Professor Julia Hippisley-Cox Professor of Clinical Epidemiology EMIS NUG committee member Director ClinRisk Ltd Director QResearch Embargoed until publication

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

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

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 Embargoed until publication

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

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

Data sourceTime period data available GP data1989- ONS cause of death1997- ONS cancer registration1997- HES Outpatient data1997- HES Inpatient data1997- HES A&E data2007-

Access Data Sharing Manager My Agreements. Select Reporting click QResearch Embargoed until publication

Clinical practice & benefit Clinical questions Research + innovation Integration into clinical systems

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

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 scoresoutcomeWeb link QRISK2CVDwww.qrisk.org QDiabetesType 2 diabeteswww.qdiabetes.org QStrokeIschaemia strokewww.qstroke.org QKidneyModerate/severe renal failurewww.qkidney.org QThrombosisVTEwww.qthrombosis.org QFractureOsteoporotic fracturewww.qfracture.org QInterventionRisks benefits interventions to lower CVD and diabetes risk QCancerDetection common cancerswww.qcancer.org QAdmissionsEmergency admissionswww.qadmissions.org

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

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 Embargoed until publication

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 Embargoed until publication

Design: Cohort study Study period: Jan 2010 to Dec 2011 Patients: all aged years Baseline: 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

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 Embargoed until publication

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

QR GP+HES QR GP only CPRD GP +HES CPRD GP only Women ROC R2R D statistic Men ROC R2R D statistic Higher values indicates better discrimination Similar results CPRD and QResearch Marginal improvement using GP+HES linked data cf GP data alone Embargoed until publication

QResearch GP+HES QResearch GP data only SensitivityObserved risk (PPV) SensitivityObserved risk (PPV) Top 1% Top 5% Top 10% Top 20% 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 Embargoed until publication

Observed risk very close to predicted risk Similar results for GP +HES linked data and GP data alone Similar results for CPRD All show its well calibrated Embargoed until publication

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 LFTs has a 29% risk of having an emergency admission within the next two years Embargoed until publication

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

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

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

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 31 st Jan 2014 Embargoed until publication

QResearch derivation QResearch validation CPRD validation Total admissions 265,573132,723234,204 Admission method A&E70%69%73% GP Direct18%19%17% GP Bed Bureau 2% 1% Consultant3% Other7% 6% V large numbers of emergency admissions in each data source Increases reliability of results Method of admission representative of all national emergency admissions Embargoed until publication