Research Topics and Trends in <scp>MIMIC</scp> ‐ <scp>IV:</scp> A Large <scp>ICU</scp> Database Relevant for Critical Care Nursing

Abstract

BACKGROUND: The Medical Information Mart for Intensive Care-IV (MIMIC-IV) clinical database has become a central resource for data-driven critical care research, enabling advances in clinical informatics, machine learning and nursing science. Despite its rapid uptake, no prior study has provided a transparent, methodologically grounded, bibliometrics-based overview of MIMIC-IV-related research output. AIM: This paper aims to map the major research themes associated with the MIMIC-IV database (2021-2024) and to evaluate their relevance to critical care nursing research and practice. STUDY DESIGN: A study of 1150 publications retrieved from the Web of Science Core Collection (SCI-Expanded). Explicit search strategies, front-page filtering and publication counts were used to identify and analyse keyword-based research themes. RESULTS: Keyword analyses identified mortality prediction, sepsis, acute kidney injury, intensive care workflows and machine learning as dominant research areas, many of which are directly relevant to nursing-sensitive outcomes and bedside clinical decision-making. CONCLUSIONS: This review provides the first focused mapping of research themes within MIMIC-IV publications. These findings clarify the thematic landscape of current MIMIC-IV-based research and underscore topics of particular importance to critical care nursing. RELEVANCE TO CLINICAL PRACTICE: MIMIC-IV supports the generation of evidence on essential nursing concerns. Recognising global research patterns enables nurses, clinicians and informatics teams to identify emerging tools, prioritise data-driven competencies and translate large-scale analytics into improved ICU care and patient outcomes.

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