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一个用于虚拟队列验证和分析的开源统计网络应用程序。

An open source statistical web application for validation and analysis of virtual cohorts.

作者信息

Ohmann Christian, Khorchani Takoua, Cracanel Alexandru, Brüning Jan, Verde Pablo Emilio

机构信息

European Clinical Research Infrastructures Network (ECRIN), Kaiserswerther, Strasse 70, 40477, Düsseldorf, Germany.

European Clinical Research Infrastructure Network (ECRIN), 30 Bd Saint-Jacques, 75014, Paris, France.

出版信息

Sci Rep. 2025 May 6;15(1):15744. doi: 10.1038/s41598-025-99720-3.

DOI:10.1038/s41598-025-99720-3
PMID:40328940
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12056029/
Abstract

The conventional approach to developing medical treatments and medical devices usually covers pre-clinical and in-vitro investigations, in-vivo animal studies and clinical trials with humans. In-silico trials and virtual cohorts present a promising avenue for addressing the challenges inherent in clinical research and improving its efficiency. Despite considerable advancements in the field of in-silico trials, several notable gaps and challenges still need to be addressed, one is the limited availability of open and user-friendly statistical tools to support the specific analysis of virtual cohorts and in-silico trials. In the EU-Horizon funded project SIMCor we have developed a web application, providing a R-statistical environment supporting the validation of virtual cohorts and the application of validated cohorts for in-silico trials. It provides a practical platform for validating cohorts and has implemented existing statistical techniques that can be applied to compare virtual cohorts with real datasets. It is fully open, generic and menu driven and provides user guidance and help ( https://github.com/ecrin-github/SIMCor , https://zenodo.org/records/14718597 ).The tool has been developed according to specified user requirements and has been extensively tested and validated. Important next steps are to gain more experience with the tool in other domains and research environments and to extend its functionality.

摘要

开发医学治疗方法和医疗设备的传统方法通常包括临床前和体外研究、体内动物研究以及人体临床试验。计算机模拟试验和虚拟队列提供了一条有前景的途径,以应对临床研究中固有的挑战并提高其效率。尽管计算机模拟试验领域取得了显著进展,但仍有几个明显的差距和挑战需要解决,其中之一是支持虚拟队列和计算机模拟试验特定分析的开放且用户友好的统计工具的可用性有限。在欧盟地平线资助的项目SIMCor中,我们开发了一个网络应用程序,提供一个R统计环境,支持虚拟队列的验证以及将经过验证的队列应用于计算机模拟试验。它为验证队列提供了一个实用平台,并实施了可用于将虚拟队列与真实数据集进行比较的现有统计技术。它完全开放、通用且由菜单驱动,并提供用户指导和帮助(https://github.com/ecrin-github/SIMCor,https://zenodo.org/records/14718597)。该工具是根据特定用户需求开发的,并经过了广泛的测试和验证。接下来的重要步骤是在其他领域和研究环境中积累使用该工具的更多经验,并扩展其功能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b674/12056029/9a3ba2f2957e/41598_2025_99720_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b674/12056029/e5faa040dc72/41598_2025_99720_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b674/12056029/77e787939a9c/41598_2025_99720_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b674/12056029/9a3ba2f2957e/41598_2025_99720_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b674/12056029/e5faa040dc72/41598_2025_99720_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b674/12056029/77e787939a9c/41598_2025_99720_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b674/12056029/9a3ba2f2957e/41598_2025_99720_Fig3_HTML.jpg

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本文引用的文献

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Toward trustworthy medical device clinical trials: a hierarchical framework for establishing credibility and strategies for overcoming key challenges.迈向值得信赖的医疗器械临床试验:建立可信度的分层框架及克服关键挑战的策略
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Generation of synthetic aortic valve stenosis geometries for in silico trials.
用于计算机模拟试验的合成主动脉瓣狭窄几何形状的生成。
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In-silico trial of intracranial flow diverters replicates and expands insights from conventional clinical trials.颅内血流分流器的计算机模拟试验复制并拓展了传统临床试验的见解。
Nat Commun. 2021 Jun 23;12(1):3861. doi: 10.1038/s41467-021-23998-w.
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Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility.科学和监管评估药物研发中机制计算药物和疾病模型:建立模型可信度。
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