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Truong B, Hornsby L, Fox B, Chou C, Zheng J, Qian J. Benefit and risk of oral anticoagulant initiation strategies in patients with atrial fibrillation and cancer: a target trial emulation using the SEER-Medicare database. J Thromb Thrombolysis 2024; 57:638-649. [PMID: 38504063 PMCID: PMC11026243 DOI: 10.1007/s11239-024-02958-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/06/2024] [Indexed: 03/21/2024]
Abstract
Oral anticoagulants (OACs) are recommended for patients with atrial fibrillation (AFib) having CHA2DS2-VASc score ≥ 2. However, the benefits of OAC initiation in patients with AFib and cancer at different levels of CHA2DS2-VASc is unknown. We included patients with new AFib diagnosis and a record of cancer (breast, prostate, or lung) from the 2012-2019 Surveillance, Epidemiology, and End Results (SEER)-Medicare database (n = 39,915). Risks of stroke and bleeding were compared between 5 treatment strategies: (1) initiated OAC when CHA2DS2-VASc ≥ 1 (n = 6008), (2) CHA2DS2-VASc ≥ 2 (n = 8694), (3) CHA2DS2-VASc ≥ 4 (n = 20,286), (4) CHA2DS2-VASc ≥ 6 (n = 30,944), and (5) never initiated OAC (reference group, n = 33,907). Confounders were adjusted using inverse probability weighting through cloning-censoring-weighting approach. Weighted pooled logistic regressions were used to estimate treatment effect [hazard ratios (HRs) and 95% confidence interval (95% CIs)]. We found that only patients who initiated OACs at CHA2DS2-VASc ≥ 6 had lower risk of stroke compared without OAC initiation (HR 0.64, 95% CI 0.54-0.75). All 4 active treatment strategies had reduced risk of bleeding compared to non-initiators, with OAC initiation at CHA2DS2-VASc ≥ 6 being the most beneficial strategy (HR = 0.49, 95% CI 0.44-0.55). In patients with lung cancer or regional/metastatic cancer, OAC initiation at any CHA2DS2-VASc level increased risk of stroke and did not reduce risk of bleeding (except for Regimen 4). In conclusion, among cancer patients with new AFib diagnosis, OAC initiation at higher risk of stroke (CHA2DS2-VASc score ≥ 6) is more beneficial in preventing ischemic stroke and bleeding. Patients with advanced cancer or low life-expectancy may initiate OACs when CHA2DS2-VASc score ≥ 6.
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Affiliation(s)
- Bang Truong
- Department of Health Outcomes Research and Policy, Auburn University Harrison College of Pharmacy, 4306d Walker Building, Auburn, AL, 36849, USA
| | - Lori Hornsby
- Department of Pharmacy Practice, Auburn University Harrison College of Pharmacy, Auburn, AL, USA
| | - Brent Fox
- Department of Health Outcomes Research and Policy, Auburn University Harrison College of Pharmacy, 4306d Walker Building, Auburn, AL, 36849, USA
| | - Chiahung Chou
- Department of Health Outcomes Research and Policy, Auburn University Harrison College of Pharmacy, 4306d Walker Building, Auburn, AL, 36849, USA
| | - Jingyi Zheng
- Department of Mathematics and Statistics, Auburn University College of Sciences and Mathematics, Auburn, AL, USA
| | - Jingjing Qian
- Department of Health Outcomes Research and Policy, Auburn University Harrison College of Pharmacy, 4306d Walker Building, Auburn, AL, 36849, USA.
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Martinuka O, Hazard D, Marateb HR, Mansourian M, Mañanas MÁ, Romero S, Rubio-Rivas M, Wolkewitz M. Methodological biases in observational hospital studies of COVID-19 treatment effectiveness: pitfalls and potential. Front Med (Lausanne) 2024; 11:1362192. [PMID: 38576716 PMCID: PMC10991758 DOI: 10.3389/fmed.2024.1362192] [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] [Received: 12/27/2023] [Accepted: 02/20/2024] [Indexed: 04/06/2024] Open
Abstract
Introduction This study aims to discuss and assess the impact of three prevalent methodological biases: competing risks, immortal-time bias, and confounding bias in real-world observational studies evaluating treatment effectiveness. We use a demonstrative observational data example of COVID-19 patients to assess the impact of these biases and propose potential solutions. Methods We describe competing risks, immortal-time bias, and time-fixed confounding bias by evaluating treatment effectiveness in hospitalized patients with COVID-19. For our demonstrative analysis, we use observational data from the registry of patients with COVID-19 who were admitted to the Bellvitge University Hospital in Spain from March 2020 to February 2021 and met our predefined inclusion criteria. We compare estimates of a single-dose, time-dependent treatment with the standard of care. We analyze the treatment effectiveness using common statistical approaches, either by ignoring or only partially accounting for the methodological biases. To address these challenges, we emulate a target trial through the clone-censor-weight approach. Results Overlooking competing risk bias and employing the naïve Kaplan-Meier estimator led to increased in-hospital death probabilities in patients with COVID-19. Specifically, in the treatment effectiveness analysis, the Kaplan-Meier estimator resulted in an in-hospital mortality of 45.6% for treated patients and 59.0% for untreated patients. In contrast, employing an emulated trial framework with the weighted Aalen-Johansen estimator, we observed that in-hospital death probabilities were reduced to 27.9% in the "X"-treated arm and 40.1% in the non-"X"-treated arm. Immortal-time bias led to an underestimated hazard ratio of treatment. Conclusion Overlooking competing risks, immortal-time bias, and confounding bias leads to shifted estimates of treatment effects. Applying the naïve Kaplan-Meier method resulted in the most biased results and overestimated probabilities for the primary outcome in analyses of hospital data from COVID-19 patients. This overestimation could mislead clinical decision-making. Both immortal-time bias and confounding bias must be addressed in assessments of treatment effectiveness. The trial emulation framework offers a potential solution to address all three methodological biases.
