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

Autor: de Kok JWTM; Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands., van Bussel BCT; 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., Schnabel R; Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands., van Herpt TTW; Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands., Driessen RGH; Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands; Department of Cardiology, Maastricht University Medical Centre+, Maastricht, The Netherlands., Meijs DAM; Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands., Goossens JA; Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands., Mertens HJMM; Maastricht University Medical Centre+, Maastricht, The Netherlands., van Kuijk SMJ; Department of Clinical Epidemiology and Medical Technology Assessment (KEMTA), Maastricht University Medical Centre+, Maastricht, The Netherlands., Wynants L; Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands; Department of Development and Regeneration, KU Leuven, Leuven, Belgium., van der Horst ICC; Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands., van Rosmalen F; Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands. Electronic address: frank.van.rosmalen@mumc.nl.
Jazyk: angličtina
Zdroj: Journal of clinical epidemiology [J Clin Epidemiol] 2024 Jun; Vol. 170, pp. 111342. Date of Electronic Publication: 2024 Apr 02.
DOI: 10.1016/j.jclinepi.2024.111342
Abstrakt: 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.
Competing Interests: Declaration of competing interest The authors declare that there are no competing interests.
(Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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