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Verspoor K. The Evolution of Clinical Knowledge During COVID-19: Towards a Global Learning Health System. Yearb Med Inform 2021; 30:176-184. [PMID: 34479389 PMCID: PMC8416229 DOI: 10.1055/s-0041-1726503] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVES We examine the knowledge ecosystem of COVID-19, focusing on clinical knowledge and the role of health informatics as enabling technology. We argue for commitment to the model of a global learning health system to facilitate rapid knowledge translation supporting health care decision making in the face of emerging diseases. METHODS AND RESULTS We frame the evolution of knowledge in the COVID-19 crisis in terms of learning theory, and present a view of what has occurred during the pandemic to rapidly derive and share knowledge as an (underdeveloped) instance of a global learning health system. We identify the key role of information technologies for electronic data capture and data sharing, computational modelling, evidence synthesis, and knowledge dissemination. We further highlight gaps in the system and barriers to full realisation of an efficient and effective global learning health system. CONCLUSIONS The need for a global knowledge ecosystem supporting rapid learning from clinical practice has become more apparent than ever during the COVID-19 pandemic. Continued effort to realise the vision of a global learning health system, including establishing effective approaches to data governance and ethics to support the system, is imperative to enable continuous improvement in our clinical care.
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Affiliation(s)
- Karin Verspoor
- School of Computing Technologies, RMIT University, Melbourne VIC 3000 Australia
- Centre for Digital Transformation of Health, The University of Melbourne, Melbourne VIC 3010 Australia
- School of Computing and Information Systems, The University of Melbourne, Melbourne VIC 3010 Australia
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Hripcsak G, Schuemie MJ, Madigan D, Ryan PB, Suchard MA. Drawing Reproducible Conclusions from Observational Clinical Data with OHDSI. Yearb Med Inform 2021; 30:283-289. [PMID: 33882595 PMCID: PMC8416226 DOI: 10.1055/s-0041-1726481] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVE The current observational research literature shows extensive publication bias and contradiction. The Observational Health Data Sciences and Informatics (OHDSI) initiative seeks to improve research reproducibility through open science. METHODS OHDSI has created an international federated data source of electronic health records and administrative claims that covers nearly 10% of the world's population. Using a common data model with a practical schema and extensive vocabulary mappings, data from around the world follow the identical format. OHDSI's research methods emphasize reproducibility, with a large-scale approach to addressing confounding using propensity score adjustment with extensive diagnostics; negative and positive control hypotheses to test for residual systematic error; a variety of data sources to assess consistency and generalizability; a completely open approach including protocol, software, models, parameters, and raw results so that studies can be externally verified; and the study of many hypotheses in parallel so that the operating characteristics of the methods can be assessed. RESULTS OHDSI has already produced findings in areas like hypertension treatment that are being incorporated into practice, and it has produced rigorous studies of COVID-19 that have aided government agencies in their treatment decisions, that have characterized the disease extensively, that have estimated the comparative effects of treatments, and that the predict likelihood of advancing to serious complications. CONCLUSIONS OHDSI practices open science and incorporates a series of methods to address reproducibility. It has produced important results in several areas, including hypertension therapy and COVID-19 research.
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Affiliation(s)
- George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
- Observational Health Data Sciences and Informatics, New York, New York, USA
| | - Martijn J. Schuemie
- Observational Health Data Sciences and Informatics, New York, New York, USA
- Epidemiology Analytics, Janssen Research and Development, Titusville, New Jersey, USA
| | - David Madigan
- Observational Health Data Sciences and Informatics, New York, New York, USA
- Northeastern University, Boston, Massachusetts, USA
| | - Patrick B. Ryan
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
- Observational Health Data Sciences and Informatics, New York, New York, USA
- Epidemiology Analytics, Janssen Research and Development, Titusville, New Jersey, USA
| | - Marc A. Suchard
- Observational Health Data Sciences and Informatics, New York, New York, USA
- Fielding School of Public Health, Department of Biostatistics, University of California, Los Angeles, Los Angeles, USA
- David Geffen School of Medicine, Department of Biomathematics, University of California, Los Angeles, Los Angeles, USA
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Holmes JH, Beinlich J, Boland MR, Bowles KH, Chen Y, Cook TS, Demiris G, Draugelis M, Fluharty L, Gabriel PE, Grundmeier R, Hanson CW, Herman DS, Himes BE, Hubbard RA, Kahn CE, Kim D, Koppel R, Long Q, Mirkovic N, Morris JS, Mowery DL, Ritchie MD, Urbanowicz R, Moore JH. Why Is the Electronic Health Record So Challenging for Research and Clinical Care? Methods Inf Med 2021; 60:32-48. [PMID: 34282602 DOI: 10.1055/s-0041-1731784] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
BACKGROUND The electronic health record (EHR) has become increasingly ubiquitous. At the same time, health professionals have been turning to this resource for access to data that is needed for the delivery of health care and for clinical research. There is little doubt that the EHR has made both of these functions easier than earlier days when we relied on paper-based clinical records. Coupled with modern database and data warehouse systems, high-speed networks, and the ability to share clinical data with others are large number of challenges that arguably limit the optimal use of the EHR OBJECTIVES: Our goal was to provide an exhaustive reference for those who use the EHR in clinical and research contexts, but also for health information systems professionals as they design, implement, and maintain EHR systems. METHODS This study includes a panel of 24 biomedical informatics researchers, information technology professionals, and clinicians, all of whom have extensive experience in design, implementation, and maintenance of EHR systems, or in using the EHR as clinicians or researchers. All members of the panel are affiliated with Penn Medicine at the University of Pennsylvania and have experience with a variety of different EHR platforms and systems and how they have evolved over time. RESULTS Each of the authors has shared their knowledge and experience in using the EHR in a suite of 20 short essays, each representing a specific challenge and classified according to a functional hierarchy of interlocking facets such as usability and usefulness, data quality, standards, governance, data integration, clinical care, and clinical research. CONCLUSION We provide here a set of perspectives on the challenges posed by the EHR to clinical and research users.
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Affiliation(s)
- John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
| | - James Beinlich
- Information Technology Entity Services and Corporate Information Services, University of Pennsylvania Health System, Philadelphia, Pennsylvania, United States
| | - Mary R Boland
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
| | - Kathryn H Bowles
- Department of Biobehavioral Health Sciences, University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania, United States
| | - Yong Chen
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
| | - Tessa S Cook
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
| | - George Demiris
- Department of Biobehavioral Health Sciences, University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania, United States
| | - Michael Draugelis
- Department of Predictive Health Care, University of Pennsylvania Health System, Philadelphia, Pennsylvania, United States
| | - Laura Fluharty
- Clinical Research Operations, University of Pennsylvania Health System, Philadelphia, Pennsylvania, United States
| | - Peter E Gabriel
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
| | - Robert Grundmeier
- Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - C William Hanson
- Department of Anesthesiology and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
| | - Daniel S Herman
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine Philadelphia, Pennsylvania, United States
| | - Blanca E Himes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
| | - Rebecca A Hubbard
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
| | - Charles E Kahn
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
| | - Ross Koppel
- Department of Sociology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Qi Long
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
| | - Nebojsa Mirkovic
- Department of Research Analytics, University of Pennsylvania Health System, Philadelphia, Pennsylvania, United States
| | - Jeffrey S Morris
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
| | - Ryan Urbanowicz
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
| | - Jason H Moore
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
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Recalde M, Roel E, Pistillo A, Sena AG, Prats-Uribe A, Ahmed WUR, Alghoul H, Alshammari TM, Alser O, Areia C, Burn E, Casajust P, Dawoud D, DuVall SL, Falconer T, Fernández-Bertolín S, Golozar A, Gong M, Lai LYH, Lane JCE, Lynch KE, Matheny ME, Mehta PP, Morales DR, Natarjan K, Nyberg F, Posada JD, Reich CG, Rijnbeek PR, Schilling LM, Shah K, Shah NH, Subbian V, Zhang L, Zhu H, Ryan P, Prieto-Alhambra D, Kostka K, Duarte-Salles T. Characteristics and outcomes of 627 044 COVID-19 patients living with and without obesity in the United States, Spain, and the United Kingdom. Int J Obes (Lond) 2021; 45:2347-2357. [PMID: 34267326 PMCID: PMC8281807 DOI: 10.1038/s41366-021-00893-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 06/07/2021] [Accepted: 06/24/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND A detailed characterization of patients with COVID-19 living with obesity has not yet been undertaken. We aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 patients living with obesity (PLWO) to those of patients living without obesity. METHODS We conducted a cohort study based on outpatient/inpatient care and claims data from January to June 2020 from Spain, the UK, and the US. We used six databases standardized to the OMOP common data model. We defined two non-mutually exclusive cohorts of patients diagnosed and/or hospitalized with COVID-19; patients were followed from index date to 30 days or death. We report the frequency of demographics, prior medical conditions, and 30-days outcomes (hospitalization, events, and death) by obesity status. RESULTS We included 627 044 (Spain: 122 058, UK: 2336, and US: 502 650) diagnosed and 160 013 (Spain: 18 197, US: 141 816) hospitalized patients with COVID-19. The prevalence of obesity was higher among patients hospitalized (39.9%, 95%CI: 39.8-40.0) than among those diagnosed with COVID-19 (33.1%; 95%CI: 33.0-33.2). In both cohorts, PLWO were more often female. Hospitalized PLWO were younger than patients without obesity. Overall, COVID-19 PLWO were more likely to have prior medical conditions, present with cardiovascular and respiratory events during hospitalization, or require intensive services compared to COVID-19 patients without obesity. CONCLUSION We show that PLWO differ from patients without obesity in a wide range of medical conditions and present with more severe forms of COVID-19, with higher hospitalization rates and intensive services requirements. These findings can help guiding preventive strategies of COVID-19 infection and complications and generating hypotheses for causal inference studies.
