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Glasziou P, Sanders S, Byambasuren O, Thomas R, Hoffmann T, Greenwood H, van der Merwe M, Clark J. Clinical trials and their impact on policy during COVID-19: a review. Wellcome Open Res 2024; 9:20. [PMID: 38434720 PMCID: PMC10905118 DOI: 10.12688/wellcomeopenres.19305.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/19/2023] [Indexed: 03/05/2024] Open
Abstract
Background Of over 8,000 recorded randomised trials addressing COVID-19, around 80% were of treatments, and 17% have reported results. Approximately 1% were adaptive or platform trials, with 25 having results available, across 29 journal articles and 10 preprint articles. Methods We conducted an extensive literature review to address four questions about COVID-19 trials, particularly the role and impact of platform/adaptive trials and lessons learned. Results The key findings were: Q1. Social value in conducting trials and uptake into policy? COVID-19 drug treatments varied substantially and changed considerably, with drugs found effective in definitive clinical trials replacing unproven drugs. Dexamethasone has likely saved ½-2 million lives, and was cost effective across a range of countries and populations, whereas the cost effectiveness of remdesivir is uncertain. Published economic and health system impacts of COVID-19 treatments were infrequent. Q2. Issues with adaptive trial designs. Of the 77 platform trials registered, 6 major platform trials, with approximately 50 treatment arms, recruited ~135,000 participants with funding over $100 million. Q3. Models of good practice. Streamlined set-up processes such as flexible and fast-track funding, ethics, and governance approvals are vital. To facilitate recruitment, simple and streamlined research processes, and pre-existing research networks to coordinate trial planning, design, conduct and practice change are crucial to success. Q4. Potential conflicts to avoid? When treating patients through trials, balancing individual and collective rights and allocating scarce resources between healthcare and research are challenging. Tensions occur between commercial and non-commercial sectors, and academic and public health interests, such as publication and funding driven indicators and the public good. Conclusion There is a need to (i) reduce small, repetitive, single centre trials, (ii) increase coordination to ensure robust research conducted for treatments, and (iii) a wider adoption of adaptive/platform trial designs to respond to fast-evolving evidence landscape.
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Affiliation(s)
- Paul Glasziou
- IEBH, Health Science and Medicine, Bond University, Robina, Queensland, Australia
| | - Sharon Sanders
- IEBH, Health Science and Medicine, Bond University, Robina, Queensland, Australia
| | | | - Rae Thomas
- IEBH, Health Science and Medicine, Bond University, Robina, Queensland, Australia
| | - Tammy Hoffmann
- IEBH, Health Science and Medicine, Bond University, Robina, Queensland, Australia
| | - Hannah Greenwood
- IEBH, Health Science and Medicine, Bond University, Robina, Queensland, Australia
| | | | - Justin Clark
- IEBH, Health Science and Medicine, Bond University, Robina, Queensland, Australia
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2
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Horvat CM, King AJ, Huang DT. Designing and Implementing "Living and Breathing" Clinical Trials: An Overview and Lessons Learned from the COVID-19 Pandemic. Crit Care Clin 2023; 39:717-732. [PMID: 37704336 PMCID: PMC9935272 DOI: 10.1016/j.ccc.2023.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
The practice of medicine is characterized by uncertainty, and the findings of randomized clinical trials (RCTs) are meant to help curb that uncertainty. Traditional RCTs, however, have many limitations. To overcome some of these limitations, new trial paradigms rooted in the origins of evidence-based medicine are beginning to disrupt the traditional mold. These new designs recognize uncertainty permeates medical decision making and aim to capitalize on modern health system infrastructure to integrate investigation as a component of care delivery. This article provides an overview of "living, breathing" trials, including current state, anticipated developments, and areas of controversy.
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Affiliation(s)
- Christopher M Horvat
- UPMC Children's Hospital of Pittsburgh, Faculty Pavilion, 4401 Penn Avenue, Suite 0200, Pittsburgh, PA 15224, USA; Department of Critical Care Medicine, University of Pittsburgh School of Medicine, 3550 Terrace Street, 603A, Pittsburgh, PA 15261, USA.
| | - Andrew J King
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, 3550 Terrace Street, 603A, Pittsburgh, PA 15261, USA
| | - David T Huang
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, 3550 Terrace Street, 603A, Pittsburgh, PA 15261, USA
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3
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Quintana M, Saville BR, Vestrucci M, Detry MA, Chibnik L, Shefner J, Berry JD, Chase M, Andrews J, Sherman AV, Yu H, Drake K, Cudkowicz M, Paganoni S, Macklin EA. Design and Statistical Innovations in a Platform Trial for Amyotrophic Lateral Sclerosis. Ann Neurol 2023; 94:547-560. [PMID: 37245090 DOI: 10.1002/ana.26714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/12/2023] [Accepted: 05/17/2023] [Indexed: 05/29/2023]
Abstract
Platform trials allow efficient evaluation of multiple interventions for a specific disease. The HEALEY ALS Platform Trial is testing multiple investigational products in parallel and sequentially in persons with amyotrophic lateral sclerosis (ALS) with the goal of rapidly identifying novel treatments to slow disease progression. Platform trials have considerable operational and statistical efficiencies compared with typical randomized controlled trials due to their use of shared infrastructure and shared control data. We describe the statistical approaches required to achieve the objectives of a platform trial in the context of ALS. This includes following regulatory guidance for the disease area of interest and accounting for potential differences in outcomes of participants within the shared control (potentially due to differences in time of randomization, mode of administration, and eligibility criteria). Within the HEALEY ALS Platform Trial, the complex statistical objectives are met using a Bayesian shared parameter analysis of function and survival. This analysis serves to provide a common integrated estimate of treatment benefit, overall slowing in disease progression, as measured by function and survival while accounting for potential differences in the shared control group using Bayesian hierarchical modeling. Clinical trial simulation is used to provide a better understanding of this novel analysis method and complex design. ANN NEUROL 2023;94:547-560.
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Affiliation(s)
| | - Benjamin R Saville
- Berry Consultants, Austin, Texas, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | | | - Lori Chibnik
- Biostatistics Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Jeremy Shefner
- Department of Neurology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - James D Berry
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Marianne Chase
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Jinsy Andrews
- Neurological Institute of New York, Columbia University, New York, New York, USA
| | - Alexander V Sherman
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Hong Yu
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Kristin Drake
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Merit Cudkowicz
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Sabrina Paganoni
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, Massachusetts, USA
| | - Eric A Macklin
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Barrow Neurological Institute, Phoenix, Arizona, USA
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4
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Davies A, Ormel I, Bernier A, Harriss E, Mumba N, Gobat N, Schwartz L, Cheah PY. A rapid review of community engagement and informed consent processes for adaptive platform trials and alternative design trials for public health emergencies. Wellcome Open Res 2023; 8:194. [PMID: 37654739 PMCID: PMC10465998 DOI: 10.12688/wellcomeopenres.19318.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/19/2023] [Indexed: 09/02/2023] Open
Abstract
Background : Public Health Emergencies (PHE) demand expeditious research responses to evaluate new or repurposed therapies and prevention strategies. Alternative Design Trials (ADTs) and Adaptive Platform Trials (APTs) have enabled efficient large-scale testing of biomedical interventions during recent PHEs. Design features of these trials may have implications for engagement and/or informed consent processes. We aimed to rapidly review evidence on engagement and informed consent for ADTs and APTs during PHE to consider what (if any) recommendations can inform practice. Method : In 2022, we searched 8 prominent databases for relevant peer reviewed publications and guidelines for ADTs/APTs in PHE contexts. Articles were selected based on pre-identified inclusion and exclusion criteria. We reviewed protocols and informed consent documents for a sample of large platform trials and consulted with key informants from ADTs/APT trial teams. Data were extracted and summarised using narrative synthesis. Results : Of the 49 articles included, 10 were guidance documents, 14 discussed engagement, 10 discussed informed consent, and 15 discussed both. Included articles addressed ADTs delivered during the West African Ebola epidemic and APTs delivered during COVID-19. PHE clinical research guidance documents highlight the value of ADTs/APTs and the importance of community engagement, but do not provide practice-specific guidance for engagement or informed consent. Engagement and consent practice for ADTs conducted during the West African Ebola epidemic have been well-documented. For COVID-19, engagement and consent practice was described for APTs primarily delivered in high income countries with well-developed health service structures. A key consideration is strong communication of the complexity of trial design in clear, accessible ways. Conclusion: We highlight key considerations for best practice in community engagement and informed consent relevant to ADTs and APTs for PHEs which may helpfully be included in future guidance. Protocol: The review protocol is published online at Prospero on 15/06/2022: registration number CRD42022334170.
