ArticleAging clinical and experimental research2025
Risk factors for frequent unplanned hospital visits in older multimorbid patients after an emergency department visit: a retrospective cohort study.
Article in Aging clinical and experimental research, 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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1 citing paper in PubMed.
- The C-Reactive Protein/Albumin Ratio Predicts In-Hospital Mortality Across Age Groups.Nutrients · 2026Article
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7 authors.
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Abstract
backgroundHospital readmissions are common among older multimorbid patients, and hospitalisation is associated with functional decline and reduced quality of life. Identifying patient characteristics and risk factors for repeated hospital visits following an emergency department (ED) encounter is important for guiding targeted interventions to prevent future revisits.
aimTo characterise multimorbid older patients with frequent unplanned hospital visits following an emergency department (ED) visit, compare them to those with fewer revisits, and identify risk factors for frequent unplanned hospital revisits.
methodsA retrospective analysis of 25 436 multimorbid patients aged ≥ 75 years who visited 8 EDs in Sweden in 2017 was performed. Patients with ≥ 3 ED visits or unplanned hospital admissions in the following year were classified as frequent hospital visitors. Their characteristics were compared to those of infrequent visitors, and binomial logistic regression was used to identify factors predictive of frequent hospital visits.
resultsAn increasing number of hospital visits in the prior 12 months (odds ratio (OR) 1.25 95% confidence interval (95% CI) 1.21-1.29) and an increasing number of comorbidities (OR 1.11 95% CI 1.09-1.13) were the most impactful risk factors for multiple unplanned hospital revisits. Female sex (OR 0.81 95% CI 0.75-0.88) was the most important protective factor.
conclusionA history of frequent hospital visits was the strongest risk factor for future unplanned hospital visits. Several other factors were also identified, suggesting the potential for predictive models to identify at-risk patients before frequent visits occur.
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