Immunogenicity of COVID-19 mRNA Vaccines in Immunocompromised Patients: A Systematic Review and Meta-Analysis

Immunogenicity of COVID-19 mRNA Vaccines in Immunocompromised Patients: A Systematic Review and Meta-Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Immunogenicity of COVID-19 mRNA Vaccines in Immunocompromised Patients: A Systematic Review and Meta-Analysis Mohammad-Mehdi Mehrabinejad, Fatemeh Moosaie, Hojat Dehghanbanadaki, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-994503/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Immunocompromised (IC) patients are at higher risk of severe SARS-CoV-2 infection, morbidity, and mortality compared to general population. They should be prioritized for primary prevention through vaccination. In this study, we aimed to evaluate the efficacy of COVID-19 mRNA vaccines in IC patients through a systematic review and meta-analysis approach. Method PubMed-MEDLINE, Scopus, and Web of Science were searched for original articles reporting the immunogenicity of two doses of mRNA COVID-19 vaccines in adult patients with IC condition between June 1, 2020 and September 1, 2021. Meta-analysis was performed using either random or fixed effect according to the heterogeneity of the studies. Subgroup analysis was performed to identify potential sources of heterogeneity. Results A total of 26 studies on 3207 IC patients and 1726 healthy individuals were included. The risk of seroconversion in IC patients was 48% lower than those in controls (RR= 0.52 [0.42, 0.65]). IC patients with autoimmune condition were 54% and patients with malignancy were 42% more likely to have positive seroconversion compared to those with transplant ( P <0.01). Subgroup meta-analysis based on type of malignancy, revealed significantly higher proportion of positive seroconversion in solid organ compared to hematologic malignancies (RR= 0.88 [0.85, 0.92] vs. 0.61 [0.44, 0.86], P = 0.03). Subgroup meta-analysis based on type of transplantation (kidney vs. others), showed no statically significant between group difference of seroconversion ( P = 0.55). Conclusions IC patients, especially transplant patients, developed lower immunogenicity with two-dose of COVID-19 mRNA vaccines. Among patients with IC, those with autoimmune condition and solid organ malignancies are mostly benefited from COVID-19 vaccination. Findings from this meta-analysis, could aid health care policy makers upon making decision regarding the importance of the booster dose or more strict personal protections in the IC patients. Pulmonology Immunology Medical Genetics COVID-19 SARS-CoV-2 Vaccination Immunocompromised patient Malignancy Transplantation Autoimmune Efficacy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Immunocompromised (IC) conditions are estimated to affect approximately 2.7% of United States adults (1). Such patients are at higher risk of severe SARS-CoV-2 infection, extended hospitalization, intensive care admission, and mortality compared to general population (2-6). Besides, prolonged viral shedding and potential sources of novel SARS-CoV-2 variants in this population are also of particular importance (7-9). Thus, IC patients should be prioritized for primary prevention through Coronavrus infectious disease 2019 (COVID-19) vaccination. Global efforts have been taken to develop SARS-CoV-2 vaccines since the initiation of the current COVID-19 pandemic. The mRNA vaccines (i.e., mRNA-1273 and BNT162b2) are the most commonly approved vaccines worldwide which are utilized in different clinical trials in global scale (10). The overall efficacy and safety of COVID-19 vaccines in phase III trials were promising (11), sparking global hope toward ending the current outbreak. However, the application of COVID-19 vaccines in patients with impaired immune system remains as an ongoing subject of debate as they were excluded from the original trials (12, 13). IC patients due to either the primary disease or the immunosuppressive treatments are more likely to show weak or suboptimal immune response to COVID-19 vaccines given previous studies on influenza vaccines (14). Hence, the real-world statistics regarding the efficacy of COVID-19 vaccines are required to provide physicians a better insight towards decision-making in this group of high-risk patients. In this study, we aimed to systematically review the literature and analyze the pooled effectiveness of COVID-19 vaccination in IC patients compared to healthy controls using meta-analysis. We also assessed the efficacy of mRNA vaccines in IC patients based on their etiological factor including malignancy, transplantation, and autoimmune diseases. Methods And Materials Protocol and Literature search This systematic review and meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed-MEDLINE, Scopus, and Web of Science were searched for original articles reporting the efficacy in adult patients with IC condition between June 1, 2020 and September 1, 2021. The search terms were as follows: ((COVID-19) OR (SARS-CoV-2) OR (novel coronavirus)) AND ((vaccine) OR (vaccination)) OR (vaccinated)) AND ((immunocompromised) OR (immunosuppressed) OR (corticosteroid) OR (chemotherapy) OR (cancer) OR (malignancy) OR (rheumatologic disease) OR (immunodeficiency) OR (autoimmune) OR (AIDS) OR (HIV) OR (transplant)). The references of the selected articles were further screened to search for potentially relevant articles. Two reviewers independently performed the literature search, and any disagreement regarding study inclusion was resolved by consensus. The authors were not blinded to the authors, institutions, or journals while selecting studies or extracting data. EndNote version X9 was used for literature management. Eligibility criteria Studies investigating the immunogenicity of COVID-19 mRNA vaccination in IC patients were eligible for inclusion. The included studies met the following criteria: (1) Population : studies on IC patients with a sample size ≥ 30 participants and control group of healthy individuals. IC patients included patients with solid organ or hematologic malignancies who receive chemotherapy, patients with inherited or acquired immunodeficiency diseases, patients with autoimmune or rheumatologic diseases, patients with other conditions (i.e., asthma) receiving long-term corticosteroid, and transplant recipients. (2) Intervention : mRNA COVID-19 vaccination (3) Study design : all retrospective and prospective studies as well as clinical trials with healthy control group were included. (4) Outcomes : main outcome of this study was seroconversion in IC patients using anti-SARS-CoV-2 spike IgG ≥ 14 days after the second dose of COVID-19 mRNA vaccines. The subgroup analysis was performed to determine the efficacy of COVID-19 mRNA vaccines in different groups of patients based on etiology of IC condition. The exclusion criteria were as follows: (1) reviews and editorials; (2) case reports or case series <30 patients; (3) partially overlapping patient cohorts; (4) articles not written in English; (5) single-arm studies or with non-healthy control group; and (6) non-human studies. Two reviewers independently reviewed the literature in consensus. Data Collection Eligible studies were evaluated by two experts independently and the following data was extracted from each included publication: author, date of publications, country of origin, study design, study sample size, definition of IC conditions, inclusion and exclusion criteria, the number of IC patients, variables matched, the proportion of male, mean age, duration of disease, type and etiology of the immunodeficiency and its proportion to the total population, type of vaccination, and efficacy of the vaccination. Any conflicts in data extraction were discussed or consulted by a third expert and resolved. Quality assessment National Institutes of Health (NIH) quality assessment tool (15) to evaluate the included studies. The scores of 11–14, 6–10, and 0–5 were considered as good, fair, and poor quality, respectively. Moreover, the studies were evaluated in terms of methodology by two experts, independently; any conflict of opinion was discussed or referred to third expert and resolved. Statistical analyses STATA version 16 for Windows (Stata Corp, College Station, Texas) was utilized for the meta-analysis. At least three studies in each group were required to synthesize the data on outcomes. The heterogeneity of studies was measured using I² or Q test. A fixed model was employed, if the heterogeneity