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Affiliation(s)
- Alun Davies
- Health Systems Collaborative, Nuffield Department of Medicine, University of Oxford, Oxford, England, UK
| | - Ilja Ormel
- Faculty of Health Sciences, Department of Health Research Methods, Evidence & Impact, McMaster University, Hamilton, Ontario, Canada
| | - Alexe Bernier
- Faculty of Social Sciences, School of Social Work, McMaster University, Hamilton, Ontario, Canada
| | - Eli Harriss
- Bodleian Health Care Libraries, University of Oxford, Oxford, England, UK
| | - Noni Mumba
- The KEMRI-Wellcome Trust Research Programme, Kilifi, 80108, Kenya
| | - Nina Gobat
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, England, UK
| | - Lisa Schwartz
- Faculty of Health Sciences, Department of Health Research Methods, Evidence & Impact, McMaster University, Hamilton, Ontario, Canada
| | - Phaik Yeong Cheah
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Salaya, Nakhon Pathom, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, England, UK
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5
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Morris AH, Horvat C, Stagg B, Grainger DW, Lanspa M, Orme J, Clemmer TP, Weaver LK, Thomas FO, Grissom CK, Hirshberg E, East TD, Wallace CJ, Young MP, Sittig DF, Suchyta M, Pearl JE, Pesenti A, Bombino M, Beck E, Sward KA, Weir C, Phansalkar S, Bernard GR, Thompson BT, Brower R, Truwit J, Steingrub J, Hiten RD, Willson DF, Zimmerman JJ, Nadkarni V, Randolph AG, Curley MAQ, Newth CJL, Lacroix J, Agus MSD, Lee KH, deBoisblanc BP, Moore FA, Evans RS, Sorenson DK, Wong A, Boland MV, Dere WH, Crandall A, Facelli J, Huff SM, Haug PJ, Pielmeier U, Rees SE, Karbing DS, Andreassen S, Fan E, Goldring RM, Berger KI, Oppenheimer BW, Ely EW, Pickering BW, Schoenfeld DA, Tocino I, Gonnering RS, Pronovost PJ, Savitz LA, Dreyfuss D, Slutsky AS, Crapo JD, Pinsky MR, James B, Berwick DM. Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy. J Am Med Inform Assoc 2022; 30:178-194. [PMID: 36125018 PMCID: PMC9748596 DOI: 10.1093/jamia/ocac143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 07/27/2022] [Accepted: 08/22/2022] [Indexed: 12/15/2022] Open
Abstract
How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.
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Affiliation(s)
- Alan H Morris
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Christopher Horvat
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Brian Stagg
- Department of Ophthalmology and Visual Sciences, Moran Eye Center, University of Utah, Salt Lake City, Utah, USA
| | - David W Grainger
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
| | - Michael Lanspa
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - James Orme
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Terry P Clemmer
- Department of Internal Medicine (Critical Care), Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Lindell K Weaver
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Frank O Thomas
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Colin K Grissom
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Ellie Hirshberg
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Thomas D East
- SYNCRONYS - Chief Executive Officer, Albuquerque, New Mexico, USA
| | - Carrie Jane Wallace
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Michael P Young
- Department of Critical Care, Renown Regional Medical Center, Reno, Nevada, USA
| | - Dean F Sittig
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, Texas, USA
| | - Mary Suchyta
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - James E Pearl
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Antinio Pesenti
- Faculty of Medicine and Surgery—Anesthesiology, University of Milan, Milano, Lombardia, Italy
| | - Michela Bombino
- Department of Emergency and Intensive Care, San Gerardo Hospital, Monza (MB), Italy
| | - Eduardo Beck
- Faculty of Medicine and Surgery - Anesthesiology, University of Milan, Ospedale di Desio, Desio, Lombardia, Italy
| | - Katherine A Sward
- Department of Biomedical Informatics, College of Nursing, University of Utah, Salt Lake City, Utah, USA
| | - Charlene Weir
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Shobha Phansalkar
- Wolters Kluwer Health—Clinical Solutions—Medical Informatics, Wolters Kluwer Health, Newton, Massachusetts, USA
| | - Gordon R Bernard
- Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - B Taylor Thompson
- Pulmonary and Critical Care Division, Department of Internal Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Roy Brower
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Jonathon Truwit
- Department of Internal Medicine, Pulmonary and Critical Care, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Jay Steingrub
- Department of Internal Medicine, Pulmonary and Critical Care, University of Massachusetts Medical School, Baystate Campus, Springfield, Massachusetts, USA
| | - R Duncan Hiten
- Department of Internal Medicine, Pulmonary and Critical Care, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Douglas F Willson
- Pediatric Critical Care, Department of Pediatrics, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Jerry J Zimmerman
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Washington School of Medicine, Seattle, Washington, USA
| | - Vinay Nadkarni
- Department of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Adrienne G Randolph
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Martha A Q Curley
- University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania, USA
| | - Christopher J L Newth
- Childrens Hospital Los Angeles, Department of Anesthesiology and Critical Care, University of Southern California Keck School of Medicine, Los Angeles, California, USA
| | - Jacques Lacroix
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, Université de Montréal Faculté de Médecine, Montreal, Quebec, Canada
| | - Michael S D Agus
- Division of Medical Pediatric Critical Care, Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Kang Hoe Lee
- Department of Intensive Care Medicine, Ng Teng Fong Hospital and National University Centre of Transplantation, National University Singapore Yong Loo Lin School of Medicine, Singapore
| | - Bennett P deBoisblanc
- Department of Internal Medicine, Pulmonary and Critical Care, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USA
| | - Frederick Alan Moore
- Department of Surgery, University of Florida College of Medicine, Gainesville, Florida, USA
| | - R Scott Evans
- Department of Medical Informatics, Intermountain Healthcare, and Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Dean K Sorenson
- Department of Medical Informatics, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Anthony Wong
- Department of Data Science Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Illinois, USA
| | - Michael V Boland
- Department of Ophthalmology, Massachusetts Ear and Eye Infirmary, Harvard Medical School, Boston, Massachusetts, USA
| | - Willard H Dere
- Endocrinology and Metabolism Division, Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Alan Crandall
- Department of Ophthalmology and Visual Sciences, Moran Eye Center, University of Utah, Salt Lake City, Utah, USA
- Posthumous
| | - Julio Facelli
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Stanley M Huff
- Department of Medical Informatics, Intermountain Healthcare, Department of Biomedical Informatics, University of Utah, and Graphite Health, Salt Lake City, Utah, USA
| | - Peter J Haug
