Proinflammatory IgG Fc structures in patients with severe COVID-19.

Severe acute respiratory syndrome coronavirus 2 infections can cause coronavirus disease 2019 (COVID-19), which manifests with a range of severities from mild illness to life-threatening pneumonia and multi-organ failure. Severe COVID-19 is characterized by an inflammatory signature, including high levels of inflammatory cytokines, alveolar inflammatory infiltrates and vascular microthrombi. Here we show that patients with severe COVID-19 produced a unique serologic signature, including an increased likelihood of IgG1 with afucosylated Fc glycans. This Fc modification on severe acute respiratory syndrome coronavirus 2 IgGs enhanced interactions with the activating Fcγ receptor FcγRIIIa; when incorporated into immune complexes, Fc afucosylation enhanced production of inflammatory cytokines by monocytes, including interleukin-6 and tumor necrosis factor. These results show that disease severity in COVID-19 correlates with the presence of proinflammatory IgG Fc structures, including afucosylated IgG1.

Germline-encoded specificities and the predictability of the B cell response.

Antibodies result from the competition of B cell lineages evolving under selection for improved antigen recognition, a process known as affinity maturation. High-affinity antibodies to pathogens such as HIV, influenza, and SARS-CoV-2 are frequently reported to arise from B cells whose receptors, the precursors to antibodies, are encoded by particular immunoglobulin alleles. This raises the possibility that the presence of particular germline alleles in the B cell repertoire is a major determinant of the quality of the antibody response. Alternatively, initial differences in germline alleles' propensities to form high-affinity receptors might be overcome by chance events during affinity maturation. We first investigate these scenarios in simulations: when germline-encoded fitness differences are large relative to the rate and effect size variation of somatic mutations, the same germline alleles persistently dominate the response of different individuals. In contrast, if germline-encoded advantages can be easily overcome by subsequent mutations, allele usage becomes increasingly divergent over time, a pattern we then observe in mice experimentally infected with influenza virus. We investigated whether affinity maturation might nonetheless strongly select for particular amino acid motifs across diverse genetic backgrounds, but we found no evidence of convergence to similar CDR3 sequences or amino acid substitutions. These results suggest that although germline-encoded specificities can lead to similar immune responses between individuals, diverse evolutionary routes to high affinity limit the genetic predictability of responses to infection and vaccination.

Normalizing Microbiota-Induced Retinoic Acid Deficiency Stimulates Protective CD8(+) T Cell-Mediated Immunity in Colorectal Cancer.

Although all-trans-retinoic acid (atRA) is a key regulator of intestinal immunity, its role in colorectal cancer (CRC) is unknown. We found that mice with colitis-associated CRC had a marked deficiency in colonic atRA due to alterations in atRA metabolism mediated by microbiota-induced intestinal inflammation. Human ulcerative colitis (UC), UC-associated CRC, and sporadic CRC specimens have similar alterations in atRA metabolic enzymes, consistent with reduced colonic atRA. Inhibition of atRA signaling promoted tumorigenesis, whereas atRA supplementation reduced tumor burden. The benefit of atRA treatment was mediated by cytotoxic CD8(+) T cells, which were activated due to MHCI upregulation on tumor cells. Consistent with these findings, increased colonic expression of the atRA-catabolizing enzyme, CYP26A1, correlated with reduced frequencies of tumoral cytotoxic CD8(+) T cells and with worse disease prognosis in human CRC. These results reveal a mechanism by which microbiota drive colon carcinogenesis and highlight atRA metabolism as a therapeutic target for CRC.

Measuring the effectiveness of online search ads on blood donor recruitment and donation.