of studies was below 40% and a random effect model in case of heterogeneity above 40%. Effect measures were calculated. Also, based on the heterogeneity of studies, either meta-regression analysis or subgroup analysis was performed for potential moderators. Moreover, funnel plot asymmetry and the Eggers test were used to assess publication bias. In case of significant publication bias, the adjustment was performed for the effect size using the trim and fill method. A P -value less than 0.05 was considered statistically significant. Results Study selection The study selection flowchart is presented in Figure 1 . The literature search, after removing duplicates, resulted in 2093 studies, of which 1992 were considered irrelevant following title and abstract screening. Of the remaining 101, a further 75 were removed according to the exclusion criteria. Therefore, in total, 26 studies (16-41) were eligible for the meta-analysis of seroconversion after the second dose of the vaccine. Characteristics of included studies Characteristics of the included studies are provided in Table 1 . All 26 included studies on 3207 IC patients and 1726 healthy controls showed that 65.8 % IC patients and 99.2 % healthy controls had seropositive IgG test following second dose of COVID-19 mRNA vaccines. All of the studies were conducted in 2021. Sample sizes, from which relevant data were available for extraction, varied from 40 to 807. Participants’ mean age ranged from 42 to 71.4 years. The majority of the studies (18, 20, 23-25, 27, 29-34, 36-41) had a prospective cohort design (n = 18). Five studies (17, 19, 21, 22, 28) had a retrospective cohort design and three (16, 26, 35) were cross-sectional. Table 1 Details of the data presented by the included studies. Study (First Author) Country Study design Total sample size Case Control Etiology of IC condition Type of vaccine No of cases Male% of cases Age No of Non-cases (if applicable) Male% of Non-cases Age Sattler A Germany Prospective Cohort 78 39 71.8 57.3 39 51.2 53.0 Transplant BNT162b2 (Pfizer/BionTech) Rincon-Arevalo H Germany Prospective Cohort 75 40 70 62.4 [51.2-69.5]* 35 57.1 51 [34-80]* Transplant BNT162b2 (Pfizer/BionTech) Korth J Germany Prospective Cohort 46 23 48 57.7 23 39 44.4 Transplant BNT162b2 (Pfizer/BionTech) Rabinowich L Israel Cross-sectional 105 80 70 60.1 25 32 52.7 Transplant BNT162b2 (Pfizer/BionTech) Schramm R Germany Prospective Cohort 100 50 64 55 50 34 47 Transplant BNT162b2 (Pfizer/BionTech) Cao J USA Retrospective Cohort 47 37 72.9 64 [50-69]* 10 20 66 [57-75]* Transplant mRNA-1273 (Moderna) or BNT162b2 (Pfizer/BionTech) Grupper A Israel Retrospective Cohort 151 136 81.7 58.6 25 32 52.7 Transplant BNT162b2 (Pfizer/BionTech) Marinaki S Greece Prospective Cohort 150 34 79.4 60 [49.1-68.4]* 116 - - Transplant BNT162b2 (Pfizer/BionTech) Rashidi-Alavijeh J Germany Prospective Cohort 63 43 60.5 57 [ 49 – 64 ]* 20 45 43.5 [38-53.5]* Transplant BNT162b2 (Pfizer/BionTech) Hod T Israel Prospective Cohort 322 120 80 59.7 141 30.2 57.04 Transplant BNT162b2 (Pfizer/BionTech) Stumpf J Germany Prospective Cohort 512 368 65.5 57.3 144 23.6 48 Transplant (a) mRNA-1273 (Moderna) (n=143); (b) BNT162b2 (Pfizer/BionTech) (n=369) Firket L USA Retrospective Cohort 40 20 45 51.2 20 65 48.3 Transplant BNT162b2 (Pfizer/BionTech) Peled Y Israel Prospective Cohort 213 77 64 62 [49-68]* 136 37 63 Transplant BNT162b2 (Pfizer/BionTech) Monin L UK Prospective Cohort 205 151 52 73 [64.5-79.5]* 54 52 40.5 [31.3-50]* Malignancy BNT162b2 (Pfizer/BionTech) Pimpinelli F Italy Prospective Cohort 128 92 53/2 70* 36 0 81 Malignancy BNT162b2 (Pfizer/BionTech) Massarweh A Israel Prospective Cohort 180 102 57 66 [56-72]* 78 32 62 [49-70]* Malignancy BNT162b2 (Pfizer/BionTech) Agbarya A Israel Cross-sectional 355 140 54 65.3 215 37.2 62.5 Malignancy BNT162b2 (Pfizer/BionTech) Herishanu Y Israel Prospective Cohort 219 167 67.1 71 [63-76]* 52 - 69 [63-73.7]* Malignancy BNT162b2 (Pfizer/BionTech) Iacono D Italy Cross-sectional 108 36 41.6 82* 72 - ≥66 Malignancy BNT162b2 (Pfizer/BionTech) Malard F France Retrospective Cohort 225 195 60 68.9* 30 - - Malignancy BNT162b2 (Pfizer/BionTech) Eliakim-Raz N Israel Prospective Cohort 161 95 58 65 [56-72]* 66 32 62 [50-70]* Malignancy BNT162b2 (Pfizer/BionTech) Herzog Tzarfati K Israel Prospective Cohort 423 315 56 71 [61-78]* 108 44 69 [58-74]* Malignancy BNT162b2 (Pfizer/BionTech) Reuken P Germany Prospective Cohort 55 28 46.4 42 [ 36 – 59 ]* 27 - - Autoimmune BNT162b2 (Pfizer/BionTech) Geisen UM Germany Retrospective Cohort 68 42 35.7 50.5 26 30.8 37.5 Autoimmune mRNA-1273 (Moderna) or BNT162b2 (Pfizer/BionTech) Furer V Israel Prospective Cohort 807 686 30.7 59 [19-88]* 121 35 50* Autoimmune BNT162b2 (Pfizer/BionTech) Prendecki M UK Prospective Cohort 155 85 52.1 52 [39.9-63.9]* 70 - 41.4* Autoimmune BNT162b2 (Pfizer/BionTech) *: Median [IQR] is reported; otherwise the mean is reported. Quality assessment of included studies Quality assessment of the included studies is presented in Supplementary Table S1 . The majority of the studies (n = 18) (16-18, 21-23, 25, 27, 28, 30, 31, 33, 34, 37-41) were of good quality and 8 (19, 20, 24, 26, 29, 32, 35, 36) had fair quality. Seroconversion in immunocompromised patients vs. controls Meta-analysis of 26 studies revealed that the risk of positive seroconversion in IC patients were 48% lower than healthy controls. (RR= 0.52; 95% CI: 0.42, 0.65; P < 0.01). Subgroup meta-analysis based on type of IC (i.e. autoimmune, transplant, and malignancy), revealed a statistically significant between-group difference ( P < 0.01) (Figure 2 ). When comparing each two subtypes of immunodeficiency, the results showed that IC patients due to transplant were less likely to develop positive seroconversion than IC patients due to autoimmune disorder ( P < 0.01) as well as IC patients due to malignancy ( P < 0.01). There was no statistically significant difference in seroconversion between IC patients with autoimmune disorder and those with malignancy ( P = 0.19). Seroconversion in patients with autoimmune disease vs. controls Four (20, 21, 34, 37) of the included studies were conducted on IC patients with autoimmune immunodeficiency. Although the proportion of positive seroconversion in these patients was lower than the controls, the pooled analysis showed no statistically significant difference in relative risk of seroconversion between two groups. (RR= 0.87; 95% CI: 0.75, 1.01; P = 0.07) (Figure 2 ). Seroconversion in patients with malignancy vs. controls Meta-analysis of 9 studies (16, 18, 23, 24, 26, 28, 30, 31, 33) revealed IC patients with malignancy were 0.75 times as likely to seroconvert than healthy controls (RR = 0.75; 95% CI: 0.63, 0.89; P < 0.01). Subgroup meta-analysis was conducted based on type of malignancy (hematologic vs. solid organ). Four (23, 24, 28, 33) of the studies were on patients with hematologic malignancy and three (16, 18, 30) were on patients with solid organ malignancy. The relative risk of seroconversion among IC patients with solid organ was significantly higher than those with hematologic malignancies (RR= 0.88; 95% CI: 0.85, 0.92 vs. RR= 0.61; 95% CI: 0.44, 0.86; P = 0.0.03) ( Figure 3 ). Seroconversion in transplant patients vs. controls Of the included studies, 13 (17, 19, 22, 25, 27, 29, 32, 35, 36, 38-41) were on IC patients with transplant. The meta-analysis showed transplant patients were 67% less likely to develop seroconversion than controls (RR= 0.33; 95% CI: 0.24, 0.47; P < 0.01). Seven (19, 22, 25, 27, 38, 39, 41) of the included studies were on patients with kidney transplant and the remaining (17, 29, 32, 35, 36, 40) were on patients with different transplants; none of which with more than three studies to be separated in the subgroup analysis. Hence, subgroup meta-analysis was conducted based on type of transplantation (kidney vs. others). The analysis did not reveal any statistically significant difference in relative risk of seroconversion in patients with kidney transplant compared to other types of transplants (RR = 0.30; 95% CI: 0.20, 0.47) vs. RR = 0.38; 95% CI: 0.21, 0.66; P = 0.55) ( Figure 4 ). Publication bias Funnel plot for seroconversion was asymmetrical and Egger test showed statistically significant evidence of publication bias ( P < 0.01, z = -9.09). Trim and fill method was used to adjust the effect size (Pooled estimate = 0.87; 95% CI: 0.85, 0.88; number of studies = 84) ( Figure 5 ) . Discussion Immunodeficiency comprises a wide range of disorders from