- Department of Medical Informatics, Intermountain Healthcare, and Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Ulrike Pielmeier
- Aalborg University Faculty of Engineering and Science - Department of Health Science and Technology, Respiratory and Critical Care Group, Aalborg, Nordjylland, Denmark
| | - Stephen E Rees
- Aalborg University Faculty of Engineering and Science - Department of Health Science and Technology, Respiratory and Critical Care Group, Aalborg, Nordjylland, Denmark
| | - Dan S Karbing
- Aalborg University Faculty of Engineering and Science - Department of Health Science and Technology, Respiratory and Critical Care Group, Aalborg, Nordjylland, Denmark
| | - Steen Andreassen
- Aalborg University Faculty of Engineering and Science - Department of Health Science and Technology, Respiratory and Critical Care Group, Aalborg, Nordjylland, Denmark
| | - Eddy Fan
- Internal Medicine, Pulmonary and Critical Care Division, Institute of Health Policy, Management and Evaluation, University of Toronto Faculty of Medicine, Toronto, Ontario, Canada
| | - Roberta M Goldring
- Department of Internal Medicine, Pulmonary and Critical Care, New York University School of Medicine, New York, New York, USA
| | - Kenneth I Berger
- Department of Internal Medicine, Pulmonary and Critical Care, New York University School of Medicine, New York, New York, USA
| | - Beno W Oppenheimer
- Department of Internal Medicine, Pulmonary and Critical Care, New York University School of Medicine, New York, New York, USA
| | - E Wesley Ely
- Internal Medicine, Pulmonary and Critical Care, Critical Illness, Brain Dysfunction, and Survivorship (CIBS) Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Tennessee Valley Veteran’s Affairs Geriatric Research Education Clinical Center (GRECC), Nashville, Tennessee, USA
| | - Brian W Pickering
- Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota, USA
| | - David A Schoenfeld
- Biostatistics Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Irena Tocino
- Department of Radiology, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Russell S Gonnering
- Department of Ophthalmology and Visual Sciences, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Peter J Pronovost
- Department of Anesthesiology and Critical Care Medicine, University Hospitals, Highland Hills, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Lucy A Savitz
- Northwest Center for Health Research, Kaiser Permanente, Oakland, California, USA
| | - Didier Dreyfuss
- Assistance Publique—Hôpitaux de Paris, Université de Paris, Sorbonne Université - INSERM unit UMR S_1155 (Common and Rare Kidney Diseases), Paris, France
| | - Arthur S Slutsky
- Interdepartmental Division of Critical Care Medicine, Keenan Research Center, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, University of Toronto, Toronto, Ontario, Canada
| | - James D Crapo
- Department of Internal Medicine, National Jewish Health, Denver, Colorado, USA
| | - Michael R Pinsky
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Brent James
- Department of Internal Medicine, Clinical Excellence Research Center (CERC), Stanford University School of Medicine, Stanford, California, USA
| | - Donald M Berwick
- Institute for Healthcare Improvement, Cambridge, Massachusetts, USA
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6
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McCreary EK, Lemon L, Megli C, Oakes A, Seymour CW. Monoclonal Antibodies for Treatment of SARS-CoV-2 Infection During Pregnancy : A Cohort Study. Ann Intern Med 2022; 175:1707-1715. [PMID: 36375150 PMCID: PMC9747093 DOI: 10.7326/m22-1329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Monoclonal antibody (mAb) treatment decreases hospitalization and death in high-risk outpatients with mild to moderate COVID-19. However, no studies have evaluated adverse events and effectiveness of mAbs in pregnant persons compared with no mAb treatment. OBJECTIVE To determine the frequency of drug-related adverse events and obstetric-associated safety outcomes after treatment with mAb compared with no mAb treatment of pregnant persons, and the association between mAb treatment and a composite of 28-day COVID-19-related hospital admission or emergency department (ED) visit, COVID-19-associated delivery, or mortality. DESIGN Retrospective, propensity score-matched, cohort study. SETTING UPMC Health System from 30 April 2021 to 21 January 2022. PARTICIPANTS Persons aged 12 years or older with a pregnancy episode and any documented positive SARS-CoV-2 test (polymerase chain reaction or antigen test). INTERVENTION Bamlanivimab and etesevimab, casirivimab and imdevimab, or sotrovimab treatment compared with no mAb treatment. MEASUREMENTS Drug-related adverse events, obstetric-associated safety outcomes among persons who delivered, and a risk-adjusted composite of 28-day COVID-19-related hospital admission or ED visit, COVID-19-associated delivery, or mortality. RESULTS Among 944 pregnant persons (median age [interquartile range (IQR)], 30 years [26 to 33 years]; White (79.5%; n = 750); median Charlson Comorbidity Index score [IQR], 0 [0 to 0]), 552 received mAb treatment (58%). Median gestational age at COVID-19 diagnosis or treatment was 179 days (IQR, 123 to 227), and most persons received sotrovimab (69%; n = 382). Of those with known vaccination status, 392 (62%) were fully vaccinated. Drug-related adverse events were uncommon (n = 8; 1.4%), and there were no differences in any obstetric-associated outcome among 778 persons who delivered. In the total population, the risk ratio for mAb treatment of the composite 28-day COVID-19-associated outcome was 0.71 (95% CI, 0.37 to 1.4). The propensity score-matched risk ratio was 0.61 (95% CI, 0.34 to 1.1). There were no deaths among mAb-treated patients compared with 1 death in the nontreated control patients. There were more non-COVID-19-related hospital admissions in the mAb-treated persons in the unmatched cohort (14 [2.5%] vs. 2 [0.5%]; risk ratio, 5.0; 95% CI, 1.1 to 21.7); however, there was no difference in the propensity score-matched rates, which were 2.5% mAb-treated vs. 2% untreated (risk ratio, 1.3; 95% CI, 0.58% to 2.8%). LIMITATIONS Drug-related adverse events were patient and provider reported and potentially underrepresented. Symptom severity at the time of SARS-CoV-2 testing was not available for nontreated patients. CONCLUSION In pregnant persons with mild to moderate COVID-19, adverse events after mAb treatment were mild and rare. There was no difference in obstetric-associated safety outcomes between mAb treatment and no treatment among persons who delivered. There was no difference in 28-day COVID-19-associated outcomes and non-COVID-19-related hospital admissions for mAb treatment compared with no mAb treatment in a propensity score-matched cohort. PRIMARY FUNDING SOURCE No funding was received for this study.
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Affiliation(s)
- Erin K McCreary
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (E.K.M.)
| | - Lara Lemon
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, and Magee-Womens Research Institute, Pittsburgh, Pennsylvania (L.L., C.M.)
| | - Christina Megli
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, and Magee-Womens Research Institute, Pittsburgh, Pennsylvania (L.L., C.M.)
| | - Amber Oakes
- Department of Pharmacy, Magee-Womens Hospital, UPMC, Pittsburgh, Pennsylvania (A.O.)
| | - Christopher W Seymour
- Office of Healthcare Innovation, UPMC, Pittsburgh, Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, and Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (C.W.S.)
