Suppr超能文献

利用现有数据解决重症监护中的重要临床问题。

Using existing data to address important clinical questions in critical care.

机构信息

Department of Medicine, Division of Pulmonary & Critical Care Medicine, University of Michigan, Ann Arbor, MI, USA.

出版信息

Crit Care Med. 2013 Mar;41(3):886-96. doi: 10.1097/CCM.0b013e31827bfc3c.

Abstract

OBJECTIVE

With important technological advances in healthcare delivery and the Internet, clinicians and scientists now have access to overwhelming number of available databases capturing patients with critical illness. Yet, investigators seeking to answer important clinical or research questions with existing data have few resources that adequately describe the available sources and the strengths and limitations of each. This article reviews an approach to selecting a database to address health services and outcomes research questions in critical care, examines several databases that are commonly used for this purpose, and briefly describes some strengths and limitations of each.

DATA SOURCES

Narrative review of the medical literature.

SUMMARY

The available databases that collect information on critically ill patients are numerous and vary in the types of questions they can optimally answer. Selection of a data source must consider not only accessibility but also the quality of the data contained within the database, and the extent to which it captures the necessary variables for the research question. Questions seeking causal associations (e.g., effect of treatment on mortality) usually either require secondary data that contain detailed information about demographics, laboratories, and physiology to best address nonrandom selection or sophisticated study design. Purely descriptive questions (e.g., incidence of respiratory failure) can often be addressed using secondary data with less detail such as administrative claims. Although each database has its own inherent limitations, all secondary analyses will be subject to the same challenges of appropriate study design and good observational research.

CONCLUSION

The literature demonstrates that secondary analyses can have significant impact on critical care practice. While selection of the optimal database for a particular question is a necessary part of high-quality analyses, it is not sufficient to guarantee an unbiased study. Thoughtful and well-constructed study design and analysis approaches remain equally important pillars of robust science. Only through responsible use of existing data will investigators ensure that their study has the greatest impact on critical care practice and outcomes.

摘要

目的

随着医疗保健技术的进步和互联网的普及,临床医生和科学家现在可以访问大量可用于捕捉危重病患者的数据库。然而,寻求利用现有数据回答重要临床或研究问题的研究人员,可用于描述可用资源以及每个资源的优势和局限性的资源很少。本文回顾了一种选择数据库的方法,以解决重症监护中的卫生服务和结果研究问题,考察了几种常用于此目的的数据库,并简要描述了每个数据库的一些优势和局限性。

资料来源

对医学文献的叙述性综述。

总结

收集危重病患者信息的可用数据库数量众多,在能够最佳回答的问题类型上存在差异。数据源的选择不仅要考虑可访问性,还要考虑数据库中数据的质量,以及数据库在多大程度上包含研究问题所需的变量。寻求因果关系的问题(例如,治疗对死亡率的影响)通常需要包含详细信息的二级数据,这些信息可以最佳地解决非随机选择或复杂的研究设计问题,例如人口统计学、实验室和生理学信息。纯粹描述性的问题(例如,呼吸衰竭的发生率)通常可以使用包含较少详细信息的二级数据(例如行政索赔)来解决。尽管每个数据库都有其固有的局限性,但所有二次分析都将面临适当研究设计和良好观察性研究的相同挑战。

结论

文献表明,二次分析可以对重症监护实践产生重大影响。虽然选择特定问题的最佳数据库是高质量分析的必要组成部分,但它并不能保证研究无偏倚。深思熟虑且精心构建的研究设计和分析方法仍然是稳健科学的同等重要支柱。只有通过负责任地使用现有数据,研究人员才能确保他们的研究对重症监护实践和结果产生最大影响。

相似文献

2
The future of Cochrane Neonatal.考克兰新生儿协作网的未来。
Early Hum Dev. 2020 Nov;150:105191. doi: 10.1016/j.earlhumdev.2020.105191. Epub 2020 Sep 12.
4
Critical Care Network in the State of Qatar.卡塔尔国重症监护网络。
Qatar Med J. 2019 Nov 7;2019(2):2. doi: 10.5339/qmj.2019.qccc.2. eCollection 2019.

引用本文的文献

4
Observational Studies.观察性研究。
Respir Care. 2023 Nov;68(11):1585-1597. doi: 10.4187/respcare.11170. Epub 2023 Jun 20.
8
The Blessing and the Curse of the Administrative Database.行政数据库的福与祸
Ann Am Thorac Soc. 2020 Feb;17(2):174-175. doi: 10.1513/AnnalsATS.201906-430ED.

本文引用的文献

2
Hospital-level variation in the use of intensive care.医疗机构之间重症监护使用情况的差异。
Health Serv Res. 2012 Oct;47(5):2060-80. doi: 10.1111/j.1475-6773.2012.01402.x. Epub 2012 Mar 30.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验