primary (e.g., congenital) to numerous secondary conditions acquired consequently to a disease process or its treatment (e.g., human immunodeficiency virus (HIV) infection, radiation therapy, and immunosuppressive medications) (42). Although inconclusive, it has been shown that IC patients might be at a higher risk of severe COVID-19 (43, 44). On the other hand, limited number of studies revealed reduced vaccine efficacy of vaccines in IC patients (45). Nevertheless, data are limited on the efficacy of COVID-19 vaccines in this critical group of patients. In this meta-analysis on the immunogenicity of COVID-19 mRNA vaccines in IC patients, we found lower risk of positive seroconversion in this group of patients compared to healthy controls. In addition, subgroup analysis revealed significantly lower risk of positive seroconversion in transplant patients in comparison with autoimmune disorder and patients with malignancy. Intriguingly, COVID-19 mRNA vaccines seem to achieve lower efficacy in patients with hematologic malignancies compared to solid organ. We found significantly lower risk of positive seroconversion after the second dose of the vaccine in IC patients than controls. The controls were all healthy individuals, and this finding might not be surprising as observed with the administration of previous vaccines (e.g., Influenza vaccine) (46). However, it does not undermine the importance of vaccine in IC patients, as evidence highlights that the immune response after vaccines is more robust than that of natural SARS-CoV-2 infection (47, 48). It can also imply the importance of booster dose administration in this group of patients. As per recent Center for Disease Control and Prevention (CDC) guidelines, patients with moderately to severely compromised immune systems are recommended to receive an additional dose of COVID-19 mRNA vaccine (49). Furthermore, studies have shown the promotion of immune response in transplant patients receiving the third dose of mRNA vaccines, namely mRNA-1273 (Moderna) and BNT162b2 (Pfizer-BioNTech) (50, 51). However, a dichotomous view toward the booster dose seems to be insufficient since the degree and etiology of immunosuppression tend to be two important factors regarding immune response and the need for an additional dose (52). Whether a booster dose is necessarily associated with an enhanced immune response is also a matter of debate. There is evidence that initial post-vaccine antibody titer was predictive of response to booster, and some IC patients will never mount an antibody response (53) and a more restricted personal protection is highly recommended even after vaccination (54). Interestingly, our analysis revealed significantly lower relative risk of positive seroconversion in patients with transplant compared to patients with autoimmune disorder and patients with malignancy. A study by Evison et al. , on the efficacy of Influenza vaccine showed that vaccine response rate was higher among patients with HIV and patients who received dialysis compared to renal transplant recipients and patients with rheumatologic disease (55). This can be justified by the fact that treatment regimen may be an important contributing factor. Mycophenolate mofetil has been shown to accompany less immune response compared to a regimen consisting of prednisone, cyclosporine, and azathioprine (56-58). These drugs which are used to prevent allograft rejection interfere with T and B cell activation and proliferation leading to impediment of antibody generation (59). Although we did find any significant difference between kidney transplant and other organ transplant recipients, transplant recipients seem to be more vulnerable to vaccine failures in general, and special attention should be directed toward this group of patients. Studies proposed some approach to increase immunogenicity of vaccine in transplant recipients such as modulation of immunosuppression, adjuvants, intradermal injection, high antigen doses, and booster administration (59). Hematologic diseases are believed to have the highest level of immunosuppression amongst malignancies (60). This group of patients also 3-4-fold higher rates of severe/critical COVID-19 disease and mortality (61, 62). Hematologic malignancies are associated with immune dysfunction with alterations in both innate and adaptive immunity (63). Cytopenia, B/plasma cells reduction, hypogammaglobulinemia, anti-cancer therapy are amongst the underlying cause of immunodeficiency in these patients (64); thus, lower vaccine efficacy might be observed consequently, which is consistent with our findings about the lower immunogenicity of mRNA vaccines in patients with hematologic malignancies. It is also worth mentioning that there are numerous approaches to the assessment of immune response after vaccine administration which are related to anti-SARS-COV-2 recombinant spike, receptor binding domain or neutralizing IgG or total antibodies (52). We included articles with the main outcome of anti-SARS-CoV-2 spike IgG level; however, seropositivity may not necessarily show protection against SARS-CoV-2 (53), and routine assessment COVID-19 vaccine responses is not recommended (53). We confined this meta-analysis to mRNA vaccines due to limited studies on other COVID-19 vaccine types and to reduce heterogeneity. However, a study by Boekel et al. , on the development of antibody in patients with autoimmune disease did not show any significant different between immunogenicity induced by an mRNA vaccine (BNT162b2) and a viral vector type (ChAdOx1 nCoV-19) (65). It has also been shown that inactivated COVID-19 vaccine (CoronaVac) can induce immune response in patients with immune-mediated disease; still, the titer of antibody is associated with age and type of immunosuppressive therapy (66). This study indeed has some limitations. There was a lack data regarding HIV and other primary immunodeficiency disorders, and they are not included in this meta-analysis. Furthermore, we included studies with both retrospective and prospective design, which may reduce the level of evidence. Conclusion The risk of positive seroconversion in IC patients was almost half of those in healthy individuals. However, IC conditions due to autoimmune disorders did not lower the risk of positive seroconversion. Among IC conditions, transplantation induced lowest immunogenicity with 67% lower risk of seroconversion than healthy individuals. Besides, we found that vaccination among IC patients with hematological malignancy induced lower risk of seroconversion than those among IC patients with solid organ malignancy. Findings from this meta-analysis, could aid health care policy makers upon making decision regarding the importance of the booster dose or more strict personal protections in the IC patients. Abbreviations IC: Immunocompromised; COVID-19: Coronavrus infectious disease 2019; HIV: human immunodeficiency virus. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials The authors stated that all information provided in this article could be shared. Competing interests The authors declare that there is no conflict of interest regarding the publication of this manuscript. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' contributions The conception and design of the study: MM, HD, SS, NR; acquisition of data: MM, AH, AA, MS, MT; drafting the article: MM, FM, MT; revising it critically for important intellectual content: SS, MM, NR, HD; final approval of the version to be submitted: NR, SS. All authors read and approved the final manuscript. Acknowledgements Not applicable References Harpaz R, Dahl RM, Dooling KL. Prevalence of immunosuppression among US adults, 2013. Jama. 2016;316(23):2547–8. Abkhoo A, Shaker E, Mehrabinejad M-M, Azadbakht J, Sadighi N, Salahshour F. Factors Predicting Outcome in Intensive Care Unit-Admitted COVID-19 Patients: Using Clinical, Laboratory, and Radiologic Characteristics. Critical Care Research and Practice. 2021;2021. Dumortier J, Duvoux C, Roux O, Altieri M, Barraud H, Besch C, et al. Covid-19 in liver transplant recipients: the French SOT COVID registry. Clinics research in hepatology gastroenterology. 2021;45(4):101639. 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Massarweh A, Eliakim-Raz N, Stemmer A, Levy-Barda A, Yust-Katz S, Zer A, et al. Evaluation of Seropositivity Following BNT162b2 Messenger RNA Vaccination for SARS-CoV-2 in Patients Undergoing Treatment for Cancer. JAMA oncology. Monin L, Laing AG, Munoz-Ruiz M, McKenzie DR, Del Barrio ID, Alaguthurai T, et al. Safety and immunogenicity of one versus two doses of the COVID-19 vaccine BNT162b2 for patients with cancer: interim analysis of a prospective observational study. Lancet Oncology. 