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

## TL;DR

This study tracks immune mediator levels in COVID-19 patients over time to predict disease outcomes based on immune response patterns.

## Contribution

The study identifies specific immune mediators and their longitudinal patterns that predict survival or death in COVID-19 patients.

## Key findings

- Death outcome is linked to persistent high levels of immune mediators, especially chemokines and cytokines.
- Discharge outcome shows a balanced and decreasing pattern of pro-inflammatory cytokines over time.
- IL-6, CCL2, and CXCL8 are key predictors of disease outcome with high accuracy.

## Abstract

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.

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).

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.

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.