Efficacy and safety of azvudine versus nirmatrelvir/ritonavir in cancer patients with COVID-19

Cancer significantly contributes to the unfavorable prognosis of coronavirus disease 2019 (COVID-19) patients. The efficacy and safety of azvudine and nirmatrelvir/ritonavir (Paxlovid) in cancer patients with COVID-19 remain uncertain. Therefore, we designed a comprehensive retrospective study encompassing clinical data of 32,864 hospitalized COVID-19 patients, 691 of whom were cancer patients treated with azvudine and 200 were cancer patients treated with Paxlovid. After 2:1 propensity score matching, 397 patients in the azvudine group and 199 patients in the Paxlovid group were enrolled. Cox regression analysis revealed the risk of all-cause death (HR: 1.84, 95% CI: 1.059-3.182, P = 0.030) and composite disease progression (HR: 1.70, 95% CI: 1.043-2.757, P = 0.033) were greater in the Paxlovid group than in the azvudine group. Two sensitivity analyses confirmed the robustness of our findings. The safety analysis of adverse events revealed no statistically significant differences between the two groups. In conclusion, we carried out the first analysis to compare the efficacy and safety of azvudine and Paxlovid in cancer patients with COVID-19 and demonstrated that azvudine significantly reduced the risk of all-cause death and composite disease progression among cancer patients with COVID-19 compared with Paxlovid.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-03-31
DOI
https://doi.org/10.1038/s41598-025-85677-w
Citations
1
Primary Topic
COVID-19 and healthcare impacts
Type
article
Field-Weighted Citation Impact
1.05

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Efficacy and safety of azvudine versus nirmatrelvir/ritonavir in cancer patients with COVID-19

Guotao Li, Xiaobo Hu, Zhigang Ren, Ling Wang et al.
1 citations
Scientific Reports
COVID-19 and healthcare impacts
1.05
article

Efficacy and safety of azvudine versus nirmatrelvir/ritonavir in cancer patients with COVID-19

Guotao Li, Xiaobo Hu, Zhigang Ren, Ling Wang, Guowu Qian, Hong Luo, Hongxia Liang, Donghua Zhang, Bohan Jia, Di Zhu, Shixi Zhang, Guangming Li, Zujiang Yu, Haiyu Wang, Silin Li, Junyi Sun
article en
1 citations

Abstract

Cancer significantly contributes to the unfavorable prognosis of coronavirus disease 2019 (COVID-19) patients. The efficacy and safety of azvudine and nirmatrelvir/ritonavir (Paxlovid) in cancer patients with COVID-19 remain uncertain. Therefore, we designed a comprehensive retrospective study encompassing clinical data of 32,864 hospitalized COVID-19 patients, 691 of whom were cancer patients treated with azvudine and 200 were cancer patients treated with Paxlovid. After 2:1 propensity score matching, 397 patients in the azvudine group and 199 patients in the Paxlovid group were enrolled. Cox regression analysis revealed the risk of all-cause death (HR: 1.84, 95% CI: 1.059-3.182, P = 0.030) and composite disease progression (HR: 1.70, 95% CI: 1.043-2.757, P = 0.033) were greater in the Paxlovid group than in the azvudine group. Two sensitivity analyses confirmed the robustness of our findings. The safety analysis of adverse events revealed no statistically significant differences between the two groups. In conclusion, we carried out the first analysis to compare the efficacy and safety of azvudine and Paxlovid in cancer patients with COVID-19 and demonstrated that azvudine significantly reduced the risk of all-cause death and composite disease progression among cancer patients with COVID-19 compared with Paxlovid.

Scientific ReportsVol. 15(1)
Zhengzhou University (CN), People’s Hospital of Wenshan Prefecture (CN), The Fifth People's Hospital of Anyang (CN), Shangqiu First People's Hospital (CN), Nanyang Institute of Technology (CN), Luoyang Central Hospital Affiliated to Zhengzhou University (CN), Henan Provincial Chest Hospital (CN), First Affiliated Hospital of Zhengzhou University (CN)
National Key Research and Development Program of China
Good health and well-being
Openalex Percentile: Top 24%
COVID-19 and healthcare impacts
1.05
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.