2021;22(6):765–78. Peled Y, Ram E, Lavee J, Sternik L, Segev A, Wieder-Finesod A, et al. BNT162b2 vaccination in heart transplant recipients: Clinical experience and antibody response. The Journal of heart lung transplantation: the official publication of the International Society for Heart Transplantation. 2021;40(8):759–62. Pimpinelli F, Marchesi F, Piaggio G, Giannarelli D, Papa E, Falcucci P, et al. Fifth-week immunogenicity and safety of anti-SARS-CoV-2 BNT162b2 vaccine in patients with multiple myeloma and myeloproliferative malignancies on active treatment: preliminary data from a single institution. J Hematol Oncol. 2021;14(1):81. Prendecki M, Clarke C, Edwards H, McIntyre S, Mortimer P, Gleeson S, et al. Humoral and T-cell responses to SARS-CoV-2 vaccination in patients receiving immunosuppression. Annals of the rheumatic diseases; 2021. Rabinowich L, Grupper A, Baruch R, Ben-Yehoyada M, Halperin T, Turner D, et al. Low immunogenicity to SARS-CoV-2 vaccination among liver transplant recipients. Journal of hepatology. 2021;75(2):435–8. Rashidi-Alavijeh J, Frey A, Passenberg M, Korth J, Zmudzinski J, Anastasiou OE, et al. Humoral Response to SARS-Cov-2 Vaccination in Liver Transplant Recipients-A Single-Center Experience. Vaccines. 2021;9(7). Reuken PA, Andreas N, Grunert PC, Glöckner S, Kamradt T, Stallmach A. T cell response after SARS-CoV-2 vaccination in immunocompromised patients with inflammatory bowel disease. Journal of Crohn's & colitis; 2021. Rincon-Arevalo H, Choi M, Stefanski AL, Halleck F, Weber U, Szelinski F, et al. Impaired humoral immunity to SARS-CoV-2 BNT162b2 vaccine in kidney transplant recipients and dialysis patients. Sci Immunol. 2021;6(60). Sattler A, Schrezenmeier E, Weber UA, Potekhin A, Bachmann F, Straub-Hohenbleicher H, et al. Impaired humoral and cellular immunity after SARS-CoV-2 BNT162b2 (tozinameran) prime-boost vaccination in kidney transplant recipients. The Journal of clinical investigation. 2021;131(14). Schramm R, Costard-Jackle A, Rivinius R, Fischer B, Muller B, Boeken U, et al. Poor humoral and T-cell response to two-dose SARS-CoV-2 messenger RNA vaccine BNT162b2 in cardiothoracic transplant recipients. Clin Res Cardiol. 2021;110(8):1142–9. Stumpf J, Siepmann T, Lindner T, Karger C, Schwöbel J, Anders L, et al. Humoral and cellular immunity to SARS-CoV-2 vaccination in renal transplant versus dialysis patients: A prospective, multicenter observational study using mRNA-1273 or BNT162b2 mRNA vaccine. The Lancet regional health Europe. 2021:100178. Duly K, Farraye FA, Bhat S. COVID-19 vaccine use in immunocompromised patients: A commentary on evidence and recommendations. American Journal of Health-System Pharmacy; 2021. Fung M, Babik JM. COVID-19 in immunocompromised hosts: what we know so far. Clin Infect Dis. 2021;72(2):340–50. Salahshour F, Mehrabinejad M-M, Toosi MN, Gity M, Ghanaati H, Shakiba M, et al. Clinical and chest CT features as a predictive tool for COVID-19 clinical progress: introducing a novel semi-quantitative scoring system. European Radiology. 2021:1–11. Prevention CfDCa. Interim Clinical Considerations for Use of COVID-19 Vaccines Currently Approved or Authorized in the United States 2021 [Available from: https://www.cdc.gov/vaccines/covid-19/clinical-considerations/covid-19-vaccines-us.html . Felldin M, Studahl M, Svennerholm B, Friman V. The antibody response to pandemic H1N1 2009 influenza vaccine in adult organ transplant patients. Transpl Int. 2012;25(2):166–71. Altawalah H. Antibody Responses to Natural SARS-CoV-2 Infection or after COVID-19 Vaccination. Vaccines. 2021;9(8):910. Greaney AJ, Loes AN, Gentles LE, Crawford KHD, Starr TN, Malone KD, et al. Antibodies elicited by mRNA-1273 vaccination bind more broadly to the receptor binding domain than do those from SARS-CoV-2 infection. Sci Transl Med. 2021;13:600. Control CfD. Prevention. COVID-19 vaccines for moderately to severely immunocompromised people. Last Accessed September. 2021;7. Hall VG, Ferreira VH, Ku T, Ierullo M, Majchrzak-Kita B, Chaparro C, et al. Randomized trial of a third dose of mRNA-1273 vaccine in transplant recipients. New England Journal of Medicine. 2021. Del Bello A, Abravanel F, Marion O, Couat C, Esposito L, Lavayssière L, et al. Efficiency of a boost with a third dose of anti-SARS‐CoV‐2 messenger RNA‐based vaccines in solid organ transplant recipients. American Journal of Transplantation. 2021. Lee ARYB, Wong SY, Chai LYA, Lee SC, Lee M, Muthiah MD, et al. Efficacy of COVID-19 vaccines in immunocompromised patients: A systematic review and meta-analysis. medRxiv. 2021. Haidar G, Agha M, Lukanski A, Linstrum K, Troyan R, Bilderback A, et al. Immunogenicity of COVID-19 vaccination in immunocompromised patients: an observational, prospective cohort study interim analysis. medRxiv. 2021. Tabatabaeizadeh S-A. Airborne transmission of COVID-19 and the role of face mask to prevent it: a systematic review and meta-analysis. Eur J Med Res. 2021;26(1):1–6. Evison J, Farese S, Seitz M, Uehlinger DE, Furrer H, Mühlemann K. Randomized, double-blind comparative trial of subunit and virosomal influenza vaccines for immunocompromised patients. Clinical infectious diseases. 2009;48(10):1402–12. Cavdar C, Sayan M, Sifil A, Artuk C, Yilmaz N, Bahar H, et al. The comparison of antibody response to influenza vaccination in continuous ambulatory peritoneal dialysis, hemodialysis and renal transplantation patients. Scand J Urol Nephrol. 2003;37(1):71–6. Smith KG, Isbel NM, Catton MG, Leydon JA, Becker GJ, Walker RG. Suppression of the humoral immune response by mycophenolate mofetil. Nephrology Dialysis Transplantation. 1998;13(1):160–4. Scharpé J, Evenepoel P, Maes B, Bammens B, Claes K, Osterhaus A, et al. Influenza vaccination is efficacious and safe in renal transplant recipients. Am J Transplant. 2008;8(2):332–7. Caillard S, Thaunat O. COVID-19 vaccination in kidney transplant recipients. Nature Reviews Nephrology. 2021:1–3. Chemaly RF, Ghosh S, Bodey GP, Rohatgi N, Safdar A, Keating MJ, et al. Respiratory viral infections in adults with hematologic malignancies and human stem cell transplantation recipients: a retrospective study at a major cancer center. Medicine. 2006;85(5):278–87. Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, et al. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area. Jama. 2020;323(20):2052–9. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. Jama. 2020;323(13):1239–42. Atkins S, He F. Chemotherapy and beyond: infections in the era of old and new treatments for hematologic malignancies. Infectious Disease Clinics. 2019;33(2):289–309. Dhodapkar MV, Dhodapkar KM, Ahmed R. Viral immunity and vaccines in hematologic malignancies: implications for COVID-19. Blood cancer discovery. 2021;2(1):9. Boekel L, Steenhuis M, Hooijberg F, Besten YR, van Kempen ZL, Kummer LY, et al. Antibody development after COVID-19 vaccination in patients with autoimmune diseases in the Netherlands: a substudy of data from two prospective cohort studies. The Lancet Rheumatology. 2021. Seyahi E, Bakhdiyarli G, Oztas M, Kuskucu MA, Tok Y, Sut N, et al. Antibody response to inactivated COVID-19 vaccine (CoronaVac) in immune-mediated diseases: a controlled study among hospital workers and elderly. Rheumatology international. 2021:1–12. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-994503","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":63244114,"identity":"5530f3cc-b513-4e68-97db-eab727e5003d","order_by":0,"name":"Mohammad-Mehdi Mehrabinejad","email":"","orcid":"","institution":"Tehran University of Medical Sciences School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohammad-Mehdi","middleName":"","lastName":"Mehrabinejad","suffix":""},{"id":63244115,"identity":"c89dc86f-2f9f-459a-91ec-92cdbde1097b","order_by":1,"name":"Fatemeh 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10:17:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-994503/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-994503/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":15643153,"identity":"7cbcd2d9-a13c-4d22-9f2b-0896936840ed","added_by":"auto","created_at":"2021-11-17 18:01:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80504,"visible":true,"origin":"","legend":"The PRISMA 2009 flow diagram of the study.","description":"","filename":"Onlinefig1.png","url":"https://assets-eu.researchsquare.com/files/rs-994503/v1/dd8256c622e4493b25a8bf62.png"},{"id":15643240,"identity":"1d26e22f-9e68-4185-a545-cb427af9515b","added_by":"auto","created_at":"2021-11-17 18:04:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70483,"visible":true,"origin":"","legend":"Meta-analysis of seroconversion in immunocompromised patients vs controls, based on type of immunodefeciency","description":"","filename":"Onlinefig2.png","url":"https://assets-eu.researchsquare.com/files/rs-994503/v1/58665dcb1852d337f2db2af0.png"},{"id":15643156,"identity":"9da60ed7-add8-4650-9de9-4ccd315ca2be","added_by":"auto","created_at":"2021-11-17 18:01:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":44774,"visible":true,"origin":"","legend":"Meta-analysis of seroconversion in immunocompromised patients with malignancy vs controls, based on type of maliganacy","description":"","filename":"Onlinefig3.png","url":"https://assets-eu.researchsquare.com/files/rs-994503/v1/75063faca5c1eca0986f40f2.png"},{"id":15643241,"identity":"e10eea0e-d0b9-41c5-ab31-9488ae85aca1","added_by":"auto","created_at":"2021-11-17 18:04:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":34743,"visible":true,"origin":"","legend":"Meta-analysis of seroconversion in transplant patients vs controls, based on type of transplant","description":"","filename":"Onlinefig4.png","url":"https://assets-eu.researchsquare.com/files/rs-994503/v1/5f67ef50e3d01c84d897310c.png"},{"id":15643157,"identity":"06b18f4f-14ff-429f-8441-511cd880fb99","added_by":"auto","created_at":"2021-11-17 18:01:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":24808,"visible":true,"origin":"","legend":"Funnel plot (a) and trim and fill funnel plot (b) for meta-analysis of seroconversion in patients with immunodeficiency","description":"","filename":"Onlinefig5.png","url":"https://assets-eu.researchsquare.com/files/rs-994503/v1/bb692ea3c5307b8b246ccdf0.png"},{"id":15643243,"identity":"3757ae47-3c91-4362-ace9-fa71e9d335b3","added_by":"auto","created_at":"2021-11-17 18:04:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1557134,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-994503/v1/72d14a55-469e-4edf-81d6-e71e467e3778.pdf"},{"id":15643242,"identity":"c5e51f6d-e601-4240-be5d-6e3e1cdfe1b6","added_by":"auto","created_at":"2021-11-17 18:04:22","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":13402,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-994503/v1/a126f4cbf61b638a8f70f5f4.docx"}],"financialInterests":"","formattedTitle":"Immunogenicity of COVID-19 mRNA Vaccines in Immunocompromised Patients: A Systematic Review and Meta-Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eImmunocompromised (IC) conditions are estimated to affect approximately 2.7% of United States adults (1). Such patients are at higher risk of severe SARS-CoV-2 infection, extended hospitalization, intensive care admission, and mortality compared to general population (2-6). Besides, prolonged viral shedding and potential sources of novel SARS-CoV-2 variants in this population are also of particular importance (7-9). Thus, IC patients should be prioritized for primary prevention through Coronavrus infectious disease 2019 (COVID-19) vaccination.\u003c/p\u003e \u003cp\u003eGlobal efforts have been taken to develop SARS-CoV-2 vaccines since the initiation of the current COVID-19 pandemic. The mRNA vaccines (i.e., mRNA-1273 and BNT162b2) are the most commonly approved vaccines worldwide which are utilized in different clinical trials in global scale (10). The overall efficacy and safety of COVID-19 vaccines in phase III trials were promising (11), sparking global hope toward ending the current outbreak. However, the application of COVID-19 vaccines in patients with impaired immune system remains as an ongoing subject of debate as they were excluded from the original trials (12, 13). IC patients due to either the primary disease or the immunosuppressive treatments are more likely to show weak or suboptimal immune response to COVID-19 vaccines given previous studies on influenza vaccines (14). Hence, the real-world statistics regarding the efficacy of COVID-19 vaccines are required to provide physicians a better insight towards decision-making in this group of high-risk patients.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to systematically review the literature and analyze the pooled effectiveness of COVID-19 vaccination in IC patients compared to healthy controls using meta-analysis. We also assessed the efficacy of mRNA vaccines in IC patients based on their etiological factor including malignancy, transplantation, and autoimmune diseases.\u003c/p\u003e"},{"header":"Methods And Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eProtocol and Literature search\u003c/h2\u003e \u003cp\u003e This systematic review and meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.\u003c/p\u003e \u003cp\u003ePubMed-MEDLINE, Scopus, and Web of Science were searched for original articles reporting the efficacy in adult patients with IC condition between June 1, 2020 and September 1, 2021. The search terms were as follows: ((COVID-19) OR (SARS-CoV-2) OR (novel coronavirus)) AND ((vaccine) OR (vaccination)) OR (vaccinated)) AND ((immunocompromised) OR (immunosuppressed) OR (corticosteroid) OR (chemotherapy) OR (cancer) OR (malignancy) OR (rheumatologic disease) OR (immunodeficiency) OR (autoimmune) OR (AIDS) OR (HIV) OR (transplant)).\u003c/p\u003e \u003cp\u003eThe references of the selected articles were further screened to search for potentially relevant articles. Two reviewers independently performed the literature search, and any disagreement regarding study inclusion was resolved by consensus. The authors were not blinded to the authors, institutions, or journals while selecting studies or extracting data. EndNote version X9 was used for literature management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEligibility criteria\u003c/h2\u003e \u003cp\u003eStudies investigating the immunogenicity of COVID-19 mRNA vaccination in IC patients were eligible for inclusion. The included studies met the following criteria: (1) \u003cem\u003ePopulation\u003c/em\u003e: studies on IC patients with a sample size \u0026ge; 30 participants and control group of healthy individuals. IC patients included patients with solid organ or hematologic malignancies who receive chemotherapy, patients with inherited or acquired immunodeficiency diseases, patients with autoimmune or rheumatologic diseases, patients with other conditions (i.e., asthma) receiving long-term corticosteroid, and transplant recipients. (2) \u003cem\u003eIntervention\u003c/em\u003e: mRNA COVID-19 vaccination (3) \u003cem\u003eStudy design\u003c/em\u003e: all retrospective and prospective studies as well as clinical trials with healthy control group were included. (4) \u003cem\u003eOutcomes\u003c/em\u003e: main outcome of this study was seroconversion in IC patients using anti-SARS-CoV-2 spike IgG \u0026ge; 14 days after the second dose of COVID-19 mRNA vaccines. The subgroup analysis was performed to determine the efficacy of COVID-19 mRNA vaccines in different groups of patients based on etiology of IC condition.\u003c/p\u003e \u003cp\u003eThe exclusion criteria were as follows: (1) reviews and editorials; (2) case reports or case series \u0026lt;30 patients; (3) partially overlapping patient cohorts; (4) articles not written in English; (5) single-arm studies or with non-healthy control group; and (6) non-human studies. Two reviewers independently reviewed the literature in consensus.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003e Eligible studies were evaluated by two experts independently and the following data was extracted from each included publication: author, date of publications, country of origin, study design, study sample size, definition of IC conditions, inclusion and exclusion criteria, the number of IC patients, variables matched, the proportion of male, mean age, duration of disease, type and etiology of the immunodeficiency and its proportion to the total population, type of vaccination, and efficacy of the vaccination.\u003c/p\u003e \u003cp\u003eAny conflicts in data extraction were discussed or consulted by a third expert and resolved.