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Affiliation(s)
- Oksana Martinuka
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Derek Hazard
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Hamid Reza Marateb
- Biomedical Engineering Research Center (CREB), Automatic Control Department (ESAII), Universitat Politècnica de Catalunya-Barcelona Tech (UPC), Barcelona, Spain
- Department of Artificial Intelligence, Smart University of Medical Sciences, Tehran, Iran
| | - Marjan Mansourian
- Biomedical Engineering Research Center (CREB), Automatic Control Department (ESAII), Universitat Politècnica de Catalunya-Barcelona Tech (UPC), Barcelona, Spain
- Department of Epidemiology and Biostatistics, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Miguel Ángel Mañanas
- Biomedical Engineering Research Center (CREB), Automatic Control Department (ESAII), Universitat Politècnica de Catalunya-Barcelona Tech (UPC), Barcelona, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Sergio Romero
- Biomedical Engineering Research Center (CREB), Automatic Control Department (ESAII), Universitat Politècnica de Catalunya-Barcelona Tech (UPC), Barcelona, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Manuel Rubio-Rivas
- Department of Internal Medicine, Bellvitge University Hospital, Hospitalet de Llobregat, Barcelona, Spain
| | - Martin Wolkewitz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
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Senefeld JW, Gorman EK, Johnson PW, Moir ME, Klassen SA, Carter RE, Paneth NS, Sullivan DJ, Morkeberg OH, Wright RS, Fairweather D, Bruno KA, Shoham S, Bloch EM, Focosi D, Henderson JP, Juskewitch JE, Pirofski LA, Grossman BJ, Tobian AA, Franchini M, Ganesh R, Hurt RT, Kay NE, Parikh SA, Baker SE, Buchholtz ZA, Buras MR, Clayburn AJ, Dennis JJ, Diaz Soto JC, Herasevich V, Klompas AM, Kunze KL, Larson KF, Mills JR, Regimbal RJ, Ripoll JG, Sexton MA, Shepherd JR, Stubbs JR, Theel ES, van Buskirk CM, van Helmond N, Vogt MN, Whelan ER, Wiggins CC, Winters JL, Casadevall A, Joyner MJ. Rates Among Hospitalized Patients With COVID-19 Treated With Convalescent Plasma: A Systematic Review and Meta-Analysis. Mayo Clin Proc Innov Qual Outcomes 2023; 7:499-513. [PMID: 37859995 PMCID: PMC10582279 DOI: 10.1016/j.mayocpiqo.2023.09.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023] Open
Abstract
Objective To examine the association of COVID-19 convalescent plasma transfusion with mortality and the differences between subgroups in hospitalized patients with COVID-19. Patients and Methods On October 26, 2022, a systematic search was performed for clinical studies of COVID-19 convalescent plasma in the literature from January 1, 2020, to October 26, 2022. Randomized clinical trials and matched cohort studies investigating COVID-19 convalescent plasma transfusion compared with standard of care treatment or placebo among hospitalized patients with confirmed COVID-19 were included. The electronic search yielded 3841 unique records, of which 744 were considered for full-text screening. The selection process was performed independently by a panel of 5 reviewers. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Data were extracted by 5 independent reviewers in duplicate and pooled using an inverse-variance random effects model. The prespecified end point was all-cause mortality during hospitalization. Results Thirty-nine randomized clinical trials enrolling 21,529 participants and 70 matched cohort studies enrolling 50,160 participants were included in the systematic review. Separate meta-analyses reported that transfusion of COVID-19 convalescent plasma was associated with a decrease in mortality compared with the control cohort for both randomized clinical trials (odds ratio [OR], 0.87; 95% CI, 0.76-1.00) and matched cohort studies (OR, 0.76; 95% CI, 0.66-0.88). The meta-analysis of subgroups revealed 2 important findings. First, treatment with convalescent plasma containing high antibody levels was associated with a decrease in mortality compared with convalescent plasma containing low antibody levels (OR, 0.85; 95% CI, 0.73 to 0.99). Second, earlier treatment with COVID-19 convalescent plasma was associated with a decrease in mortality compared with the later treatment cohort (OR, 0.63; 95% CI, 0.48 to 0.82). Conclusion During COVID-19 convalescent plasma use was associated with a 13% reduced risk of mortality, implying a mortality benefit for hospitalized patients with COVID-19, particularly those treated with convalescent plasma containing high antibody levels treated earlier in the disease course.
