To develop predictive criteria for COVID-19-associated cytokine storm (CS), a severe hyperimmune response that results in organ damage in some patients infected with COVID-19. We hypothesised that criteria for inflammation and cell death would predict this type of CS.
We analysed 513 hospitalised patients who were positive for COVID-19 reverse transcriptase PCR and for ground-glass opacity by chest high-resolution CT. To achieve an early diagnosis, we analysed the laboratory results of the first 7 days of hospitalisation. We implemented logistic regression and principal component analysis to determine the predictive criteria. We used a 'genetic algorithm' to derive the cut-offs for each laboratory result. We validated the criteria with a second cohort of 258 patients.
We found that the criteria for macrophage activation syndrome, haemophagocytic lymphohistiocytosis and the HScore did not identify the COVID-19 cytokine storm (COVID-CS). We developed new predictive criteria, with sensitivity and specificity of 0.85 and 0.80, respectively, comprising three clusters of laboratory results that involve (1) inflammation, (2) cell death and tissue damage, and (3) prerenal electrolyte imbalance. The criteria identified patients with longer hospitalisation and increased mortality. These results highlight the relevance of hyperinflammation and tissue damage in the COVID-CS.
We propose new early predictive criteria to identify the CS occurring in patients with COVID-19. The criteria can be readily used in clinical practice to determine the need for an early therapeutic regimen, block the hyperimmune response and possibly decrease mortality.