BACKGROUND: Acutely highlighted during the COVID-19 pandemic, the tenuousness of the blood supply continues to be a lynchpin of the most important medical procedures. Online advertisements have become a mainstay in donor recruitment. We set out to determine the effectiveness of online search ads and variations thereof on blood donations with an emphasis on first-time donors. STUDY DESIGN AND METHODS: From September 01, 2022 through March 31, 2023, we performed a campaign comparison experiment through a major search-ads platform with two distinct messages: one altruistic ("Altruistic") and one with a prospect of rewards ("Promotion"). We developed a method to track donation outcomes and associated them with impressions, click-throughs, and conversions. We compared the performance of the Altruistic and Promotion arms to a control group that was not associated with any search-ads ("Baseline"). RESULTS: Analyzing 34,157 donations during the study period, the Promotion group, and not Altruistic, had a significant difference of first-time donors over Baseline (24% vs. 12%, p = 7e-6). We analyzed 49,305 appointments and discovered that appointments made from the Altruistic arm resulted in a significantly higher percentage of donations when compared to Baseline (57% vs. 53%, p = .009); however, the Promotion group had a higher percentage of donations from first-time donors when compared to Baseline (12% vs. 8%, p = .006). CONCLUSION: We developed a method for determining the effectiveness of online search ads on donation outcomes. Rewards/promotions messaging was most effective at recruiting first-time donors. Our methodology is generalizable to different blood centers to explore messaging effectiveness among their unique communities.

Hepatitis E virus seropositivity in an ethnically diverse community blood donor population.

BACKGROUND AND OBJECTIVES: Hepatitis E virus (HEV) is an underrecognized and emerging infectious disease that may threaten the safety of donor blood supply in many parts of the world. We sought to elucidate whether our local community blood supply is at increased susceptibility for transmission of transfusion-associated HEV infections. MATERIALS AND METHODS: We screened 10,002 randomly selected donations over an 8-month period between 2017 and 2018 at the Stanford Blood Center for markers of HEV infection using commercial IgM/IgG serological tests and reverse transcriptase quantitative polymerase chain reaction assays (RT-qPCR). Donor demographic information, including gender, age, self-identified ethnicity, location of residence and recent travel, were obtained from the donor database and used to generate multivariate binary logistic regressions for risk factors of IgG seropositivity. RESULTS: A total of 10,002 blood donations from 7507 unique donors were screened, and there was no detectable HEV RNA by RT-qPCR. The overall seropositivity rate was 12.1% for IgG and 0.56% for IgM. Multivariate analysis of unique donors revealed a significantly higher risk of IgG seropositivity with increasing age, White/Asian ethnicities and residence in certain local counties. CONCLUSION: Although HEV IgG seroprevalence in the San Francisco Bay Area is consistent with ongoing infection, the screening of a large donor population did not identify any viraemic blood donors. While HEV is an underrecognized and emerging infection in other regions, there is no evidence to support routine blood screening for HEV in our local blood supply currently; however, periodic monitoring may still be required to assess the ongoing risk.

Factors associated with first-time and repeat blood donation: Adverse reactions and effects on donor behavior.

BACKGROUND: Blood centers have a dual mission to protect donors and patients; donor safety is paramount to maintaining an adequate blood supply. Elucidating donor factors associated with adverse reactions (AR) is critical to this mission. STUDY DESIGN/METHODS: A retrospective cohort analysis of whole blood donors from 2003 to 2020 was conducted at a single blood center in northern California. Adjusted odds ratios (AORs) with 95% CIs for ARs were estimated via multivariable logistic regression on demographics, donation history, and physical examination data. Where appropriate, Wilcoxon-Rank Sum and chi-squared tests were used to determine significance. RESULTS: First-time blood donors (FTD) exhibited a higher AR rate than repeat donors (4.4% vs. 1.9% p < .0001). When compared with FTDs without AR, FTDs with ARs (FT-AR) were less likely to return (30.0% vs. 47.3%, p < .0001), and, of those who returned, had a higher rate of reaction 20.2% versus 2.8% (p < .001). Factors found to be associated with FT-AR (younger age, increased heart rate, and higher diastolic blood pressure) still correlated positively with AR on return donation, but to a lower degree. FTD who potentially witnessed an AR had a lower return rate (44.6% vs. 47.3%, p = <.001) and donated fewer units (2.38 vs. 3.37, p < .001) when compared to FTD who did not witness an AR. CONCLUSION: The AR on FTD increases the AR likelihood of return donation. Longitudinal analysis shows that a time-based deferral policy targeted at FT-AR young donors can reduce the number of ARs while not dramatically impacting the blood supply.

Implementation strategy for complete pathogen reduction technology treated apheresis platelet inventory.

