JMIR Publications

ABSTRACT

Background:

Intensive 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.

Objective:

The 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.

Methods:

The research was conducted using three databases (PubMed – Medline, Embase, Web of science) from the inception of each database to August 1st, 2022. We included original articles based on four open databases of patients admitted to intensive care units: Amsterdam University Medical Centers Database (AmsterdamUMC), eICU Collaborative Research Database (eICU-CRD), High time resolution ICU dataset (HiRID), Medical Information Mart for Intensive Care (MIMIC 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.

Results:

We observed a consistent increase in the number of publications from these databases since 2016. The MIMIC 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 [2.8 – 5.8]. Cardiovascular and infectious topics were predominant, accounting for 333 (25.5%) and 324 (24.8%) articles, respectively. Logistic regression emerged as the most commonly employed 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, are under the curve (AUC) was the most frequent performance measure, with a median value of 0.840 [0.780 – 0.890].

Conclusions:

The 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 journals of lower impact factors.