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7
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Shang Y, Wu J, Liu J, Long Y, Xie J, Zhang D, Hu B, Zong Y, Liao X, Shang X, Ding R, Kang K, Liu J, Pan A, Xu Y, Wang C, Xu Q, Zhang X, Zhang J, Liu L, Zhang J, Yang Y, Yu K, Guan X, Chen D. Expert consensus on the diagnosis and treatment of severe and critical coronavirus disease 2019 (COVID-19). JOURNAL OF INTENSIVE MEDICINE 2022; 2:199-222. [PMID: 36785648 PMCID: PMC9411033 DOI: 10.1016/j.jointm.2022.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 07/24/2022] [Indexed: 12/16/2022]
Affiliation(s)
- You Shang
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Jianfeng Wu
- Department of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong 510010, China
| | - Jinglun Liu
- Department of Emergency and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Yun Long
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Science, Beijing 100730, China
| | - Jianfeng Xie
- Department of Critical Care Medicine, Jiangsu Provincial Key Laboratory of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Dong Zhang
- Department of Critical Care Medicine, The First Hospital of Jilin University, Changchun, Jilin 130021, China
| | - Bo Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China
| | - Yuan Zong
- Department of Critical Care Medicine, Shaanxi Provincial Hospital, Xi'an, Shannxi 710068, China
| | - Xuelian Liao
- Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Xiuling Shang
- Department of Critical Care Medicine, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian 350001, China
| | - Renyu Ding
- Department of Critical Care Medicine, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Kai Kang
- Department of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China
| | - Jiao Liu
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Aijun Pan
- Department of Critical Care Medicine, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China
| | - Yonghao Xu
- Department of Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120, China
| | - Changsong Wang
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang 150001, China
| | - Qianghong Xu
- Department of Critical Care Medicine, Zhejiang Hospital Affiliated to Medical College of Zhejiang University, Hangzhou, Zhejiang 310013, China
| | - Xijing Zhang
- Department of Critical Care Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, Shannxi 710032, China
| | - Jicheng Zhang
- Department of Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Ling Liu
- Department of Critical Care Medicine, Jiangsu Provincial Key Laboratory of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Jiancheng Zhang
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Yi Yang
- Department of Critical Care Medicine, Jiangsu Provincial Key Laboratory of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Kaijiang Yu
- Department of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China
- Corresponding authors: Dechang Chen, Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China. Xiangdong Guan, Department of Critical Care Medicine, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong 510080, China. Kaijiang Yu, Department of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China.
| | - Xiangdong Guan
- Department of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong 510010, China
- Corresponding authors: Dechang Chen, Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China. Xiangdong Guan, Department of Critical Care Medicine, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong 510080, China. Kaijiang Yu, Department of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China.
| | - Dechang Chen
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Corresponding authors: Dechang Chen, Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China. Xiangdong Guan, Department of Critical Care Medicine, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong 510080, China. Kaijiang Yu, Department of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China.
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8
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Huang DT, McCreary EK, Bariola JR, Minnier TE, Wadas RJ, Shovel JA, Albin D, Marroquin OC, Kip KE, Collins K, Schmidhofer M, Wisniewski MK, Nace DA, Sullivan C, Axe M, Meyers R, Weissman A, Garrard W, Peck-Palmer OM, Wells A, Bart RD, Yang A, Berry LR, Berry S, Crawford AM, McGlothlin A, Khadem T, Linstrum K, Montgomery SK, Ricketts D, Kennedy JN, Pidro CJ, Nakayama A, Zapf RL, Kip PL, Haidar G, Snyder GM, McVerry BJ, Yealy DM, Angus DC, Seymour CW. Effectiveness of Casirivimab-Imdevimab and Sotrovimab During a SARS-CoV-2 Delta Variant Surge: A Cohort Study and Randomized Comparative Effectiveness Trial. JAMA Netw Open 2022; 5:e2220957. [PMID: 35834252 PMCID: PMC10881222 DOI: 10.1001/jamanetworkopen.2022.20957] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 05/17/2022] [Indexed: 11/14/2022] Open
Abstract
Importance The effectiveness of monoclonal antibodies (mAbs), casirivimab-imdevimab and sotrovimab, is unknown in patients with mild to moderate COVID-19 caused by the SARS-CoV-2 Delta variant. Objective To evaluate the effectiveness of mAb against the Delta variant compared with no mAb treatment and to ascertain the comparative effectiveness of casirivimab-imdevimab and sotrovimab. Design, Setting, and Participants This study comprised 2 parallel studies: (1) a propensity score-matched cohort study of mAb treatment vs no mAb treatment and (2) a randomized comparative effectiveness trial of casirivimab-imdevimab and sotrovimab. The cohort consisted of patients who received mAb treatment at the University of Pittsburgh Medical Center outpatient infusion centers and emergency departments from July 14 to September 29, 2021. Participants were patients with a positive SARS-CoV-2 test result who were eligible to receive mAbs according to emergency use authorization criteria. Exposure For the trial, patients were randomized to either intravenous casirivimab-imdevimab or sotrovimab according to a system therapeutic interchange policy. Main Outcomes and Measures For the cohort study, risk ratio (RR) estimates for the primary outcome of hospitalization or death by 28 days were compared between mAb treatment and no mAb treatment using propensity score-matched models. For the comparative effectiveness trial, the primary outcome was hospital-free days (days alive and free of hospitalization) within 28 days after mAb treatment, where patients who died were assigned -1 day in a bayesian cumulative logistic model adjusted for treatment location, age, sex, and time. Inferiority was defined as a 99% posterior probability of an odds ratio (OR) less than 1. Equivalence was defined as a 95% posterior probability that the OR was within a given bound. Results A total of 3069 patients (1023 received mAb treatment: mean [SD] age, 53.2 [16.4] years; 569 women [56%]; 2046 had no mAb treatment: mean [SD] age, 52.8 [19.5] years; 1157 women [57%]) were included in the prospective cohort study, and 3558 patients (mean [SD] age, 54 [18] years; 1919 women [54%]) were included in the randomized comparative effectiveness trial. In propensity score-matched models, mAb treatment was associated with reduced risk of hospitalization or death (RR, 0.40; 95% CI, 0.28-0.57) compared with no treatment. Both casirivimab-imdevimab (RR, 0.31; 95% CI, 0.20-0.50) and sotrovimab (RR, 0.60; 95% CI, 0.37-1.00) were associated with reduced hospitalization or death compared with no mAb treatment. In the clinical trial, 2454 patients were randomized to receive casirivimab-imdevimab and 1104 patients were randomized to receive sotrovimab. The median (IQR) hospital-free days were 28 (28-28) for both mAb treatments, the 28-day mortality rate was less than 1% (n = 12) for casirivimab-imdevimab and less than 1% (n = 7) for sotrovimab, and the hospitalization rate by day 28 was 12% (n = 291) for casirivimab-imdevimab and 13% (n = 140) for sotrovimab. Compared with patients who received casirivimab-imdevimab, those who received sotrovimab had a median adjusted OR for hospital-free days of 0.88 (95% credible interval, 0.70-1.11). This OR yielded 86% probability of inferiority for sotrovimab vs casirivimab-imdevimab and 79% probability of equivalence. Conclusions and Relevance In this propensity score-matched cohort study and randomized comparative effectiveness trial, the effectiveness of casirivimab-imdevimab and sotrovimab against the Delta variant was similar, although the prespecified criteria for statistical inferiority or equivalence were not met. Both mAb treatments were associated with a reduced risk of hospitalization or death in nonhospitalized patients with mild to moderate COVID-19 caused by the Delta variant. Trial Registration ClinicalTrials.gov Identifier: NCT04790786.
