Clinical features of COVID-19 patients with rebound phenomenon after corticosteroid therapy

Abstract

Rational Corticosteroid therapy plays a key role in the treatment of COVID-19 patients with respiratory failure. However, a rebound phenomenon after steroid cessation rarely occurs. Here, we investigated the clinical features of patients with rebound after steroid therapy.Methods In total, 84 patients with COVID-19 treated with corticosteroids were enrolled and analysed retrospectively. A rebound was defined as when a patient’s respiratory status deteriorated after the cessation of corticosteroid therapy, without secondary bacterial infection.Results Subjects in the rebound group were more likely to having severe respiratory failure than those in the non-rebound group. While the duration of steroid therapy was longer in the rebound group (8 days vs 10 days, p=0.0009), the dosage of steroid and the timing of the start or termination of steroid therapy did not show any differences between the two groups (p=0.17 and 0.68, respectively). The values of soluble interleukin-2 receptor (sIL-2R) at the baseline and the values of C reactive protein (CRP) or lactate dehydrogenase (LDH) at the end of steroid therapy were significantly higher in the rebound group (937 vs 1336 U/mL; p=0.002, 0.63 vs 3.96 mg/dL; p=0.01 and 278 vs 451 IU/mL; p=0.01, respectively). No patient in the rebound group suffered from thromboses, and the causes of death were exacerbation of COVID-19, ventilator-associated pneumonia or sepsis. The prediction model using baseline features for the rebound phenomenon included four variables of age >68 years, required supplemental oxygen >5 L/min, lymphocyte counts <792 /µL and sIL-2R >1146 U/mL. The discrimination ability of this model was 0.906 (0.755–0.968).Conclusion These findings suggest that severe respiratory failure has a higher risk for the rebound phenomenon after the cessation of corticosteroids, and the values of sIL-2R, LDH and CRP are useful to assess the probability of developing rebound. A multivariate model was developed to predict rebound risk, which showed acceptable discrimination ability.