BACKGROUND: Bacterial contamination in platelets remain a major public health concern, which prompted the US Food and Drug Administration guidance for bacterial contamination mitigation. Pathogen reduction technology (PRT) is one mitigation strategy that has shown success in Europe over the last decade. Therefore, our center sought to transition from a dual system of bacterial culturing (BacT) and PRT to full PRT. METHODS: A 1 month pilot study was conducted to simulate 100% PRT conditions. Our center also collected baseline data on key platelet production metrics in the 4 months prior to 100% PRT and compared it to the 4 months post-implementation. RESULTS: The pilot study showed no statistical differences in split rate, proportion of low-yield products, or proportion of single, double, and triple collections. The only observed difference was an 11 min increase in the average duration of double collections. Our baseline versus post-implementation monitoring showed no difference in split rate, discard rate, percentage of low-yield units, and average yield of low yield units. Statistical differences were detected in the proportion of single, double, and triple collections, as well as the average yield of full dose products. Roughly 20% of our inventory consisted of low-yield products. DISCUSSION: With suitable mitigation strategies, transitioning to a full PRT inventory may result in higher net margins while not adversely affecting overall platelet production. A pilot study is a good way to project potential effects of switching from a dual BacT and PRT inventory to full PRT, and can be adopted by other centers aiming to make the transition.

Interferon-γ Release Assay for Accurate Detection of Severe Acute Respiratory Syndrome Coronavirus 2 T-Cell Response.

We investigated feasibility and accuracy of an interferon-γ release assay (IGRA) for detection of T-cell responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Whole blood IGRA accurately distinguished between convalescent and uninfected healthy blood donors with a predominantly CD4+ T-cell response. SARS-CoV-2 IGRA may serve as a useful diagnostic tool in managing the coronavirus disease 2019 pandemic.

How do I implement pathogen-reduced platelets?

BACKGROUND: Several risk mitigation steps have improved the safety of platelets in regard to bacterial contamination, but this continues to be a concern today. A Food and Drug Administration (FDA) Guidance issued in December 2018 aims to further limit this risk. The guidance offers multiple pathways for compliance, and hospital blood banks will have to collaborate with blood donor centers to assess various factors before deciding which method is most appropriate for them. METHODS AND MATERIALS: Our institution considered several factors before moving forward with pathogen reduction technology. This included an assessment of platelet shelf-life, bacterial testing requirements, the efficacy of low-yield platelets, and managing a mixed platelet inventory. The decision to transition to pathogen-reduced platelets was associated with complex collection and processing limitations that resulted in either an increase in platelets that were over-concentrated or products with a low platelet yield. RESULTS: Through trials of various collection settings with unique target volumes and target platelet yields, our blood donor center was able to optimize the production. At the hospital end, this transition required a thorough review of low-yield platelet products and their clinical efficacy. Additionally, this implementation necessitated collaboration with clinical colleagues, comprehensive education, and training. CONCLUSIONS: Pathogen-reduced platelets would be the most efficient way for our institution to be compliant. This summary may serve as a roadmap for other institutions that are considering which FDA prescribed method to use and provide support for those that have decided on pathogen reduction technology but need to optimize their collections to best utilize low-yield products.

Identifying correlations between donor demographics and isohemagglutinin titers as a potential method to screen for low-titer group O whole blood.

BACKGROUND: With more hospitals using low-titer group O whole blood in trauma resuscitation, having an efficient screening method for low-titer donors is critical. Our blood center uses an automated screen for high-titer isohemagglutinins in our platelet donations while collecting detailed donor demographic information. Using this data, we can identify key demographics often associated with titer status, thereby helping develop a donor-triaging method for titering. STUDY DESIGN AND METHODS: Titer results were read with an automated microplate system as either high or low, based on agglutination, with a cutoff equivalent to 1:256 (both anti-A and anti-B). Donor demographic data analyzed included date of donation, blood group, age, gender, and ethnicity. RESULTS: 57,508 donations were collected from 2073 unique donors between 2014 and 2018. We found the following demographics to be correlated with titer status: gender, ABO blood group, age, and ethnicity. Variability in titer status was identified in 215 individuals. This represented around 10 % of the total unique donors and was split equally amongst gender. We also found that donors between the ages of 41-60 ha d the highest likelihood of having variability in titer status, peaking at 13 %, and this proportion declined past age 60. CONCLUSION: Titer status is associated with the following donor demographics: gender, ABO type, age, and ethnicity. We also discovered that variability in titer status is correlated with age. In blood centers that do not have automated and routine titer screening procedure, these findings could be used as a method to efficiently identify low-titer donors a-priori.

