F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.36396.1 Research Article Articles Association between convalescent plasma and the risk of mortality among patients with COVID-19: a meta-analysis

[version 1; peer review: 1 not approved]

 Wardhani Shinta Oktya Conceptualization Investigation Supervision Validation Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0001-8889-0930 1 Fajar Jonny Karunia Conceptualization Data Curation Formal Analysis Investigation Methodology Software Supervision Validation Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-0309-5813 a 2 Wulandari Laksmi Conceptualization Investigation Supervision Writing – Original Draft Preparation Writing – Review & Editing 3 Soegiarto Gatot Conceptualization Formal Analysis Investigation Supervision Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-9197-3873 4 Purnamasari Yeni Methodology Software https://orcid.org/0000-0001-5830-3571 5 Asmiragani Anisa Investigation Methodology Software https://orcid.org/0000-0002-5707-6060 5 Maliga Helnida Anggun Investigation Methodology Software https://orcid.org/0000-0002-3702-6306 5 Ilmawan Muhammad Investigation Methodology Software https://orcid.org/0000-0002-9459-609X 5 Seran Gloriana Data Curation Investigation Project Administration Resources Visualization 6 Iskandar Dheka Sapti Data Curation Project Administration Resources Visualization 7 Ndapa Conchita Emiliana Data Curation Project Administration Resources Visualization 8 Hamat Viviana Data Curation Investigation Resources Visualization 8 Wahyuni Rafika Ajeng Investigation Project Administration Resources Visualization 5 Cyntia Linda Oktaviana Suci Data Curation Investigation Project Administration Resources Visualization 5 Maarang Feronika Maryanti Data Curation Investigation Project Administration Resources 9 Beo Yosef Andrian Data Curation Project Administration Resources Visualization 9 Adar Olivera Agnes Data Curation Project Administration Resources Visualization 8 Rakhmadhan Iraky Mardya Data Curation Project Administration Resources Visualization 5 Shantikaratri Emilia Tiara Data Curation Project Administration Resources Visualization 5 Putri Ayu Sekarani Damana Data Curation Project Administration Resources Visualization 5 Wahdini Rizqa Data Curation Investigation Project Administration Resources 5 Broto Endang Pati Conceptualization Project Administration Resources 10 Suwanto Agnes Wanda Conceptualization Resources Visualization https://orcid.org/0000-0002-1095-5789 11 Tamara Fredo Data Curation Project Administration Resources Visualization https://orcid.org/0000-0002-2534-9108 2 Mahendra Aditya Indra Data Curation Project Administration Resources https://orcid.org/0000-0003-0983-993X 2 Winoto Eden Suryoiman Data Curation Methodology Resources Visualization https://orcid.org/0000-0001-8427-5017 2 Krisna Pratista Adi Data Curation Project Administration Visualization 2 Harapan Harapan Supervision Validation Writing – Review & Editing https://orcid.org/0000-0001-7630-8413 12 Division of Hematology and Oncology, Department of Internal Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, 65145, Indonesia Brawijaya Internal Medicine Research Center, Department of Internal Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, 65145, Indonesia Department of Pulmonology and Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, 60286, Indonesia Division of Allergy & Immunology, Department of Internal Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, 60286, Indonesia Faculty of Medicine, Universitas Brawijaya, Malang, 65145, Indonesia Faculty of Medicine, Universitas Indonesia, Jakarta, 10430, Indonesia Department of Biomedical Sciences, Faculty of Medicine, Universitas Brawijaya, Malang, 65145, Indonesia Department of Midwifery, Faculty of Medicine, Universitas Brawijaya, Malang, 65145, Indonesia Department of Nursing, Faculty of Medicine, Universitas Brawijaya, Malang, 65145, Indonesia Department of Neurosurgery, Faculty of Medicine, Universitas Airlangga, Surabaya, 60286, Indonesia Department of Radiology, Faculty of Medicine, Universitas Brawijaya, Malang, 65145, Indonesia Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia gembyok@gmail.com

No competing interests were disclosed.

