Professor Julia Hippisley-Cox Professor of Clinical Epidemiology EMIS NUG committee member Director...
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Transcript of Professor Julia Hippisley-Cox Professor of Clinical Epidemiology EMIS NUG committee member Director...
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QAdmissions -risk of emergency hospital admissionProfessor Julia Hippisley-CoxProfessor of Clinical EpidemiologyEMIS NUG committee memberDirector ClinRisk LtdDirector QResearch
Embargoed until publication
acknowledgements
Co-author – Dr Carol Coupland QResearch database - EMIS
practices, EMIS, Nottingham University
ClinRisk Ltd (development & software)
HSCIC (pseudonymised HES data) CPRD (validation data source) East London Commissioning Support
Unit EMIS NUG for suggesting topic 2yrs
ago
Embargoed until publication
Outline
QResearch database Open Pseudonymiser & data linkage Overview of QPrediction scores QAdmissions risk profiling Qinnovation competition
Embargoed until publication
QResearch database www.qresearch.org
Established 2002 joint venture EMIS & UoN
Patient level pseudonymised data Only used for research No patient identifiers, no free text Strong IG framework with no
breeches Approved by ethics, BMA/RCGP Advisory board with NUG & practice
reps Currently 680 practices Can contribute if LV or EMIS Web
Information on QResearch – GP derived data
Demographic data – age, sex, ethnicity, SHA, deprivation
Diagnoses Clinical values –blood pressure, body
mass index Laboratory tests – FBC, U&E, LFTs etc Prescribed medication – drug, dose,
duration, frequency, route Referrals Consultations
QResearch database already linked to deprivation data in 2002 cause of death data in 2007
Very useful for research better definition & capture of outcomes Health inequality analysis Improved performance of QRISK2 and similar
scores
Developed new open source technique for data linkage using pseudonymised data
QResearch Data Linkage Project
www.openpseudonymiser.org
Scrambles NHS number BEFORE extraction from clinical system
Takes NHS number + project specific encrypted ‘salt code’
One way hashing algorithm (SHA2-256) Cant be reversed engineered Applied twice in two separate locations
before data leaves source Apply identical software to external dataset Allows two pseudonymised datasets to be
linked Open source – free for all to use
QResearch Database + data linked in 2013
Data source Time period data available
GP data 1989-
ONS cause of death 1997-
ONS cancer registration 1997-
HES Outpatient data 1997-
HES Inpatient data 1997-
HES A&E data 2007-
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Activating QResearch in EMIS Web
Access Data Sharing Manager
My Agreements.
Select Reporting
click QResearch
Clinical Research Cycle
QPrediction ScoresA new family of Risk Prediction tools
Individual assessment Who is most at risk of preventable disease? What is level of that risk and how does it compare? Who is likely to benefit from interventions? What is the balance of risks and benefits for my
patient? Enable informed consent and shared decisions
Population level Risk stratification Identification of rank ordered list of patients for
recall or reassurance GP systems integration
Allow updates tool over time, audit of impact on services and outcomes
Criteria for choosing clinical outcomes
Major cause morbidity & mortality Represents real clinical need Related intervention which can be
targeted Related to national priorities (ideally) Necessary data in clinical record Help inform decisions at the point of
care Can be implemented into everyday
clinical practice
+Published & validated scores
scores outcome Web link
QRISK2 CVD www.qrisk.org
QDiabetes Type 2 diabetes www.qdiabetes.org
QStroke Ischaemia stroke www.qstroke.org
QKidney Moderate/severe renal failure
www.qkidney.org
QThrombosis VTE www.qthrombosis.org
QFracture Osteoporotic fracture www.qfracture.org
QIntervention Risks benefits interventions to lower CVD and diabetes risk
www.qintervention.org
QCancer Detection common cancers www.qcancer.org
QAdmissions Emergency admissions www.qadmissions.org
Embargoed until publication
QAdmissions: background
Emergency admissions cost 11 billion/year
Some potentially avoidable NHS England new DES to reward
practices for management of high risk patients to lower risk
Problems with current risk assessment tools Out of date Not validated or published Expensive
Embargoed until publication
QAdmissions: Aim
Develop new risk algorithm which Includes clinically relevant variables
ameliorable to change Account for ethnicity & deprivation to
avoid worsening health inequalities Include geographical weighting Based on contemporaneous English data Can be updated regularly Can be implemented in routine general
practice