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Affiliation(s)
- Jonathon W. Senefeld
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
- Department of Kinesiology and Community Health, University of Illinois Urbana-Champaign, Urbana, IL
| | - Ellen K. Gorman
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Patrick W. Johnson
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL
| | - M. Erin Moir
- Department of Kinesiology, University of Wisconsin-Madison, Madison
| | - Stephen A. Klassen
- Department of Kinesiology, Brock University, St. Catharines, Ontario, Canada
| | - Rickey E. Carter
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL
| | - Nigel S. Paneth
- Department of Epidemiology and Biostatistics and Department of Pediatrics and Human Development, Michigan State University, East Lansing
| | - David J. Sullivan
- Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, ML
| | - Olaf H. Morkeberg
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - R. Scott Wright
- Human Research Protection Program, Mayo Clinic, Rochester, MN
| | | | - Katelyn A. Bruno
- Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL
- Division of Cardiovascular Medicine, University of Florida, Gainesville
| | - Shmuel Shoham
- Department of Medicine, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Evan M. Bloch
- Department of Pathology Johns Hopkins University School of Medicine, Baltimore, ML
| | - Daniele Focosi
- North-Western Tuscany Blood Bank, Pisa University Hospital, Italy
| | - Jeffrey P. Henderson
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine in St. Louis, MO
- Department of Molecular Microbiology, Washington University School of Medicine in St. Louis, MO
| | | | - Liise-Anne Pirofski
- Division of Infectious Diseases, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY
| | - Brenda J. Grossman
- Department of Pathology and Immunology, Washington University School of Medicine in St. Louis, MO
| | - Aaron A.R. Tobian
- Department of Pathology Johns Hopkins University School of Medicine, Baltimore, ML
| | - Massimo Franchini
- Division of Transfusion Medicine, Carlo Poma Hospital, Mantua, Italy
| | - Ravindra Ganesh
- Department of General Internal Medicine, Mayo Clinic, Rochester, MN
| | - Ryan T. Hurt
- Department of General Internal Medicine, Mayo Clinic, Rochester, MN
| | - Neil E. Kay
- Division of Hematology, Mayo Clinic, Rochester, MN
- Department of Immunology, Mayo Clinic, Rochester, MN
| | | | - Sarah E. Baker
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Zachary A. Buchholtz
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Matthew R. Buras
- Department of Quantitative Health Sciences, Mayo Clinic, Scottsdale, AZ
| | - Andrew J. Clayburn
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Joshua J. Dennis
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Juan C. Diaz Soto
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Vitaly Herasevich
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Allan M. Klompas
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Katie L. Kunze
- Department of Quantitative Health Sciences, Mayo Clinic, Scottsdale, AZ
| | | | - John R. Mills
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Riley J. Regimbal
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Juan G. Ripoll
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Matthew A. Sexton
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - John R.A. Shepherd
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - James R. Stubbs
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Elitza S. Theel
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | | | - Noud van Helmond
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Matthew N.P. Vogt
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Emily R. Whelan
- Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL
| | - Chad C. Wiggins
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Jeffrey L. Winters
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Arturo Casadevall
- Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, ML
| | - Michael J. Joyner
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
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Zuo H, Yu L, Campbell SM, Yamamoto SS, Yuan Y. The implementation of target trial emulation for causal inference: a scoping review. J Clin Epidemiol 2023; 162:29-37. [PMID: 37562726 DOI: 10.1016/j.jclinepi.2023.08.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 07/25/2023] [Accepted: 08/02/2023] [Indexed: 08/12/2023]
Abstract
OBJECTIVES We aim to investigate the implementation of Target Trial Emulation (TTE) for causal inference, involving research topics, frequently used strategies, and issues indicating the need for future improvements. STUDY DESIGN AND SETTING We performed a scoping review by following the Joanna Briggs Institute (JBI) guidance and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. A health research-focused librarian searched multiple medical databases, and two independent reviewers completed screening and extraction within covidence review management software. RESULTS Our search resulted in 1,240 papers, of which 96 papers were eligible for data extraction. Results show a significant increase in the use of TTE in 2018 and 2021. The study topics varied and focused primarily on cancer, cardiovascular and cerebrovascular diseases, and infectious diseases. However, not all papers specified well all three critical components for generating robust causal evidence: time-zero, random assignment simulation, and comparison strategy. Some common issues were observed from retrieved papers, and key limitations include residual confounding, limited generalizability, and a lack of reporting guidance that need to be improved. CONCLUSION Uneven adherence to the TTE framework exists, and future improvements are needed to progress applications using causal inference with observational data.