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Affiliation(s)
- David T. Huang
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Erin K. McCreary
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - J. Ryan Bariola
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Tami E. Minnier
- Wolff Center, University of Pittsburgh Medical Center (UPMC), Pittsburgh, Pennsylvania
| | - Richard J. Wadas
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Judith A. Shovel
- Wolff Center, University of Pittsburgh Medical Center (UPMC), Pittsburgh, Pennsylvania
| | - Debbie Albin
- Supply Chain Management/HC Pharmacy, UPMC, Pittsburgh, Pennsylvania
| | | | - Kevin E. Kip
- Clinical Analytics, UPMC, Pittsburgh, Pennsylvania
| | | | - Mark Schmidhofer
- Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Mary Kay Wisniewski
- Wolff Center, University of Pittsburgh Medical Center (UPMC), Pittsburgh, Pennsylvania
| | - David A. Nace
- Division of Geriatric Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Colleen Sullivan
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Health System Office of Healthcare Innovation, UPMC, Pittsburgh, Pennsylvania
| | - Meredith Axe
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Russell Meyers
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Alexandra Weissman
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | | | - Octavia M. Peck-Palmer
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Alan Wells
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Robert D. Bart
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Health Services Division, UPMC, Pittsburgh, Pennsylvania
| | - Anne Yang
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | | | | | | | | | - Tina Khadem
- Health System Office of Healthcare Innovation, UPMC, Pittsburgh, Pennsylvania
| | - Kelsey Linstrum
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Health System Office of Healthcare Innovation, UPMC, Pittsburgh, Pennsylvania
| | - Stephanie K. Montgomery
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Health System Office of Healthcare Innovation, UPMC, Pittsburgh, Pennsylvania
| | - Daniel Ricketts
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Jason N. Kennedy
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Caroline J. Pidro
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Anna Nakayama
- Health System Office of Healthcare Innovation, UPMC, Pittsburgh, Pennsylvania
| | - Rachel L. Zapf
- Wolff Center, University of Pittsburgh Medical Center (UPMC), Pittsburgh, Pennsylvania
| | - Paula L. Kip
- Wolff Center, University of Pittsburgh Medical Center (UPMC), Pittsburgh, Pennsylvania
| | - Ghady Haidar
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Graham M. Snyder
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Bryan J. McVerry
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Donald M. Yealy
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Derek C. Angus
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Health System Office of Healthcare Innovation, UPMC, Pittsburgh, Pennsylvania
| | - Christopher W. Seymour
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Health System Office of Healthcare Innovation, UPMC, Pittsburgh, Pennsylvania
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9
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Hirsch C, Park YS, Piechotta V, Chai KL, Estcourt LJ, Monsef I, Salomon S, Wood EM, So-Osman C, McQuilten Z, Spinner CD, Malin JJ, Stegemann M, Skoetz N, Kreuzberger N. SARS-CoV-2-neutralising monoclonal antibodies to prevent COVID-19. Cochrane Database Syst Rev 2022; 6:CD014945. [PMID: 35713300 PMCID: PMC9205158 DOI: 10.1002/14651858.cd014945.pub2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
BACKGROUND Monoclonal antibodies (mAbs) are laboratory-produced molecules derived from the B cells of an infected host. They are being investigated as potential prophylaxis to prevent coronavirus disease 2019 (COVID-19). OBJECTIVES To assess the effects of SARS-CoV-2-neutralising mAbs, including mAb fragments, to prevent infection with SARS-CoV-2 causing COVID-19; and to maintain the currency of the evidence, using a living systematic review approach. SEARCH METHODS We searched the Cochrane COVID-19 Study Register, MEDLINE, Embase, and three other databases on 27 April 2022. We checked references, searched citations, and contacted study authors to identify additional studies. SELECTION CRITERIA We included randomised controlled trials (RCTs) that evaluated SARS-CoV-2-neutralising mAbs, including mAb fragments, alone or combined, versus an active comparator, placebo, or no intervention, for pre-exposure prophylaxis (PrEP) and postexposure prophylaxis (PEP) of COVID-19. We excluded studies of SARS-CoV-2-neutralising mAbs to treat COVID-19, as these are part of another review. DATA COLLECTION AND ANALYSIS Two review authors independently assessed search results, extracted data, and assessed risk of bias using Cochrane RoB 2. Prioritised outcomes were infection with SARS-CoV-2, development of clinical COVID-19 symptoms, all-cause mortality, admission to hospital, quality of life, adverse events (AEs), and serious adverse events (SAEs). We rated the certainty of evidence using GRADE. MAIN RESULTS We included four RCTs of 9749 participants who were previously uninfected and unvaccinated at baseline. Median age was 42 to 76 years. Around 20% to 77.5% of participants in the PrEP studies and 35% to 100% in the PEP studies had at least one risk factor for severe COVID-19. At baseline, 72.8% to 82.2% were SARS-CoV-2 antibody seronegative. We identified four ongoing studies, and two studies awaiting classification. Pre-exposure prophylaxis Tixagevimab/cilgavimab versus placebo One study evaluated tixagevimab/cilgavimab versus placebo in participants exposed to SARS-CoV-2 wild-type, Alpha, Beta, and Delta variant. About 39.3% of participants were censored for efficacy due to unblinding and 13.8% due to vaccination. Within six months, tixagevimab/cilgavimab probably decreases infection with SARS-CoV-2 (risk ratio (RR) 0.45, 95% confidence interval (CI) 0.29 to 0.70; 4685 participants; moderate-certainty evidence), decreases development of clinical COVID-19 symptoms (RR 0.18, 95% CI 0.09 to 0.35; 5172 participants; high-certainty evidence), and may decrease admission to hospital (RR 0.03, 95% CI 0 to 0.59; 5197 participants; low-certainty evidence). Tixagevimab/cilgavimab may result in little to no difference on mortality within six months, all-grade AEs, and SAEs (low-certainty evidence). Quality of life was not reported. Casirivimab/imdevimab versus placebo One study evaluated casirivimab/imdevimab versus placebo in participants who may have been exposed to SARS-CoV-2 wild-type, Alpha, and Delta variant. About 36.5% of participants opted for SARS-CoV-2 vaccination and had a mean of 66.1 days between last dose of intervention and vaccination. Within six months, casirivimab/imdevimab may decrease infection with SARS-CoV-2 (RR 0.01, 95% CI 0 to 0.14; 825 seronegative participants; low-certainty evidence) and may decrease development of clinical COVID-19 symptoms (RR 0.02, 95% CI 0 to 0.27; 969 participants; low-certainty evidence). We are uncertain whether casirivimab/imdevimab affects mortality regardless of the SARS-CoV-2 antibody serostatus. Casirivimab/imdevimab may increase all-grade AEs slightly (RR 1.14, 95% CI 0.98 to 1.31; 969 participants; low-certainty evidence). The evidence is very