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An evidence mapping and analysis of registered COVID-19 clinical trials in China

BACKGROUND: This article aims to summarize the key characteristics of registered trials of 2019 novel coronavirus (COVID-19), in terms of their spatial and temporal distributions, types of design and interventions, and patient characteristics among others. METHODS: A comprehensive search of the regi...

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Autores principales: Lu, Liming, Li, Fan, Wen, Hao, Ge, Shuqi, Zeng, Jingchun, Luo, Wen, Wang, Lai, Tang, Chunzhi, Xu, Nenggui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7268588/
https://www.ncbi.nlm.nih.gov/pubmed/32493331
http://dx.doi.org/10.1186/s12916-020-01612-y
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author Lu, Liming
Li, Fan
Wen, Hao
Ge, Shuqi
Zeng, Jingchun
Luo, Wen
Wang, Lai
Tang, Chunzhi
Xu, Nenggui
author_facet Lu, Liming
Li, Fan
Wen, Hao
Ge, Shuqi
Zeng, Jingchun
Luo, Wen
Wang, Lai
Tang, Chunzhi
Xu, Nenggui
author_sort Lu, Liming
collection PubMed
description BACKGROUND: This article aims to summarize the key characteristics of registered trials of 2019 novel coronavirus (COVID-19), in terms of their spatial and temporal distributions, types of design and interventions, and patient characteristics among others. METHODS: A comprehensive search of the registered COVID-19 trials has been performed on platforms including ClinicalTrials.gov, WHO International Clinical Trials Registry Platform (WHO ICTRP), Chinese Clinical Trials Registry (CHiCTR), Australian Clinical Trials Registry, Britain’s National Research Register (BNRR), Current Control Trials (CCT), and Glaxo Smith Kline Register. Trials registered at the first 8 weeks of the COVID-19 outbreak are included, without language restrictions. For each study, the registration information, study design, and administrator information are collected and summarized. RESULTS: A total of 220 registered trials were evaluated as of February 27, 2020. Hospital-initiated trials were the majority and account for 80% of the sample. Among the trials, pilot studies and phase 4 trials are more common and represent 35% and 19.1% of the sample, respectively. The median sample size of the registered trials is 100, with interquartile range 60–240. Further, 45.9% of the trials mentioned information on a data monitoring committee. 54.5% of the trials did not specify the disease severity among patients they intend to recruit. Four types of interventions are most common in the experimental groups across the registered studies: antiviral drugs, Traditional Chinese Medicine (TCM), biological agents, and hormone drugs. Among them, the TCM and biological agents are frequently used in pilot study and correspond to a variety of primary endpoints. In contrast, trials with antiviral drugs have more targeted primary outcomes such as “COVID-19 nucleic acid test” and “28-day mortality.” CONCLUSIONS: We provide an evidence mapping and analysis of registered COVID-19 clinical trials in China. In particular, it is critical for ongoing and future studies to refine their research hypothesis and better identify their intervention therapies and the corresponding primary outcomes. It is also imperative for multiple public health divisions and research institutions to work together for integrative clinical data capture and sharing, with a common objective of improving future studies that evaluate COVID-19 interventions.
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spelling pubmed-72685882020-06-04 An evidence mapping and analysis of registered COVID-19 clinical trials in China Lu, Liming Li, Fan Wen, Hao Ge, Shuqi Zeng, Jingchun Luo, Wen Wang, Lai Tang, Chunzhi Xu, Nenggui BMC Med Research Article BACKGROUND: This article aims to summarize the key characteristics of registered trials of 2019 novel coronavirus (COVID-19), in terms of their spatial and temporal distributions, types of design and interventions, and patient characteristics among others. METHODS: A comprehensive search of the registered COVID-19 trials has been performed on platforms including ClinicalTrials.gov, WHO International Clinical Trials Registry Platform (WHO ICTRP), Chinese Clinical Trials Registry (CHiCTR), Australian Clinical Trials Registry, Britain’s National Research Register (BNRR), Current Control Trials (CCT), and Glaxo Smith Kline Register. Trials registered at the first 8 weeks of the COVID-19 outbreak are included, without language restrictions. For each study, the registration information, study design, and administrator information are collected and summarized. RESULTS: A total of 220 registered trials were evaluated as of February 27, 2020. Hospital-initiated trials were the majority and account for 80% of the sample. Among the trials, pilot studies and phase 4 trials are more common and represent 35% and 19.1% of the sample, respectively. The median sample size of the registered trials is 100, with interquartile range 60–240. Further, 45.9% of the trials mentioned information on a data monitoring committee. 54.5% of the trials did not specify the disease severity among patients they intend to recruit. Four types of interventions are most common in the experimental groups across the registered studies: antiviral drugs, Traditional Chinese Medicine (TCM), biological agents, and hormone drugs. Among them, the TCM and biological agents are frequently used in pilot study and correspond to a variety of primary endpoints. In contrast, trials with antiviral drugs have more targeted primary outcomes such as “COVID-19 nucleic acid test” and “28-day mortality.” CONCLUSIONS: We provide an evidence mapping and analysis of registered COVID-19 clinical trials in China. In particular, it is critical for ongoing and future studies to refine their research hypothesis and better identify their intervention therapies and the corresponding primary outcomes. It is also imperative for multiple public health divisions and research institutions to work together for integrative clinical data capture and sharing, with a common objective of improving future studies that evaluate COVID-19 interventions. BioMed Central 2020-06-01 /pmc/articles/PMC7268588/ /pubmed/32493331 http://dx.doi.org/10.1186/s12916-020-01612-y Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Lu, Liming
Li, Fan
Wen, Hao
Ge, Shuqi
Zeng, Jingchun
Luo, Wen
Wang, Lai
Tang, Chunzhi
Xu, Nenggui
An evidence mapping and analysis of registered COVID-19 clinical trials in China
title An evidence mapping and analysis of registered COVID-19 clinical trials in China
title_full An evidence mapping and analysis of registered COVID-19 clinical trials in China
title_fullStr An evidence mapping and analysis of registered COVID-19 clinical trials in China
title_full_unstemmed An evidence mapping and analysis of registered COVID-19 clinical trials in China
title_short An evidence mapping and analysis of registered COVID-19 clinical trials in China
title_sort evidence mapping and analysis of registered covid-19 clinical trials in china
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7268588/
https://www.ncbi.nlm.nih.gov/pubmed/32493331
http://dx.doi.org/10.1186/s12916-020-01612-y
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