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表 0;记录从临床数据库到研究数据集的步骤。

Table 0; documenting the steps to go from clinical database to research dataset.

机构信息

Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands.

Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands; Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands.

出版信息

J Clin Epidemiol. 2024 Jun;170:111342. doi: 10.1016/j.jclinepi.2024.111342. Epub 2024 Apr 2.

Abstract

OBJECTIVES

Data-driven decision support tools have been increasingly recognized to transform health care. However, such tools are often developed on predefined research datasets without adequate knowledge of the origin of this data and how it was selected. How a dataset is extracted from a clinical database can profoundly impact the validity, interpretability and interoperability of the dataset, and downstream analyses, yet is rarely reported. Therefore, we present a case study illustrating how a definitive patient list was extracted from a clinical source database and how this can be reported.

STUDY DESIGN AND SETTING

A single-center observational study was performed at an academic hospital in the Netherlands to illustrate the impact of selecting a definitive patient list for research from a clinical source database, and the importance of documenting this process. All admissions from the critical care database admitted between January 1, 2013, and January 1, 2023, were used.

RESULTS

An interdisciplinary team collaborated to identify and address potential sources of data insufficiency and uncertainty. We demonstrate a stepwise data preparation process, reducing the clinical source database of 54,218 admissions to a definitive patient list of 21,553 admissions. Transparent documentation of the data preparation process improves the quality of the definitive patient list before analysis of the corresponding patient data. This study generated seven important recommendations for preparing observational health-care data for research purposes.

CONCLUSION

Documenting data preparation is essential for understanding a research dataset originating from a clinical source database before analyzing health-care data. The findings contribute to establishing data standards and offer insights into the complexities of preparing health-care data for scientific investigation. Meticulous data preparation and documentation thereof will improve research validity and advance critical care.

摘要

目的

数据驱动的决策支持工具已越来越被认为可以改变医疗保健。然而,这些工具通常是在预定义的研究数据集上开发的,而对这些数据的来源以及数据的选择方式缺乏足够的了解。从临床数据库中提取数据集的方式会极大地影响数据集的有效性、可解释性和互操作性,以及下游分析,但这一点很少有报道。因此,我们提出了一个案例研究,说明如何从临床源数据库中提取确定的患者列表,以及如何报告这一点。

研究设计和设置

在荷兰的一家学术医院进行了一项单中心观察性研究,以说明从临床源数据库中为研究选择确定的患者列表的影响,以及记录这一过程的重要性。使用了 2013 年 1 月 1 日至 2023 年 1 月 1 日期间从重症监护数据库中收录的所有入院记录。

结果

一个跨学科团队合作,以确定和解决潜在的数据不足和不确定性的来源。我们展示了一个逐步的数据准备过程,将临床源数据库中的 54218 次入院记录减少到 21553 次确定的患者列表。在分析相应的患者数据之前,对数据准备过程进行透明的记录可以提高确定的患者列表的质量。本研究为准备观察性医疗保健数据用于研究目的提出了七个重要建议。

结论

在分析医疗保健数据之前,记录数据准备对于理解源自临床源数据库的研究数据集至关重要。研究结果有助于建立数据标准,并为准备医疗保健数据进行科学研究的复杂性提供了见解。细致的数据准备和记录将提高研究的有效性,并推动重症监护的发展。

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