uncertain about the effects on grade 3 to 4 AEs and SAEs within six months. Admission to hospital and quality of life were not reported. Postexposure prophylaxis Bamlanivimab versus placebo One study evaluated bamlanivimab versus placebo in participants who may have been exposed to SARS-CoV-2 wild-type. Bamlanivimab probably decreases infection with SARS-CoV-2 versus placebo by day 29 (RR 0.76, 95% CI 0.59 to 0.98; 966 participants; moderate-certainty evidence), may result in little to no difference on all-cause mortality by day 60 (R 0.83, 95% CI 0.25 to 2.70; 966 participants; low-certainty evidence), may increase all-grade AEs by week eight (RR 1.12, 95% CI 0.86 to 1.46; 966 participants; low-certainty evidence), and may increase slightly SAEs (RR 1.46, 95% CI 0.73 to 2.91; 966 participants; low-certainty evidence). Development of clinical COVID-19 symptoms, admission to hospital within 30 days, and quality of life were not reported. Casirivimab/imdevimab versus placebo One study evaluated casirivimab/imdevimab versus placebo in participants who may have been exposed to SARS-CoV-2 wild-type, Alpha, and potentially, but less likely to Delta variant. Within 30 days, casirivimab/imdevimab decreases infection with SARS-CoV-2 (RR 0.34, 95% CI 0.23 to 0.48; 1505 participants; high-certainty evidence), development of clinical COVID-19 symptoms (broad-term definition) (RR 0.19, 95% CI 0.10 to 0.35; 1505 participants; high-certainty evidence), may result in little to no difference on mortality (RR 3.00, 95% CI 0.12 to 73.43; 1505 participants; low-certainty evidence), and may result in little to no difference in admission to hospital. Casirivimab/imdevimab may slightly decrease grade 3 to 4 AEs (RR 0.50, 95% CI 0.24 to 1.02; 2617 participants; low-certainty evidence), decreases all-grade AEs (RR 0.70, 95% CI 0.61 to 0.80; 2617 participants; high-certainty evidence), and may result in little to no difference on SAEs in participants regardless of SARS-CoV-2 antibody serostatus. Quality of life was not reported. AUTHORS' CONCLUSIONS For PrEP, there is a decrease in development of clinical COVID-19 symptoms (high certainty), infection with SARS-CoV-2 (moderate certainty), and admission to hospital (low certainty) with tixagevimab/cilgavimab. There is low certainty of a decrease in infection with SARS-CoV-2, and development of clinical COVID-19 symptoms; and a higher rate for all-grade AEs with casirivimab/imdevimab. For PEP, there is moderate certainty of a decrease in infection with SARS-CoV-2 and low certainty for a higher rate for all-grade AEs with bamlanivimab. There is high certainty of a decrease in infection with SARS-CoV-2, development of clinical COVID-19 symptoms, and a higher rate for all-grade AEs with casirivimab/imdevimab. Although there is high-to-moderate certainty evidence for some outcomes, it is insufficient to draw meaningful conclusions. These findings only apply to people unvaccinated against COVID-19. They are only applicable to the variants prevailing during the study and not other variants (e.g. Omicron). In vitro, tixagevimab/cilgavimab is effective against Omicron, but there are no clinical data. Bamlanivimab and casirivimab/imdevimab are ineffective against Omicron in vitro. Further studies are needed and publication of four ongoing studies may resolve the uncertainties.
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Affiliation(s)
- Caroline Hirsch
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Yun Soo Park
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Vanessa Piechotta
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Khai Li Chai
- Transfusion Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Lise J Estcourt
- Haematology/Transfusion Medicine, NHS Blood and Transplant, Oxford, UK
| | - Ina Monsef
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Susanne Salomon
- Laboratory of Experimental Immunology, Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Erica M Wood
- Transfusion Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | | | - Zoe McQuilten
- Transfusion Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | | | - Jakob J Malin
- Department I for Internal Medicine, Division of Infectious Diseases, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Miriam Stegemann
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Nicole Skoetz
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Nina Kreuzberger
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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10
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McCreary EK, Bariola JR, Minnier TE, Wadas RJ, Shovel JA, Albin D, Marroquin OC, Kip KE, Collins K, Schmidhofer M, Wisniewski MK, Nace DA, Sullivan C, Axe M, Meyers R, Weissman A, Garrard W, Peck-Palmer OM, Wells A, Bart RD, Yang A, Berry LR, Berry S, Crawford AM, McGlothlin A, Khadem T, Linstrum K, Montgomery SK, Ricketts D, Kennedy JN, Pidro CJ, Haidar G, Snyder GM, McVerry BJ, Yealy DM, Angus DC, Nakayama A, Zapf RL, Kip PL, Seymour CW, Huang DT. The comparative effectiveness of COVID-19 monoclonal antibodies: A learning health system randomized clinical trial. Contemp Clin Trials 2022; 119:106822. [PMID: 35697146 PMCID: PMC9187853 DOI: 10.1016/j.cct.2022.106822] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 05/09/2022] [Accepted: 06/06/2022] [Indexed: 11/28/2022]
Abstract
Background Monoclonal antibodies (mAb) that neutralize SARS-CoV-2 decrease hospitalization and death compared to placebo in patients with mild to moderate COVID-19; however, comparative effectiveness is unknown. We report the comparative effectiveness of bamlanivimab, bamlanivimab-etesevimab, and casirivimab-imdevimab. Methods A learning health system platform trial in a U.S. health system enrolled patients meeting mAb Emergency Use Authorization criteria. An electronic health record-embedded application linked local mAb inventory to patient encounters and provided random mAb allocation. Primary outcome was hospital-free days to day 28. Primary analysis was a Bayesian model adjusting for treatment location, age, sex, and time. Inferiority was defined as 99% posterior probability of an odds ratio < 1. Equivalence was defined as 95% posterior probability the odds ratio is within a given bound. Findings Between March 10 and June 25, 2021, 1935 patients received treatment. Median hospital-free days were 28 (IQR 28, 28) for each mAb. Mortality was 0.8% (1/128), 0.8% (7/885), and 0.7% (6/922) for bamlanivimab, bamlanivimab-etesevimab, and casirivimab-imdevimab, respectively. Relative to casirivimab-imdevimab (n = 922), median adjusted odds ratios were 0.58 (95% credible interval [CI] 0.30–1.16) and 0.94 (95% CI 0.72–1.24) for bamlanivimab (n = 128) and bamlanivimab-etesevimab (n = 885), respectively. These odds ratios yielded 91% and 94% probabilities of inferiority of bamlanivimab versus bamlanivimab-etesevimab and casirivimab-imdevimab, and an 86% probability of equivalence between bamlanivimab-etesevimab and casirivimab-imdevimab. Interpretation Among patients with mild to moderate COVID-19, bamlanivimab-etesevimab or casirivimab-imdevimab treatment resulted in 86% probability of equivalence. No treatment met prespecified criteria for statistical equivalence. Median hospital-free days to day 28 were 28 (IQR 28, 28) for each mAb. Funding and registration This work received no external funding. The U.S. government provided the reported mAb. This trial is registered at ClinicalTrials.gov, NCT04790786.