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eQuality assessment\u003c/h2\u003e \u003cp\u003eNational Institutes of Health (NIH) quality assessment tool (15) to evaluate the included studies. The scores of 11\u0026ndash;14, 6\u0026ndash;10, and 0\u0026ndash;5 were considered as good, fair, and poor quality, respectively. Moreover, the studies were evaluated in terms of methodology by two experts, independently; any conflict of opinion was discussed or referred to third expert and resolved.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eSTATA version 16 for Windows (Stata Corp, College Station, Texas) was utilized for the meta-analysis. At least three studies in each group were required to synthesize the data on outcomes. The heterogeneity of studies was measured using I\u0026sup2; or Q test. A fixed model was employed, if the heterogeneity of studies was below 40% and a random effect model in case of heterogeneity above 40%. Effect measures were calculated. Also, based on the heterogeneity of studies, either meta-regression analysis or subgroup analysis was performed for potential moderators. Moreover, funnel plot asymmetry and the Eggers test were used to assess publication bias. In case of significant publication bias, the adjustment was performed for the effect size using the trim and fill method. A \u003cem\u003eP\u003c/em\u003e-value less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003e\u003cstrong\u003eStudy selection\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe study selection flowchart is presented in Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The literature search, after removing duplicates, resulted in 2093 studies, of which 1992 were considered irrelevant following title and abstract screening. Of the remaining 101, a further 75 were removed according to the exclusion criteria. Therefore, in total, 26 studies (16-41) were eligible for the meta-analysis of seroconversion after the second dose of the vaccine.\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003e\u003cstrong\u003eCharacteristics of included studies\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eCharacteristics of the included studies are provided in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. All 26 included studies on 3207 IC patients and 1726 healthy controls showed that 65.8 % IC patients and 99.2 % healthy controls had seropositive IgG test following second dose of COVID-19 mRNA vaccines. All of the studies were conducted in 2021. Sample sizes, from which relevant data were available for extraction, varied from 40 to 807. Participants\u0026rsquo; mean age ranged from 42 to 71.4 years. The majority of the studies (18, 20, 23-25, 27, 29-34, 36-41) had a prospective cohort design (n = 18). Five studies (17, 19, 21, 22, 28) had a retrospective cohort design and three (16, 26, 35) were cross-sectional.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDetails of the data presented by the included studies.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"12\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eStudy (First Author)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eStudy design\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal sample size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCase\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEtiology of IC condition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eType of vaccine\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo of cases\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale% of cases\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo of Non-cases (if applicable)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale% of Non-cases\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSattler A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransplant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRincon-Arevalo H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.4 [51.2-69.5]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 [34-80]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransplant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKorth J\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransplant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRabinowich L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransplant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSchramm R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransplant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCao J\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetrospective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 [50-69]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 [57-75]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransplant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emRNA-1273 (Moderna) or BNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrupper A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetrospective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransplant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarinaki S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreece\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 [49.1-68.4]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransplant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRashidi-Alavijeh J\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.5 [38-53.5]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransplant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHod T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransplant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStumpf J\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransplant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(a) mRNA-1273 (Moderna) (n=143);\u003c/p\u003e\n \u003cp\u003e(b) BNT162b2 (Pfizer/BionTech) (n=369)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirket L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetrospective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransplant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeled Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 [49-68]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransplant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonin L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73 [64.5-79.5]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.5 [31.3-50]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePimpinelli F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMassarweh A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 [56-72]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 [49-70]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgbarya A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHerishanu Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71 [63-76]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 [63-73.7]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIacono D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalard F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetrospective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.9*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEliakim-Raz N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 [56-72]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 [50-70]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHerzog Tzarfati K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71 [61-78]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 [58-74]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReuken P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutoimmune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeisen UM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetrospective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutoimmune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emRNA-1273 (Moderna) or BNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFurer V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 [19-88]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutoimmune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrendecki M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 [39.9-63.9]*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.4*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutoimmune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNT162b2 (Pfizer/BionTech)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"12\"\u003e\n \u003cp\u003e*: Median [IQR] is reported; otherwise the mean is reported.