Background: Uncontrolled inflammation can result in severe status and even death in influenza patients, and there is a lack of early clinical evaluation models.

Methods: We recruited patients with influenza pneumonia and healthy controls from the emergency departments of three urban teaching hospitals in Beijing, China, during the winter of 2018-2019. Donated plasma samples were screened using protein and lectin microarrays to assess changes in the glycosylation patterns of immunoglobulins. These changes were used to develop and validate an Immunoglobulin Glycosylation Profile for Severe Status Identification Algorithm (IGPSSIA). A combined model of IGPSSIA score and clinical indicators was constructed to identify severe influenza pneumonia cases.

Results: We enrolled 114 patients, including 56 in the mild and 58 in the severe groups, and recruited 27 volunteers as healthy controls. We screened out the differentially expressed glycan moieties between the mild and the severe groups and included them in the LASSO regression analysis. In the training set (70% of patients, n = 80), IGPSSIA = 1.113 × [GNL?IgG4 Man] - 2.499 × [LCA?IgG1 Man] + 0.029 × [BanLec?IgA2 Man] + 0.529 × [HHL, AL?IgM Man] - 2.210 × [sWGA?IgG GlcNAc] + 0.001 × [PSA?IgG4 Man] + 0.027 × [Ricin B Chain?IgG2 Gal & GalNAc]. Finally, we constructed a clinical diagnostic model using age, time interval from onset to admission, lymphocyte count, platelet count and IGPSSIA score, and achieved an AUC of 0.839 (95% CI 0.767-0.911).

Conclusions: Changes in immunoglobulin glycosylation profiles can be a promising tool for identifying severe status in patients with influenza pneumonia.