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Affiliation(s)
- Martina Recalde
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.,Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Elena Roel
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.,Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Andrea Pistillo
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Anthony G Sena
- Janssen Research & Development, Titusville, NJ, USA.,Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Albert Prats-Uribe
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
| | - Waheed-Ul-Rahman Ahmed
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Oxford, UK.,College of Medicine and Health, University of Exeter, St Luke's Campus, Exeter, UK
| | - Heba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine
| | | | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Edward Burn
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.,Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
| | - Paula Casajust
- Real-World Evidence, Trial Form Support, Barcelona, Spain
| | - Dalia Dawoud
- Cairo University, Faculty of Pharmacy, Cairo, Egypt
| | - Scott L DuVall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.,Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Sergio Fernández-Bertolín
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Asieh Golozar
- Department of Epidemiology, Johns Hopkins School of Public, Baltimore, MD, USA.,Pharmacoepidemiology, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | | | - Lana Yin Hui Lai
- Division of Cancer Sciences, School of Medical Sciences, University of Manchester, Manchester, UK
| | - Jennifer C E Lane
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Oxford, UK
| | - Kristine E Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.,Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Michael E Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Paras P Mehta
- College of Medicine, The University of Arizona, Tucson, AZ, USA
| | - Daniel R Morales
- Division of Population Health and Genomics, University of Dundee, Dundee, UK
| | - Karthik Natarjan
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,New York-Presbyterian Hospital, New York, NY, USA
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Jose D Posada
- Department of Medicine, Stanford University, Palo Alto, CA, USA
| | | | - Peter R Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Lisa M Schilling
- Data Science to Patient Value Program, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Karishma Shah
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Oxford, UK
| | - Nigam H Shah
- Department of Medicine, Stanford University, Palo Alto, CA, USA
| | - Vignesh Subbian
- College of Engineering, The University of Arizona, Tucson, AZ, USA
| | - Lin Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.,Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Hong Zhu
- Institute of Health Management, Southern Medical University, Guangzhou, China.,Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Patrick Ryan
- Janssen Research & Development, Titusville, NJ, USA.,Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Daniel Prieto-Alhambra
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.,Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
| | - Kristin Kostka
- Real World Solutions, IQVIA, Cambridge, MA, USA.,The OHDSI Center at the Roux Institute, Northeastern University, Portland, ME, USA
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.
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Hedberg P, Karlsson Valik J, van der Werff S, Tanushi H, Requena Mendez A, Granath F, Bell M, Mårtensson J, Dyrdak R, Hertting O, Färnert A, Ternhag A, Naucler P. Clinical phenotypes and outcomes of SARS-CoV-2, influenza, RSV and seven other respiratory viruses: a retrospective study using complete hospital data. Thorax 2021; 77:154-163. [PMID: 34226206 PMCID: PMC8260304 DOI: 10.1136/thoraxjnl-2021-216949] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 05/25/2021] [Indexed: 12/29/2022]
Abstract
Background An understanding of differences in clinical phenotypes and outcomes COVID-19 compared with other respiratory viral infections is important to optimise the management of patients and plan healthcare. Herein we sought to investigate such differences in patients positive for SARS-CoV-2 compared with influenza, respiratory syncytial virus (RSV) and other respiratory viruses. Methods We performed a retrospective cohort study of hospitalised adults and children (≤15 years) who tested positive for SARS-CoV-2, influenza virus A/B, RSV, rhinovirus, enterovirus, parainfluenza viruses, metapneumovirus, seasonal coronaviruses, adenovirus or bocavirus in a respiratory sample at admission between 2011 and 2020. Results A total of 6321 adult (1721 SARS-CoV-2) and 6379 paediatric (101 SARS-CoV-2) healthcare episodes were included in the study. In adults, SARS-CoV-2 positivity was independently associated with younger age, male sex, overweight/obesity, diabetes and hypertension, tachypnoea as well as better haemodynamic measurements, white cell count, platelet count and creatinine values. Furthermore, SARS-CoV-2 was associated with higher 30-day mortality as compared with influenza (adjusted HR (aHR) 4.43, 95% CI 3.51 to 5.59), RSV (aHR 3.81, 95% CI 2.72 to 5.34) and other respiratory viruses (aHR 3.46, 95% CI 2.61 to 4.60), as well as higher 90-day mortality, ICU admission, ICU mortality and pulmonary embolism in adults. In children, patients with SARS-CoV-2 were older and had lower prevalence of chronic cardiac and respiratory diseases compared with other viruses. Conclusions SARS-CoV-2 is associated with more severe outcomes compared with other respiratory viruses, and although associated with specific patient and clinical characteristics at admission, a substantial overlap precludes discrimination based on these characteristics.
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Affiliation(s)
- Pontus Hedberg
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm, Sweden .,Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - John Karlsson Valik
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.,Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
| | - Suzanne van der Werff
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.,Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
| | - Hideyuki Tanushi
- Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
| | - Ana Requena Mendez
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.,Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
| | - Fredrik Granath
- Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
| | - Max Bell
- Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden.,Department of Perioperative Medicine and Intensive Care, Karolinska University Hospital, Stockholm, Sweden
| | - Johan Mårtensson
- Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden.,Department of Perioperative Medicine and Intensive Care, Karolinska University Hospital, Stockholm, Sweden
| | - Robert Dyrdak
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm, Sweden.,Department of Clinical Microbiology, Karolinska University Hospital, Stockholm, Sweden
| | - Olof Hertting
- Department of Women's and Children's Health, Karolinska Institute, Stockholm, Sweden.,Department of Paediatric Infectious Diseases, Astrid Lindgren's Hospital, Karolinska University Hospital, Stockholm, Sweden
| | - Anna Färnert
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.,Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
| | - Anders Ternhag
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.,Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
| | - Pontus Naucler
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.,Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
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Catala M, Coma E, Alonso S, Álvarez-Lacalle E, Cordomi S, López D, Fina F, Medina-Peralta M, Prats C, Prieto-Alhambra D. Risk Diagrams Based on Primary Care Electronic Medical Records and Linked Real-Time PCR Data to Monitor Local COVID-19 Outbreaks During the Summer 2020: A Prospective Study Including 7,671,862 People in Catalonia. Front Public Health 2021; 9:693956. [PMID: 34291033 PMCID: PMC8287173 DOI: 10.3389/fpubh.2021.693956] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 06/07/2021] [Indexed: 11/23/2022] Open
Abstract
Monitoring transmission is a prerequisite for containing COVID-19. We report on effective potential growth (EPG) as a novel measure for the early identification of local outbreaks based on primary care electronic medical records (EMR) and PCR-confirmed cases. Secondly, we studied whether increasing EPG precedes local hospital and intensive care (ICU) admissions and mortality. Population-based cohort including all Catalan citizens' PCR tests, hospitalization, intensive care (ICU) and mortality between 1/07/2020 and 13/09/2020; linked EMR covering 88.6% of the Catalan population was obtained. Nursing home residents were excluded. COVID-19 counts were ascertained based on EMR and PCRs separately. Weekly empirical propagation (ρ7) and 14-day cumulative incidence (A14) and 95% confidence intervals were estimated at care management area (CMA) level, and combined as EPG = ρ7 × A14. Overall, 7,607,201 and 6,798,994 people in 43 CMAs were included for PCR and EMR measures, respectively. A14, ρ7, and EPG increased in numerous CMAs during summer 2020. EMR identified 2.70-fold more cases than PCRs, with similar trends, a median (interquartile range) 2 (1) days earlier, and better precision. Upticks in EPG preceded increases in local hospital admissions, ICU occupancy, and mortality. Increasing EPG identified localized outbreaks in Catalonia, and preceded local hospital and ICU admissions and subsequent mortality. EMRs provided similar estimates to PCR, but some days earlier and with better precision. EPG is a useful tool for the monitoring of community transmission and for the early identification of COVID-19 local outbreaks.
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Affiliation(s)
- Marti Catala
- Computational Biology and Complex Systems (BIOCOM-SC), Department of Physics, Universitat Politècnica de Catalunya, Castelldefels, Spain.,Comparative Medicine and Bioimage Centre of Catalonia (CMCiB), Germans Trias i Pujol Research Institute (IGTP), Badalona, Spain
| | - Ermengol Coma
- Sistemes d'Informació dels Serveis d'Atenció Primària (SISAP), Institut Català de la Salut (ICS), Barcelona, Spain
| | - Sergio Alonso
- Computational Biology and Complex Systems (BIOCOM-SC), Department of Physics, Universitat Politècnica de Catalunya, Castelldefels, Spain
| | - Enrique Álvarez-Lacalle
- Computational Biology and Complex Systems (BIOCOM-SC), Department of Physics, Universitat Politècnica de Catalunya, Castelldefels, Spain
| | - Silvia Cordomi
- Direcció d'Estratègia i Qualitat, Institut Català de la Salut, Barcelona, Spain
| | - Daniel López
- Computational Biology and Complex Systems (BIOCOM-SC), Department of Physics, Universitat Politècnica de Catalunya, Castelldefels, Spain
| | - Francesc Fina
- Sistemes d'Informació dels Serveis d'Atenció Primària (SISAP), Institut Català de la Salut (ICS), Barcelona, Spain
| | - Manuel Medina-Peralta
- Sistemes d'Informació dels Serveis d'Atenció Primària (SISAP), Institut Català de la Salut (ICS), Barcelona, Spain
| | - Clara Prats
- Computational Biology and Complex Systems (BIOCOM-SC), Department of Physics, Universitat Politècnica de Catalunya, Castelldefels, Spain.,Comparative Medicine and Bioimage Centre of Catalonia (CMCiB), Germans Trias i Pujol Research Institute (IGTP), Badalona, Spain
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57
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Almeida JR, Silva JF, Matos S, Oliveira JL. A two-stage workflow to extract and harmonize drug mentions from clinical notes into observational databases. J Biomed Inform 2021; 120:103849. [PMID: 34214696 DOI: 10.1016/j.jbi.2021.103849] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 06/04/2021] [Accepted: 06/19/2021] [Indexed: 01/02/2023]
Abstract
BACKGROUND The content of the clinical notes that have been continuously collected along patients' health history has the potential to provide relevant information about treatments and diseases, and to increase the value of structured data available in Electronic Health Records (EHR) databases. EHR databases are currently being used in observational studies which lead to important findings in medical and biomedical sciences. However, the information present in clinical notes is not being used in those studies, since the computational analysis of this unstructured data is much complex in comparison to structured data. METHODS We propose a two-stage workflow for solving an existing gap in Extraction, Transformation and Loading (ETL) procedures regarding observational databases. The first stage of the workflow extracts prescriptions present in patient's clinical notes, while the second stage harmonises the extracted information into their standard definition and stores the resulting information in a common database schema used in observational studies. RESULTS We validated this methodology using two distinct data sets, in which the goal was to extract and store drug related information in a new Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) database. We analysed the performance of the used annotator as well as its limitations. Finally, we described some practical examples of how users can explore these datasets once migrated to OMOP CDM databases. CONCLUSION With this methodology, we were able to show a strategy for using the information extracted from the clinical notes in business intelligence tools, or for other applications such as data exploration through the use of SQL queries. Besides, the extracted information complements the data present in OMOP CDM databases which was not directly available in the EHR database.
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Affiliation(s)
- João Rafael Almeida
- DETI/IEETA, University of Aveiro, Aveiro, Portugal; Department of Computation, University of A Coruña, A Coruña, Spain.
| | | | - Sérgio Matos
- DETI/IEETA, University of Aveiro, Aveiro, Portugal.
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Oi I, Ito I, Hirabayashi M, Endo K, Emura M, Kojima T, Tsukao H, Tomii K, Nakagawa A, Otsuka K, Akai M, Oi M, Sugita T, Fukui M, Inoue D, Hasegawa Y, Takahashi K, Yasui H, Fujita K, Ishida T, Ito A, Kita H, Kaji Y, Tsuchiya M, Tomioka H, Yamada T, Terada S, Nakaji H, Hamao N, Shirata M, Nishioka K, Yamazoe M, Shiraishi Y, Ogimoto T, Hosoya K, Ajimizu H, Shima H, Matsumoto H, Tanabe N, Hirai T. Pneumonia Caused by Severe Acute Respiratory Syndrome Coronavirus 2 and Influenza Virus: A Multicenter Comparative Study. Open Forum Infect Dis 2021; 8:ofab282. [PMID: 34291119 PMCID: PMC8244664 DOI: 10.1093/ofid/ofab282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 05/28/2021] [Indexed: 12/15/2022] Open
Abstract
Background Detailed differences in clinical information between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia (CP), which is the main phenotype of SARS-CoV-2 disease, and influenza pneumonia (IP) are still unclear. Methods A prospective, multicenter cohort study was conducted by including patients with CP who were hospitalized between January and June 2020 and a retrospective cohort of patients with IP hospitalized from 2009 to 2020. We compared the clinical presentations and studied the prognostic factors of CP and IP. Results Compared with the IP group (n = 66), in the multivariate analysis, the CP group (n = 362) had a lower percentage of patients with underlying asthma or chronic obstructive pulmonary disease (P < .01), lower neutrophil-to-lymphocyte ratio (P < .01), lower systolic blood pressure (P < .01), higher diastolic blood pressure (P < .01), lower aspartate aminotransferase level (P < .05), higher serum sodium level (P < .05), and more frequent multilobar infiltrates (P < .05). The diagnostic scoring system based on these findings showed excellent differentiation between CP and IP (area under the receiver operating characteristic curve, 0.889). Moreover, the prognostic predictors were different between CP and IP. Conclusions Comprehensive differences between CP and IP were revealed, highlighting the need for early differentiation between these 2 pneumonias in clinical settings.
