Introduction
Preventing hospital readmission is a common and costly issue and emergency readmission within 30 days of discharge is used as a measure of care and often deemed avoidable, although non-elective readmissions are often not preventable or predictable. Recent analysis by the Nuffield Trust reported an increase of 19.2% in 30-day readmissions in England between 2010/11 and 2016/17 from 1,157,570 to 1,379,790 [1].
Several authors have developed predictive tools to assist clinicians to identify patients at risk of death or readmission within 30 days of discharge [2,3,4,5,6]. However, several systematic reviews have highlighted the majority of the predictive tools had poor discriminatory ability [7,8,9]. The LACE index identified four variables that were predictive (length of stay, acuity, comorbidities and emergency department use) and was found to have reasonable discrimination [10], the BOOST tool identified eight variables [11]. However little data is available on their use and application within the UK population.
The aim of the study was to undertake a prospective study to determine the efficacy of two models (LACE and BOOST) in predicting unplanned hospital readmission.