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003e\u003cstrong\u003eQuality assessment of included studies\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eQuality assessment of the included studies is presented in \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e. The majority of the studies (n = 18) (16-18, 21-23, 25, 27, 28, 30, 31, 33, 34, 37-41) were of good quality and 8 (19, 20, 24, 26, 29, 32, 35, 36) had fair quality.\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003e\u003cstrong\u003eSeroconversion in immunocompromised patients vs. controls\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eMeta-analysis of 26 studies revealed that the risk of positive seroconversion in IC patients were 48% lower than healthy controls. (RR= 0.52; 95% CI: 0.42, 0.65; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). Subgroup meta-analysis based on type of IC (i.e. autoimmune, transplant, and malignancy), revealed a statistically significant between-group difference (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) (Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). When comparing each two subtypes of immunodeficiency, the results showed that IC patients due to transplant were less likely to develop positive seroconversion than IC patients due to autoimmune disorder (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) as well as IC patients due to malignancy (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). There was no statistically significant difference in seroconversion between IC patients with autoimmune disorder and those with malignancy (\u003cem\u003eP\u003c/em\u003e= 0.19).\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003e\u003cstrong\u003eSeroconversion in patients with autoimmune disease vs. controls\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eFour (20, 21, 34, 37) of the included studies were conducted on IC patients with autoimmune immunodeficiency. Although the proportion of positive seroconversion in these patients was lower than the controls, the pooled analysis showed no statistically significant difference in relative risk of seroconversion between two groups. (RR= 0.87; 95% CI: 0.75, 1.01; \u003cem\u003eP\u003c/em\u003e = 0.07) (Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003e\u003cstrong\u003eSeroconversion in patients with malignancy vs. controls\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eMeta-analysis of 9 studies (16, 18, 23, 24, 26, 28, 30, 31, 33) revealed IC patients with malignancy were 0.75 times as likely to seroconvert than healthy controls (RR = 0.75; 95% CI: 0.63, 0.89; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). Subgroup meta-analysis was conducted based on type of malignancy (hematologic vs. solid organ). Four (23, 24, 28, 33) of the studies were on patients with hematologic malignancy and three (16, 18, 30) were on patients with solid organ malignancy. The relative risk of seroconversion among IC patients with solid organ was significantly higher than those with hematologic malignancies (RR= 0.88; 95% CI: 0.85, 0.92 vs. RR= 0.61; 95% CI: 0.44, 0.86; \u003cem\u003eP\u003c/em\u003e = 0.0.03) \u003cstrong\u003e(\u003c/strong\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003e\u003cstrong\u003eSeroconversion in transplant patients vs. controls\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eOf the included studies, 13 (17, 19, 22, 25, 27, 29, 32, 35, 36, 38-41) were on IC patients with transplant. The meta-analysis showed transplant patients were 67% less likely to develop seroconversion than controls (RR= 0.33; 95% CI: 0.24, 0.47; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). Seven (19, 22, 25, 27, 38, 39, 41) of the included studies were on patients with kidney transplant and the remaining (17, 29, 32, 35, 36, 40) were on patients with different transplants; none of which with more than three studies to be separated in the subgroup analysis. Hence, subgroup meta-analysis was conducted based on type of transplantation (kidney vs. others). The analysis did not reveal any statistically significant difference in relative risk of seroconversion in patients with kidney transplant compared to other types of transplants (RR = 0.30; 95% CI: 0.20, 0.47) vs. RR = 0.38; 95% CI: 0.21, 0.66; \u003cem\u003eP\u003c/em\u003e = 0.55) \u003cstrong\u003e(\u003c/strong\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003e\u003cstrong\u003ePublication bias\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eFunnel plot for seroconversion was asymmetrical and Egger test showed statistically significant evidence of publication bias (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, z = -9.09). Trim and fill method was used to adjust the effect size (Pooled estimate = 0.87; 95% CI: 0.85, 0.88; number of studies = 84) \u003cstrong\u003e(\u003c/strong\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eImmunodeficiency comprises a wide range of disorders from primary (e.g., congenital) to numerous secondary conditions acquired consequently to a disease process or its treatment (e.g., human immunodeficiency virus (HIV) infection, radiation therapy, and immunosuppressive medications) (42). Although inconclusive, it has been shown that IC patients might be at a higher risk of severe COVID-19 (43, 44). On the other hand, limited number of studies revealed reduced vaccine efficacy of vaccines in IC patients (45). Nevertheless, data are limited on the efficacy of COVID-19 vaccines in this critical group of patients.\u003c/p\u003e \u003cp\u003eIn this meta-analysis on the immunogenicity of COVID-19 mRNA vaccines in IC patients, we found lower risk of positive seroconversion in this group of patients compared to healthy controls. In addition, subgroup analysis revealed significantly lower risk of positive seroconversion in transplant patients in comparison with autoimmune disorder and patients with malignancy. Intriguingly, COVID-19 mRNA vaccines seem to achieve lower efficacy in patients with hematologic malignancies compared to solid organ.\u003c/p\u003e \u003cp\u003eWe found significantly lower risk of positive seroconversion after the second dose of the vaccine in IC patients than controls. The controls were all healthy individuals, and this finding might not be surprising as observed with the administration of previous vaccines (e.g., Influenza vaccine) (46). However, it does not undermine the importance of vaccine in IC patients, as evidence highlights that the immune response after vaccines is more robust than that of natural SARS-CoV-2 infection (47, 48). It can also imply the importance of booster dose administration in this group of patients. As per recent Center for Disease Control and Prevention (CDC) guidelines, patients with moderately to severely compromised immune systems are recommended to receive an additional dose of COVID-19 mRNA vaccine (49). Furthermore, studies have shown the promotion of immune response in transplant patients receiving the third dose of mRNA vaccines, namely mRNA-1273 (Moderna) and BNT162b2 (Pfizer-BioNTech) (50, 51). However, a dichotomous view toward the booster dose seems to be insufficient since the degree and etiology of immunosuppression tend to be two important factors regarding immune response and the need for an additional dose (52). Whether a booster dose is necessarily associated with an enhanced immune response is also a matter of debate. There is evidence that initial post-vaccine antibody titer was predictive of response to booster, and some IC patients will never mount an antibody response (53) and a more restricted personal protection is highly recommended even after vaccination (54).\u003c/p\u003e \u003cp\u003eInterestingly, our analysis revealed significantly lower relative risk of positive seroconversion in patients with transplant compared to patients with autoimmune disorder and patients with malignancy. A study by Evison \u003cem\u003eet al.\u003c/em\u003e, on the efficacy of Influenza vaccine showed that vaccine response rate was higher among patients with HIV and patients who received dialysis compared to renal transplant recipients and patients with rheumatologic disease (55). This can be justified by the fact that treatment regimen may be an important contributing factor. Mycophenolate mofetil has been shown to accompany less immune response compared to a regimen consisting of prednisone, cyclosporine, and azathioprine (56-58). These drugs which are used to prevent allograft rejection interfere with T and B cell activation and proliferation leading to impediment of antibody generation (59). Although we did find any significant difference between kidney transplant and other organ transplant recipients, transplant recipients seem to be more vulnerable to vaccine failures in general, and special attention should be directed toward this group of patients. Studies proposed some approach to increase immunogenicity of vaccine in transplant recipients such as modulation of immunosuppression, adjuvants, intradermal injection, high antigen doses, and booster administration (59).