Exploring the timeline and network interplay of immune mediators in COVID-19 patients according to disease outcome

Introduction: The present study is an observational descriptive follow-up investigation designed to characterize the profile of serum immune mediators in COVID-19 patients further categorized according to disease outcome. Methods: A total of 92 COVID-19 patients were enrolled in a timeline kinetics, starting at hospital admission (Day 0) throughout consecutive timepoint intervals (Day 3-7, Day 8-14 and Day 15-40). Immune mediators (chemokines, cytokines and growth factors) were quantified by a high-throughput multiplex assay and compared with a pre-pandemic healthy control group (HC). Results: Data demonstrated that COVID-19 exhibited a classical immune mediator storm, with prominent increase of chemokines and pro-inflammatory cytokines. Longitudinal follow-up revealed that the "death" outcome was associated with a persistent increase of immune mediators across all timepoints, with higher imbalance at Day 8-14. Conversely, the "discharge" outcome evolved with a balanced temporal profile with progressive waning of pro-inflammatory cytokines. Integrative network architectures uncovered that the "death" outcome exhibited a selective high-density chemokine cluster, contrasting with the balanced pattern described for "discharge" subgroups. A set of serum immune mediators (CXCL8, CCL2, CXCL10, IL-6, and IFN-γ) emerged as relevant predictors of disease outcome (AUC ≥ 0.8). Decision tree stepwise algorithms pointed out the hierarchical power (accuracy = 83%) of IL-6, CCL2, and CXCL8 to sort out patients according to disease outcome. Conclusions: Overall, these findings support clinical applicability of measuring serum immune mediators as complementary prognostic biomarkers for early classification and prediction of disease outcome in COVID-19 patients.