an increase in the risk of thromboembolic events (TEEs) due to concomitant exposure to dexamethasone and apixaban or rivaroxaban. Direct oral anticoagulants (DOACs), as well as corticosteroid dexamethasone, are commonly used to treat individuals hospitalised with COVID-19. Dexamethasone induces cytochrome P450-3A4 enzyme that also metabolises DOACs apixaban and rivaroxaban. This raises a concern about possible interaction between dexamethasone and DOACs that may reduce the efficacy of the DOACs and result in an increased risk of TEE. DESIGN We used nested case-control study design. SETTING This study was conducted in the National COVID Cohort Collaborative (N3C), the largest electronic health records repository for COVID-19 in the USA. PARTICIPANTS Study participants were adults over 18 years who were exposed to a DOAC for 10 or more consecutive days. Exposure to dexamethasone was at least 5 or more consecutive days. PRIMARY AND SECONDARY OUTCOME MEASURES Our primary exposure variable was concomitant exposure to dexamethasone for 5 or more days after exposure to either rivaroxaban or apixaban for 5 or more consecutive days. We used McNemar's Χ2 test and adjusted logistic regression to evaluate association between concomitant use of dexamethasone with either apixaban or rivaroxaban. RESULTS McNemar's Χ2 test did not find a discernible association of TEE in patients concomitantly exposed to dexamethasone and a DOAC (χ2=0.5, df=1, p=0.48). In addition, a conditional logistic regression model did not find an increase in the risk of TEE (adjusted OR 1.15, 95% CI 0.32 to 4.18). CONCLUSION This nested case-control study did not find evidence of an association between concomitant exposure to dexamethasone and a DOAC with an increase in risk of TEE. Due to small sample size, an association cannot be completely ruled out.
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Affiliation(s)
- Olga V Kravchenko
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Richard D Boyce
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | - Paul T Kocis
- Department of Pharmacology, Penn State Health Milton S Hershey Medical Center, Hershey, Pennsylvania, USA
| | | | - Malinda Tan
- Pharmacotherapy Outcomes Research Center, The University of Utah College of Pharmacy, Salt Lake City, Utah, USA
| | - Charles E Leonard
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kathleen M Andersen
- Center for Drug Safety and Effectiveness, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Hemalkumar Mehta
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - G Caleb Alexander
- Center for Drug Safety and Effectiveness, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Daniel C Malone
- College of Pharmacy, University of Utah Health, Salt Lake City, Utah, USA
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10
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Delos Santos NP, Duttke S, Heinz S, Benner C. MEPP: more transparent motif enrichment by profiling positional correlations. NAR Genom Bioinform 2022; 4:lqac075. [PMID: 36267125 PMCID: PMC9575187 DOI: 10.1093/nargab/lqac075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/18/2022] [Accepted: 09/23/2022] [Indexed: 11/11/2022] Open
Abstract
Score-based motif enrichment analysis (MEA) is typically applied to regulatory DNA to infer transcription factors (TFs) that may modulate transcription and chromatin state in different conditions. Most MEA methods determine motif enrichment independent of motif position within a sequence, even when those sequences harbor anchor points that motifs and their bound TFs may functionally interact with in a distance-dependent fashion, such as other TF binding motifs, transcription start sites (TSS), sequencing assay cleavage sites, or other biologically meaningful features. We developed motif enrichment positional profiling (MEPP), a novel MEA method that outputs a positional enrichment profile of a given TF's binding motif relative to key anchor points (e.g. transcription start sites, or other motifs) within the analyzed sequences while accounting for lower-order nucleotide bias. Using transcription initiation and TF binding as test cases, we demonstrate MEPP's utility in determining the sequence positions where motif presence correlates with measures of biological activity, inferring positional dependencies of binding site function. We demonstrate how MEPP can be applied to interpretation and hypothesis generation from experiments that quantify transcription initiation, chromatin structure, or TF binding measurements. MEPP is available for download from https://github.com/npdeloss/mepp.