Optimizing O-negative RBC utilization using a data-driven approach.

BACKGROUND: O-negative red blood cells (ON-RBC) are a precious resource and the international blood banking community has become increasingly concerned with its inappropriate utilization. AABB recently made several recommendations to address the issue. Solutions must be multifaceted and involve donor centers, blood banks, and clinical departments. From the perspective of a hospital blood bank, it is difficult to rely solely on increased donor recruitment and ubiquitous blood typing of the entire in-patient population. We therefore focused on interventions within the blood bank to optimize inventory and policies to ensure appropriate ON-RBC utilization. STUDY DESIGN AND METHODS: Transfusion data over one year was examined for the rate of out-of-group/inappropriate ON-RBC. Furthermore, we assessed whether that rate was related to product life on the day of transfusion. We also examined our stock inventory levels and how excess inventory can contribute to inappropriate ON-RBC usage. RESULTS: The ON-RBC inventory level was decreased in order to reduce the rate of inappropriate transfusions while maintaining a safe level for optimal patient care. Compared to baseline, our intervention caused ON-RBCs to be transfused earlier in their shelf-life (9.27 vs. 11.15 days from expiration [DFE], p = 0.0012). This reduced the overall rate of inappropriate ON-RBC transfusions (67% vs. 54%, p = 0.0035), approximating 185 units of ON-RBC saved over the course of 6 months. CONCLUSIONS: A data-driven approach to optimize stock inventory levels is widely applicable; it can be adopted by numerous institutions to improve utilization and establish a benchmark for the broader blood banking community.

Big data modeling to predict platelet usage and minimize wastage in a tertiary care system.

Maintaining a robust blood product supply is an essential requirement to guarantee optimal patient care in modern health care systems. However, daily blood product use is difficult to anticipate. Platelet products are the most variable in daily usage, have short shelf lives, and are also the most expensive to produce, test, and store. Due to the combination of absolute need, uncertain daily demand, and short shelf life, platelet products are frequently wasted due to expiration. Our aim is to build and validate a statistical model to forecast future platelet demand and thereby reduce wastage. We have investigated platelet usage patterns at our institution, and specifically interrogated the relationship between platelet usage and aggregated hospital-wide patient data over a recent consecutive 29-mo period. Using a convex statistical formulation, we have found that platelet usage is highly dependent on weekday/weekend pattern, number of patients with various abnormal complete blood count measurements, and location-specific hospital census data. We incorporated these relationships in a mathematical model to guide collection and ordering strategy. This model minimizes waste due to expiration while avoiding shortages; the number of remaining platelet units at the end of any day stays above 10 in our model during the same period. Compared with historical expiration rates during the same period, our model reduces the expiration rate from 10.5 to 3.2%. Extrapolating our results to the ∼2 million units of platelets transfused annually within the United States, if implemented successfully, our model can potentially save ∼80 million dollars in health care costs.

Streamlining a blood center and hospital transfusion service supply chain with an informatics vendor-managed inventory solution: development, implementation, and 3-month follow-up.

BACKGROUND: The ordering process at Stanford Health Care involved twice-daily shipments predicated upon current stock levels from the blood center to the hospital transfusion service. Manual census determination is time consuming and error prone. We aimed to enhance inventory management by developing an informatics platform to streamline the ordering process and reallocate staff productivity. STUDY DESIGN AND METHODS: The general inventory accounts for more than 50 product categories based on characteristics including component, blood type, irradiation status, and cytomegalovirus serology status. Over a 5-month calibration period, inventory levels were determined algorithmically and electronically. An in-house software program was created to determine inventory levels, optimize the electronic ordering process, and reduce labor time. A 3-month pilot period was implemented using this program. RESULTS: This system showed noninferiority while saving labor time. The average weekly transfused:stocked ratios for cryoprecipitate, plasma, and red blood cells, respectively, were 1.03, 1.21, and 1.48 before the pilot period, compared with 0.88, 1.17, and 1.40 during (p = 0.28). There were 27 (before) and 31 (during) average STAT units ordered per week (p = 0.86). The number of monthly wasted products due to expiration was 226 (before) and 196 (during) units, respectively (p = 0.28). An estimated 7 hours per week of technologist time was reallocated to other tasks. CONCLUSION: An in-house electronic ordering system can enhance information fidelity, reallocate and optimize valuable staff productivity, and further standardize ordering. This system showed noninferiority to the labor-intensive manual system while freeing up over 360 hours of staff time per year.