 3 2 2021 2021 10 64 28 1 2021 Copyright: © 2021 Wardhani SO et al. 2021 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: Convalescent plasma (CCP) has been used for treating some infectious diseases; however, the efficacy of CCP in coronavirus disease 2019 (COVID-19) remains controversial. The aim of this research was to assess the efficacy of CCP as an adjunctive treatment in COVID-19 patients.

Methods: Four bibliographic databases and a preprint database were searched for potentially relevant articles. Mortality rates between patients treated with standard treatment and standard treatment with CCP were compared using a Z test.

Results: A total of 1,937 patients treated with CCP and 3,405 patients without CCP retrieved from 12 studies were included. The risk of mortality was 1.92-fold higher in patients without CCP compared to patients treated with CCP (OR: 1.92; 95%CI: 1.33, 2.77; p=0.0005). In severe COVID-19 sub-group analysis, we found that patients without the CCP had a 1.32 times higher risk of mortality than those treated with the CCP (OR: 1.32; 95%CI: 1.09, 1.60; p=0.0040).

Conclusions: CCP, as adjunctive therapy, reduces the mortality rate among COVID-19 patients.

 convalescent plasma passive immunization COVID-19 mortality outcomes The author(s) declared that no grants were involved in supporting this work. Introduction

The management of coronavirus disease 2019 (COVID-19) remains challenging. While the guideline for the management of COVID-19 has been established, 1- 3 the mortality rate of COVID-19 remains increased over the periods. 4, 5 The guideline suggests that several treatments, including antiviral, hydroxychloroquine, steroid, anticoagulation, and other supportive treatments, should be used to treat patients with COVID-19. 1- 3 However, recent evidence from large scale studies failed to clarify the efficacy of those suggested treatments. 6- 8 Moreover, the findings from the World Health Organization (WHO) solidarity trials also failed to clarify the benefits of hydroxychloroquine, remdesivir, interferon regimens, and lopinavir in the management of COVID-19. 8 Therefore, new approaches to COVID-19 management are required.

Convalescent plasma (CCP), an immunological therapy, is suggested to have promising efficacy for managing several infectious diseases. 9 CCP, a strategy of passive immunization, was first introduced by von Behring and Kitasato in 1890. Initially, it was used to manage diphtheria and other infectious diseases such as scarlet fever and pertussis. 10 Moreover, due to its good efficacy, this therapy was also used for the management of Ebola, severe acute respiratory syndrome (SARS), and Middle East respiratory syndrome (MERS). 11 In patients with MERS, SARS, and Ebola, the clinical improvement and reduced mortality rate were observed in patients receiving CCP than patients without CPP. 12 However, the efficacy of CCP against COVID-19 is conflicting. Furthermore, previous meta-analyses resulted in inconclusive findings due to the lack of structured methodology. Therefore, a holistic meta-analysis is needed to provide insight into the clinical efficacy of CCP for the management of COVID-19.

 Methods Study design

A systematic review and meta-analysis covering the period July 2020 - December 2020 was conducted to assess the efficacy of CCP as an adjunctive treatment in COVID-19 patients. Studies from prominent bibliographic databases were searched, and the protocols followed the checklist from Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA). 13

Eligibility criteria

Relevant articles were assessed for inclusion and exclusion criteria before the final analysis. Our analysis included articles with the following criteria: (1) observational or randomized controlled trial studies; (2) providing sufficient data of COVID-19 diagnosis methods; and (3) well-identified methodologies represented with Newcastle-Ottawa Scale (NOS). Case reports, case series, letters to the editor, reviews, commentaries, low method quality, and those with pre-post test comparison were excluded.

 Search strategy and data extraction

Relevant studies in four bibliographic databases (Embase, PubMed, Web of Science, and Cochrane) and a preprint database MedRix were searched as of 2 December 2020. The searches limited to English only using Medical Subjects Heading: (“COVID-19” OR “SARS-CoV-2”) AND (“convalescent plasma” OR “serotherapy” OR “hyperimmune globulin therapy” OR “convalescent plasma treatment”). A reference list of the relevant articles was also retrieved for additional references. Information of: (1) name of the first author; (2) year of publication; (3) country of origin; (4) sample size of cases and controls, (5) CCP administration, and (6) mortality rate were collected from each article.

