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.