Human B-cell isotype switching origins of IgE.

BACKGROUND: B cells expressing IgE contribute to immunity against parasites and venoms and are the source of antigen specificity in allergic patients, yet the developmental pathways producing these B cells in human subjects remain a subject of debate. Much of our knowledge of IgE lineage development derives from model studies in mice rather than from human subjects. OBJECTIVE: We evaluate models for isotype switching to IgE in human subjects using immunoglobulin heavy chain (IGH) mutational lineage data. METHODS: We analyzed IGH repertoires in 9 allergic and 24 healthy adults using high-throughput DNA sequencing of 15,843,270 IGH rearrangements to identify clonal lineages of B cells containing members expressing IgE. Somatic mutations in IGH inherited from common ancestors within the clonal lineage are used to infer the relationships between B cells. RESULTS: Data from 613,641 multi-isotype B-cell clonal lineages, of which 592 include an IgE member, are consistent with indirect switching to IgE from IgG- or IgA-expressing lineage members in human subjects. We also find that these inferred isotype switching frequencies are similar in healthy and allergic subjects. CONCLUSIONS: We found evidence that secondary isotype switching of mutated IgG1-expressing B cells is the primary source of IgE in human subjects, with lesser contributions from precursors expressing other switched isotypes and rarely IgM or IgD, suggesting that IgE is derived from previously antigen-experienced B cells rather than naive B cells that typically express low-affinity unmutated antibodies. These data provide a basis from which to evaluate allergen-specific human antibody repertoires in healthy and diseased subjects.

Effects of aging, cytomegalovirus infection, and EBV infection on human B cell repertoires.

Elderly humans show decreased humoral immunity to pathogens and vaccines, yet the effects of aging on B cells are not fully known. Chronic viral infection by CMV is implicated as a driver of clonal T cell proliferations in some aging humans, but whether CMV or EBV infection contributes to alterations in the B cell repertoire with age is unclear. We have used high-throughput DNA sequencing of IGH gene rearrangements to study the BCR repertoires over two successive years in 27 individuals ranging in age from 20 to 89 y. Some features of the B cell repertoire remain stable with age, but elderly subjects show increased numbers of B cells with long CDR3 regions, a trend toward accumulation of more highly mutated IgM and IgG Ig genes, and persistent clonal B cell populations in the blood. Seropositivity for CMV or EBV infection alters B cell repertoires, regardless of the individual's age: EBV infection correlates with the presence of persistent clonal B cell expansions, whereas CMV infection correlates with the proportion of highly mutated Ab genes. These findings isolate effects of aging from those of chronic viral infection on B cell repertoires and provide a baseline for understanding human B cell responses to vaccination or infectious stimuli.

How do I implement an automated screen for high-titer ABO antibody as an inventory management tool for ABO plasma-incompatible platelets?

BACKGROUND: Plasma volume reduction (PVR) may reduce the risk of hemolysis associated with transfusion of plateletpheresis blood products (PLTs) containing ABO-incompatible plasma. But PVR may delay PLT issue. In collaboration with our blood donor center we evaluated an automated screen of PLT for high-titer ABO antibody and to apply PVR to high-titer PLTs. STUDY DESIGN AND METHODS: At the donor center, plasma from PLT donors was tested using an automated microplate system (PK7300, Beckman). PK settings were set for a detection cutoff equivalent to 1 in 256 using a manual tube method. The donors associated with high-titer PLTs were characterized by sex and age. In the transfusion service, the number of PVR procedures was evaluated before and after implementation of the high-titer screen. RESULTS: During validation, 157 of 1008 PLT units (15%) were positive by the automated method versus 121 (12%) by manual method. After implementation, 2112 of 15,240 PLT units were high-titer, with higher frequency in donations from females versus males (18% vs. 12%, p < 0.0001). The PLT PVR rate was reduced by 50%. CONCLUSION: Implementation of an automated method to screen PLTs for high-titer ABO antibody at the donor center improves the inventory management of PLTs containing ABO-incompatible plasma at the hospital transfusion service.

Disease diagnostics using machine learning of immune receptors.