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Affiliation(s)
- Hanxiao Zuo
- School of Public Health, University of Alberta, Edmonton, Alberta T6G 1C9, Canada.
| | - Lin Yu
- School of Public Health, University of Alberta, Edmonton, Alberta T6G 1C9, Canada
| | - Sandra M Campbell
- John W. Scott Health Sciences Library, University of Alberta, Edmonton, Alberta T6G 2R7, Canada
| | - Shelby S Yamamoto
- School of Public Health, University of Alberta, Edmonton, Alberta T6G 1C9, Canada
| | - Yan Yuan
- School of Public Health, University of Alberta, Edmonton, Alberta T6G 1C9, Canada
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5
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Hansford HJ, Cashin AG, Jones MD, Swanson SA, Islam N, Douglas SRG, Rizzo RRN, Devonshire JJ, Williams SA, Dahabreh IJ, Dickerman BA, Egger M, Garcia-Albeniz X, Golub RM, Lodi S, Moreno-Betancur M, Pearson SA, Schneeweiss S, Sterne JAC, Sharp MK, Stuart EA, Hernán MA, Lee H, McAuley JH. Reporting of Observational Studies Explicitly Aiming to Emulate Randomized Trials: A Systematic Review. JAMA Netw Open 2023; 6:e2336023. [PMID: 37755828 PMCID: PMC10534275 DOI: 10.1001/jamanetworkopen.2023.36023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/22/2023] [Indexed: 09/28/2023] Open
Abstract
Importance Observational (nonexperimental) studies that aim to emulate a randomized trial (ie, the target trial) are increasingly informing medical and policy decision-making, but it is unclear how these studies are reported in the literature. Consistent reporting is essential for quality appraisal, evidence synthesis, and translation of evidence to policy and practice. Objective To assess the reporting of observational studies that explicitly aimed to emulate a target trial. Evidence Review We searched Medline, Embase, PsycINFO, and Web of Science for observational studies published between March 2012 and October 2022 that explicitly aimed to emulate a target trial of a health or medical intervention. Two reviewers double-screened and -extracted data on study characteristics, key predefined components of the target trial protocol and its emulation (eligibility criteria, treatment strategies, treatment assignment, outcome[s], follow-up, causal contrast[s], and analysis plan), and other items related to the target trial emulation. Findings A total of 200 studies that explicitly aimed to emulate a target trial were included. These studies included 26 subfields of medicine, and 168 (84%) were published from January 2020 to October 2022. The aim to emulate a target trial was explicit in 70 study titles (35%). Forty-three studies (22%) reported use of a published reporting guideline (eg, Strengthening the Reporting of Observational Studies in Epidemiology). Eighty-five studies (43%) did not describe all key items of how the target trial was emulated and 113 (57%) did not describe the protocol of the target trial and its emulation. Conclusion and Relevance In this systematic review of 200 studies that explicitly aimed to emulate a target trial, reporting of how the target trial was emulated was inconsistent. A reporting guideline for studies explicitly aiming to emulate a target trial may improve the reporting of the target trial protocols and other aspects of these emulation attempts.
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Affiliation(s)
- Harrison J. Hansford
- School of Health Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
- Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
| | - Aidan G. Cashin
- School of Health Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
- Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
| | - Matthew D. Jones
- School of Health Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
- Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
| | - Sonja A. Swanson
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Nazrul Islam
- Oxford Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
- Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | - Susan R. G. Douglas
- School of Health Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
| | - Rodrigo R. N. Rizzo
- School of Health Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
- Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
| | - Jack J. Devonshire
- Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
| | - Sam A. Williams
- Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
| | - Issa J. Dahabreh
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Barbra A. Dickerman
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Matthias Egger
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Centre for Infectious Disease Epidemiology and Research, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Xabier Garcia-Albeniz
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- RTI Health Solutions, Barcelona, Spain
| | - Robert M. Golub
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Sara Lodi
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Margarita Moreno-Betancur
- Clinical Epidemiology & Biostatistics Unit, Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville, Victoria, Australia
- Department of Paediatrics, The University of Melbourne, Parkville, Victoria, Australia
| | - Sallie-Anne Pearson
- School of Population Health, Faculty of Medicine and Health, UNSW Sydney, New South Wales, Australia
| | - Sebastian Schneeweiss
- Division of Pharmacoepidemiology, Department of Medicine, Brigham & Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jonathan A. C. Sterne
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- NIHR Bristol Biomedical Research Centre, Bristol, United Kingdom
- Health Data Research UK South-West, Bristol, United Kingdom
| | - Melissa K. Sharp
- Department of Public Health and Epidemiology, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Elizabeth A. Stuart
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Miguel A. Hernán
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Hopin Lee
- University of Exeter Medical School, Exeter, United Kingdom
| | - James H. McAuley
- School of Health Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
- Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
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Purbey PK, Roy K, Gupta S, Paul MK. Mechanistic insight into the protective and pathogenic immune-responses against SARS-CoV-2. Mol Immunol 2023; 156:111-126. [PMID: 36921486 PMCID: PMC10009586 DOI: 10.1016/j.molimm.2023.03.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 02/20/2023] [Accepted: 03/07/2023] [Indexed: 03/12/2023]
Abstract
COVID-19 is a severe respiratory illness that has emerged as a devasting health problem worldwide. The disease outcome is heterogeneous, which is most likely dependent on the immunity of an individual. Asymptomatic and mildly/moderate symptomatic (non-severe) patients likely develop an effective early immune response and clear the virus. However, severe symptoms dominate due to a failure in the generation of an effective and specific early immune response against SARS-CoV-2. Moreover, a late surge in pathogenic inflammation involves dysregulated innate and adaptive immune responses leading to local and systemic tissue damage and the emergence of severe disease symptoms. In this review, we describe the potential mechanisms of protective and pathogenic immune responses in "mild/moderate" and "severe" symptomatic SARS-CoV-2 infected people, respectively, and discuss the immune components that are currently targeted for therapeutic intervention.
