Altered Fc glycosylation of anti-HLA alloantibodies in hemato-oncological patients receiving platelet transfusions

Background: The formation of alloantibodies directed against class I human leukocyte antigens (HLA) continues to be a clinically challenging complication after platelet transfusions, which can lead to platelet refractoriness (PR) and occurs in approximately 5%–15% of patients with chronic platelet support. Interestingly, anti-HLA IgG levels in alloimmunized patients do not seem to predict PR, suggesting functional or qualitative differences among anti-HLA IgG. The binding of these alloantibodies to donor platelets can result in rapid clearance after transfusion, presumably via FcγR-mediated phagocytosis and/or complement activation, which both are affected by the IgG-Fc glycosylation. Objectives: To characterize the Fc glycosylation profile of anti-HLA class I antibodies formed after platelet transfusion and to investigate its effect on clinical outcome. Patients/Methods: We screened and captured anti-HLA class I antibodies (anti-HLA A2, anti-HLA A24, and anti-HLA B7) developed after platelet transfusions in hemato-oncology patients, who were included in the PREPAReS Trial. Using liquid chromatography-mass spectrometry, we analyzed the glycosylation profiles of total and anti-HLA IgG1 developed over time. Subsequently, the glycosylation data was linked to the patients' clinical information and posttransfusion increments. Results: The glycosylation profile of anti-HLA antibodies was highly variable between patients. In general, Fc galactosylation and sialylation levels were elevated compared to total plasma IgG, which correlated negatively with the platelet count increment. Furthermore, high levels of afucosylation were observed for two patients. Conclusions: These differences in composition of anti-HLA Fc-glycosylation profiles could potentially explain the variation in clinical severity between patients

Fc galactosylation of anti-platelet human IgG1 alloantibodies enhances complement activation on platelets

Approximately 20% of patients receiving multiple platelet transfusions develop platelet alloantibodies, which can be directed against human leukocyte antigens (HLA) and, to a lesser extent, against human platelet antigens (HPA). These antibodies can lead to the rapid clearance of donor platelets, presumably through IgG-Fc receptor (FcγR)-mediated phagocytosis or via complement activation, resulting in platelet refractoriness. Strikingly, not all patients with anti-HLA or -HPA antibodies develop platelet refractoriness upon unmatched platelet transfusions. Previously, we found that IgG Fc glycosylation of anti-HLA antibodies was highly variable between patients with platelet refractoriness, especially with respect to galactosylation and sialylation of the Fc-bound sugar moiety. Here, we produced recombinant glycoengineered anti-HLA and anti- HPA-1a monoclonal antibodies with varying Fc galactosylation and sialylation levels and studied their ability to activate the classical complement pathway. We observed that anti-HLA monoclonal antibodies with different specificities, binding simultaneously to the same HLA-molecules, or anti-HLA in combination with anti-HPA-1a monoclonal antibodies interacted synergistically with C1q, the first component of the classical pathway. Elevated Fc galactosylation and, to a lesser extent, sialylation significantly increased the complement-activating properties of anti-HLA and anti-HPA-1a monoclonal antibodies. We propose that both the breadth of the polyclonal immune response, with recognition of different HLA epitopes and in some cases HPA antigens, and the type of Fc glycosylation can provide an optimal stoichiometry for C1q binding and subsequent complement activation. These factors can shift the effect of a platelet alloimmune response to a clinically relevant response, leading to complement-mediated clearance of donor platelets, as observed in platelet refractoriness

Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse

Background: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. Methods: The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. Results: A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH2O. Conclusion: Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes