Discussion
This is the first study that compares two predictive scores in a
prospective study. Mean BOOST score was 4.0 (IQR ± 1.0) and was similar
to an earlier retrospective study in another London hospital of 324
patients [11]. The LACE mean score of 12 (± 2.0) is similar to other
studies with slightly higher scores in those re-admitted compared to
those who were not re-admitted [15].
The readmission rate was 33% in this study and older patients had
higher readmission rates, this differs from others where rates of 8% to
22% have been reported [10,15]. However, neither of these studies
explicitly examined older patients and thus, our readmission rate is not
unexpected. The higher readmission rate may have impacted our data in
lowering the number of predicted non-events in our power calculation
(which used the literature readmission rate of 14% for our population.
This may have led our study to be underpowered.
Examining the area under the receiver operating characteristic (ROC)
curve was 0.667 for BOOST and 0.685 for LACE, both of which are
significant predictors of readmission and the results are comparable to
previous studies [9,10,15]. AUCs of less than 0.7 are deemed ‘good’
predictive capacity which reflects the overlap in predictions of those
readmitted versus those who are not. The discriminatory ability of LACE
has previously been reported as poor in the elderly population [16]
and systematic review found that most models performed poorly (mainly in
US population), but suggested that they may be useful with wider
implementation needed [7]. This was similar to our findings with
both BOOST and LACE having poor (but significant) discrimination for
this group. Interestingly the systematic review, which included BOOST
but not LACE, commented that few of the tools included overall health,
illness severity and social determinants of health [7].
Relating to the multivariate analysis, the strongest predictor of
readmission is previous admission in this cohort and hospital
readmission continues to be a common phenomenon and perhaps not
unexpected [9]. Given this, we perhaps need to examine the issue in
a different manner and focus on transitional care following discharge
from hospital for this cohort of patients as a way to prevent/reduce
hospital readmission. There are now established hospital in the home and
transitional programmes that allow early hospital discharge and prevent
hospital admission are beneficial [17,18,19,20]. A systematic review
of interventions to reduce early hospital readmissions concluded that
interventions were complex with more recent ones less effective in their
review from 2009 to 2013 [8]. Singaporean-based RCT showed some
positive results but again highlighted the issues facing clinicians with
patients with multiple comorbidity and complex care needs [21].
Co-ordination of care has been cited as an issue and this continues to
be a problem in the UK [22]. The recurrent theme of previous
hospitalisations as a strong indicator of future readmission, it may be
that predictor models do not have benefit over a well performed history
with emphasis on previous admissions. This would be an area worth future
investigation.
It may be that patients require a ‘step-down’ or managed approach
following hospital discharge and the role of a hospital in the home has
the potential to improve post-discharge outcomes [19,23]. Within the
local area, the @home service set up in 2014 manages 300-400 patients
per month for short-term acute follow-up with positive results in terms
of patient satisfaction [24], but did not demonstrate significant
reduction in local emergency department attendances [25]. Given that
hospital in the home services are now embedded into the healthcare
system and integrated care is being established around the country, an
exploration of targeted services for patients with high BOOST and/or
LACE scores is required. This would determine if early identification of
patients who had high scores and were referred for hospital in the home
services translates to a lower readmission rate and better clinical
outcomes for patients. Clearly, further research is needed on the
various hospital in the home programmes as they are not standardised
service.
A limitation of this study lies in the selection of a sub-group of
patients that were admitted and discharged in a short time, mostly with
length of stays less than two weeks, and not transferred to an inpatient
ward. This study validates the use of the BOOST tool for recognising
risk factors for readmission in these patients, but does not directly
validate its use in those patients who are admitted to inpatient wards
for greater lengths of stay. However, unlike the LACE score the BOOST
tool does not select for length of stay and is designed only to flag
patients with risk factors for readmission regardless of duration.
This single centre, prospective cohort study aimed to determine the
efficacy of two models at predicting unplanned readmissions in those
aged 75 and older and we have demonstrated that the mean BOOST and LACE
scores for those readmitted was significantly higher than those not
readmitted. Whilst the multivariate logistic regression model accurately
predicted the highest number of cases with 74.5% of correct cases
identified and the area under the curve was acceptable, sensitivity and
specificity could be improved. Overall the predictive power is not
optimal, these tools still hold some value in preventing readmissions.
This study shows the strongest predictor of readmission is previous
admission, and health literacy. It may be that we need to focus on
education intervention to increase patient involvement in their care and
ongoing management of their health. This approach has the ability to
improve continuity of care and along with care coordination; there could
be some benefit for decreased hospitalisations. The BOOST 2 tool has the
potential to provide a pathway for quality improvement where
interventions (such as teach back) based on identified risk factors
(i.e. literacy) could help in preventing readmissions.
Predicting hospital readmission remains a complex task and any tool
needs to be clinically relevant and reliably measured. Further
prospective studies using these predictive tools may be useful in
planning transitioned and hospital in the home programmes for those at
high-risk of readmission.