\u003c/p\u003e \u003cp\u003eHematologic diseases are believed to have the highest level of immunosuppression amongst malignancies (60). This group of patients also 3-4-fold higher rates of severe/critical COVID-19 disease and mortality (61, 62). Hematologic malignancies are associated with immune dysfunction with alterations in both innate and adaptive immunity (63). Cytopenia, B/plasma cells reduction, hypogammaglobulinemia, anti-cancer therapy are amongst the underlying cause of immunodeficiency in these patients (64); thus, lower vaccine efficacy might be observed consequently, which is consistent with our findings about the lower immunogenicity of mRNA vaccines in patients with hematologic malignancies.\u003c/p\u003e \u003cp\u003eIt is also worth mentioning that there are numerous approaches to the assessment of immune response after vaccine administration which are related to anti-SARS-COV-2 recombinant spike, receptor binding domain or neutralizing IgG or total antibodies (52). We included articles with the main outcome of anti-SARS-CoV-2 spike IgG level; however, seropositivity may not necessarily show protection against SARS-CoV-2 (53), and routine assessment COVID-19 vaccine responses is not recommended (53).\u003c/p\u003e \u003cp\u003eWe confined this meta-analysis to mRNA vaccines due to limited studies on other COVID-19 vaccine types and to reduce heterogeneity. However, a study by Boekel \u003cem\u003eet al.\u003c/em\u003e, on the development of antibody in patients with autoimmune disease did not show any significant different between immunogenicity induced by an mRNA vaccine (BNT162b2) and a viral vector type (ChAdOx1 nCoV-19) (65). It has also been shown that inactivated COVID-19 vaccine (CoronaVac) can induce immune response in patients with immune-mediated disease; still, the titer of antibody is associated with age and type of immunosuppressive therapy (66).\u003c/p\u003e \u003cp\u003eThis study indeed has some limitations. There was a lack data regarding HIV and other primary immunodeficiency disorders, and they are not included in this meta-analysis. Furthermore, we included studies with both retrospective and prospective design, which may reduce the level of evidence.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe risk of positive seroconversion in IC patients was almost half of those in healthy individuals. However, IC conditions due to autoimmune disorders did not lower the risk of positive seroconversion. Among IC conditions, transplantation induced lowest immunogenicity with 67% lower risk of seroconversion than healthy individuals. Besides, we found that vaccination among IC patients with hematological malignancy induced lower risk of seroconversion than those among IC patients with solid organ malignancy. Findings from this meta-analysis, could aid health care policy makers upon making decision regarding the importance of the booster dose or more strict personal protections in the IC patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eIC: Immunocompromised; COVID-19: Coronavrus infectious disease 2019; HIV: human immunodeficiency virus.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors stated that all information provided in this article could be shared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest regarding the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe conception and design of the study: MM, HD, SS, NR; acquisition of data: MM, AH, AA, MS, MT; drafting the article: MM, FM, MT; revising it critically for important intellectual content: SS, MM, NR, HD; final approval of the version to be submitted: NR, SS. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHarpaz R, Dahl RM, Dooling KL. Prevalence of immunosuppression among US adults, 2013. Jama. 2016;316(23):2547\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbkhoo A, Shaker E, Mehrabinejad M-M, Azadbakht J, Sadighi N, Salahshour F. Factors Predicting Outcome in Intensive Care Unit-Admitted COVID-19 Patients: Using Clinical, Laboratory, and Radiologic Characteristics. Critical Care Research and Practice. 2021;2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDumortier J, Duvoux C, Roux O, Altieri M, Barraud H, Besch C, et al. Covid-19 in liver transplant recipients: the French SOT COVID registry. 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Rheumatology international. 2021:1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, SARS-CoV-2, Vaccination, Immunocompromised patient, Malignancy, Transplantation, Autoimmune, Efficacy","lastPublishedDoi":"10.21203/rs.3.rs-994503/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-994503/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eImmunocompromised (IC) patients are at higher risk of severe SARS-CoV-2 infection, morbidity, and mortality compared to general population. They should be prioritized for primary prevention through vaccination. In this study, we aimed to evaluate the efficacy of COVID-19 mRNA vaccines in IC patients through a systematic review and meta-analysis approach.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003ePubMed-MEDLINE, Scopus, and Web of Science were searched for original articles reporting the immunogenicity of two doses of mRNA COVID-19 vaccines in adult patients with IC condition between June 1, 2020 and September 1, 2021. Meta-analysis was performed using either random or fixed effect according to the heterogeneity of the studies. Subgroup analysis was performed to identify potential sources of heterogeneity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 26 studies on 3207 IC patients and 1726 healthy individuals were included. The risk of seroconversion in IC patients was 48% lower than those in controls (RR= 0.52 [0.42, 0.65]). IC patients with autoimmune condition were 54% and patients with malignancy were 42% more likely to have positive seroconversion compared to those with transplant (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01). Subgroup meta-analysis based on type of malignancy, revealed significantly higher proportion of positive seroconversion in solid organ compared to hematologic malignancies (RR= 0.88 [0.85, 0.92] vs. 0.61 [0.44, 0.86], \u003cem\u003eP\u003c/em\u003e= 0.03). Subgroup meta-analysis based on type of transplantation (kidney vs. others), showed no statically significant between group difference of seroconversion (\u003cem\u003eP\u003c/em\u003e= 0.55).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIC patients, especially transplant patients, developed lower immunogenicity with two-dose of COVID-19 mRNA vaccines. Among patients with IC, those with autoimmune condition and solid organ malignancies are mostly benefited from COVID-19 vaccination. Findings from this meta-analysis, could aid health care policy makers upon making decision regarding the importance of the booster dose or more strict personal protections in the IC patients.\u003c/p\u003e","manuscriptTitle":"Immunogenicity of COVID-19 mRNA Vaccines in Immunocompromised Patients: A Systematic Review and Meta-Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-11-17 18:01:20","doi":"10.21203/rs.3.rs-994503/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2021-12-20T08:24:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-11-10T13:43:12+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-11-03T12:02:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-10-21T04:28:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2021-10-19T06:16:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c10fcab4-c2dc-41bf-94ae-1c485311a5cc","owner":[],"postedDate":"November 17th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":8566853,"name":"Pulmonology"},{"id":8566854,"name":"Immunology"},{"id":8566855,"name":"Medical Genetics"}],"tags":[],"updatedAt":"2022-01-29T07:01:38+00:00","versionOfRecord":[],"versionCreatedAt":"2021-11-17 18:01:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-994503","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-994503","identity":"rs-994503","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}