Contribution of Open Access Databases to Intensive Care Medicine Research: Scoping Review

BackgroundIntensive care units (ICUs) handle the most critical patients with a high risk of mortality. Due to those conditions, close monitoring is necessary and therefore, a large volume of data is collected. Collaborative ventures have enabled the emergence of large open ac...

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Main Authors: Julien Kallout, Antoine Lamer, Julien Grosjean, Gaétan Kerdelhué, Guillaume Bouzillé, Thomas Clavier, Benjamin Popoff
Format: Article
Language:English
Published: JMIR Publications 2025-01-01
Series:Journal of Medical Internet Research
Online Access:https://www.jmir.org/2025/1/e57263
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author Julien Kallout
Antoine Lamer
Julien Grosjean
Gaétan Kerdelhué
Guillaume Bouzillé
Thomas Clavier
Benjamin Popoff
author_facet Julien Kallout
Antoine Lamer
Julien Grosjean
Gaétan Kerdelhué
Guillaume Bouzillé
Thomas Clavier
Benjamin Popoff
author_sort Julien Kallout
collection DOAJ
description BackgroundIntensive care units (ICUs) handle the most critical patients with a high risk of mortality. Due to those conditions, close monitoring is necessary and therefore, a large volume of data is collected. Collaborative ventures have enabled the emergence of large open access databases, leading to numerous publications in the field. ObjectiveThe aim of this scoping review is to identify the characteristics of studies using open access intensive care databases and to describe the contribution of these studies to intensive care research. MethodsThe research was conducted using 3 databases (PubMed–MEDLINE, Embase, and Web of Science) from the inception of each database to August 1, 2022. We included original articles based on 4 open databases of patients admitted to ICUs: Amsterdam University Medical Centers Database, eICU Collaborative Research Database, High time resolution ICU dataset, Medical Information Mart for Intensive Care (II to IV). A double-blinded screening for eligibility was performed, first on the title and abstract and subsequently on the full-text articles. Characteristics relating to publication journals, study design, and statistical analyses were extracted and analyzed. ResultsWe observed a consistent increase in the number of publications from these databases since 2016. The Medical Information Mart for Intensive Care databases were the most frequently used. The highest contributions came from China and the United States, with 689 (52.7%) and 370 (28.3%) publications respectively. The median impact factor of publications was 3.8 (IQR 2.8-5.8). Topics related to cardiovascular and infectious diseases were predominant, accounting for 333 (25.5%) and 324 (24.8%) articles, respectively. Logistic regression emerged as the most commonly used statistical model for both inference and prediction questions, featuring in 396 (55.5%) and 281 (47.5%) studies, respectively. A majority of the inference studies yielded statistically significant results (84.0%). In prediction studies, area under the curve was the most frequent performance measure, with a median value of 0.840 (IQR 0.780-0.890). ConclusionsThe abundance of scientific outputs resulting from these databases, coupled with the diversity of topics addressed, highlight the importance of these databases as valuable resources for clinical research. This suggests their potential impact on clinical practice within intensive care settings. However, the quality and clinical relevance of these studies remains highly heterogeneous, with a majority of articles being published in low–impact factor journals.
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spelling doaj-art-aa065d666a494a4b9c8fd249e31091d52025-01-09T21:00:44ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-01-0127e5726310.2196/57263Contribution of Open Access Databases to Intensive Care Medicine Research: Scoping ReviewJulien Kallouthttps://orcid.org/0009-0007-8086-5360Antoine Lamerhttps://orcid.org/0000-0002-9546-1808Julien Grosjeanhttps://orcid.org/0000-0002-7446-644XGaétan Kerdelhuéhttps://orcid.org/0000-0001-5803-5554Guillaume Bouzilléhttps://orcid.org/0000-0002-3637-6558Thomas Clavierhttps://orcid.org/0000-0001-8630-7537Benjamin Popoffhttps://orcid.org/0000-0003-2854-0909 BackgroundIntensive care units (ICUs) handle the most critical patients with a high risk of mortality. Due to those conditions, close monitoring is necessary and therefore, a large volume of data is collected. Collaborative ventures have enabled the emergence of large open access databases, leading to numerous publications in the field. ObjectiveThe aim of this scoping review is to identify the characteristics of studies using open access intensive care databases and to describe the contribution of these studies to intensive care research. MethodsThe research was conducted using 3 databases (PubMed–MEDLINE, Embase, and Web of Science) from the inception of each database to August 1, 2022. We included original articles based on 4 open databases of patients admitted to ICUs: Amsterdam University Medical Centers Database, eICU Collaborative Research Database, High time resolution ICU dataset, Medical Information Mart for Intensive Care (II to IV). A double-blinded screening for eligibility was performed, first on the title and abstract and subsequently on the full-text articles. Characteristics relating to publication journals, study design, and statistical analyses were extracted and analyzed. ResultsWe observed a consistent increase in the number of publications from these databases since 2016. The Medical Information Mart for Intensive Care databases were the most frequently used. The highest contributions came from China and the United States, with 689 (52.7%) and 370 (28.3%) publications respectively. The median impact factor of publications was 3.8 (IQR 2.8-5.8). Topics related to cardiovascular and infectious diseases were predominant, accounting for 333 (25.5%) and 324 (24.8%) articles, respectively. Logistic regression emerged as the most commonly used statistical model for both inference and prediction questions, featuring in 396 (55.5%) and 281 (47.5%) studies, respectively. A majority of the inference studies yielded statistically significant results (84.0%). In prediction studies, area under the curve was the most frequent performance measure, with a median value of 0.840 (IQR 0.780-0.890). ConclusionsThe abundance of scientific outputs resulting from these databases, coupled with the diversity of topics addressed, highlight the importance of these databases as valuable resources for clinical research. This suggests their potential impact on clinical practice within intensive care settings. However, the quality and clinical relevance of these studies remains highly heterogeneous, with a majority of articles being published in low–impact factor journals.https://www.jmir.org/2025/1/e57263
spellingShingle Julien Kallout
Antoine Lamer
Julien Grosjean
Gaétan Kerdelhué
Guillaume Bouzillé
Thomas Clavier
Benjamin Popoff
Contribution of Open Access Databases to Intensive Care Medicine Research: Scoping Review
Journal of Medical Internet Research
title Contribution of Open Access Databases to Intensive Care Medicine Research: Scoping Review
title_full Contribution of Open Access Databases to Intensive Care Medicine Research: Scoping Review
title_fullStr Contribution of Open Access Databases to Intensive Care Medicine Research: Scoping Review
title_full_unstemmed Contribution of Open Access Databases to Intensive Care Medicine Research: Scoping Review
title_short Contribution of Open Access Databases to Intensive Care Medicine Research: Scoping Review
title_sort contribution of open access databases to intensive care medicine research scoping review
url https://www.jmir.org/2025/1/e57263
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