key: cord-0770619-znwsbtrw authors: Haidar, Ghady; Agha, Mounzer; Bilderback, Andrew; Lukanski, Amy; Linstrum, Kelsey; Troyan, Rachel; Rothenberger, Scott; McMahon, Deborah K; Crandall, Melissa D; Sobolewksi, Michele D; Enick, P Nathan; Jacobs, Jana L; Collins, Kevin; Klamar-Blain, Cynthia; Macatangay, Bernard J C; Parikh, Urvi M; Heaps, Amy; Coughenour, Lindsay; Schwartz, Marc B; Dueker, Jeffrey M; Silveira, Fernanda P; Keebler, Mary E; Humar, Abhinav; Luketich, James D; Morrell, Matthew R; Pilewski, Joseph M; McDyer, John F; Pappu, Bhanu; Ferris, Robert L; Marks, Stanley M; Mahon, John; Mulvey, Katie; Hariharan, Sundaram; Updike, Glenn M; Brock, Lorraine; Edwards, Robert; Beigi, Richard H; Kip, Paula L; Wells, Alan; Minnier, Tami; Angus, Derek C; Mellors, John W title: Prospective evaluation of COVID-19 vaccine responses across a broad spectrum of immunocompromising conditions: the COVICS study date: 2022-02-18 journal: Clin Infect Dis DOI: 10.1093/cid/ciac103 sha: 5225da3fa595699a74bd24d2febe71c43b2a991a doc_id: 770619 cord_uid: znwsbtrw BACKGROUND: We studied humoral responses after COVID-19 vaccination across varying causes of immunodeficiency. METHODS: Prospective study of fully-vaccinated immunocompromised adults (solid organ transplant (SOT), hematologic malignancy, solid cancers, autoimmune conditions, HIV infection) versus non-immunocompromised healthcare-workers (HCW). The primary outcome was the proportion with a reactive test (seropositive) for IgG to SARS-CoV-2 receptor-binding domain. Secondary outcomes were comparisons of antibody levels and their correlation with pseudovirus neutralization titers. Stepwise logistic regression was used to identify factors associated with seropositivity. RESULTS: 1271 participants enrolled: 1,099 immunocompromised and 172 HCW. Compared to HCW (92.4% seropositive), seropositivity was lower among participants with SOT (30.7%), hematological malignancies (50.0%), autoimmune conditions (79.1%), solid tumors (78.7%), and HIV (79.8%) (p<0.01). Factors associated with poor seropositivity included age, greater immunosuppression, time since vaccination, anti-CD20 monoclonal antibodies, and vaccination with BNT162b2 (Pfizer) or adenovirus vector vaccines versus mRNA-1273 (Moderna). mRNA-1273 was associated with higher antibody levels than BNT162b2 or adenovirus vector vaccines, after adjusting for time since vaccination, age, and underlying condition. Antibody levels were strongly correlated with pseudovirus neutralization titers (Spearman r=0.89, p<0.0001), but in seropositive participants with intermediate antibody levels, neutralization titers were significantly lower in immunocompromised individuals versus HCW. CONCLUSION: Antibody responses to COVID-19 vaccines were lowest among SOT and anti-CD20 monoclonal recipients, and recipients of vaccines other than mRNA-1273. Among those with intermediate antibody levels, pseudovirus neutralization titers were lower in immunocompromised patients than HCW. Additional SARS-CoV-2 preventive approaches are needed for immunocompromised persons, which may need to be tailored to the cause of immunodeficiency. Recent studies in immunocompromised individuals have shown that coronavirus disease 2019 (COVID- 19) vaccines elicit poor antibody responses [1] [2] [3] [4] . Several unknowns persist, however, including factors associated with inadequate humoral responses across varied causes of immunocompromising conditions, and whether antibodies from immunocompromised patients have similar neutralizing ability as those of nonimmunocompromised individuals. To address these knowledge gaps, we performed the COVID-19 Vaccination in the Immunocompromised Study (COVICS). Our objectives were to measure antibody responses and neutralization titers after COVID-19 vaccination in individuals with a broad range of immunocompromising conditions compared to nonimmunocompromised healthcare workers (HCW). COVICS is a prospective observational electronic medical record (EMR)-embedded study of adults who had completed their COVID-19 vaccine series. The study was approved by the University of Pittsburgh Institutional Review Board (Study21030056). Enrollment began on April 14 th , 2021 and occurred online. To obtain serum across the University of Pittsburgh Medical Center (UPMC) Health System, a study-specific severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) IgG order was built in the EMR without billing of the patient. Serum could be drawn at any of 16 UPMC hospital-based labs across Western Pennsylvania. Test results were shared via the EMR. Detailed information on recruitment materials (all of which were IRB-approved), the consent process, and enrollment metrics can be found in the Supplement (including Supplementary figures S8 and S9). Briefly, the study primarily