ABSTRACT
Background: Little data exists to guide the treatment of persistent COVID-19 in immunocompromised patients. We have employed a unique protocol combining tixegavimab/cilgavimab, and short-term combination antivirals including remdesivir. Methods: A retrospective single-center analysis of persistent COVID-19 in immunocompromised patients. Response was assessed by symptom resolution, declining C-reactive protein (CRP) levels and increasing SARS-CoV-2-PCR cycle-threshold (Ct) values. Results: Fourteen patients were included, including 2 kidney transplant recipients, 11 with B-cell lymphoproliferative disease, treated with anti-CD20 or ibrutinib, and 1 with rheumatoid arthritis, treated with anti-CD20. Median Ct-value was 27 (interquartile range (IQR):24-32). All patients received tixegavimab/cilgavimab and a 5-day course of remdesivir. Eleven also received nirmaltrevir/ritonavir and one received molnupiravir. Median follow-up was 45 days (IQR:12-89). Eleven patients had complete responses including symptom resolution, decrease in CRP, and increase in Ct values (all with either a negative PCR or Ct value>30 on day 4-16). Three patients had a partial response with relapses requiring re-admission. One had died, and two responded to prolonged antiviral treatments. Conclusions: A combination of monoclonal antibodies with antivirals has led to complete resolution of persistent COVID-19 in most severely-immunocompromised patients. Controlled studies will further direct the treatment of these patients, while more effective antivirals are urgently needed.
Subject(s)
Lymphoma, B-Cell , COVID-19 , Arthritis, RheumatoidABSTRACT
Facing the rapidly spreading novel coronavirus disease (COVID-19), evidence to inform decision-making at both the clinical and policy-making level is highly needed. Based on the results of a study by Petrilli et al, we have developed a calculator using patient data at admission to predict the risk of critical illness (intensive care unit admission, use of mechanical ventilation, discharge to hospice, or death). We report a retrospective validation of the risk calculator on 145 consecutive patients admitted with COVID-19 to a single hospital in Israel. Of the 18 patients with critical illness, 17 were correctly identified by the model(sensitivity: 94.4%, 95% CI, 72.7% to 99.9%; specificity: 81.9%, 95% CI, 74.1% to 88.2%). Of the 127 patients with non-critical illness, 104 were correctly identified. This, despite considerable differences between the original and validation study populations. Our results show that data from published knowledge can be used to provide reliable, patient level, automated risk assessment, potentially reducing the cognitive burden on physicians and helping policy makers better prepare for future needs.