ArticleBMC pediatrics2025
Prognostic factors associated with length of stay in children with an acute admission to a pediatric ward: a historical cohort study.
Article in BMC pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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
1 citing paper in PubMed.
- From Triage to Outcome: How ED Parameters and Comorbidities Shape Pediatric Critical Illness Prognosis.Children (Basel, Switzerland) · 2026Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAn acute (i.e. unplanned) admission implies uncertainty about the length of stay (LOS) for children, their parents and hospital staff. An improved understanding of the prognostic factors associated with LOS in children is essential for adequate discharge planning, optimal use of bed capacity, and patient counseling. We aimed to explore which prognostic factors were associated with LOS in children between 0 and 18 years old, who were acutely admitted to a pediatric academic hospital ward.
methodsWe conducted a historical cohort study using electronic data from all children who were acutely admitted to the Emma Children’s Hospital, the Netherlands, between 2017 and 2022. Selection of potential prognostic factors was based on literature and expert opinion. Univariable linear regression analysis was used to select prognostic factors associated with LOS, followed by multivariable regression analysis to obtain the final model.
resultsWe included 9209 children with a median LOS of 2.7 days (IQR 1.1 to 5.8). Out of 16 potential prognostic factors, 10 were associated with an increased LOS, while 4 decreased the LOS. The strongest associations were (1) the involvement of ≥ 3 sub-specialties during the first 24 h of admission, (2) the severity of illness and (3) medical home care after discharge, and were all prognostic for an increased LOS. The final model explained 29% of the variance in LOS.
conclusionsThe results of this study support patient and hospital expectations and bed capacity management. The explained variance is limited despite the inclusion of multiple factors, suggesting predicting LOS for this population is challenging.
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