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Affiliation(s)
- Nathaniel P Delos Santos
- Department of Biomedical Informatics, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0634, USA
| | - Sascha Duttke
- School of Molecular Biosciences, College of Veterinary Medicine, Washington State University, Pullman, WA, USA
| | - Sven Heinz
- Department of Medicine, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0634, USA
| | - Christopher Benner
- Department of Medicine, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0634, USA
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11
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Huang Y, Liu Y, Steel PAD, Axsom KM, Lee JR, Tummalapalli SL, Wang F, Pathak J, Subramanian L, Zhang Y. Deep significance clustering: a novel approach for identifying risk-stratified and predictive patient subgroups. J Am Med Inform Assoc 2021; 28:2641-2653. [PMID: 34571540 PMCID: PMC8500061 DOI: 10.1093/jamia/ocab203] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/04/2021] [Accepted: 09/02/2021] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVE Deep significance clustering (DICE) is a self-supervised learning framework. DICE identifies clinically similar and risk-stratified subgroups that neither unsupervised clustering algorithms nor supervised risk prediction algorithms alone are guaranteed to generate. MATERIALS AND METHODS Enabled by an optimization process that enforces statistical significance between the outcome and subgroup membership, DICE jointly trains 3 components, representation learning, clustering, and outcome prediction while providing interpretability to the deep representations. DICE also allows unseen patients to be predicted into trained subgroups for population-level risk stratification. We evaluated DICE using electronic health record datasets derived from 2 urban hospitals. Outcomes and patient cohorts used include discharge disposition to home among heart failure (HF) patients and acute kidney injury among COVID-19 (Cov-AKI) patients, respectively. RESULTS Compared to baseline approaches including principal component analysis, DICE demonstrated superior performance in the cluster purity metrics: Silhouette score (0.48 for HF, 0.51 for Cov-AKI), Calinski-Harabasz index (212 for HF, 254 for Cov-AKI), and Davies-Bouldin index (0.86 for HF, 0.66 for Cov-AKI), and prediction metric: area under the Receiver operating characteristic (ROC) curve (0.83 for HF, 0.78 for Cov-AKI). Clinical evaluation of DICE-generated subgroups revealed more meaningful distributions of member characteristics across subgroups, and higher risk ratios between subgroups. Furthermore, DICE-generated subgroup membership alone was moderately predictive of outcomes. DISCUSSION DICE addresses a gap in current machine learning approaches where predicted risk may not lead directly to actionable clinical steps. CONCLUSION DICE demonstrated the potential to apply in heterogeneous populations, where having the same quantitative risk does not equate with having a similar clinical profile.
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Affiliation(s)
- Yufang Huang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Yifan Liu
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Peter A D Steel
- Department of Emergency Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Kelly M Axsom
- Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, USA
| | - John R Lee
- Department of Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Sri Lekha Tummalapalli
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
- Department of Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Jyotishman Pathak
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Lakshminarayanan Subramanian
- Courant Institute of Mathematical Sciences, New York University, New York, New York, USA
- Department of Population Health, New York University School of Medicine, New York, New York, USA
| | - Yiye Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
- Department of Emergency Medicine, Weill Cornell Medicine, New York, New York, USA
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12
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Cirnaru MD, Song S, Tshilenge KT, Corwin C, Mleczko J, Galicia Aguirre C, Benlhabib H, Bendl J, Apontes P, Fullard J, Creus-Muncunill J, Reyahi A, Nik AM, Carlsson P, Roussos P, Mooney SD, Ellerby LM, Ehrlich ME. Unbiased identification of novel transcription factors in striatal compartmentation and striosome maturation. eLife 2021; 10:e65979. [PMID: 34609283 PMCID: PMC8492065 DOI: 10.7554/elife.65979] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 08/20/2021] [Indexed: 02/06/2023] Open
Abstract
Many diseases are linked to dysregulation of the striatum. Striatal function depends on neuronal compartmentation into striosomes and matrix. Striatal projection neurons are GABAergic medium spiny neurons (MSNs), subtyped by selective expression of receptors, neuropeptides, and other gene families. Neurogenesis of the striosome and matrix occurs in separate waves, but the factors regulating compartmentation and neuronal differentiation are largely unidentified. We performed RNA- and ATAC-seq on sorted striosome and matrix cells at postnatal day 3, using the Nr4a1-EGFP striosome reporter mouse. Focusing on the striosome, we validated the localization and/or role of Irx1, Foxf2, Olig2, and Stat1/2 in the developing striosome and the in vivo enhancer function of a striosome-specific open chromatin region 4.4 Kb downstream of Olig2. These data provide novel tools to dissect and manipulate the networks regulating MSN compartmentation and differentiation, including in human iPSC-derived striatal neurons for disease modeling and drug discovery.