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Affiliation(s)
- Issei Oi
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Department of Internal Medicine, Sugita Genpaku Memorial Obama Municipal Hospital, Obama, Japan
| | - Isao Ito
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Department of Internal Medicine, Sugita Genpaku Memorial Obama Municipal Hospital, Obama, Japan
| | - Masataka Hirabayashi
- Department of Respiratory Medicine, Hyogo Prefectural Amagasaki General Medical Center, Amagasaki, Japan
| | - Kazuo Endo
- Department of Respiratory Medicine, Hyogo Prefectural Amagasaki General Medical Center, Amagasaki, Japan
| | - Masahito Emura
- Department of Respiratory Medicine, Kyoto City Hospital, Kyoto, Japan
| | - Toru Kojima
- Department of Respiratory Medicine, Fukui Prefectural Hospital, Fukui, Japan
| | - Hitokazu Tsukao
- Department of Respiratory Medicine, Fukui Prefectural Hospital, Fukui, Japan
| | - Keisuke Tomii
- Department of Respiratory Medicine, Kobe City Medical Center General Hospital, Kobe, Japan
| | - Atsushi Nakagawa
- Department of Respiratory Medicine, Kobe City Medical Center General Hospital, Kobe, Japan
| | - Kojiro Otsuka
- Department of Respiratory Medicine, Shinko Hospital, Kobe, Japan
| | - Masaya Akai
- Department of Respiratory Medicine, Japanese Red Cross Fukui Hospital, Fukui, Japan
| | - Masahiro Oi
- Department of Respiratory Medicine, Japanese Red Cross Fukui Hospital, Fukui, Japan
| | - Takakazu Sugita
- Department of Respiratory Medicine, Japan Red Cross Wakayama Medical Center, Wakayama, Japan
| | - Motonari Fukui
- Respiratory Disease Center, Kitano Hospital, Tazuke Kofukai Medical Research Institute, Osaka, Japan
| | - Daiki Inoue
- Respiratory Disease Center, Kitano Hospital, Tazuke Kofukai Medical Research Institute, Osaka, Japan
| | - Yoshinori Hasegawa
- Department of Respiratory Medicine, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan
| | - Kenichi Takahashi
- Department of Respiratory Medicine, Kishiwada City Hospital, Kishiwaada, Japan
| | - Hiroaki Yasui
- Department of Internal Medicine, Horikawa Hospital, Kyoto, Japan
| | - Kohei Fujita
- Division of Respiratory Medicine, Center for Respiratory Disease, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | - Tadashi Ishida
- Department of Respiratory Medicine, Ohara Healthcare Foundation, Kurashiki Central Hospital, Kurashiki, Japan
| | - Akihiro Ito
- Department of Respiratory Medicine, Ohara Healthcare Foundation, Kurashiki Central Hospital, Kurashiki, Japan
| | - Hideo Kita
- Department of Respiratory Medicine, Takatsuki Red Cross Hospital, Takatsuki, Japan
| | - Yusuke Kaji
- Department of Respiratory Medicine, Tenri Hospital, Tenri, Japan
| | - Michiko Tsuchiya
- Department of Respiratory Medicine, Rakuwakai Otowa Hospital, Kyoto, Japan
| | - Hiromi Tomioka
- Department of Respiratory Medicine, Kobe City Medical Center West Hospital, Kobe, Japan
| | - Takashi Yamada
- Department of Respiratory Medicine, Shizuoka City Shizuoka Hospital, Shizuoka, Japan
| | - Satoru Terada
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Respiratory Medicine and General Practice, Terada Clinic, Himeji, Japan
| | - Hitoshi Nakaji
- Department of Respiratory Medicine, Toyooka Hospital, Toyooka, Japan
| | - Nobuyoshi Hamao
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Department of Internal Medicine, Sugita Genpaku Memorial Obama Municipal Hospital, Obama, Japan
| | - Masahiro Shirata
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kensuke Nishioka
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Masatoshi Yamazoe
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Department of Respiratory Medicine, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan
| | - Yusuke Shiraishi
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Respiratory Disease Center, Kitano Hospital, Tazuke Kofukai Medical Research Institute, Osaka, Japan
| | - Tatsuya Ogimoto
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Department of Respiratory Medicine, Kishiwada City Hospital, Kishiwaada, Japan
| | - Kazutaka Hosoya
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Department of Respiratory Medicine, Kishiwada City Hospital, Kishiwaada, Japan
| | - Hitomi Ajimizu
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Department of Respiratory Medicine, Rakuwakai Otowa Hospital, Kyoto, Japan
| | - Hiroshi Shima
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Department of Respiratory Medicine, Toyooka Hospital, Toyooka, Japan
| | - Hisako Matsumoto
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Naoya Tanabe
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Toyohiro Hirai
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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59
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Khera R, Mortazavi BJ, Sangha V, Warner F, Young HP, Ross JS, Shah ND, Theel ES, Jenkinson WG, Knepper C, Wang K, Peaper D, Martinello RA, Brandt CA, Lin Z, Ko AI, Krumholz HM, Pollock BD, Schulz WL. Accuracy of Computable Phenotyping Approaches for SARS-CoV-2 Infection and COVID-19 Hospitalizations from the Electronic Health Record. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021. [PMID: 34013299 PMCID: PMC8132274 DOI: 10.1101/2021.03.16.21253770] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Objective: Real-world data have been critical for rapid-knowledge generation throughout the COVID-19 pandemic. To ensure high-quality results are delivered to guide clinical decision making and the public health response, as well as characterize the response to interventions, it is essential to establish the accuracy of COVID-19 case definitions derived from administrative data to identify infections and hospitalizations. Methods: Electronic Health Record (EHR) data were obtained from the clinical data warehouse of the Yale New Haven Health System (Yale, primary site) and 3 hospital systems of the Mayo Clinic (validation site). Detailed characteristics on demographics, diagnoses, and laboratory results were obtained for all patients with either a positive SARS-CoV-2 PCR or antigen test or ICD-10 diagnosis of COVID-19 (U07.1) between April 1, 2020 and March 1, 2021. Various computable phenotype definitions were evaluated for their accuracy to identify SARS-CoV-2 infection and COVID-19 hospitalizations. Results: Of the 69,423 individuals with either a diagnosis code or a laboratory diagnosis of a SARS-CoV-2 infection at Yale, 61,023 had a principal or a secondary diagnosis code for COVID-19 and 50,355 had a positive SARS-CoV-2 test. Among those with a positive laboratory test, 38,506 (76.5%) and 3449 (6.8%) had a principal and secondary diagnosis code of COVID-19, respectively, while 8400 (16.7%) had no COVID-19 diagnosis. Moreover, of the 61,023 patients with a COVID-19 diagnosis code, 19,068 (31.2%) did not have a positive laboratory test for SARS-CoV-2 in the EHR. Of the 20 cases randomly sampled from this latter group for manual review, all had a COVID-19 diagnosis code related to asymptomatic testing with negative subsequent test results. The positive predictive value (precision) and sensitivity (recall) of a COVID-19 diagnosis in the medical record for a documented positive SARS-CoV-2 test were 68.8% and 83.3%, respectively. Among 5,109 patients who were hospitalized with a principal diagnosis of COVID-19, 4843 (94.8%) had a positive SARS-CoV-2 test within the 2 weeks preceding hospital admission or during hospitalization. In addition, 789 hospitalizations had a secondary diagnosis of COVID-19, of which 446 (56.5%) had a principal diagnosis consistent with severe clinical manifestation of COVID-19 (e.g., sepsis or respiratory failure). Compared with the cohort that had a principal diagnosis of COVID-19, those with a secondary diagnosis had a more than 2-fold higher in-hospital mortality rate (13.2% vs 28.0%, P<0.001). In the validation sample at Mayo Clinic, diagnosis codes more consistently identified SARS-CoV-2 infection (precision of 95%) but had lower recall (63.5%) with substantial variation across the 3 Mayo Clinic sites. Similar to Yale, diagnosis codes consistently identified COVID-19 hospitalizations at Mayo, with hospitalizations defined by secondary diagnosis code with 2-fold higher in-hospital mortality compared to those with a primary diagnosis of COVID-19. Conclusions: COVID-19 diagnosis codes misclassified the SARS-CoV-2 infection status of many people, with implications for clinical research and epidemiological surveillance. Moreover, the codes had different performance across two academic health systems and identified groups with different risks of mortality. Real-world data from the EHR can be used to in conjunction with diagnosis codes to improve the identification of people infected with SARS-CoV-2.
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60
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Puig-Domingo M, Marazuela M, Yildiz BO, Giustina A. COVID-19 and endocrine and metabolic diseases. An updated statement from the European Society of Endocrinology. Endocrine 2021; 72:301-316. [PMID: 33963516 PMCID: PMC8105151 DOI: 10.1007/s12020-021-02734-w] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 04/16/2021] [Indexed: 01/08/2023]
Abstract
BACKGROUND COVID-19 has completely changed our daily clinical practice as well as our social relations. Many organs and biological systems are involved in SARS-Cov-2 infection, either due to direct virus-induced damage or to indirect effects that can have systemic consequences. Endocrine system is not only an exception but its involvement in COVID-19 is so relevant that an "endocrine phenotype" of COVID-19 has progressively acquired clinical relevance. AIM We have been appointed by the European Society of Endocrinology (ESE) to update with the current statement ESE members and the whole endocrine community on the emerging endocrine phenotype of COVID-19 and its implication for the prevention and management of the disease. CONCLUSIONS Diabetes has a major role in this phenotype since it is one of the most frequent comorbidities associated with severity and mortality of COVID-19. Careful management including treatment modifications may be required for protecting our patients rather with known diabetes from the most dangerous consequences of COVID-19 or hospitalized with COVID-19, but also in patients with SARS-CoV-2 induced newly onset diabetes. Obesity increases susceptibility to SARS-CoV-2 and the risk for COVID-19 adverse outcome. Adequate nutritional management needs to be granted to patients with obesity or undernourishment in order to limit their increased susceptibility and severity of COVID-19 infection. Lack of vitamin D, hypocalcemia and vertebral fractures have also emerged as frequent findings in the hospitalized COVID-19 population and may negatively impact on the outcome of such patients. Also, in patients with adrenal insufficiency prompt adaptation of glucocorticoid doses may be needed. Moreover, in this updated statement role of sex hormones as well as peculiar pituitary and thyroid aspects of COVID-19 have been included. Finally, in view of the mass vaccination, potential implications for endocrine patients should be considered.
