Developing and validating a predictive model for future emergency hospital admissions

Neophytos Stylianou, Jason Young, Carol Peden, Christos Vasilakis

OAI: oai:purehost.bath.ac.uk:openaire_cris_publications/158df507-e057-4adb-a89b-1f0cc3c7c906 • DOI: https://doi.org/10.1177/14604582221101538

Although many emergency hospital admissions may be unavoidable, a proportion of these admissions represent a failure of the care system. The adverse consequences of avoidable emergency hospital admissions affect patients, carers, care systems and substantially increase care costs. The aim of this study was to develop and validate a risk prediction model to estimate the individual probability of emergency admission in the next 12 months within a regional population. We deterministically linked routinely collected data from secondary care with population level data, resulting in a comprehensive research dataset of 190,466 individuals. The resulting risk prediction tool is based on a logistic regression model with five independent variables. The model indicated a discrimination of area under the receiver operating characteristic curve of 0.9384 (95% CI 0.9325 – 0.9443). We also experimented with different probability cut-off points for identifying high risk patients and found the model’s overall prediction accuracy to be over 95% throughout. In summary, the internally validated model we developed can predict with high accuracy the individual risk of emergency admission to hospital within the next year. Its relative simplicity makes it easily implementable within a decision support tool to assist with the management of individual patients in the community.

Emergency hospital admission Decision support system health care Risk prediction /dk/atira/pure/subjectarea/asjc/2700/2718 name=Health Informatics