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Affiliation(s)
- Maria-Daniela Cirnaru
- Department of Neurology, Icahn School of Medicine at Mount SinaiNew YorkUnited States
| | - Sicheng Song
- Department of Biomedical Informatics and Medical Education, University of WashingtonSeattleUnited States
| | | | - Chuhyon Corwin
- Department of Neurology, Icahn School of Medicine at Mount SinaiNew YorkUnited States
| | - Justyna Mleczko
- Department of Neurology, Icahn School of Medicine at Mount SinaiNew YorkUnited States
| | | | - Houda Benlhabib
- Department of Biomedical Informatics and Medical Education, University of WashingtonSeattleUnited States
| | - Jaroslav Bendl
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount SinaiNew YorkUnited States
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount SinaiNew YorkUnited States
- Department of Psychiatry, Icahn School of Medicine at Mount SinaiNew YorkUnited States
| | - Pasha Apontes
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount SinaiNew YorkUnited States
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount SinaiNew YorkUnited States
- Department of Psychiatry, Icahn School of Medicine at Mount SinaiNew YorkUnited States
| | - John Fullard
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount SinaiNew YorkUnited States
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount SinaiNew YorkUnited States
- Department of Psychiatry, Icahn School of Medicine at Mount SinaiNew YorkUnited States
| | - Jordi Creus-Muncunill
- Department of Neurology, Icahn School of Medicine at Mount SinaiNew YorkUnited States
| | - Azadeh Reyahi
- Department of Chemistry and Molecular Biology, University of GothenburgGothenburgSweden
| | - Ali M Nik
- Department of Chemistry and Molecular Biology, University of GothenburgGothenburgSweden
| | - Peter Carlsson
- Department of Chemistry and Molecular Biology, University of GothenburgGothenburgSweden
| | - Panos Roussos
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount SinaiNew YorkUnited States
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount SinaiNew YorkUnited States
- Department of Psychiatry, Icahn School of Medicine at Mount SinaiNew YorkUnited States
- Mental Illness Research, Education, and Clinical Center (VISN 2 South)BronxUnited States
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, University of WashingtonSeattleUnited States
| | | | - Michelle E Ehrlich
- Department of Neurology, Icahn School of Medicine at Mount SinaiNew YorkUnited States
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13
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Cooley NP, Wright ES. Accurate annotation of protein coding sequences with IDTAXA. NAR Genom Bioinform 2021; 3:lqab080. [PMID: 34541527 PMCID: PMC8445202 DOI: 10.1093/nargab/lqab080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 07/07/2021] [Accepted: 08/25/2021] [Indexed: 11/12/2022] Open
Abstract
The observed diversity of protein coding sequences continues to increase far more rapidly than knowledge of their functions, making classification algorithms essential for assigning a function to proteins using only their sequence. Most pipelines for annotating proteins rely on searches for homologous sequences in databases of previously annotated proteins using BLAST or HMMER. Here, we develop a new approach for classifying proteins into a taxonomy of functions and demonstrate its utility for genome annotation. Our algorithm, IDTAXA, was more accurate than BLAST or HMMER at assigning sequences to KEGG ortholog groups. Moreover, IDTAXA correctly avoided classifying sequences with novel functions to existing groups, which is a common error mode for classification approaches that rely on E-values as a proxy for confidence. We demonstrate IDTAXA's utility for annotating eukaryotic and prokaryotic genomes by assigning functions to proteins within a multi-level ontology and applied IDTAXA to detect genome contamination in eukaryotic genomes. Finally, we re-annotated 8604 microbial genomes with known antibiotic resistance phenotypes to discover two novel associations between proteins and antibiotic resistance. IDTAXA is available as a web tool (http://DECIPHER.codes/Classification.html) or as part of the open source DECIPHER R package from Bioconductor.
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Affiliation(s)
- Nicholas P Cooley
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
| | - Erik S Wright
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
- Center for Evolutionary Biology and Medicine, Pittsburgh, PA 15219, USA
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14
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Rodriguez VA, Bhave S, Chen R, Pang C, Hripcsak G, Sengupta S, Elhadad N, Green R, Adelman J, Metitiri KS, Elias P, Groves H, Mohan S, Natarajan K, Perotte A. Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients. J Am Med Inform Assoc 2021; 28:1480-1488. [PMID: 33706377 PMCID: PMC7989331 DOI: 10.1093/jamia/ocab029] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 01/09/2021] [Accepted: 02/05/2021] [Indexed: 12/28/2022] Open
Abstract
OBJECTIVE Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020. MATERIALS AND METHODS For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)-positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model's calibration and evaluated feature importances to interpret model output. RESULTS The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve-MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve-MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition. DISCUSSION Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients. CONCLUSIONS We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results.