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Affiliation(s)
- M Puig-Domingo
- Endocrinology and Nutrition Service, Department of Medicine, Germans Trias i Pujol Health Science Research Institute and Hospital, Universitat Autònoma de Barcelona, Badalona, Spain
| | - M Marazuela
- Department of Endocrinology, Hospital Universitario de la Princesa, Instituto de Investigación de la Princesa, Universidad Autónoma de Madrid, Madrid, Spain
| | - B O Yildiz
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Hacettepe University School of Medicine, Hacettepe Ankara, Turkey
| | - A Giustina
- Institute of Endocrine and Metabolic Sciences, Vita-Salute San Raffaele University, IRCCS San Raffaele Hospital, Milano, Italy.
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61
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Chen X, Wang Z, Bromfield SG, DeVries A, Pryor D, Willey V. Identification and Comparison of Patient Characteristics for Those Hospitalized with COVID-19 versus Influenza Using Machine Learning in a Commercially Insured US Population. Pragmat Obs Res 2021; 12:9-13. [PMID: 33935528 PMCID: PMC8080116 DOI: 10.2147/por.s304220] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 04/06/2021] [Indexed: 11/23/2022] Open
Affiliation(s)
- Xiaoxue Chen
- Anthem Translational Research, HealthCore Inc., Wilmington, DE, USA
| | - Zhi Wang
- Anthem Translational Research, HealthCore Inc., Wilmington, DE, USA
| | | | - Andrea DeVries
- Anthem Translational Research, HealthCore Inc., Wilmington, DE, USA
| | - David Pryor
- Clinical Strategy and Innovation, Anthem, Inc., Indianapolis, IN, USA
| | - Vincent Willey
- Scientific Affairs, HealthCore Inc., Wilmington, DE, USA
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62
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Dagliati A, Malovini A, Tibollo V, Bellazzi R. Health informatics and EHR to support clinical research in the COVID-19 pandemic: an overview. Brief Bioinform 2021; 22:812-822. [PMID: 33454728 PMCID: PMC7929411 DOI: 10.1093/bib/bbaa418] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 10/29/2020] [Accepted: 12/19/2020] [Indexed: 12/15/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has clearly shown that major challenges and threats for humankind need to be addressed with global answers and shared decisions. Data and their analytics are crucial components of such decision-making activities. Rather interestingly, one of the most difficult aspects is reusing and sharing of accurate and detailed clinical data collected by Electronic Health Records (EHR), even if these data have a paramount importance. EHR data, in fact, are not only essential for supporting day-by-day activities, but also they can leverage research and support critical decisions about effectiveness of drugs and therapeutic strategies. In this paper, we will concentrate our attention on collaborative data infrastructures to support COVID-19 research and on the open issues of data sharing and data governance that COVID-19 had made emerge. Data interoperability, healthcare processes modelling and representation, shared procedures to deal with different data privacy regulations, and data stewardship and governance are seen as the most important aspects to boost collaborative research. Lessons learned from COVID-19 pandemic can be a strong element to improve international research and our future capability of dealing with fast developing emergencies and needs, which are likely to be more frequent in the future in our connected and intertwined world.
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Affiliation(s)
- Arianna Dagliati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy
| | | | | | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy
- IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
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63
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Prieto-Alhambra D, Kostka K, Duarte-Salles T, Prats-Uribe A, Sena A, Pistillo A, Khalid S, Lai L, Golozar A, Alshammari TM, Dawoud D, Nyberg F, Wilcox A, Andryc A, Williams A, Ostropolets A, Areia C, Jung CY, Harle C, Reich C, Blacketer C, Morales D, Dorr DA, Burn E, Roel E, Tan EH, Minty E, DeFalco F, de Maeztu G, Lipori G, Alghoul H, Zhu H, Thomas J, Bian J, Park J, Roldán JM, Posada J, Banda JM, Horcajada JP, Kohler J, Shah K, Natarajan K, Lynch K, Liu L, Schilling L, Recalde M, Spotnitz M, Gong M, Matheny M, Valveny N, Weiskopf N, Shah N, Alser O, Casajust P, Park RW, Schuff R, Seager S, DuVall S, You SC, Song S, Fernández-Bertolín S, Fortin S, Magoc T, Falconer T, Subbian V, Huser V, Ahmed WUR, Carter W, Guan Y, Galvan Y, He X, Rijnbeek P, Hripcsak G, Ryan P, Suchard M. Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS. RESEARCH SQUARE 2021:rs.3.rs-279400. [PMID: 33688639 PMCID: PMC7941629 DOI: 10.21203/rs.3.rs-279400/v1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response [1,2]. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) [3] Characterizing Health Associated Risks, and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD. Methods: We conducted a descriptive cohort study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11 th June 2020 and are iteratively updated via GitHub [4]. Findings: We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19 , and 113,627 hospitalized with COVID-19 requiring intensive services . All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts, and are available in an interactive website: https://data.ohdsi.org/Covid19CharacterizationCharybdis/. Interpretation: CHARYBDIS findings provide benchmarks that contribute to our understanding of COVID-19 progression, management and evolution over time. This can enable timely assessment of real-world outcomes of preventative and therapeutic options as they are introduced in clinical practice.
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Affiliation(s)
- Daniel Prieto-Alhambra
- Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, UK
| | | | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | | | - Anthony Sena
- Janssen R&D, Titusville NJ, USA, 2) Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Andrea Pistillo
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Sara Khalid
- Centre for Statistics in Medicine, NDORMS, University of Oxford, UK
| | - Lana Lai
- Division of Cancer Sciences, School of Medical Sciences, University of Manchester, UK
| | - Asieh Golozar
- Regeneron Pharmaceuticals, NY USA, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD USA
| | - Thamir M Alshammari
- Medication Safety Research Chair, King Saud University, Riyadh, Saudi Arabia
| | - Dalia Dawoud
- National Institute for Health and Care Excellence, London, UK
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Adam Wilcox
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA, 2) UW Medicine, Seattle, WA, USA
| | | | - Andrew Williams
- Tufts Institute for Clinical Research and Health Policy Studies, US
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Chi Young Jung
- Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Daegu Catholic University Medical Center, Daegu, Korea
| | | | | | - Clair Blacketer
- Janssen R&D, Titusville NJ, USA, 2) Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Daniel Morales
- Division of Population Health and Genomics, University of Dundee, UK
| | - David A Dorr
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Edward Burn
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | | | - Eng Hooi Tan
- Centre for Statistics in Medicine, NDORMS, University of Oxford, UK
| | - Evan Minty
- O'Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Canada
| | | | | | | | - Heba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Palestine
| | - Hong Zhu
- Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Jason Thomas
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | | | - Jimyung Park
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea
| | - Jordi Martínez Roldán
- Director of Innovation and Digital Transformation, Hospital del Mar, Barcelona, Spain
| | - Jose Posada
- Stanford University School of Medicine, Stanford, California, USA
| | - Juan M Banda
- Georgia State University, Department of Computer Science, Atlanta, GA, USA
| | - Juan P Horcajada
- Department of Infectious Diseases, Hospital del Mar, Institut Hospital del Mar d'Investigació Mèdica (IMIM), Universitat Autònoma de Barcelona. Universitat Pompeu Fabra, Barcelo
| | - Julianna Kohler
- United States Agency for International Development, Washington, DC, USA
| | - Karishma Shah
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA, 2) New York-Presbyterian Hospital, 622 W 168 St, PH20 New York, NY 10032 USA
| | - Kristine Lynch
- VINCI, VA Salt Lake City Health Care System, Salt Lake City, VA, & Division of Epidemiology, University of Utah, Salt Lake City, UT
| | - Li Liu
- Biomedical Big Data Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Lisa Schilling
- Data Science to Patient Value Program, University of Colorado Anschutz Medical Campus
| | - Martina Recalde
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Matthew Spotnitz
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA
| | | | - Michael Matheny
- VINCI, Tennessee Valley Healthcare System VA, Nashville, TN & Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | | | - Nicole Weiskopf
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | | | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Rae Woong Park
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea
| | - Robert Schuff
- Knight Cancer Institute, Oregon Health & Science University
| | | | - Scott DuVall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Seng Chan You
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea
| | - Seokyoung Song
- Department of Anesthesiology and Pain Medicine, Catholic University of Daegu, School of Medicine, Daegu, Korea
| | - Sergio Fernández-Bertolín
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Stephen Fortin
- Observational Health Data Analytics, Janssen Research and Development, Raritan, NJ, USA
| | | | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Vignesh Subbian
- College of Engineering, The University of Arizona, Tucson, Arizona, USA
| | - Vojtech Huser
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Waheed-Ul-Rahman Ahmed
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK, 2) College of Medicine and Health, University of Exeter, St Luke's Campus, E
| | - William Carter
- Data Science to Patient Value Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Yin Guan
- DHC Technologies Co. Ltd, Beijing, China
| | | | - Xing He
- University of Florida Health
| | - Peter Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA, 2) New York-Presbyterian Hospital, 622 W 168 St, PH20 New York, NY 10032 USA
| | | | - Marc Suchard
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles
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64
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Sang S, Sun R, Coquet J, Carmichael H, Seto T, Hernandez-Boussard T. Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study. J Med Internet Res 2021; 23:e23026. [PMID: 33534724 PMCID: PMC7901593 DOI: 10.2196/23026] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 10/09/2020] [Accepted: 02/01/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, artificial intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particularly in preparation for an impending crisis or pandemic. OBJECTIVE This study aimed to develop and test the feasibility of a "patients-like-me" framework to predict the deterioration of patients with COVID-19 using a retrospective cohort of patients with similar respiratory diseases. METHODS Our framework used COVID-19-like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-19-like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acute respiratory distress syndrome (ARDS) at an academic medical center from 2008 to 2019. In total, 15 training cohorts were created using different combinations of the COVID-19-like cohorts with the ARDS cohort for exploratory purposes. In this study, two machine learning models were developed: one to predict invasive mechanical ventilation (IMV) within 48 hours for each hospitalized day, and one to predict all-cause mortality at the time of admission. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value. We established model interpretability by calculating SHapley Additive exPlanations (SHAP) scores to identify important features. RESULTS Compared to the COVID-19-like cohorts (n=16,509), the patients hospitalized with COVID-19 (n=159) were significantly younger, with a higher proportion of patients of Hispanic ethnicity, a lower proportion of patients with smoking history, and fewer patients with comorbidities (P<.001). Patients with COVID-19 had a lower IMV rate (15.1 versus 23.2, P=.02) and shorter time to IMV (2.9 versus 4.1 days, P<.001) compared to the COVID-19-like patients. In the COVID-19-like training data, the top models achieved excellent performance (AUROC>0.90). Validating in the COVID-19 cohort, the top-performing model for predicting IMV was the XGBoost model (AUROC=0.826) trained on the viral pneumonia cohort. Similarly, the XGBoost model trained on all 4 COVID-19-like cohorts without ARDS achieved the best performance (AUROC=0.928) in predicting mortality. Important predictors included demographic information (age), vital signs (oxygen saturation), and laboratory values (white blood cell count, cardiac troponin, albumin, etc). Our models had class imbalance, which resulted in high negative predictive values and low positive predictive values. CONCLUSIONS We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic.