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Affiliation(s)
- Prabhat K Purbey
- Department of Microbiology, Immunology and Molecular Genetics, University of California Los Angeles (UCLA), Los Angeles, CA 90095, USA.
| | - Koushik Roy
- Microbiology and Immunology, Department of Pathology, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
| | - Sandeep Gupta
- Department of Neurobiology, University of California Los Angeles (UCLA), Los Angeles, CA 90095, USA
| | - Manash K Paul
- Department of Pulmonary and Critical Care Medicine, David Geffen School of Medicine, University of California Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Microbiology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India.
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7
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Martinuka O, von Cube M, Hazard D, Marateb HR, Mansourian M, Sami R, Hajian MR, Ebrahimi S, Wolkewitz M. Target Trial Emulation Using Hospital-Based Observational Data: Demonstration and Application in COVID-19. Life (Basel) 2023; 13:777. [PMID: 36983933 PMCID: PMC10053871 DOI: 10.3390/life13030777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/02/2023] [Accepted: 03/08/2023] [Indexed: 03/17/2023] Open
Abstract
Methodological biases are common in observational studies evaluating treatment effectiveness. The objective of this study is to emulate a target trial in a competing risks setting using hospital-based observational data. We extend established methodology accounting for immortal time bias and time-fixed confounding biases to a setting where no survival information beyond hospital discharge is available: a condition common to coronavirus disease 2019 (COVID-19) research data. This exemplary study includes a cohort of 618 hospitalized patients with COVID-19. We describe methodological opportunities and challenges that cannot be overcome applying traditional statistical methods. We demonstrate the practical implementation of this trial emulation approach via clone-censor-weight techniques. We undertake a competing risk analysis, reporting the cause-specific cumulative hazards and cumulative incidence probabilities. Our analysis demonstrates that a target trial emulation framework can be extended to account for competing risks in COVID-19 hospital studies. In our analysis, we avoid immortal time bias, time-fixed confounding bias, and competing risks bias simultaneously. Choosing the length of the grace period is justified from a clinical perspective and has an important advantage in ensuring reliable results. This extended trial emulation with the competing risk analysis enables an unbiased estimation of treatment effects, along with the ability to interpret the effectiveness of treatment on all clinically important outcomes.
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Affiliation(s)
- Oksana Martinuka
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Centre, University of Freiburg, 79104 Freiburg, Germany
| | - Maja von Cube
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Centre, University of Freiburg, 79104 Freiburg, Germany
| | - Derek Hazard
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Centre, University of Freiburg, 79104 Freiburg, Germany
| | - Hamid Reza Marateb
- Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan 81746-73441, Iran
- Biomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Politècnica de Catalunya-Barcelona Tech (UPC) Building H, Floor 4, Av. Diagonal 647, 08028 Barcelona, Spain
| | - Marjan Mansourian
- Biomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Politècnica de Catalunya-Barcelona Tech (UPC) Building H, Floor 4, Av. Diagonal 647, 08028 Barcelona, Spain
- Department of Epidemiology and Biostatistics, School of Health, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
| | - Ramin Sami
- Department of Internal Medicine, School of Medicine, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
| | - Mohammad Reza Hajian
- Department of Internal Medicine, School of Medicine, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
| | - Sara Ebrahimi
- Alzahra Research Institute, Alzahra University Hospital, Isfahan University of Medical Sciences, Isfahan 81746-75731, Iran
| | - Martin Wolkewitz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Centre, University of Freiburg, 79104 Freiburg, Germany
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8
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Zhou CK, Bennett MM, Villa CH, Hammonds KP, Lu Y, Ettlinger J, Priest EL, Gottlieb RL, Davis S, Mays E, Clarke TC, Shoaibi A, Wong HL, Anderson SA, Kelly RJ. Multi-center matched cohort study of convalescent plasma for hospitalized patients with COVID-19. PLoS One 2022; 17:e0273223. [PMID: 35980913 PMCID: PMC9387784 DOI: 10.1371/journal.pone.0273223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 08/04/2022] [Indexed: 11/18/2022] Open
Abstract
Background
Although frequently used in the early pandemic, data on the effectiveness of COVID-19 convalescent plasma (CCP) remain mixed. We investigated the effectiveness and safety of CCP in hospitalized COVID-19 patients in real-world practices during the first two waves of the pandemic in a multi-hospital healthcare system in Texas.
