Evidence map›Paper›PMID 39284764›Full record

ArticleInfluenza and other respiratory viruses2024

Antiviral Effectiveness, Clinical Outcomes, and Artificial Intelligence Imaging Analysis for Hospitalized COVID-19 Patients Receiving Antivirals.

Yuan Gao, Yixi Dong, Qiushi Bu, Zhijie Gong, Wei Wang, Zhongkai Zhou, Yunyi Gao, Liwei Liu, Menghua Wu, Jiaying Zhang and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Influenza and other respiratory viruses, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Yuan GaoFourth Department of Liver Disease Center, Beijing You'An Hospital, Capital Medical University, Beijing, China.ORCID 0000-0002-0670-9104
Yixi DongSchool of Management, University of Science and Technology of China, Hefei, China.
Qiushi BuAcademy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.
Zhijie GongSchool of Management, University of Science and Technology of China, Hefei, China.
Wei WangDepartment of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China.
Zhongkai ZhouDepartment of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China.
Yunyi GaoSchool of Basic Medicine, Qingdao University, Qingdao, China.
Liwei LiuFourth Department of Liver Disease Center, Beijing You'An Hospital, Capital Medical University, Beijing, China.
Menghua WuDepartment of Urology, Beijing You'An Hospital, Capital Medical University, Beijing, China.
Jiaying ZhangDepartment of Infectious Diseases, Beijing You'An Hospital, Capital Medical University, Beijing, China.
Lianchun LiangDepartment of Infectious Diseases, Beijing You'An Hospital, Capital Medical University, Beijing, China.
Hongjun LiDepartment of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China.
Mengxi JiangDepartment of Pharmacology, School of Basic Medical Sciences, Capital Medical University, Beijing, China.
Zujin LuoDepartment of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Yingmin MaDepartment of Respiratory and Critical Care Medicine, Beijing You'An Hospital, Capital Medical University, Beijing, China.
Xinyu ZhangSchool of Management, University of Science and Technology of China, Hefei, China.
Zhongjie HuLiver Disease Center, Beijing You'An Hospital, Capital Medical University, Beijing, China.

Funding

Construction Project of High-level Technology Talents in Public HealthHigh-Level Public Health Technology Talent Project 2022-2-005
6 · The paper itself

Abstract

introductionThere is still a lack of clinical evidence comprehensively evaluating the effectiveness of antiviral treatments for COVID-19 hospitalized patients.

methodsA retrospective cohort study was conducted at Beijing You'An Hospital, focusing on patients treated with nirmatrelvir/ritonavir or azvudine. The study employed a tripartite analysis-viral dynamics, survival curve analysis, and AI-based radiological analysis of pulmonary CT images-aiming to assess the severity of pneumonia.

resultsOf 370 patients treated with either nirmatrelvir/ritonavir or azvudine as monotherapy, those in the nirmatrelvir/ritonavir group experienced faster viral clearance than those treated with azvudine (5.4 days vs. 8.4 days, p < 0.001). No significant differences were observed in the survival curves between the two drug groups. AI-based radiological analysis revealed that patients in the nirmatrelvir group had more severe pneumonia conditions (infection ratio is 11.1 vs. 5.35, p = 0.007). Patients with an infection ratio higher than 9.2 had nearly three times the mortality rate compared to those with an infection ratio lower than 9.2.

conclusionsOur study suggests that in real-world studies regarding hospitalized patients with COVID-19 pneumonia, the antiviral effect of nirmatrelvir/ritonavir is significantly superior to azvudine, but the choice of antiviral agents is not necessarily linked to clinical outcomes; the severity of pneumonia at admission is the most important factor to determine prognosis. Additionally, our findings indicate that pulmonary AI imaging analysis can be a powerful tool for predicting patient prognosis and guiding clinical decision-making.

Indexed as

Antiviral AgentsArtificial IntelligenceCOVID-19COVID-19 Drug TreatmentRitonavirSARS-CoV-2AdultAgedDrug CombinationsFemaleHospitalizationHumansLungMaleMiddle AgedPandemicsAntiviral AgentsDrug CombinationsRitonavirantiviralartificial intelligence imageCOVID‐19viral dynamics

Identifiers

PMID39284764
PMCPMC11405122

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.