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Affiliation(s)
- Shengtian Sang
- Department of Medicine, Biomedical Informatics, Stanford University, Stanford, CA, United States
| | - Ran Sun
- Department of Medicine, Biomedical Informatics, Stanford University, Stanford, CA, United States
| | - Jean Coquet
- Department of Medicine, Biomedical Informatics, Stanford University, Stanford, CA, United States
| | | | - Tina Seto
- Technology and Digital Solutions, Stanford University, Stanford, CA, United States
| | - Tina Hernandez-Boussard
- Department of Medicine, Biomedical Informatics, Stanford University, Stanford, CA, United States
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Burn E, Sena AG, Prats-Uribe A, Spotnitz M, DuVall S, Lynch KE, Matheny ME, Nyberg F, Ahmed WUR, Alser O, Alghoul H, Alshammari T, Zhang L, Casajust P, Areia C, Shah K, Reich C, Blacketer C, Andryc A, Fortin S, Natarajan K, Gong M, Golozar A, Morales D, Rijnbeek P, Subbian V, Roel E, Recalde M, Lane JCE, Vizcaya D, Posada JD, Shah NH, Jonnagaddala J, Lai LYH, Avilés-Jurado FX, Hripcsak G, Suchard MA, Ranzani OT, Ryan P, Prieto-Alhambra D, Kostka K, Duarte-Salles T. Use of dialysis, tracheostomy, and extracorporeal membrane oxygenation among 842,928 patients hospitalized with COVID-19 in the United States. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021. [PMID: 33269356 PMCID: PMC7709172 DOI: 10.1101/2020.11.25.20229088] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Objective To estimate the proportion of patients hospitalized with COVID-19 who undergo dialysis, tracheostomy, and extracorporeal membrane oxygenation (ECMO). Design A network cohort study. Setting Seven databases from the United States containing routinely-collected patient data: HealthVerity, Premier, IQVIA Hospital CDM, IQVIA Open Claims, Optum EHR, Optum SES, and VA-OMOP. Patients Patients hospitalized with a clinical diagnosis or a positive test result for COVID-19. Interventions Dialysis, tracheostomy, and ECMO. Measurements and Main Results 842,928 patients hospitalized with COVID-19 were included (22,887 from HealthVerity, 77,853 from IQVIA Hospital CDM, 533,997 from IQVIA Open Claims, 36,717 from Optum EHR, 4,336 from OPTUM SES, 156,187 from Premier, and 10,951 from VA-OMOP). Across the six databases, 35,192 (4.17% [95% CI: 4.13% to 4.22%]) patients received dialysis, 6,950 (0.82% [0.81% to 0.84%]) had a tracheostomy, and 1,568 (0.19% [95% CI: 0.18% to 0.20%]) patients underwent ECMO over the 30 days following hospitalization. Use of ECMO was more common among patients who were younger, male, and with fewer comorbidities. Tracheostomy was broadly used for a similar proportion of patients regardless of age, sex, or comorbidity. While dialysis was generally used for a similar proportion among younger and older patients, it was more frequent among male patients and among those with chronic kidney disease. Conclusion Use of dialysis among those hospitalized with COVID-19 is high at around 4%. Although less than one percent of patients undergo tracheostomy and ECMO, the absolute numbers of patients who have undergone these interventions is substantial.
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Affiliation(s)
- Edward Burn
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.,Centre for Statistics in Medicine, NDORMS, University of Oxford
| | - Anthony G Sena
- Janssen Research & Development, Titusville, NJ, USA.,Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - Matthew Spotnitz
- Department of Biomedical Informatics, Columbia University, New York, NY, US
| | - Scott DuVall
- Department of Veterans Affairs, Salt Lake City, UT, US.,University of Utah School of Medicine, Salt Lake City, UT, US
| | - Kristine E Lynch
- Department of Veterans Affairs, Salt Lake City, UT, US.,Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Michael E Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Fredrik Nyberg
- School of Public Health and Community Medicine,, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Waheed-Ul-Rahman Ahmed
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, OX3 7LD, UK.,College of Medicine and Health, University of Exeter, St Luke's Campus, Heavitree Road, Exeter, EX1 2LU, UK
| | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Heba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Palestine
| | - Thamir Alshammari
- Medication Safety Research Chair, King Saud University , Riyadh, Saudi Arabia
| | - Lin Zhang
- School of Population Medicine and Public Health, Peking Union Medical College and Chinese Academy of Medical Sciences.,School of Population and Global Health, The University of Melbourne
| | - Paula Casajust
- Real-World Evidence, Trial Form Support, Barcelona, Spain
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford
| | - Karishma Shah
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, OX3 7LD, UK
| | | | | | - Alan Andryc
- Janssen Research & Development, Titusville, NJ, USA
| | | | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University, New York, NY, US
| | | | - Asieh Golozar
- Regeneron Pharmaceuticals, NY US.,Johns Hopkins Bloomberg School of Public Health, Baltimore, MD US
| | - Daniel Morales
- Division of Population Health and Genomics, University of Dundee
| | - Peter Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Vignesh Subbian
- College of Engineering, The University of Arizona, Tucson, Arizona, USA
| | - Elena Roel
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Martina Recalde
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.,Universitat Autònoma de Barcelona, Spain
| | | | | | | | | | | | - Lana Yin Hui Lai
- Division of Cancer Sciences, School of Medical Sciences, University of Manchester
| | - Francesc Xavier Avilés-Jurado
- Otorhinolaryngology Head-Neck Surgery Department, Hospital Clínic, IDIBAPS Universitat de Barcelona, Villarroel 170, 08036, Barcelona, Spain.,Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR, Generalitat de Catalunya, 2017-SGR-01581, Barcelona, Spain
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, US
| | - Marc A Suchard
- Department of Biostatistic, UCLA Fielding School of Public Health, University of California, Los Angeles
| | - Otavio T Ranzani
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain.,Pulmonary Division, Heart Institute (InCor, Hospital das Clinicas, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Patrick Ryan
- Janssen Research & Development, Titusville, NJ, USA.,Columbia University, New York, NY, US
| | | | | | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
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Mayer MA, Vidal-Alaball J, Puigdellívol-Sánchez A, Marín Gomez FX, Leis A, Mendioroz Peña J. Clinical Characterization of Patients With COVID-19 in Primary Care in Catalonia: Retrospective Observational Study. JMIR Public Health Surveill 2021; 7:e25452. [PMID: 33496668 PMCID: PMC7871981 DOI: 10.2196/25452] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 01/02/2021] [Accepted: 01/04/2021] [Indexed: 02/06/2023] Open
Abstract
Background The country of Spain has one of the highest incidences of COVID-19, with more than 1,000,000 cases as of the end of October 2020. Patients with a history of chronic conditions, obesity, and cancer are at greater risk from COVID-19; moreover, concerns surrounding the use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin type II receptor blockers (ARBs) and its relationship to COVID-19 susceptibility have increased since the beginning of the pandemic. Objective The objectives of this study were to compare the characteristics of patients diagnosed with COVID-19 to those of patients without COVID-19 in primary care; to determine the risk factors associated with the outcome of mortality; and to determine the potential influence of certain medications, such as ACEIs and ARBs, on the mortality of patients with COVID-19. Methods An observational retrospective study of patients diagnosed with COVID-19 in the Catalan Central Region of Spain between March 1 and August 17, 2020, was conducted. The data were obtained from the Primary Care Services Information Technologies System of the Catalan Institute of Health in Barcelona, Spain. Results The study population included 348,596 patients (aged >15 years) registered in the Primary Care Services Information Technologies System of the Catalan Central Region. The mean age of the patients was 49.53 years (SD 19.42), and 31.17% of the patients were aged ≥60 years. 175,484/348,596 patients (50.34%) were women. A total of 23,844/348,596 patients (6.84%) in the population studied were diagnosed with COVID-19 during the study period, and the most common clinical conditions of these patients were hypertension (5267 patients, 22.1%) and obesity (5181 patients, 21.7%). Overall, 2680/348,596 patients in the study population (0.77%) died during the study period. The number of deaths among patients without COVID-19 was 1825/324,752 (0.56%; mean age 80.6 years, SD 13.3), while among patients diagnosed with COVID-19, the number of deaths was 855/23,844 (3.58%; mean age 83.0 years, SD 10.80) with an OR of 6.58 (95% CI 6.06-7.15). Conclusions We observed that women were more likely to contract COVID-19 than men. In addition, our study did not show that hypertension, obesity, or being treated with ACEIs or ARBs was linked to an increase in mortality in patients with COVID-19. Age is the main factor associated with mortality in patients infected with SARS-CoV-2.