 Assessment of the methodology quality

The quality of potential papers was assessed using the Newcastle-Ottawa Scale (NOS). 14 This scoring system evaluates the sample selection, group comparison, and the outcome. The quality of the articles could be classified as low, moderate, and high quality. Articles with low quality were excluded from our analysis. The assessment was carried out by three independent investigators (MI, AAA & YP), and when there was a discrepancy among the investigators, a discussion was performed with a senior researcher (JKF).

 Outcome measure

The primary outcome measure was all causes of mortality among COVID-19 patients treated with and without CCP. The predictor variable was COVID-19 patients treated with CCP. A sub-group analysis was conducted based on the severity of COVID-19 patients treated with CCP (e.i. mild and severe).

 Statistical analysis

The association between CCP and the reduction of the risk of mortality among COVID-19 patients was assessed using a Z test. Before assessing the association, the potency of bias and heterogeneity was assessed. To assess the risk of bias, an Egger test was employed to calculate tau-squared, and a p-value of less than 0.05 indicates that the potency of bias was found. A Q test was used to assess the heterogeneity among the included papers. The p-value of less than 0.10 was considered that heterogeneity across the studies was found, and the correlation was therefore determined using a random-effect model; otherwise, a fixed-effect model was employed. All analyses were carried out using Review Manager (Revman Cochrane, London, UK) version 5.3, and the cumulative calculation was presented using a forest plot.

 Results Studies selection

A total of 1,143 papers were identified, and 1,105 papers were excluded because they had irrelevant topics. A total of 38 papers were included for review in full-text, and 26 additional papers were excluded because of review, pre-post test model, commentary, and low-quality papers. In the final process, 12 papers were included in our analysis. 15- 26 The article selection flowchart is depicted in Figure 1, and the study characteristics are presented in Table 1.

A flowchart of study selection in our meta-analysis. CCP efficacy against COVID-19

A total of 1,937 patients treated with CCP and 3,405 patients without CCP, collected from 12 papers, were included in our analysis. Data suggest that COVID-19 patients without the CCP had a 1.92-fold higher risk of mortality than patients treated with the CCP (OR: 1.92; 95%CI: 1.33, 2.77; p=0.0005) ( Figure 2A). A sub-group analysis among severe COVID-19 patients who were treated with CCP was conducted. This sub-group consisted of nine papers with 1,458 patients treated with CCP and 2,706 patients without CCP. The data revealed a 1.32-fold higher risk of mortality in COVID-19 patients without CCP compared to patients treated with CCP (OR: 1.32; 95%CI: 1.09, 1.60; p=0.0040) ( Figure 2B).

Forest plot of the association between convalescent plasma and the risk of mortality.

A). All cases (OR: 1.92; 95%CI: 1.33, 2.77; p = 0.0005; p Egger: 0.3620; p Heterogeneity: 0.0600; I-squared: 43.00%). B). Severe COVID-19 (OR: 1.32; 95%CI: 1.09, 1.60; p = 0.0040; p Egger: 0.3790; p Heterogeneity: 0.1200; I-squared: 37.00%).

 Baseline characteristics of articles included in our meta-analysis.
Name Country City Hospital Sample size CCP volume Recipient NOS
CCP Control
Abolghassemi et al 2020 Iran Mixed Multicenter 115 74 500 mL Mild and severe cases 9
Altuntas et al 2020 Turkey Mixed Multicenter 888 888 200 - 600 mL Severe cases 8
Chen et al 2020 China Hangzhou Zhejiang University 19 10 200-500 mL Severe cases 7
Gharbharan et al 2020 Netherlands Mixed Multicenter 43 43 300 mL Mild and severe cases 8
Hegerova et al 2020 USA Washington Multicenter 20 20 200 mL Severe cases 9
Li et al 2020 China Wuhan Multicenter 52 51 100 mL Severe cases 8
Rasheed et al 2020 Iraq Bagdad Multicenter 21 28 400 mL Severe cases 8
Salazar et al 2020 (a) US Mixed Multicenter 321 582 NA Mild and severe cases 8
Salazar et al 2020 (b) US Mixed Multicenter 85 158 NA Severe cases 9
Xia et al 2020 China Wuhan Wuhan Huoshenshan Hospital 138 1430 200-1200 mL Severe cases 8
Zeng et al 2020 China Hangzhou Multicenter 6 15 300 mL Severe cases 9

Note: CCP, convalescent plasma; NOS, Newcastle-ottawa scale.

