Serial measurement of cytokines strongly predict COVID-19 outcome.

Purpose: Cytokines are major mediators of COVID-19 pathogenesis and several of them are already being regarded as predictive markers for the clinical course and outcome of COVID-19 cases. A major pitfall of many COVID-19 cytokine studies is the lack of a benchmark sampling timing. Since cytokines and their relative change during an infectious disease course is quite dynamic, we evaluated the predictive value of serially measured cytokines for COVID-19 cases.

Methods: In this single-center, prospective study, a broad spectrum of cytokines were determined by multiplex ELISA assay in samples collected at admission and at the third day of hospitalization. Appropriateness of cytokine levels in predicting mortality were assessed by receiver-operating characteristic (ROC) analyses for both sampling times in paralel to conventional biomarkers.

Results: At both sampling points, higher levels of IL-6, IL-7, IL-10, IL-15, IL-27 IP-10, MCP-1, and GCSF were found to be more predictive for mortality (p<0.05). Some of these cytokines, such as IL-6, IL-10, IL-7 and GCSF, had higher sensitivity and specificity in predicting mortality. AUC values of IL-6, IL-10, IL-7 and GCSF were 0.85 (0.65 to 0.92), 0.88 (0.73 to 0.96), 0.80 (0.63 to 0.91) and 0.86 (0.70 to 0.95), respectively at hospital admission. Compared to hospital admission, on the 3rd day of hospitalization serum levels of IL-6 and, IL-10 decreased significantly in the survivor group, unlike the non-survivor group (IL-6, p = 0.015, and IL-10, p = 0.016).

Conclusion: Our study results suggest that single-sample-based cytokine analyzes can be misleading and that cytokine levels measured serially at different sampling times provide a more precise and accurate estimate for the outcome of COVID-19 patients.