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Affiliation(s)
- Miguel Angel Mayer
- Research Programme on Biomedical Informatics, Hospital del Mar Medical Research Institute, Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Josep Vidal-Alaball
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain.,Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
| | - Anna Puigdellívol-Sánchez
- Facultat de Medicina, Universitat de Vic-Universitat Central de Catalunya, Vic, Spain.,CAP Anton de Borja, Consorci Sanitari de Terrassa, Terrassa, Spain.,Unitat d'Anatomia Humana Facultat de Medicina, Universitat de Barcelona, Barcelona, Spain
| | - Francesc X Marín Gomez
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain.,Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
| | - Angela Leis
- Research Programme on Biomedical Informatics, Hospital del Mar Medical Research Institute, Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Jacobo Mendioroz Peña
- Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Spain.,COVID-19 Response Unit, Department of Health, Generalitat de Catalunya, Barcelona, Spain
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67
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Kim JH, Ta CN, Liu C, Sung C, Butler AM, Stewart LA, Ena L, Rogers JR, Lee J, Ostropolets A, Ryan PB, Liu H, Lee SM, Elkind MSV, Weng C. Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials. J Am Med Inform Assoc 2021; 28:14-22. [PMID: 33260201 PMCID: PMC7798960 DOI: 10.1093/jamia/ocaa276] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 10/21/2020] [Indexed: 01/26/2023] Open
Abstract
Objective This research aims to evaluate the impact of eligibility criteria on recruitment and observable clinical outcomes of COVID-19 clinical trials using electronic health record (EHR) data. Materials and Methods On June 18, 2020, we identified frequently used eligibility criteria from all the interventional COVID-19 trials in ClinicalTrials.gov (n = 288), including age, pregnancy, oxygen saturation, alanine/aspartate aminotransferase, platelets, and estimated glomerular filtration rate. We applied the frequently used criteria to the EHR data of COVID-19 patients in Columbia University Irving Medical Center (CUIMC) (March 2020–June 2020) and evaluated their impact on patient accrual and the occurrence of a composite endpoint of mechanical ventilation, tracheostomy, and in-hospital death. Results There were 3251 patients diagnosed with COVID-19 from the CUIMC EHR included in the analysis. The median follow-up period was 10 days (interquartile range 4–28 days). The composite events occurred in 18.1% (n = 587) of the COVID-19 cohort during the follow-up. In a hypothetical trial with common eligibility criteria, 33.6% (690/2051) were eligible among patients with evaluable data and 22.2% (153/690) had the composite event. Discussion By adjusting the thresholds of common eligibility criteria based on the characteristics of COVID-19 patients, we could observe more composite events from fewer patients. Conclusions This research demonstrated the potential of using the EHR data of COVID-19 patients to inform the selection of eligibility criteria and their thresholds, supporting data-driven optimization of participant selection towards improved statistical power of COVID-19 trials.
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Affiliation(s)
- Jae Hyun Kim
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Casey N Ta
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Cong Liu
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Cynthia Sung
- Health Services and Systems Research, Duke-NUS Medical School, Singapore
| | - Alex M Butler
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Latoya A Stewart
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Lyudmila Ena
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - James R Rogers
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Junghwan Lee
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Patrick B Ryan
- Department of Biomedical Informatics, Columbia University, New York, New York, USA.,Observational Health Data Sciences and Informatics, New York, New York, USA.,Epidemiology Analytics, Janssen Research and Development, Titusville, New Jersey, USA
| | - Hao Liu
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Shing M Lee
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York, USA
| | - Mitchell S V Elkind
- Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA.,Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
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68
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Burn E, Tebé C, Fernandez-Bertolin S, Aragon M, Recalde M, Roel E, Prats-Uribe A, Prieto-Alhambra D, Duarte-Salles T. The natural history of symptomatic COVID-19 during the first wave in Catalonia. Nat Commun 2021; 12:777. [PMID: 33536436 PMCID: PMC7858639 DOI: 10.1038/s41467-021-21100-y] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 01/07/2021] [Indexed: 11/15/2022] Open
Abstract
The natural history of coronavirus disease 2019 (COVID-19) has yet to be fully described. Here, we use patient-level data from the Information System for Research in Primary Care (SIDIAP) to summarise COVID-19 outcomes in Catalonia, Spain. We included 5,586,521 individuals from the general population. Of these, 102,002 had an outpatient diagnosis of COVID-19, 16,901 were hospitalised with COVID-19, and 5273 died after either being diagnosed or hospitalised with COVID-19 between 1st March and 6th May 2020. Older age, being male, and having comorbidities were all generally associated with worse outcomes. These findings demonstrate the continued need to protect those at high risk of poor outcomes, particularly older people, from COVID-19 and provide appropriate care for those who develop symptomatic disease. While risks of hospitalisation and death were lower for younger populations, there is a need to limit their role in community transmission.
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Affiliation(s)
- Edward Burn
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
| | - Cristian Tebé
- Biostatistics Unit at Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet, Barcelona, Spain
- Universitat de Barcelona, Barcelona, Spain
| | - Sergio Fernandez-Bertolin
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Maria Aragon
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Martina Recalde
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Elena Roel
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Albert Prats-Uribe
- Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK.
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
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Rho Y, Cho DY, Son Y, Lee YJ, Kim JW, Lee HJ, You SC, Park RW, Lee JY. COVID-19 International Collaborative Research by the Health Insurance Review and Assessment Service Using Its Nationwide Real-world Data: Database, Outcomes, and Implications. J Prev Med Public Health 2021; 54:8-16. [PMID: 33618494 PMCID: PMC7939755 DOI: 10.3961/jpmph.20.616] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 01/18/2021] [Indexed: 12/11/2022] Open
Abstract
This article aims to introduce the inception and operation of the COVID-19 International Collaborative Research Project, the world’s first coronavirus disease 2019 (COVID-19) open data project for research, along with its dataset and research method, and to discuss relevant considerations for collaborative research using nationwide real-world data (RWD). COVID-19 has spread across the world since early 2020, becoming a serious global health threat to life, safety, and social and economic activities. However, insufficient RWD from patients was available to help clinicians efficiently diagnose and treat patients with COVID-19, or to provide necessary information to the government for policy-making. Countries that saw a rapid surge of infections had to focus on leveraging medical professionals to treat patients, and the circumstances made it even more difficult to promptly use COVID-19 RWD. Against this backdrop, the Health Insurance Review and Assessment Service (HIRA) of Korea decided to open its COVID-19 RWD collected through Korea’s universal health insurance program, under the title of the COVID-19 International Collaborative Research Project. The dataset, consisting of 476 508 claim statements from 234 427 patients (7590 confirmed cases) and 18 691 318 claim statements of the same patients for the previous 3 years, was established and hosted on HIRA’s in-house server. Researchers who applied to participate in the project uploaded analysis code on the platform prepared by HIRA, and HIRA conducted the analysis and provided outcome values. As of November 2020, analyses have been completed for 129 research projects, which have been published or are in the process of being published in prestigious journals.