Methods and findings
Among 11,322 hospitalized patients with confirmed COVID-19 infection from July 1, 2020 to April 15, 2021, we included patients who received CCP and matched them with those who did not receive CCP within ±2 days of the transfusion date across sites within strata of sex, age groups, days and use of dexamethasone from hospital admission to the match date, and oxygen requirements 4–12 hours prior to the match date. Cox proportional hazards model estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for effectiveness outcomes in a propensity score 1:1 matched cohort. Pre-defined safety outcomes were described. We included 1,245 patients each in the CCP treated and untreated groups. Oxygen support was required by 93% of patients at the baseline. The pre-defined primary effectiveness outcome of 28-day in-hospital all-cause mortality (HR = 0.85; 95%CI: 0.66,1.10) were similar between treatment groups. Sensitivity and stratified analyses found similar null results. CCP-treated patients were less likely to be discharged alive (HR = 0.82; 95%CI: 0.74, 0.91), and more likely to receive mechanical ventilation (HR = 1.48; 95%CI: 1.12, 1.96). Safety outcomes were rare and similar between treatment groups.
Conclusion
The findings in this large, matched cohort of patients hospitalized with COVID-19 and mostly requiring oxygen support at the time of treatment, do not support a clinical benefit in 28-day in-hospital all-cause mortality for CCP. Future studies should assess the potential benefits with specifically high-titer units in perhaps certain subgroups of patients (e.g. those with early disease or immunocompromised).
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Affiliation(s)
- Cindy Ke Zhou
- Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Monica M. Bennett
- Baylor Scott & White Research Institute, Dallas, TX, United States of America
| | - Carlos H. Villa
- Office of Blood Research and Review, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Kendall P. Hammonds
- Baylor Scott & White Research Institute, Dallas, TX, United States of America
| | - Yun Lu
- Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Jason Ettlinger
- Baylor Scott & White Research Institute, Dallas, TX, United States of America
| | - Elisa L. Priest
- Baylor Scott & White Research Institute, Dallas, TX, United States of America
| | - Robert L. Gottlieb
- Baylor Scott & White Research Institute, Dallas, TX, United States of America
- Baylor University Medical Center, Dallas, Texas, United States of America
- Baylor Heart and Vascular Hospital, Dallas, Texas, United States of America
- Baylor Scott and White The Heart Hospital, Plano, Texas, United States of America
- Texas A&M Health Science Center, Dallas, Texas, United States of America
- TCU and University of North Texas Health Science Center, Fort Worth, Texas, United States of America
| | - Steven Davis
- Baylor Scott & White Medical Center–Irving, Irving, Texas, United States of America
| | - Edward Mays
- Baylor University Medical Center, Dallas, Texas, United States of America
| | - Tainya C. Clarke
- Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Azadeh Shoaibi
- Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Hui-Lee Wong
- Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Steven A. Anderson
- Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Ronan J. Kelly
- Baylor University Medical Center, Dallas, Texas, United States of America
- Charles A. Sammons Cancer Center Baylor University Medical Center, Dallas, Texas, United States of America
- * E-mail:
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9
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Qian Z, Zhang Z, Ma H, Shao S, Kang H, Tong Z. The efficiency of convalescent plasma in COVID-19 patients: A systematic review and meta-analysis of randomized controlled clinical trials. Front Immunol 2022; 13:964398. [PMID: 35967398 PMCID: PMC9366612 DOI: 10.3389/fimmu.2022.964398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 06/29/2022] [Indexed: 11/13/2022] Open
Abstract
