Post by Nadica (She/Her) on Nov 26, 2024 5:24:09 GMT
Validation study for assessing COVID-19 pneumonia treatments - Published Nov 25, 2024
Abstract
This study investigates the effectiveness of Azvudine and nirmatrelvir-ritonavir (Paxlovid) in treating COVID-19 pneumonia through an analysis of real-world clinical data. We retrospectively collected data from COVID-19 patients hospitalized at the Second Xiangya Hospital of Central South University between December 21, 2022, and January 18, 2023. Using kernel density estimation, box-and-whisker plots, and Schoenfeld residual plots, we evaluated the transition of patients to negative status and assessed factors such as age, disease severity, and treatment effects. The findings revealed that both Azvudine and Paxlovid significantly reduced recovery times, with Azvudine showing notable benefits for patients aged 50–80. Our analysis indicated that these drugs improved lung CT values and reduced disease severity in moderate cases. The Cox model demonstrated robustness in predicting outcomes, and a nomogram was developed for individualized recovery probability assessment. These results provide important insights into optimizing COVID-19 treatment and the potential of predictive models in clinical decision-making.
Abstract
This study investigates the effectiveness of Azvudine and nirmatrelvir-ritonavir (Paxlovid) in treating COVID-19 pneumonia through an analysis of real-world clinical data. We retrospectively collected data from COVID-19 patients hospitalized at the Second Xiangya Hospital of Central South University between December 21, 2022, and January 18, 2023. Using kernel density estimation, box-and-whisker plots, and Schoenfeld residual plots, we evaluated the transition of patients to negative status and assessed factors such as age, disease severity, and treatment effects. The findings revealed that both Azvudine and Paxlovid significantly reduced recovery times, with Azvudine showing notable benefits for patients aged 50–80. Our analysis indicated that these drugs improved lung CT values and reduced disease severity in moderate cases. The Cox model demonstrated robustness in predicting outcomes, and a nomogram was developed for individualized recovery probability assessment. These results provide important insights into optimizing COVID-19 treatment and the potential of predictive models in clinical decision-making.