 Heterogeneity and potency of bias across the studies

The analysis revealed evidence of heterogeneity in total case of COVID-19. Therefore, a random-effect model was applied to assess the association. In the severe COVID-19 sub-group, we found no heterogeneity, and we used a fixed-effect model to evaluate the correlation. Our analysis using an Egger test found no publication bias in both the total and the severe COVID-19 sub-group.

 Discussion

Our data suggest that CCP treatment associated with a reduction of mortality both in all cases and severe COVID-19 patients. Our current findings are consistent with the results of previous meta-analyses. 27- 32 The theory underlying the mechanism of CCP in COVID-19 patients remains open to controversy. Briefly, plasma transfer is the potential aspect that bridges the CCP and the reduced risk of mortality in COVID-19 patients. Plasma consists of various immunity components, including antibodies, anti-inflammatory cytokines, clotting and or anti-clotting factors, albumin, and protein C and S. 33, 34 It is believed that CCP in COVID-19 may modulate the immune response through antiviral effects and has immunomodulatory effects. 35 Antiviral effects of CCP may occur through neutralizing antibodies, and it was reported that IgG of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and IgM SARS-CoV-2 were the primary isotype antibodies identified from COVID-19 patients treated with CCP. 36 This humoral immune response may inhibit protein S of SARS-CoV-2. 37 Thereafter, they may exert the protective effects against COVID-19. The immunomodulatory effects of CCP may occur through the neutralization of cytokines and complements. 35, 38 These effects may inhibit the overactive immune system, including cytokine storm, complement activation, and hypercoagulable state regulation. 39 These mechanisms may be responsible for causing clinical improvement of COVID-19 patients. Of them, it was considered that immunoglobulin transfer is the essential factor in modulating the protective effect of CCP. 40 In SARS and influenza, it was reported that immunoglobulin transfer plays a vital role in governing clinical improvement. 9, 11 Moreover, in MERS, the CCP administration with the titers of antibodies 1:80 provided a significant immune response, and the titers of antibodies 1:40 did not provide a similar response. 41 Additionally, in Ebola, MERS, and SARS, the antibodies from the CCP may bind to the CD4 binding site on the viral envelope, and therefore may reduce the viral load and the risk of infection of the new cells. 42 It was also supported by previous studies that antibody titers from CCP donors also governed the clinical improvement of COVID-19 patients treated with CCP, 43, 44 suggesting that antibody transfer might influence the outcomes of clinical improvement.

Six meta-analyses assessing the role of CCP in COVID-19 have been reported ( Table 2). 27- 32 However, they had some significant limitations: (a) they involved a smaller sample size. In our current study, we had a relatively larger sample size; (b) some studies did not perform meta-analysis calculations to synthesize the data 27, 29 ; (c) previous studies included several case reports and case series 28, 29 in which should be excluded in the meta-analysis 13 ; (d) previous meta-analyses assessed the role of CCP in similar infectious diseases (SARS and influenza), and the results were implemented to the case of COVID-19 30, 31 ; and (e) previous meta-analyses performed a mixed calculation where the data of the case vs. control model were combined with the data of pre-post intervention models, which might provide a high risk of bias due to the final effect that might be caused by other interventions. 29, 32 In the present meta-analysis, we only calculated the model of the case (standard treatment and CCP) vs. control (standard treatment only) and therefore might provide a better correlation.