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Affiliation(s)
- Erin K McCreary
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - J Ryan Bariola
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Tami E Minnier
- Wolff Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Richard J Wadas
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Judith A Shovel
- Wolff Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Debbie Albin
- Supply Chain Management/HC Pharmacy, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Oscar C Marroquin
- Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Kevin E Kip
- Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Kevin Collins
- Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Mark Schmidhofer
- Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | | | - David A Nace
- Division of Geriatric Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Colleen Sullivan
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Health System Office of Healthcare Innovation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Meredith Axe
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Russell Meyers
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Alexandra Weissman
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - William Garrard
- Clinical Analytics, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Octavia M Peck-Palmer
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Alan Wells
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Robert D Bart
- Health Services Division, University of Pittsburgh Medical Center, Pittsburgh, PA, USA; Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Anne Yang
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | | | | | | | | | - Tina Khadem
- Health System Office of Healthcare Innovation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Kelsey Linstrum
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Health System Office of Healthcare Innovation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Stephanie K Montgomery
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Health System Office of Healthcare Innovation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Daniel Ricketts
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Jason N Kennedy
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Caroline J Pidro
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Ghady Haidar
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Graham M Snyder
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Bryan J McVerry
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Donald M Yealy
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Derek C Angus
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Health System Office of Healthcare Innovation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA; Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Anna Nakayama
- Health System Office of Healthcare Innovation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Rachel L Zapf
- Wolff Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Paula L Kip
- Wolff Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Christopher W Seymour
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Health System Office of Healthcare Innovation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA; Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - David T Huang
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
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11
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Yang S, Tong Y, Chen L, Yu W. Human Identical Sequences, hyaluronan, and hymecromone ─ the new mechanism and management of COVID-19. MOLECULAR BIOMEDICINE 2022; 3:15. [PMID: 35593963 PMCID: PMC9120813 DOI: 10.1186/s43556-022-00077-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 05/04/2022] [Indexed: 02/08/2023] Open
Abstract
COVID-19 caused by SARS-CoV-2 has created formidable damage to public health and market economy. Currently, SARS-CoV-2 variants has exacerbated the transmission from person-to-person. Even after a great deal of investigation on COVID-19, SARS-CoV-2 is still rampaging globally, emphasizing the urgent need to reformulate effective prevention and treatment strategies. Here, we review the latest research progress of COVID-19 and provide distinct perspectives on the mechanism and management of COVID-19. Specially, we highlight the significance of Human Identical Sequences (HIS), hyaluronan, and hymecromone ("Three-H") for the understanding and intervention of COVID-19. Firstly, HIS activate inflammation-related genes to influence COVID-19 progress through NamiRNA-Enhancer network. Accumulation of hyaluronan induced by HIS-mediated HAS2 upregulation is a substantial basis for clinical manifestations of COVID-19, especially in lymphocytopenia and pulmonary ground-glass opacity. Secondly, detection of plasma hyaluronan can be effective for evaluating the progression and severity of COVID-19. Thirdly, spike glycoprotein of SARS-CoV-2 may bind to hyaluronan and further serve as an allergen to stimulate allergic reaction, causing sudden adverse effects after vaccination or the aggravation of COVID-19. Finally, antisense oligonucleotides of HIS or inhibitors of hyaluronan synthesis (hymecromone) or antiallergic agents could be promising therapeutic agents for COVID-19. Collectively, Three-H could hold the key to understand the pathogenic mechanism and create effective therapeutic strategies for COVID-19.
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Affiliation(s)
- Shuai Yang
- Laboratory of RNA Epigenetics, Institutes of Biomedical Sciences & Shanghai Public Health Clinical Center & Department of General Surgery, Huashan Hospital, Cancer Metastasis Institute, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Key Laboratory of Medical Epigenetics, Shanghai, 200032, People's Republic of China
| | - Ying Tong
- Laboratory of RNA Epigenetics, Institutes of Biomedical Sciences & Shanghai Public Health Clinical Center & Department of General Surgery, Huashan Hospital, Cancer Metastasis Institute, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Key Laboratory of Medical Epigenetics, Shanghai, 200032, People's Republic of China
| | - Lu Chen
- Laboratory of RNA Epigenetics, Institutes of Biomedical Sciences & Shanghai Public Health Clinical Center & Department of General Surgery, Huashan Hospital, Cancer Metastasis Institute, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Key Laboratory of Medical Epigenetics, Shanghai, 200032, People's Republic of China
| | - Wenqiang Yu
- Laboratory of RNA Epigenetics, Institutes of Biomedical Sciences & Shanghai Public Health Clinical Center & Department of General Surgery, Huashan Hospital, Cancer Metastasis Institute, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Key Laboratory of Medical Epigenetics, Shanghai, 200032, People's Republic of China.
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12
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McCreary EK, Bariola JR, Wadas RJ, Shovel JA, Wisniewski MK, Adam M, Albin D, Minnier T, Schmidhofer M, Meyers R, Marroquin OC, Collins K, Garrard W, Berry LR, Berry S, Crawford AM, McGlothlin A, Linstrum K, Nakayama A, Montgomery SK, Snyder GM, Yealy DM, Angus DC, Kip PL, Seymour CW, Huang DT, Kip KE. Association of Subcutaneous or Intravenous Administration of Casirivimab and Imdevimab Monoclonal Antibodies With Clinical Outcomes in Adults With COVID-19. JAMA Netw Open 2022; 5:e226920. [PMID: 35412625 PMCID: PMC9006104 DOI: 10.1001/jamanetworkopen.2022.6920] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
IMPORTANCE Monoclonal antibody (mAb) treatment decreases hospitalization and death in high-risk outpatients with mild to moderate COVID-19; however, only intravenous administration has been evaluated in randomized clinical trials of treatment. Subcutaneous administration may expand outpatient treatment capacity and qualified staff available to administer treatment, but the association with patient outcomes is understudied. OBJECTIVES To evaluate whether subcutaneous casirivimab and imdevimab treatment is associated with reduced 28-day hospitalization and death compared with nontreatment among mAb-eligible patients and whether subcutaneous casirivimab and imdevimab treatment is clinically and statistically similar to intravenous casirivimab and imdevimab treatment. DESIGN, SETTING, AND PARTICIPANTS This prospective cohort study evaluated high-risk outpatients in a learning health system in the US with mild to moderate COVID-19 symptoms from July 14 to October 26, 2021, who were eligible for mAb treatment under emergency use authorization. A nontreated control group of eligible patients was also studied. EXPOSURES Subcutaneous injection or intravenous administration of the combined single dose of 600 mg of casirivimab and 600 mg of imdevimab. MAIN OUTCOMES AND MEASURES The primary outcome was the 28-day adjusted risk ratio or adjusted risk difference for hospitalization or death. Secondary outcomes included 28-day adjusted risk ratios and differences in hospitalization, death, a composite end point of emergency department admission and hospitalization, and rates of adverse events. Among 1959 matched adults with mild to moderate COVID-19, 969 patients (mean [SD] age, 53.8 [16.7] years; 547 women [56.4%]) who received casirivimab and imdevimab subcutaneously had a 28-day rate of hospitalization or death of 3.4% (22 of 653 patients) compared with 7.0% (92 of 1306 patients) in nontreated controls (risk ratio, 0.48; 95% CI, 0.30-0.80; P = .002). Among 2185 patients treated with subcutaneous (n = 969) or intravenous (n = 1216; mean [SD] age, 54.3 [16.6] years; 672 women [54.4%]) casirivimab and imdevimab, the 28-day rate of hospitalization or death was 2.8% vs 1.7%, which resulted in an adjusted risk difference of 1.5% (95% CI, -0.6% to 3.5%; P = .16). Among all infusion patients, there was no difference in intensive care unit admission (adjusted risk difference, 0.7%; 95% CI, -3.5% to 5.0%) or need for mechanical ventilation (adjusted risk difference, 0.2%; 95% CI, -5.8% to 5.5%). CONCLUSIONS AND RELEVANCE In this cohort study of high-risk outpatients with mild to moderate COVID-19 symptoms, subcutaneously administered casirivimab and imdevimab was associated with reduced hospitalization and death when compared with no treatment. These results provide preliminary evidence of potential expanded use of subcutaneous mAb treatment, particularly in areas that are facing treatment capacity and/or staffing shortages.