Clinical diagnosis typically incorporates physical examination, patient history, and various laboratory tests and imaging studies, but makes limited use of the human system's own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis (Mal-ID) , an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to SARS-CoV-2, Influenza, and HIV, highlight antigen-specific receptors, and reveal distinct characteristics of Systemic Lupus Erythematosus and Type-1 Diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of human immune responses.

Targeting repetitive laboratory testing with electronic health records-embedded predictive decision support: A pre-implementation study.

INTRODUCTION: Unnecessary laboratory testing contributes to patient morbidity and healthcare waste. Despite prior attempts at curbing such overutilization, there remains opportunity for improvement using novel data-driven approaches. This study presents the development and early evaluation of a clinical decision support tool that uses a predictive model to help providers reduce low-yield, repetitive laboratory testing in hospitalized patients. METHODS: We developed an EHR-embedded SMART on FHIR application that utilizes a laboratory test result prediction model based on historical laboratory data. A combination of semi-structured physician interviews, usability testing, and quantitative analysis on retrospective laboratory data were used to inform the tool's development and evaluate its acceptability and potential clinical impact. KEY RESULTS: Physicians identified culture and lack of awareness of repeat orders as key drivers for overuse of inpatient blood testing. Users expressed an openness to a lab prediction model and 13/15 physicians believed the tool would alter their ordering practices. The application received a median System Usability Scale score of 75, corresponding to the 75th percentile of software tools. On average, physicians desired a prediction certainty of 85% before discontinuing a routine recurring laboratory order and a higher certainty of 90% before being alerted. Simulation on historical lab data indicates that filtering based on accepted thresholds could have reduced âˆ¼22% of repeat chemistry panels. CONCLUSIONS: The use of a predictive algorithm as a means to calculate the utility of a diagnostic test is a promising paradigm for curbing laboratory test overutilization. An EHR-embedded clinical decision support tool employing such a model is a novel and acceptable intervention with the potential to reduce low-yield, repetitive laboratory testing.

Differential Peripheral Blood Glycoprotein Profiles in Symptomatic and Asymptomatic COVID-19.

Glycosylation is the most common form of post-translational modification of proteins, critically affecting their structure and function. Using liquid chromatography and mass spectrometry for high-resolution site-specific quantification of glycopeptides coupled with high-throughput artificial intelligence-powered data processing, we analyzed differential protein glycoisoform distributions of 597 abundant serum glycopeptides and nonglycosylated peptides in 50 individuals who had been seriously ill with COVID-19 and in 22 individuals who had recovered after an asymptomatic course of COVID-19. As additional comparison reference phenotypes, we included 12 individuals with a history of infection with a common cold coronavirus, 16 patients with bacterial sepsis, and 15 healthy subjects without history of coronavirus exposure. We found statistically significant differences, at FDR < 0.05, for normalized abundances of 374 of the 597 peptides and glycopeptides interrogated between symptomatic and asymptomatic COVID-19 patients. Similar statistically significant differences were seen when comparing symptomatic COVID-19 patients to healthy controls (350 differentially abundant peptides and glycopeptides) and common cold coronavirus seropositive subjects (353 differentially abundant peptides and glycopeptides). Among healthy controls and sepsis patients, 326 peptides and glycopeptides were found to be differentially abundant, of which 277 overlapped with biomarkers that showed differential expression between symptomatic COVID-19 cases and healthy controls. Among symptomatic COVID-19 cases and sepsis patients, 101 glycopeptide and peptide biomarkers were found to be statistically significantly abundant. Using both supervised and unsupervised machine learning techniques, we found specific glycoprotein profiles to be strongly predictive of symptomatic COVID-19 infection. LASSO-regularized multivariable logistic regression and K-means clustering yielded accuracies of 100% in an independent test set and of 96% overall, respectively. Our findings are consistent with the interpretation that a majority of glycoprotein modifications observed which are shared among symptomatic COVID-19 and sepsis patients likely represent a generic consequence of a severe systemic immune and inflammatory state. However, there are glycoisoform changes that are specific and particular to severe COVID-19 infection. These may be representative of either COVID-19-specific consequences or susceptibility to or predisposition for a severe course of the disease. Our findings support the potential value of glycoproteomic biomarkers in the biomedical understanding and, potentially, the clinical management of serious acute infectious conditions.