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


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


Outline

QResearch database Open Pseudonymiser & data linkage Overview of QPrediction scores QAdmissions risk profiling Qinnovation competition


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

http://www.openpseudonymiser.org/

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-


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

http://www.qrisk.org/

http://www.qdiabetes.org/

http://www.qstroke.org/

http://www.qkidney.org/

http://www.qthrombosis.org/

http://www.qthrombosis.org/

http://www.qfracture.org/

http://www.qintervention.org/

http://www.qintervention.org/

http://www.qcancer.org/

http://www.qadmissions.org/

http://www.qadmissions.org/


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


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


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


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


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


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


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


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


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


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


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


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)


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


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


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

Professor Julia Hippisley-Cox Professor of Clinical Epidemiology EMIS NUG committee member Director...

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