used online infrastructure for advertisement and enrollment. HCW expressed interest about participating in the study after receiving information through word-of-mouth or department-wide e-mails. Immunocompromised patients primarily self-referred to the study after learning about it through direct messages sent to them via our patient portal "MyUPMC" (25%), a UPMC newsletter email (24%), a disease specialist (21%), or another member of the healthcare team (11%); additional recruitment materials are described in Supplementary figure S8. A link to the study website and a phone number for the study team were provided in all enrollment materials. Once an individual expressed interest in participating, their medical record was reviewed to confirm eligibility. Eligible participants were contacted by a clinical research coordinator (CRC) or the principal investigator (PI, G.H.) and given the option to either enroll on the phone with the CRC or PI, or to self-enroll by watching an 8-minute, IRBapproved, REDCap-embedded video of the PI describing the study. Participants who opted to self-enroll were also given the option to ask questions, after which they were contacted by a CRC or the PI prior to signing the consent form. Participants who preferred to enroll in person in a clinic were scheduled for a research visit. All participants were required to sign an electronic or paper consent form; once signed, an antibody order was placed by a CRC (or PI) in the EMR, cosigned by the PI, and then emailed to the participant. Immunocompromised patients were eligible if they had any of the following: solid Data were collected using the Research Electronic Data Capture (REDCap) hosted at the University of Pittsburgh 5 . For medical history adjudication, a two-step process was used: patients self-reported their own underlying immunocompromised conditions, which were then confirmed by the PI (G.H.) or a CRC by manual chart review. Our online enrollment system was designed to not allow HCW to enroll should they answer "yes" to having an immunocompromising condition, which was also confirmed by manual chart review. The REDCap database asked participants to record (by multiple-choice, with a free-text option) specific categories of medications they were taking, primarily immunosuppressive drugs for SOT or autoimmune conditions, whether they were receiving antiretroviral therapy for HIV, and whether they were receiving systemic therapy or radiation therapy for cancer. Medical records for all participants with missing or incomplete medication data were manually reviewed by the PI (G.H.) or a CRC (L.C.), who then updated the REDCap records. G.H. and L.C. also confirmed all plasma HIV RNA levels and CD4+ T cell counts. Specific cancer chemotherapy drugs and other non-immunocompromising comorbidities were extracted from the EMR with the assistance of UPMC's Clinical Analytics Group (K.C.). Serum was processed at UPMC's CLIA-88 accredited Central Lab, then aliquoted for additional testing at UPMC's Division of Infectious Diseases laboratories. The primary outcome was the proportion of immunocompromised individuals versus HCW with a reactive (seropositive) Beckman Coulter assay (see below) for IgG to SARS-CoV-2 spike (S) receptor-binding domain (RBD). We also compared the distribution of antibody levels across subgroups, compared IgG levels to RBD with those of another assay (Bio-Rad Bio-Plex; see below), and performed pseudovirus neutralization assays (described below) in a subset of participants. Antibody assays. Serum was tested using the Beckman Coulter SARS-CoV-2 platform (IgG against the S protein RBD) per the manufacturer's instructions [6] [7] [8] . Serum IgG results are expressed as extinction coefficient signal/cutoff (S/CO) ratios or "levels" and interpreted as reactive (≥ 1.00), equivocal (0.80-1.00), or non-reactive (≤ 0.80) 8 . For data analysis, reactive results were defined as seropositive, and equivocal or non-reactive results were defined as seronegative. Sera from a subset of 245 participants (197 immunocompromised and 48 HCW), stratified by S/CO antibody level (97 with levels < 1, 79 with levels 1-10, and 69 with levels > 10), also underwent testing for IgG to RBD using the Bio-Rad Bio-Plex Pro Human SARS-CoV-2 Serology Assay, as previously described 9 (characteristics in Supplementary table S1). To determine the ability of serum from vaccinated individuals to neutralize SARS-CoV-2, sera from 100 study participants (50 immunocompromised, 50 HCW) underwent testing using a previously reported pseudovirus neutralization assay 10 10 . Baseline characteristics, seropositivity with 95% Clopper-Pearson exact confidence intervals, antibody levels, and NT50 were compared between immunocompromised participants and HCW using two-sample Student