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PARR-30: a predictive model for
readmission within 30 days
Presenter: Ian Blunt
17 July 2015
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Development of a predictive model for readmission within 30
days of discharge (PARR-30)
Model developed by Billings, Blunt, Steventon, Georghiou, Lewis and
Bardsley
•
Motivation
•
Development
•
Model performance
•
Testing in hospitals
•
Conclusions
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Why predict readmissions within 30 days?
•
Readmissions are costly, suboptimal health care - costs to the NHS
estimated at £1.6 billion each year
•
DH guidance for the NHS proposes commissioners do not pay
provider hospitals for emergency readmission within 30 days of a
selected index elective admission
•
Rate of readmissions will also play an important part in monitoring
health system performance, as one of the new English Public Health
“outcome indicators”
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Not first to try this, but…
Number of international 30 day models
Predictive tools built in one setting
may not necessarily be accurate when
used in other health care settings
Used hospital episode statistics (HES)
data to develop model for NHS in
England
Make PARR-30 freely available for
use across the NHS in England
(possibly tablet/smartphone app)
Model
From
C
statistic
Halfon et al 2008
0.67
Silverstein et al
2008
0.65
Van Walraven et al
2010
0.68
Howell et al 2009
0.65
And others…
See Kansagara et al JAMA 2011
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How is PARR30 different from PARR++?
•
Readmission in next 30 days vs next 365 days
•
Tools operate in different ways, trigger different responses
•
Next year – longer for clinicians and care managers/coordinators to contact
and engage with high-risk patients, effect behavioural change
•
30 days – highest likelihood of an unplanned admission, focussing their
discharge planning efforts and post-discharge support for high-risk patient
Model
Timescale
Run by
Input
variables
Data
source
Data lag
PARR++
12 months
PCT
~250
SUS
~ 3 months
PARR30
30 days
Acute
17
PAS/notes
None
•
Aim for speed of low-variable models with accuracy of PARR
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How is PARR30 different from PARR++?
PARR30
Hospital provides SUS
Patient nears discharge
PCT runs PARR++
Risk score calculated on
ward
Patients selected for
intervention (via GP)
Any extra intervention put
in discharge plan
Predicts readmission in next year –
PPV 65%
Predicts readmission in 30 days© Nuffield Trust
– PPV ???%
Model development
Developed using 10% sample HES
from April 2006 to May 2009
Index discharges in FY 2008/09
Readmissions within 30 days
reflected 2011-12 operating
framework
Logistic regressions identify variables
that contributed most to predictions
Validated with split sample
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Model development
Hospital of current admission
Patient age
Deprivation (via post code)
History of emergency admissions:
Current? Last 30 days? Past year?
History in the prior two years of eleven
major health conditions drawn from
the Charlson co-morbidity index
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Results
The performance of the model
was respectable, with a positive
predictive value (PPV) of
59.2% and area under the ROC
curve (“c-statistic”) of 0.70.
For the higher-risk patients (risk
score > 50%), readmission rates
ranged from 47.7% up to 88.7%.
However, these patients only
represented a small share
(1.1%) of all patients analysed.
Receiver Operating Characteristic Curve (ROC) for the
bootstrapped central estimate (red line) and 95%
confidence Intervals (shaded area)
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Results
For patients risk score > 50%, mean
readmission cost per patient was
£1,088. Assuming that an
intervention can reduce the number
of readmissions by 10% for this
group, £109 per patient could be
spent at breakeven
£3,000
£2,500
Mean cost of readmission
Predictive modelling only as effective
as the intervention it is used to
trigger. Providers need to know
potential costs of readmission to
build business case for intervention
£2,000
£1,500
£1,000
£500
£0
Risk score
Mean of cost readmission (readmitted patients only)
Mean of cost readmission (all patients)
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Testing PARR-30 in hospitals
Testing:
•
•
•
Is the tool easy to use?
Bedside info vs admin systems?
Does ward PPV reflect national?
Chelsea & Westminster Hospital
running tool direct from their data
warehouse:
•
Proved it can be done easily
•
Looking into PPV and clinical
engagement
Royal Berkshire Hospital using
spreadsheet version of tool on wards:
•
Completed by junior doctors
•
Test tool stored its output
•
Later reconciled with admin
systems for analysis
Applied on four care of the elderly
wards in Feb/March 2012
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Testing PARR-30 in hospitals
Results from using spreadsheet on the wards:
•
Tool was used 88 times
•
Low risk scores – max 39%
•
Median time to complete 1m 41s
•
Em admit in last 30 days diff 10%
•
Median patient age was 86,
mostly emergency admissions
•
Em admits last year diff 20%,
±1,2
•
Average 1.3 co-morbidities, max 4
•
•
10 patients had emergency
readmission within 30 days
Even split whether tool or system
has more
•
14% where system has diagnosis
not ticked as co-morbidity
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Conclusions
Built a predictive model using a limited set of variables that were
generated from hospital episode statistics
Variables easily available from patients’ notes or from the patients
themselves – can calculate from spreadsheet or in PAS
The performance of the model was respectable - highest risk patients
had a 88.7% chance of hospital readmission within 30 days – but high
risk patients relatively rare
Cost data suggests interventions need to be lower-cost to break even
Easily used on wards in trials - less than 2 minutes per application
Some differences in data on ward, but not huge
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17 July 2015
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