The objective of this study was to assess whether convalescent plasma therapy could offer survival advantages for patients with novel coronavirus disease 2019 (COVID-19). An electronic search of Pubmed, Web of Science, Embase, Cochrane library and MedRxiv was performed from January 1st, 2020 to April 1st, 2022. We included studies containing patients with COVID-19 and treated with CCP. Data were independently extracted by two reviewers and synthesized with a random-effect analysis model. The primary outcome was 28-d mortality. Secondary outcomes included length of hospital stay, ventilation-free days, 14-d mortality, improvements of symptoms, progression of diseases and requirements of mechanical ventilation. Safety outcomes included the incidence of all adverse events (AEs) and serious adverse events (SAEs). The Cochrane risk-of-bias assessment tool 2.0 was used to assess the potential risk of bias in eligible studies. The heterogeneity of results was assessed by I^2 test and Q statistic test. The possibility of publication bias was assessed by conducting Begg and Egger test. GRADE (Grading of Recommendations Assessment, Development and Evaluation) method were used for quality of evidence. This study had been registered on PROSPERO, CRD42021273608. 32 RCTs comprising 21478 patients with Covid-19 were included. Compared to the control group, COVID-19 patients receiving CCP were not associated with significantly reduced 28-d mortality (CCP 20.0% vs control 20.8%; risk ratio 0.94; 95% CI 0.87-1.02; p = 0.16; I² = 8%). For all secondary outcomes, there were no significant differences between CCP group and control group. The incidence of AEs (26.9% vs 19.4%,; risk ratio 1.14; 95% CI 0.99-01.31; p = 0.06; I² = 38%) and SAEs (16.3% vs 13.5%; risk ratio 1.03; 95% CI 0.87-1.20; p = 0.76; I² = 42%) tended to be higher in the CCP group compared to the control group, while the differences did not reach statistical significance. In all, CCP therapy was not related to significantly improved 28-d mortality or symptoms recovery, and should not be viewed as a routine treatment for COVID-19 patients. Trial registration number CRD42021273608. Registration on February 28, 2022. Systematic review registration https://www.crd.york.ac.uk/prospero/, Identifier CRD42022313265.
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Affiliation(s)
- Zhenbei Qian
- Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Zhijin Zhang
- Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Haomiao Ma
- Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Shuai Shao
- Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Hanyujie Kang
- Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Zhaohui Tong
- Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
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10
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Belov A, Huang Y, Villa CH, Whitaker BI, Forshee R, Anderson SA, Eder A, Verdun N, Joyner MJ, Wright SR, Carter RE, Hung DT, Homer M, Hoffman C, Lauer M, Marks P. Early administration of COVID-19 convalescent plasma with high titer antibody content by live viral neutralization assay is associated with modest clinical efficacy. Am J Hematol 2022; 97:770-779. [PMID: 35303377 PMCID: PMC9082011 DOI: 10.1002/ajh.26531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 03/09/2022] [Accepted: 03/11/2022] [Indexed: 11/21/2022]
Abstract
The efficacy of COVID‐19 convalescent plasma (CCP) as a treatment for hospitalized patients with COVID‐19 remains somewhat controversial; however, many studies have not evaluated CCP documented to have high neutralizing antibody titer by a highly accurate assay. To evaluate the correlation of the administration of CCP with titer determined by a live viral neutralization assay with 7‐ and 28‐day death rates during hospitalization, a total of 23 118 patients receiving a single unit of CCP were stratified into two groups: those receiving high titer CCP (>250 50% inhibitory dilution, ID50; n = 13 636) or low titer CCP (≤250 ID50; n = 9482). Multivariable Cox regression was performed to assess risk factors. Non‐intubated patients who were transfused with high titer CCP showed 1.1% and 1.7% absolute reductions in overall 7‐ and 28‐day death rates, respectively, compared to those non‐intubated patients receiving low titer CCP. No benefit of CCP was observed in intubated patients. The relative benefit of high titer CCP was confirmed in multivariable Cox regression. Administration of CCP with high titer antibody content determined by live viral neutralization assay to non‐intubated patients is associated with modest clinical efficacy. Although shown to be only of modest clinical benefit, CCP may play a role in the future should viral variants develop that are not neutralized by other available therapeutics.
