ArticleThe British journal of general practice : the journal of the Royal College of General Practitioners2024
Predicting unplanned admissions to hospital in older adults using routinely recorded general practice data: development and validation of a prediction model.
Article in The British journal of general practice : the journal of the Royal College of General Practitioners, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Risk stratification tool for older emergency department patients: the 6-items brief geriatric assessment.BMC geriatrics · 2026Observational
- Prediction of 1 and 2 week nonelective hospitalization and sepsis hospitalization risk in adults.NPJ digital medicine · 2025Article
- Avoiding hospital admissions this winter: the challenge for general practice.The British journal of general practice : the journal of the Royal College of General Practitioners · 2024Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUnplanned admissions to hospital represent a hazardous event for older people. Timely identification of high-risk individuals using a prediction tool may facilitate preventive interventions.
aimTo develop and validate an easy-to-use prediction model for unplanned admissions to hospital in community-dwelling older adults using readily available data to allow rapid bedside assessment by GPs. DESIGN AND
settingThis was a retrospective study using the general practice electronic health records of 243 324 community-dwelling adults aged ≥65 years linked with national administrative data to predict unplanned admissions to hospital within 6 months.
methodThe dataset was geographically split into a development (
resultsIn both samples, 7.6% (development cohort:
conclusionThe models showed satisfactory predictive ability. Reducing the number of predictors and geographic validation did not have an impact on predictive performance, demonstrating the robustness of the model. An easy-to-use tool has been developed in this study that may assist GPs in decision making and with targeted preventive interventions.
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