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Professor Julia Hippisley-Cox Professor of Clinical Epidemiology EMIS NUG committee member Director ClinRisk Ltd Director QResearch. QAdmissions -risk of emergency hospital admission. acknowledgements. Co-author – Dr Carol Coupland
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Professor Julia Hippisley-Cox Professor of Clinical Epidemiology EMIS NUG committee member Director ClinRisk Ltd Director QResearch QAdmissions -risk of emergency hospital admission 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 Embargoed until publication
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 Data Linkage Project • 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
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
Activating QResearch in EMIS Web • Access Data Sharing Manager • My Agreements. • Select Reporting • click QResearch Embargoed until publication
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
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 Embargoed until publication
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 Embargoed until publication
QAdmissions - method • Design: Cohort study • Study period: Jan 2010 to Dec 2011 • Patients: all aged 18-100 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
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 Embargoed until publication
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 • Higher values indicates better discrimination • Similar results CPRD and QResearch • Marginal improvement using GP+HES linked data cf GP data alone Embargoed until publication
QAdmissions :PPV & sensitivity • 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
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 Embargoed until publication
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 LFTs • has a 29% risk of having an emergency admission within the next two years Embargoed until publication
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 • 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