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Affiliation(s)
- Artur Belov
- Center for Biologics Evaluation and Research US FDA Silver Spring Maryland USA
| | - Yin Huang
- Center for Biologics Evaluation and Research US FDA Silver Spring Maryland USA
| | - Carlos H. Villa
- Center for Biologics Evaluation and Research US FDA Silver Spring Maryland USA
| | - Barbee I. Whitaker
- Center for Biologics Evaluation and Research US FDA Silver Spring Maryland USA
| | - Richard Forshee
- Center for Biologics Evaluation and Research US FDA Silver Spring Maryland USA
| | - Steven A. Anderson
- Center for Biologics Evaluation and Research US FDA Silver Spring Maryland USA
| | - Anne Eder
- Center for Biologics Evaluation and Research US FDA Silver Spring Maryland USA
| | - Nicole Verdun
- Center for Biologics Evaluation and Research US FDA Silver Spring Maryland USA
| | - Michael J. Joyner
- Department of Anesthesiology and Perioperative Medicine Mayo Clinic Rochester Minnesota USA
| | - Scott R. Wright
- Department of Cardiology and the Human Research Protection Program Mayo Clinic Rochester Minnesota USA
| | - Rickey E. Carter
- Department of Quantitative Health Sciences Mayo Clinic Jacksonville Florida USA
| | - Deborah T. Hung
- Infectious Disease and Microbiome Program Broad Institute Cambridge Massachusetts USA
| | - Mary Homer
- Biomedical Advanced Research and Development Authority (BARDA) District of Columbia Washington USA
| | - Corey Hoffman
- Biomedical Advanced Research and Development Authority (BARDA) District of Columbia Washington USA
| | - Michael Lauer
- Office of the Director National Institutes of Health Bethesda Maryland USA
| | - Peter Marks
- Center for Biologics Evaluation and Research US FDA Silver Spring Maryland USA
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11
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Chuang YC, Tsai HW, Liu SA, Wu MJ, Liu PY. COVID-19 in Veterans: A Narrative Review. Risk Manag Healthc Policy 2022; 15:805-815. [PMID: 35502442 PMCID: PMC9056054 DOI: 10.2147/rmhp.s354814] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 04/09/2022] [Indexed: 01/08/2023] Open
Affiliation(s)
- Yu-Chuan Chuang
- Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Hung-Wen Tsai
- Medical Administration Department, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Shih-An Liu
- Center of Quality Management, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Ming-Ju Wu
- Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Po-Yu Liu
- Division of Infection, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- Correspondence: Po-Yu Liu, Division of Infection, Department of Internal Medicine, Taichung Veterans General Hospital, No. 1650, Sec. 4, Taiwan Blvd., Xitun Dist., Taichung City, 407219, Taiwan, Tel +886 4 2359 2525, Email
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12
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Kloypan C, Saesong M, Sangsuemoon J, Chantharit P, Mongkhon P. CONVALESCENT plasma for COVID-19: A meta-analysis of clinical trials and real-world evidence. Eur J Clin Invest 2021; 51:e13663. [PMID: 34375445 PMCID: PMC8420367 DOI: 10.1111/eci.13663] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/05/2021] [Accepted: 08/07/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND There is still a lack of consensus on the efficacy of convalescent plasma (CP) treatment in COVID-19 patients. We performed a systematic review and meta-analysis to investigate the efficacy of CP vs standard treatment/non-CP on clinical outcomes in COVID-19 patients. METHODS Cochrane Library, PubMed, EMBASE and ClinicalTrials.gov were searched from December 2019 to 16 July 2021, for data from clinical trials and observational studies. The primary outcome was all-cause mortality. Risk estimates were pooled using a random-effect model. Risk of bias was assessed by Cochrane Risk of Bias tool for clinical trials and Newcastle-Ottawa Scale for observational studies. RESULTS In total, 18 peer-reviewed clinical trials, 3 preprints and 26 observational studies met the inclusion criteria. In the meta-analysis of 18 peer-reviewed trials, CP use had a 31% reduced risk of all-cause mortality compared with standard treatment use (pooled risk ratio [RR] = 0.69, 95% confidence interval [CI]: 0.56-0.86, P = .001, I2 = 50.1%). Based on severity and region, CP treatment significantly reduced risk of all-cause mortality in patients with severe and critical disease and studies conducted in Asia, pooled RR = 0.61, 95% CI: 0.47-0.81, P = .001, I2 = 0.0%; pooled RR = 0.67, 95% CI: 0.49-0.92, P = .013, I2 = 0.0%; and pooled RR = 0.62, 95% CI: 0.48-0.80, P < .001, I2 = 20.3%, respectively. The meta-analysis of observational studies showed the similar results to the clinical trials. CONCLUSIONS Convalescent plasma use was associated with reduced risk of all-cause mortality in severe or critical COVID-19 patients. However, the findings were limited with a moderate degree of heterogeneity. Further studies with well-designed and larger sample size are needed.
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Affiliation(s)
- Chiraphat Kloypan
- Division of Clinical Immunology and Transfusion Science, Department of Medical Technology, School of Allied Health Sciences, University of Phayao, Phayao, Thailand.,Unit of Excellence in Integrative Molecular Biomedicine, School of Allied Health Sciences, University of Phayao, Phayao, Thailand.,Institute of Transfusion Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Matthanaporn Saesong
- Division of Clinical Immunology and Transfusion Science, Department of Medical Technology, School of Allied Health Sciences, University of Phayao, Phayao, Thailand
| | - Juthamat Sangsuemoon
- Division of Clinical Immunology and Transfusion Science, Department of Medical Technology, School of Allied Health Sciences, University of Phayao, Phayao, Thailand
| | - Prawat Chantharit
- Division of Infectious Diseases, Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Pajaree Mongkhon
- Division of Pharmacy Practice, Department of Pharmaceutical Care, Unit of Excellence on Research in Health Outcomes and Patient Safety in Elderly, School of Pharmaceutical Sciences, University of Phayao, Phayao, Thailand.,Pharmacoepidemiology and Statistics Research Center, Faculty of Pharmacy, Chiang Mai University, Chiang Mai, Thailand
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