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

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.