Predictive factors of delayed viral clearance of asymptomatic Omicron-related COVID-19 screened positive in patients with cancer receiving active anticancer treatment

Objectives: We sought to identify the predictors of delayed viral clearance in patients with cancer with asymptomatic COVID-19 when the SARS-CoV-2 Omicron variants prevailed in Hong Kong. Methods: All patients with cancer who were attending radiation therapy for head and neck malignan-cies or systemic anticancer therapy saved their deep throat saliva or nasopharyngeal swabs at least twice weekly for SARS-CoV-2 screening between January 1 and April 30, 2022. The multivariate analyses iden-tified predictors of delayed viral clearance (or slow recovery), defined as > 21 days for the cycle threshold values rising to >30 or undetectable in two consecutive samples saved within 72 hours. Three machine learning algorithms evaluated the prediction performance of the predictors.Results: A total of 200 (15%) of 1309 patients tested positive for SARS-CoV-2. Age > 65 years ( P = 0.036), male sex ( P = 0.003), high Charlson comorbidity index ( P = 0.042), lung cancer ( P = 0.018), immune checkpoint inhibitor ( P = 0.036), and receipt of one or no dose of COVID-19 vaccine ( P = 0.003) were significant predictors. The three machine learning algorithms revealed that the mean +/- SD area-under -the-curve values predicting delayed viral clearance with the cut-off cycle threshold value >30 was 0.72 +/- 0.11. Conclusion: We identified subgroups with delayed viral clearance that may benefit from targeted inter-ventions.(c) 2023 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Automated summary

This study aimed to identify the predictors of delayed viral clearance in cancer patients with asymptomatic COVID-19 when the SARS-CoV-2 Omicron variants prevailed in Hong Kong. Multiple predictors were evaluated using machine learning algorithms, and it was found that age >65 years, male sex, high Charlson comorbidity index, lung cancer, immune checkpoint inhibitor, and receipt of one or no dose of COVID-19 vaccine were significant predictors. These findings contribute to targeted interventions for patients with delayed viral clearance.