Previous meta-analyses and some potential limitations.
Author & year Number of studies Sample size Potential limitations
Bakhtawar et al 2020 10 156

No calculation of data synthesis

Seven case report or case series articles were included

One study comparing the outcome between pre and post convalescent plasma.
Devasenapathy et al 2020 6 431

The case is non COVID-19
Rabelo-da-Ponte et al 2020 5 75

Three case report or case series articles were included

The comparison was pre and post convalescent plasma.
Rajendran et al 2020 5 NA

No calculation of data synthesis
Sarkar et al 2020 7 5444

One study comparing the outcome between pre and post convalescent plasma, other studies assessing between convalescent plasma and control (Mixed calculation).

Inappropriate calculation.
Sun et al 2020 15 1879

The case is non COVID-19

Note: NA, Not available; CCP, convalescent plasma.

In the present study, we emphasized that CCP provided good efficacy to reduce the risk of mortality among COVID-19 patients. Our findings might contribute to better management of COVID-19 patients, particularly to prevent the risk of mortality. It is expected that a medical council should elaborate on the standard procedures of CCP, including the dosage, donor criteria, side effects management, and post-intervention management. Since early administration of CCP provided better clinical outcomes than those with later intervention, 45 the appropriate time of CCP administration should be determined, and further studies are warranted.

Several important limitations of this study should be discussed. Some confounding factors that might govern the final outcomes were not controlled, including the immunological status, the dosage of CCP, time of intervention, donor criteria, the titers of antibodies, comorbidities, and transmission area. The majority of the included papers were retrospective studies, and therefore a further meta-analysis of randomized-controlled trials with a bigger sample size might provide a better conclusion.

 Conclusion

Administration of the CCP is associated with a lower risk of mortality among COVID-19 patients compared to those without CCP, and this highlights its potency to be used for the treatment of COVID-19. However, studies are warranted to formulate the dosage, time of intervention, donor criteria, and the titers of antibodies to optimize the effects.

 Data availability Underlying data

All data underlying the results are available as part of the article and no additional source data are required.

 Reporting guidelines

Figshare: PRISMA checklist for ‘Association between convalescent plasma and the risk of mortality among patients with COVID-19: A meta-analysis’, https://doi.org/10.6084/m9.figshare.13490541.v1. 46

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

 Acknowledgements

We thank to Lembaga Pengelola Dana Pendidikan (LPDP) Republik Indonesia for supporting this project.

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Competing interests: No competing interests were disclosed.

 15 2 2021 Copyright: © 2021 Arab-Zozani M 2021 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation reject

This meta-analysis aimed to investigate the association between convalescent plasma and the risk of mortality among patients with COVID-19. This is a great area of research but, in my opinion, the manuscript needs some major revisions as follows:

Please indicate the name of the searched databases in the abstract section.

Please indicate the quality appraisal checklist in the abstract section.

Please indicate the method for investigating the heterogeneity and publication bias in the abstract section. 

Please indicate the type of the included studies in the abstract, results, and table 1.

What is your reason for selecting this period for your search?

Search strategy is not complete. 

Please restructure the method section following the PRISMA item as you claim.

There are some problems regarding figure 1. Was there no duplicate record? It does not make much sense. 

It needs to mention the type of the included studies and then we can speak about the quality appraisal checklist. it seems that NOS is not sufficient. NOS is for nonrandomized studies.

Please indicate inter-rater reliability between three raters. 

Result section, please add a subheading for "study characteristics" based on PRISMA and first write a brief and then refer to table 1. Also, add the type of the control in column control. 

Figure 1 has some problems. Your study is a meta-analysis. How were 11 studies included in qualitative synthesis? Which qualitative synthesis?

There is a 6 % difference between I2 for A and B, what is your rationale for selecting the fixed or random-effect model? Please provide a reference for your claim. Please add the details in the method section.

Please add the funnel plot as a supplement.

Please remove table 2 from the discussion and also discuss the added value of your study regarding the existing meta-analysis. What is the novelty of your work?

The conclusion is very optimistic. How did you come to that conclusion based solely on mortality?

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

Partly

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Partly

Are the conclusions drawn adequately supported by the results?

No

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

Reviewer Expertise:

Health-related issues and systematic review and meta-analysis methodology.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.