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Affiliation(s)
- Erin K. McCreary
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - J. Ryan Bariola
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Richard J. Wadas
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Judith A. Shovel
- Wolff Center, University of Pittsburgh Medical Center (UPMC), Pittsburgh, Pennsylvania
| | - Mary Kay Wisniewski
- Wolff Center, University of Pittsburgh Medical Center (UPMC), Pittsburgh, Pennsylvania
| | - Michelle Adam
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Debbie Albin
- Supply Chain Management/HC Pharmacy, UPMC, Pittsburgh, Pennsylvania
| | - Tami Minnier
- Wolff Center, University of Pittsburgh Medical Center (UPMC), Pittsburgh, Pennsylvania
| | - Mark Schmidhofer
- Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Russell Meyers
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | | | | | | | | | | | | | | | - Kelsey Linstrum
- Office of Healthcare Innovation, UPMC, Pittsburgh, Pennsylvania
| | - Anna Nakayama
- Office of Healthcare Innovation, UPMC, Pittsburgh, Pennsylvania
| | | | - Graham M. Snyder
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Donald M. Yealy
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Derek C. Angus
- Office of Healthcare Innovation, UPMC, Pittsburgh, Pennsylvania
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Paula L. Kip
- Office of Healthcare Innovation, UPMC, Pittsburgh, Pennsylvania
| | - Christopher W. Seymour
- Office of Healthcare Innovation, UPMC, Pittsburgh, Pennsylvania
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - David T. Huang
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Office of Healthcare Innovation, UPMC, Pittsburgh, Pennsylvania
- Clinical Research Investigation and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Kevin E. Kip
- Clinical Analytics, UPMC, Pittsburgh, Pennsylvania
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13
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Vanderbeek AM, Bliss JM, Yin Z, Yap C. Implementation of platform trials in the COVID-19 pandemic: A rapid review. Contemp Clin Trials 2021; 112:106625. [PMID: 34793985 PMCID: PMC8591985 DOI: 10.1016/j.cct.2021.106625] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 10/17/2021] [Accepted: 11/11/2021] [Indexed: 11/26/2022]
Abstract
Motivation Platform designs - master protocols that allow for new treatment arms to be added over time - have gained considerable attention in recent years. Between 2001 and 2019, 16 platform trials were initiated globally. The COVID-19 pandemic seems to have provided a new motivation for these designs. We conducted a rapid review to quantify and describe platform trials used in COVID-19. Methods We cross-referenced PubMed, ClinicalTrials.gov, and the Cytel COVID-19 Clinical Trials Tracker to identify platform trials, defined by their stated ability to add future arms. Results We identified 58 COVID-19 platform trials globally registered between January 2020 and May 2021. According to trial registries, 16 trials have added new therapies (median 3, IQR 4) and 11 have dropped arms (median 3, IQR 2.5). About 50% of trials publicly share their protocol, and 31 trials (53%) intend to share trial data. Forty-nine trials (84%) explicitly report adaptive features, and 21 trials (36%) state Bayesian methods. Conclusions During the pandemic, there has been a surge in the number of platform trials compared to historical use. While transparency in statistical methods and clarity of data sharing policies needs improvement, platform trials appear particularly well-suited for rapid evidence generation. Trials secured funding quickly and many succeeded in adding new therapies in a short time period, thus demonstrating the potential for these trial designs to be implemented beyond the pandemic. The evidence gathered here may provide ample insight to further inform operational, statistical, and regulatory aspects of future platform trial conduct.
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Affiliation(s)
- Alyssa M Vanderbeek
- Clinical Trials and Statistics Unit, The Institute of Cancer Research, London, UK
| | - Judith M Bliss
- Clinical Trials and Statistics Unit, The Institute of Cancer Research, London, UK
| | - Zhulin Yin
- Clinical Trials and Statistics Unit, The Institute of Cancer Research, London, UK
| | - Christina Yap
- Clinical Trials and Statistics Unit, The Institute of Cancer Research, London, UK.
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14
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Nathan R, Shawa I, De La Torre I, Pustizzi JM, Haustrup N, Patel DR, Huhn G. A Narrative Review of the Clinical Practicalities of Bamlanivimab and Etesevimab Antibody Therapies for SARS-CoV-2. Infect Dis Ther 2021; 10:1933-1947. [PMID: 34374951 PMCID: PMC8353431 DOI: 10.1007/s40121-021-00515-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 07/22/2021] [Indexed: 12/16/2022] Open
Abstract
The severity of coronavirus disease 2019 (COVID-19) ranges from mild to death, with high morbidity and mortality rates reported amongst a vulnerable subset of patients termed high risk. While vaccines remain the primary option for COVID-19 prevention, neutralizing monoclonal antibodies (mAbs), such as bamlanivimab and etesevimab, have been shown to benefit certain subpopulations after exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Unlike vaccine-derived immunity that develops over time, administration of neutralizing mAbs is an immediate and passive immunotherapy, with the potential to reduce disease progression, emergency room visits, hospitalizations, and death. Bamlanivimab alone and together with etesevimab hold emergency use authorizations in several countries globally, with countries increasingly transitioning to the use of bamlanivimab and etesevimab together and other authorized mAbs on the basis of their evolving variant landscape, regulatory authorizations, and access to drugs. The current guidelines for the administration of bamlanivimab alone or together with etesevimab are informed by an iterative process of testing and development. Herein the rationale for these guidelines is provided by sharing the learnings that have been gathered throughout the development process of these mAbs. In addition, this review addresses the most common clinical questions received from health care professionals (HCPs) and patients regarding indicated population, dose, use with other medications and vaccines, duration of protection, and variants in clinical practice. As prevalence of SARS-CoV-2 variants can differ by country and state, prescribing HCPs should consider the prevalence of bamlanivimab and etesevimab resistant variants in their area, where data are available, regarding potential efficacy impact when considering treatment options. Trial Registration: ClinicalTrials.gov identifier: NCT04427501; NCT04411628; NCT04497987; NCT04634409.
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Affiliation(s)
| | - Imad Shawa
- Franciscan Health, 701E County Line Rd, Ste 101, Greenwood, IN, 46143, USA
| | | | | | | | | | - Gregory Huhn
- The Ruth M. Rothstein CORE Center, Cook County Health and Hospital System, Chicago, IL, USA.
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15
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Cava C, Bertoli G, Castiglioni I. Potential drugs against COVID-19 revealed by gene expression profile, molecular docking and molecular dynamic simulation. Future Virol 2021. [PMID: 34306168 PMCID: PMC8293696 DOI: 10.2217/fvl-2020-0392] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 06/30/2021] [Indexed: 02/07/2023]
Abstract
Aim: SARS-CoV-2, an emerging betacoronavirus, is the causative agent of COVID-19. Currently, there are few specific and selective antiviral drugs for the treatment and vaccines to prevent contagion. However, their long-term effects can be revealed after several years, and new drugs for COVID-19 should continue to be investigated. Materials & methods: In the first step of our study we identified, through a gene expression analysis, several drugs that could act on the biological pathways altered in COVID-19. In the second step, we performed a docking simulation to test the properties of the identified drugs to target SARS-CoV-2. Results: The drugs that showed a higher binding affinity are bardoxolone (-8.78 kcal/mol), irinotecan (-8.40 kcal/mol) and pyrotinib (-8.40 kcal/mol). Conclusion: We suggested some drugs that could be efficient in treating COVID-19.
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Affiliation(s)
- Claudia Cava
- Institute of Molecular Bioimaging & Physiology, National Research Council (IBFM-CNR), Via F. Cervi 93, Segrate-Milan, Milan, 20090, Italy
| | - Gloria Bertoli
- Institute of Molecular Bioimaging & Physiology, National Research Council (IBFM-CNR), Via F. Cervi 93, Segrate-Milan, Milan, 20090, Italy
| | - Isabella Castiglioni
- Department of Physics "Giuseppe Occhialini", University of Milan-Bicocca Piazza dell'Ateneo Nuovo, Milan, 20126, Italy
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