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QResearch – data source
Developed using QResearch database Very large validated GP database Derived from EMIS (largest GP supplier) Representative ethnically diverse
population
Linked to Hospital Episode Statistics
Linked to ONS cause of death data
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QAdmissions - method
Design: Cohort studyStudy period: Jan 2010 to Dec 2011Patients: all aged 18-100 yearsBaseline: assessment of predictive
factors focused on clinically relevant variables Primary care
Outcome: 1 or 2 year risk of emergency admission based on HES linked data
Embargoed until publication
QAdmissions: predictors
Age, sex, BMI Ethnicity Deprivation Strategic Health Authority Smoking & alcohol Lab values
Abnormal LFTs Anaemia Raised platelets
Medication Anticoagulants Antidepressants antipsychotics NSAIDs Steroids
Prior admissions Type of Diabetes CVD, AF, CCF Chronic renal disease Venous thrombosis Cancer Asthma/COPD Manic depression or
schizophrenia Malabsorption Chronic liver/pancreas
disease Falls
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QAdmissions: Validation
Gold standard to test performance of risk tool on separate population
We used 2 validation samples Different practices in QResearch (from EMIS) Different practices in CPRD (from Vision
Practices)
Undertaken by authors with additional verification to be done by independent team Oxford University
Embargoed until publication
QAdmissions :Discrimination
QRGP+HES
QRGP only
CPRD GP +HES
CPRDGP only
Women
ROC 0.77 0.76 0.77 0.76
R2 41 37 41 38
D statistic
1.70 1.58 1.69 1.59
Men
ROC 0.78 0.77 0.77 0.77
R2 43 40 42 39
D statistic
1.76 1.66 1.74 1.64
Higher values indicates better discrimination
Similar results CPRD and QResearch
Marginal improvement using GP+HES linked data cf GP data alone
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QAdmissions :PPV & sensitivity
QResearchGP+HES
QResearchGP data only
Sensitivity
Observed risk (PPV)
Sensitivity
Observed risk (PPV)
Top 1% 7 73 6 65
Top 5% 25 53 23 50
Top 10%
39 42 37 40
Top 20%
57 30 55 29
For example, using threshold of top 10% at risk will correctly identify 39% or
emergency admissions using GP+HES linked data and
37% using GP data only
So linked data for implementation marginally better
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QAdmissions - Calibration
Observed risk very close to predicted risk
Similar results for GP +HES linked data and GP data alone
Similar results for CPRD
All show it’s well calibrated
010
2030
40
0 5 10 0 5 10
female male
Observed risk Predicted risk
2 yr
adm
issi
on ri
sk (p
erce
ntag
e)
Tenth of predicted risk
QResearch database version 35
Predicted & observed 2yr risk by tenth of predicted risk
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QAdmissions – clinical case
53 year old white man from the South West
ex-smoker drinks 7-9 units/day type 2 diabetes body mass index of
39.1 kg/m2 prescribed antidepressants
last Hb of <11g/dl abnormal LFTshas a 29% risk of having an emergency admission within the next two years
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QAdmissions – key features (1)
Robust scientific methods Based on large representative
sample so more generalizable Includes clinically relevant variables
Ethnicity, SHA, deprivation Diagnoses, medication, lab results
predicts absolute risk of emergency admission over 1 or 2 years
Embargoed until publication
QAdmissions: key features (2)
Validated on two separate populations Able to distinguish between levels of risk
(discrimination) Accurate as predicted risk close to
observed Can be updated regularly to reflect
changes in requirements Changes in populations Improvements in data capture
Transparent, peer reviewed (BMJ Open 2013)
Embargoed until publication
QAdmissions : key features (3)
Simpler to implement in clinical practice
Can run entirely on GP data (though enhanced if HES linked)
Currently being integrated into EMIS Web
Aiming for release early 2014 Paul Davis (EMIS IQ) contact
Embargoed until publication
QInnovation Competition 2014
Annual competition Prize is 10K + cut QResearch data +
advice Application form qresearch.org EMIS practices can apply Main criteria innovation likely to lead
to patient benefit 2013 – two winners including Tim
Walter for implementing QDiabetes in Newbury CCG
Closing date 31st Jan 2014
Embargoed until publication
Outcome – absolute risk emergency admission over 1 or 2 years
QResearchderivation
QResearchvalidation
CPRD validation
Total admissions
265,573
132,723
234,204
Admission method
A&E 70% 69% 73%
GP Direct 18% 19% 17%
GP Bed Bureau
2% 2% 1%
Consultant 3% 3% 3%
Other 7% 7% 6%
V large numbers of emergency admissions in each data source
Increases reliability of results
Method of admission representative of all national emergency admissions