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Affiliation(s)
| | - Shreyas Bhave
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | | | - Chao Pang
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
- Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Soumitra Sengupta
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Noemie Elhadad
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Robert Green
- Department of Emergency Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - Jason Adelman
- Division of General Medicine, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | | | - Pierre Elias
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Holden Groves
- Department of Anesthesiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Sumit Mohan
- Division of Nephrology, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Adler Perotte
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
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15
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Zhan Z, Jing Z, He B, Hosseini N, Westerhoff M, Choi EY, Garmire LX. Two-stage Cox-nnet: biologically interpretable neural-network model for prognosis prediction and its application in liver cancer survival using histopathology and transcriptomic data. NAR Genom Bioinform 2021; 3:lqab015. [PMID: 33778491 PMCID: PMC7985035 DOI: 10.1093/nargab/lqab015] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 02/01/2021] [Accepted: 02/24/2021] [Indexed: 12/11/2022] Open
Abstract
Pathological images are easily accessible data with the potential of prognostic biomarkers. Moreover, integration of heterogeneous data types from multi-modality, such as pathological image and gene expression data, is invaluable to help predicting cancer patient survival. However, the analytical challenges are significant. Here, we take the hepatocellular carcinoma (HCC) pathological image features extracted by CellProfiler, and apply them as the input for Cox-nnet, a neural network-based prognosis prediction model. We compare this model with the conventional Cox proportional hazards (Cox-PH) model, CoxBoost, Random Survival Forests and DeepSurv, using C-index and log-rank P-values. The results show that Cox-nnet is significantly more accurate than Cox-PH and Random Survival Forests models and comparable with CoxBoost and DeepSurv models, on pathological image features. Further, to integrate pathological image and gene expression data of the same patients, we innovatively construct a two-stage Cox-nnet model, and compare it with another complex neural-network model called PAGE-Net. The two-stage Cox-nnet complex model combining histopathology image and transcriptomic RNA-seq data achieves much better prognosis prediction, with a median C-index of 0.75 and log-rank P-value of 6e-7 in the testing datasets, compared to PAGE-Net (median C-index of 0.68 and log-rank P-value of 0.03). Imaging features present additional predictive information to gene expression features, as the combined model is more accurate than the model with gene expression alone (median C-index 0.70). Pathological image features are correlated with gene expression, as genes correlated to top imaging features present known associations with HCC patient survival and morphogenesis of liver tissue. This work proposes two-stage Cox-nnet, a new class of biologically relevant and interpretable models, to integrate multiple types of heterogenous data for survival prediction.
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Affiliation(s)
- Zhucheng Zhan
- School of Science and Engineering, Chinese University of Hong Kong, Shenzhen Campus, Shenzhen 518172, P.R. China
| | - Zheng Jing
- Department of Applied Statistics, University of Michigan, Ann Arbor, MI 48104, USA
| | - Bing He
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48104, USA
| | - Noshad Hosseini
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48104, USA
| | - Maria Westerhoff
- Department of Pathology, University of Michigan, Ann Arbor, MI 48104, USA
| | - Eun-Young Choi
- Department of Pathology, University of Michigan, Ann Arbor, MI 48104, USA
| | - Lana X Garmire
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48104, USA
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16
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Peng Y, Tang Y, Lee S, Zhu Y, Summers RM, Lu Z. COVID-19-CT-CXR: A Freely Accessible and Weakly Labeled Chest X-Ray and CT Image Collection on COVID-19 From Biomedical Literature. IEEE Trans Big Data 2021; 7:3-12. [PMID: 33997112 PMCID: PMC8117951 DOI: 10.1109/tbdata.2020.3035935] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 10/09/2020] [Accepted: 10/19/2020] [Indexed: 05/06/2023]
Abstract
The latest threat to global health is the COVID-19 outbreak. Although there exist large datasets of chest X-rays (CXR) and computed tomography (CT) scans, few COVID-19 image collections are currently available due to patient privacy. At the same time, there is a rapid growth of COVID-19-relevant articles in the biomedical literature, including those that report findings on radiographs. Here, we present COVID-19-CT-CXR, a public database of COVID-19 CXR and CT images, which are automatically extracted from COVID-19-relevant articles from the PubMed Central Open Access (PMC-OA) Subset. We extracted figures, associated captions, and relevant figure descriptions in the article and separated compound figures into subfigures. Because a large portion of figures in COVID-19 articles are not CXR or CT, we designed a deep-learning model to distinguish them from other figure types and to classify them accordingly. The final database includes 1,327 CT and 263 CXR images (as of May 9, 2020) with their relevant text. To demonstrate the utility of COVID-19-CT-CXR, we conducted four case studies. (1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved deep-learning (DL) performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza, another common infectious respiratory illness that may present similarly to COVID-19, and fine-tuned a baseline deep neural network to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We fine-tuned an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR. (4) From text-mined captions and figure descriptions, we compared 15 clinical symptoms and 20 clinical findings of COVID-19 versus those of influenza to demonstrate the disease differences in the scientific publications. Our database is unique, as the figures are retrieved along with relevant text with fine-grained descriptions, and it can be extended easily in the future. We believe that our work is complementary to existing resources and hope that it will contribute to medical image analysis of the COVID-19 pandemic. The dataset, code, and DL models are publicly available at https://github.com/ncbi-nlp/COVID-19-CT-CXR.