t-tests, Wilcoxon rank sum tests, or chisquare tests as appropriate. Within each group, these same variables were presented descriptively by underlying condition. For the binary outcome variable of seropositive versus seronegative, we computed the unadjusted odds ratio (ORs) and 95% confidence interval for the individual risk factors. Stepwise multivariable logistic regression analysis was then used to calculate adjusted ORs for seropositivity, which included factors found to be associated with seropositivity at the p<0.10 level. Throughout the text, only adjusted ORs are provided, which represent the ORs of seropositivity (or of being seropositive); the Tables show both adjusted and unadjusted ORs for seropositivity. We performed an additional exploratory analysis using antibody levels as a continuous outcome measure, with the same independent variables used to calculate the ORs for seropositivity. These results are presented as incidence rate ratios (IRRs) in the supplement (supplementary tables S11 -S16). The Spearman correlation coefficient was estimated between antibody levels (Beckman assay) and pseudovirus NT50, and between antibody levels by the Beckman and Bio-Rad assays. Analyses were performed using Stata SE, version 16.1 (College Station, TX). Two-sided tests with an α =0.05 was used to denote statistical significance. Between April 14 and July 19, 2021, 1271 participants were enrolled: 1,099 immunocompromised patients (86.5%) and 172 HCW (13.5%) ( Figure 1A ). Next, we examined risk factors for a negative antibody response by underlying condition. Table 3A) : By multivariate analysis, only time from vaccination (but not type of vaccine) was significantly associated with a lower odds of seropositivity (adjusted OR 0.97 (95% CI 0.94 -0.99), p = 0.004). The probability of developing a reactive antibody level decreased with each month post-vaccination, with 30-, 60-, 90-, 120-, and 150-day seropositivity of 99.8%, 99.5%, 98.6%, 96.5%, and 91.8%, respectively. There was no association between age and seropositivity. (Tables 3A and 4A) . By multivariate analysis, we identified Table 3 ). Compared to liver transplant recipients, non-liver recipients were significantly less likely to be seropositive (adjusted OR for kidney, lung, or heart versus liver transplant 0.53 (0.29 -0.98), p = 0.041; 0.21 (0.08 -0.54), p = 0.001; and 0.26 (0.13 -0.51), p < 0.001 respectively). These differences in seropositivity by organ type persisted even after adjusting for the number of immunosuppressive medications, although there was a nonstatistically significant but potentially meaningful difference in the number of immunosuppressive drugs by organ type (Supplementary Table 4 ). Neither time since vaccination nor a recent rejection episode impacted vaccine responses, though only 9 SOT recipients had been treated for rejection within 3 months before vaccination. Patients with autoimmune conditions (Tables 3A and 4A, supplementary table 7) . Vaccination with BNT162b2 or use of anti-CD20 monoclonal antibodies were associated with a lower odds of seropositivity compared to participants who received mRNA-1273 or those who had not received an anti-CD20 monoclonal antibody Patients with cancer (Tables 3B and 4B, supplementary tables 5 Administration of cancer-specific therapy within 12 months before vaccination was associated with a lower odds of seropositivity for both patients with hematological malignancies and those with solid tumors. This observation was driven by individuals who had received an anti-CD20 monoclonal antibody within the prior 12 months (OR for seropositivity after adjusting for age, vaccine type, and days since vaccination 0. Antibody levels. Antibody S/CO levels were significantly lower in immunocompromised patients compared to HCW ( Figure 1B) . This finding was driven by the higher proportion of participants with negative antibody results in the immunocompromised group. When we analyzed only seropositive study participants ( Figure 1C ), antibody levels of seropositive HCW (median 6 Figure 1D ). When antibody levels were analyzed as the continuous outcome measure (instead of seropositivity as a categorical variable) with the same predictor variables used to calculate ORs for seropositivity, we found that overall, variables associated with higher/lower antibody levels were generally similar to those associated with seropositivity (vs seronegativity) (supplementary tables S11-S16). The main differences found, compared with seropositivity as the outcome measure, were that time since vaccination was significantly associated with lower antibody levels across all subgroups (indicating that antibody titers decline over time), as was the use of non-mRNA-1273 SARS-CoV-2 vaccines (as described in detail below). Comparison of levels with 