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Affiliation(s)
- Yeunsook Rho
- HIRA Research Institute, Health Insurance Review and Assessment Service, Wonju, Korea
| | - Do Yeon Cho
- HIRA Research Institute, Health Insurance Review and Assessment Service, Wonju, Korea
| | - Yejin Son
- HIRA Research Institute, Health Insurance Review and Assessment Service, Wonju, Korea
| | - Yu Jin Lee
- HIRA Research Institute, Health Insurance Review and Assessment Service, Wonju, Korea
| | - Ji Woo Kim
- HIRA Research Institute, Health Insurance Review and Assessment Service, Wonju, Korea
| | - Hye Jin Lee
- HIRA Research Institute, Health Insurance Review and Assessment Service, Wonju, Korea
| | - Seng Chan You
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
| | - Rae Woong Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
| | - Jin Yong Lee
- HIRA Research Institute, Health Insurance Review and Assessment Service, Wonju, Korea.,Public Healthcare Center, Seoul National University Hospital, Seoul, Korea.,Deparment of Health Policy and Management, Seoul National University College of Medicine, Seoul, Korea
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Tan EH, Sena AG, Prats-Uribe A, You SC, Ahmed WUR, Kostka K, Reich C, Duvall SL, Lynch KE, Matheny ME, Duarte-Salles T, Bertolin SF, Hripcsak G, Natarajan K, Falconer T, Spotnitz M, Ostropolets A, Blacketer C, Alshammari TM, Alghoul H, Alser O, Lane JC, Dawoud DM, Shah K, Yang Y, Zhang L, Areia C, Golozar A, Relcade M, Casajust P, Jonnagaddala J, Subbian V, Vizcaya D, Lai LYH, Nyberg F, Morales DR, Posada JD, Shah NH, Gong M, Vivekanantham A, Abend A, Minty EP, Suchard M, Rijnbeek P, Ryan PB, Prieto-Alhambra D. Characteristics, outcomes, and mortality amongst 133,589 patients with prevalent autoimmune diseases diagnosed with, and 48,418 hospitalised for COVID-19: a multinational distributed network cohort analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.11.24.20236802. [PMID: 33269355 PMCID: PMC7709171 DOI: 10.1101/2020.11.24.20236802] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVE Patients with autoimmune diseases were advised to shield to avoid COVID-19, but information on their prognosis is lacking. We characterised 30-day outcomes and mortality after hospitalisation with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza. DESIGN Multinational network cohort study. SETTING Electronic health records data from Columbia University Irving Medical Center (CUIMC) (NYC, United States [US]), Optum [US], Department of Veterans Affairs (VA) (US), Information System for Research in Primary Care-Hospitalisation Linked Data (SIDIAP-H) (Spain), and claims data from IQVIA Open Claims (US) and Health Insurance and Review Assessment (HIRA) (South Korea). PARTICIPANTS All patients with prevalent autoimmune diseases, diagnosed and/or hospitalised between January and June 2020 with COVID-19, and similar patients hospitalised with influenza in 2017-2018 were included. MAIN OUTCOME MEASURES 30-day complications during hospitalisation and death. RESULTS We studied 133,589 patients diagnosed and 48,418 hospitalised with COVID-19 with prevalent autoimmune diseases. The majority of participants were female (60.5% to 65.9%) and aged ≥50 years. The most prevalent autoimmune conditions were psoriasis (3.5 to 32.5%), rheumatoid arthritis (3.9 to 18.9%), and vasculitis (3.3 to 17.6%). Amongst hospitalised patients, Type 1 diabetes was the most common autoimmune condition (4.8% to 7.5%) in US databases, rheumatoid arthritis in HIRA (18.9%), and psoriasis in SIDIAP-H (26.4%).Compared to 70,660 hospitalised with influenza, those admitted with COVID-19 had more respiratory complications including pneumonia and acute respiratory distress syndrome, and higher 30-day mortality (2.2% to 4.3% versus 6.3% to 24.6%). CONCLUSIONS Patients with autoimmune diseases had high rates of respiratory complications and 30-day mortality following a hospitalization with COVID-19. Compared to influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality. Future studies should investigate predictors of poor outcomes in COVID-19 patients with autoimmune diseases. WHAT IS ALREADY KNOWN ABOUT THIS TOPIC Patients with autoimmune conditions may be at increased risk of COVID-19 infection andcomplications.There is a paucity of evidence characterising the outcomes of hospitalised COVID-19 patients with prevalent autoimmune conditions. WHAT THIS STUDY ADDS Most people with autoimmune diseases who required hospitalisation for COVID-19 were women, aged 50 years or older, and had substantial previous comorbidities.Patients who were hospitalised with COVID-19 and had prevalent autoimmune diseases had higher prevalence of hypertension, chronic kidney disease, heart disease, and Type 2 diabetes as compared to those with prevalent autoimmune diseases who were diagnosed with COVID-19.A variable proportion of 6% to 25% across data sources died within one month of hospitalisation with COVID-19 and prevalent autoimmune diseases.For people with autoimmune diseases, COVID-19 hospitalisation was associated with worse outcomes and 30-day mortality compared to admission with influenza in the 2017-2018 season.
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Affiliation(s)
- Eng Hooi Tan
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, OX3 7LD, UK
| | - Anthony G. Sena
- Janssen Research and Development, Titusville, NJ USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Albert Prats-Uribe
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, OX3 7LD, UK
| | - Seng Chan You
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
| | - Waheed-Ul-Rahman Ahmed
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, OX3 7LD, UK
- College of Medicine and Health, University of Exeter, St Luke’s Campus, Heavitree Road, Exeter, EX1 2LU, UK
| | | | | | - Scott L. Duvall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Kristine E. Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Michael E. Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Sergio Fernandez Bertolin
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, US
- New York-Presbyterian Hospital, New York, NY, US
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University, New York, NY, US
- New York-Presbyterian Hospital, New York, NY, US
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, NY, US
| | - Matthew Spotnitz
- Department of Biomedical Informatics, Columbia University, New York, NY, US
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University, New York, NY, US
| | - Clair Blacketer
- Janssen Research and Development, Titusville, NJ USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - Heba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Palestine
| | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, Boston, 02114, Massachusetts, USA
| | - Jennifer C.E. Lane
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, OX3 7LD, UK
| | | | - Karishma Shah
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, OX3 7LD, UK
| | | | - Lin Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Science & Peking Union Medical College, Beijing 100730, China
- Melbourne School of Population and Global Health, The University of Melbourne, Victoria 3015, Australia
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU, UK
| | - Asieh Golozar
- Regeneron Pharmaceuticals, NY US
- Departament of Epidemiology, Johns Hopkins School of Public, Baltimore MD
| | - Martina Relcade
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autonoma de Barcelona, Spain
| | - Paula Casajust
- Real-World Evidence, Trial Form Support, Barcelona, Spain
| | | | - Vignesh Subbian
- College of Engineering, The University of Arizona Tucson, Arizona, USA
| | | | - Lana YH Lai
- School of Medical Sciences, University of Manchester, UK
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | | | - Jose D. Posada
- Stanford Center for Biomedical Informatics Research, Department of Medicine, School of Medicine, Stanford University
| | - Nigam H. Shah
- Stanford Center for Biomedical Informatics Research, Department of Medicine, School of Medicine, Stanford University
| | - Mengchun Gong
- Health Management Institute, Southern Medical University
| | - Arani Vivekanantham
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, OX3 7LD, UK
| | - Aaron Abend
- Autoimmune Registry Inc., 125 West Lane, Guilford, CT 06437
| | - Evan P Minty
- O’Brien School for Public Health, Faculty of Medicine, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada
| | - Marc Suchard
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA USA
| | - Peter Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Patrick B Ryan
- Janssen Research and Development, Titusville, NJ USA
- Department of Biomedical Informatics, Columbia University, New York, NY, US
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, OX3 7LD, UK
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