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Affiliation(s)
- Yifan Peng
- NCBI/NLM/NIH and Department of Population Health SciencesWeill Cornell MedicineNew YorkNY10065USA
| | - Yuxing Tang
- Imaging Biomarkers and Computer-Aided Diagnosis LaboratoryRadiology and Imaging Sciences DepartmentNational Institutes of Health (NIH) Clinical CenterBethesdaMD20892USA
| | - Sungwon Lee
- Imaging Biomarkers and Computer-Aided Diagnosis LaboratoryRadiology and Imaging Sciences DepartmentNational Institutes of Health (NIH) Clinical CenterBethesdaMD20892USA
| | - Yingying Zhu
- Imaging Biomarkers and Computer-Aided Diagnosis LaboratoryRadiology and Imaging Sciences DepartmentNational Institutes of Health (NIH) Clinical CenterBethesdaMD20892USA
- Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonTX76019USA
| | - Ronald M. Summers
- Imaging Biomarkers and Computer-Aided Diagnosis LaboratoryRadiology and Imaging Sciences DepartmentNational Institutes of Health (NIH) Clinical CenterBethesdaMD20892USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI)National Library of Medicine (NLM)National Institutes of Health (NIH)BethesdaMD20894USA
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17
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Elworth RAL, Wang Q, Kota PK, Barberan CJ, Coleman B, Balaji A, Gupta G, Baraniuk RG, Shrivastava A, Treangen T. To Petabytes and beyond: recent advances in probabilistic and signal processing algorithms and their application to metagenomics. Nucleic Acids Res 2020; 48:5217-5234. [PMID: 32338745 PMCID: PMC7261164 DOI: 10.1093/nar/gkaa265] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 03/20/2020] [Accepted: 04/04/2020] [Indexed: 02/01/2023] Open
Abstract
As computational biologists continue to be inundated by ever increasing amounts of metagenomic data, the need for data analysis approaches that keep up with the pace of sequence archives has remained a challenge. In recent years, the accelerated pace of genomic data availability has been accompanied by the application of a wide array of highly efficient approaches from other fields to the field of metagenomics. For instance, sketching algorithms such as MinHash have seen a rapid and widespread adoption. These techniques handle increasingly large datasets with minimal sacrifices in quality for tasks such as sequence similarity calculations. Here, we briefly review the fundamentals of the most impactful probabilistic and signal processing algorithms. We also highlight more recent advances to augment previous reviews in these areas that have taken a broader approach. We then explore the application of these techniques to metagenomics, discuss their pros and cons, and speculate on their future directions.
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Affiliation(s)
| | - Qi Wang
- Systems, Synthetic, and Physical Biology (SSPB) Graduate Program, Houston, TX 77005, USA
| | - Pavan K Kota
- Department of Bioengineering, Houston, TX 77005, USA
| | - C J Barberan
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Benjamin Coleman
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Advait Balaji
- Department of Computer Science, Houston, TX 77005, USA
| | - Gaurav Gupta
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Richard G Baraniuk
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Anshumali Shrivastava
- Department of Computer Science, Houston, TX 77005, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Todd J Treangen
- Department of Computer Science, Houston, TX 77005, USA
- Systems, Synthetic, and Physical Biology (SSPB) Graduate Program, Houston, TX 77005, USA
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