ArticleCritical care science2025
Frequency, financial impact, and factors associated with cost outliers in intensive care units: a cohort study in Belgium.
Article in Critical care science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Development and validation of a machine learning model for early prediction of intensive care unit acquired weakness.Intensive care medicine experimental · 2025Article
- Deep learning for cardiovascular management: optimizing pathways and cost control under diagnosis-related group models.Frontiers in artificial intelligence · 2025Review
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Authors and funding
4 authors.
Funding
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Abstract
objectiveThis study aimed to explore the association between high outliers and intensive care unit admissions and to identify the factors contributing to high intensive care unit costs.
methodsThis retrospective cohort study used data from 17 Belgian hospitals from 2018 and 2019. The study focused on the 10 most frequently admitted diagnosis-related groups in the intensive care unit. The dataset included medical discharge summaries and cost per stay from the hospital perspective.
resultsA total of 39,279 hospital stays were analyzed, 11,124 of which were intensive care unit admissions; additionally, 2,500 of these stays were high outliers. The proportion of high outliers was significantly greater in the intensive care unit group, and admission to the intensive care unit was significantly associated with high outliers in the multivariate analyses. Factors associated with high intensive care unit outliers included the medical diagnosis-related group category, patients from nursing homes, intensive care unit stay duration exceeding 4 days, and specific technical procedures (measurement of intracranial pressure, continuous hemofiltration, and mechanical ventilation).
conclusionAdmission to the intensive care unit increases the likelihood of being classified as an outlier, thus significantly impacting hospital costs. This study identified factors that can be used to predict intensive care unit outliers, which can enable adjustments to diagnosis-related group-based funding for intensive care units.
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