2 different assays: Antibody levels using the Beckman assay strongly correlated with anti-RBD titers using the Bio-Rad assay (Spearman r = 0.93, p< 0.0001, Figure 1F ), although 13% of the results were discordant. (Supplementary Second, a greater interval between vaccination and testing was associated with a lower odds of a positive antibody response in HCW and individuals with autoimmune conditions. HCW in our study underwent testing at a median of 4.4 months after vaccination, which may explain why their seropositivity (92.4%) was surprisingly lower than the 100% seroconversion described in the phase 1/2 mRNA vaccine trials, in which antibody levels were measured 1-2 months after vaccination 15, 16 . Although we found no association between time since vaccination and seropositivity among other patient groups, we did find that longer time since vaccination predicted lower antibody levels across all patient groups. It is therefore plausible that we may have observed a difference in seropositivity with longer follow-up. Furthermore, the longer interval between vaccination and antibody testing for In contrast, we found that the odds of seropositivity among SOT patients receiving two or more immunosuppressive drugs was 72% lower compared to those receiving only 1 drug, irrespective of whether the patient was receiving an antimetabolite, suggesting that the overall degree of immunosuppression, not specific drugs, is the main predictor of poor humoral responses after SOT. This finding is corroborated by our observation that antimetabolites did not impact vaccine responses in individuals with autoimmune conditions. We also found that seropositivity in lung, heart, or kidney transplant recipients was significantly lower than that of liver transplant recipients. This risk was most pronounced in lung or heart transplant recipients, in whom the odds of seropositivity was 79% and 74% lower than that of liver transplant recipients, respectively. Whether recipients of thoracic organ transplants may benefit from modified vaccine schedules and doses is not currently known. Among oncology patients, receipt of anti-cancer therapy over the preceding 12 months conferred a lower odds of seropositivity. However, this observation was driven primarily by the use of anti-CD20 monoclonal antibodies but not cytotoxic chemotherapy, checkpoint inhibitors, radiation therapy, or others. The existing literature evaluating the specific risk of poor humoral responses in the oncology population has been conflicting, with Limitations of this study include lack of assessment of cellular immunity and timing of chemotherapy in relation to vaccination, low number of HSCT recipients, and heterogeneity of immunosuppressive therapies for cancer and autoimmune conditions. In addition, that immunocompromised participants were significantly older than HCW and HCW were predominantly female, may have influenced our results, although vaccine efficacy in the phase 3 mRNA vaccine trials did not appear to be impacted by either age or gender 28, 29 . Despite using a two-tiered approach (self-report and chart review) to exclude participants with a prior history of COVID-19, a few participants with a prior positive SARS-CoV-2 PCR result outside of our system may have been missed. Similarly, although every attempt was made to ensure the accuracy of underlying medical conditions and medications, potential errors may have been introduced through either patient self-reports or EMR extraction of data. We also did not assess neutralizing antibody titers against the Delta or newlydescribed Omicron SARS-CoV-2 variants. Given that vaccine efficacy against these two variants is likely reduced 30 anti-RBD antibody levels (Signal to Cut-Off (S/CO) Ratio) measured by Beckman assay (yaxis). NT50 was defined as the highest serum dilution that neutralizes >50% of the D614G pseudovirus. Black filled circles are data from non-immunocompromised healthcare workers and red filled circles from immunocompromised participants. Figure 2B . Comparisons of antibody levels and NT50 across 100 study participants. Black, blue, and red boxes represent participants with antibody levels < 1, 1-10, and > 10, respectively. Among participants with antibody S/CO levels 1-10, NT50 were significantly lower among IC participants compared to HCW. HCW, healthcare workers; IC, immunocompromised. #Antibody levels and categories are defined by signal to cut-off ratio from the Beckman anti-RBD assay. HIV, human immunodeficiency virus; SOT, solid organ transplant. Variables with a p value of < 0.1 were entered in the multivariate model from which adjusted odds ratios were calculated. SOT, solid organ transplant; HIV, human immunodeficiency virus; IQR, interquartile range. 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