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Professor Julia Hippisley-Cox Professor of Clinical Epidemiology Director ClinRisk Ltd Director

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Presentation on theme: "Professor Julia Hippisley-Cox Professor of Clinical Epidemiology Director ClinRisk Ltd Director"— Presentation transcript:

1 Professor Julia Hippisley-Cox Professor of Clinical Epidemiology Director ClinRisk Ltd Director

2  Co-authors  QResearch database - EMIS practices, EMIS, Nottingham University  EMIS NUG (including screencasts)  ClinRisk Ltd (development & software)  Office National Statistics (mortality data)  HSCIC (pseudonymised HES data)

3  QBleed Algorithm  QBleed + QStroke  Update on tools integrated into EMIS Web Embargoed until publication

4  Individual assessment  Who is most at risk of current or preventable disease?  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 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

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6  Anticoagulants  used in prevention & treatment of VTE  To reduce risk ischaemic stroke with AF  Although use of anticoagulants in AF is in QOF uptake is low  Legitimate concerns around safety particularly risk of major bleeds  Need to quantify absolute risk of bleed to help make informed decision on risk/benefit

7  When discussing benefits and risk of anticoagulation in AF explain that  For most people benefit exceeds risk  Except for those with increased bleeding risk where careful monitoring required  Discuss options and base choice on their clinical features and preferences  Only treat after informed discussion on risks & benefits

8  Currently recommends HAS-BLED score  Scoring system major bleed in AF  Derived from 3978 hospital based patients  Not externally validated  Risk factors for HAS-BLED v similar to CHADS stroke  Simple scoring system not measure of absolute risk

9  Hypertension  Renal disease  Liver disease  Prior Stroke  Prior bleed or predisposition  Age 65 (yes/no)  Medication (antiplatelets, NSAID)  Alcohol (> 8 units/week)  Labile INR – but supposed to be for new users so INR wont be available! Embargoed until publication

10  Develop new risk algorithm which  Predict 1yr & 5yr absolute risk of GI and intracranial bleed  new users anticoagulants c.f. non-use  Includes clinically relevant variables ameliorable to change  Can be implemented in routine GP systems  Can be shared with patient to help inform decision making  Can be updated regularly Embargoed until publication

11  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

12  Design: Cohort study  Study period:  Patients: 4.4 million aged years  Baseline: assessment of predictive factors focused on  clinically relevant variables  primary care  Outcome: GI bleed or intracranial bleed on linked mortality or hospital data Embargoed until publication

13 Upper GI bleed 21,614 cases on QResearch linked hospital or mortality records Intracranial bleed 9,040 cases on QResearch linked hospital or mortality records Largest ever such study. Increases reliability of results and generalisability of findings

14  Age, sex, BMI  Ethnicity  Deprivation  Smoking & alcohol  Abnormal platelets  Medication  Antiplatelets  NSAIDS  Steroids  Antidepressants  Anticonvulsants  Atrial fibrillation  Heart Failure  Treated hypertension  Cancer  Liver disease/pancreatitis  Oesophageal varices  VTE  Prior bleed (GI, brain, haematuria,haemoptysis)

15  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)

16 WomenMen Upper GI bleed ROC R2R D statistic Intracranial bleed ROC R2R D statistic  Higher values indicates better discrimination  Similar results CPRD and QResearch

17 Fig 3 Mean predicted risks and observed risks at five years by 10th of predicted risk applying QBleed risk prediction scores to all patients in QResearch validation cohort. Hippisley-Cox J, and Coupland C BMJ 2014;349:bmj.g4606 ©2014 by British Medical Journal Publishing Group

18 Cut off 5 year risk (%) Sensitivity (%) Observed risk (%) Upper GI bleed1.4%38%2.7% Intracranial bleed0.7%51%1.5%  For example, using threshold of top 10% at risk will correctly identify  38% of those who get upper GI bleed  51% of those who get intracranial bleed

19 QBLEED  4.4 million GP patients  30,681 events  2 clear outcomes  Followed over 5 years  Absolute risk  Includes more clinically relevant factors  Externally validated  Easy to update over time HAS-BLED  4,000 hospital patients  53 events  Unclear what ‘major bleed’ is  Followed over 1 year  Simple count only  Includes INR which wont have prior to Rx  Not externally validated  Unclear about updates

20 “This is among the largest of the outpatient derivation cohorts used in this specialty to date and provides extra power to develop more robust predictive models using more candidate covariates than other scores”. “Such a model represents a change in our approach to assessing bleeding risk, from simple, point based scores, to a more inclusive, complex model”. “While there may be implications for implementation, this progression may make sense clinically—there are often patient subtleties and characteristics that inevitably increase the risk of bleeding but are not captured in simpler scores”. “While calculating bleeding risk is no longer “simple,” neither is the decision to use long term anticoagulation”. “A more comprehensive model may adjust for these factors, giving doctors and their patients a more refined estimate of absolute risk”.

21  How should GPs use risk estimates when making decisions about bleeding?  What risk is too high?  Is threshold same for every patient & every indication?  Are there patients for whom extra risk is negligible compared with underling stroke risk?

22  Estimates risk of ischaemic stroke over 1-10 years  Includes age, sex, ethnicity, deprivation  Smoking, diabetes, AF, CCF, CVD  Rheumatoid, chronic renal disease  Valvular heart disease  Treated hypertension and FH CHD  SBP, cholesterol, BMI  Integrated into EMIS WEB Embargoed until publication

23 75yr old man with AF, light smoker, heavy alcohol, NSAIDS

24 ALREADY IN EMIS WEB  QRISk2  QDiabetes  QStroke  QFracture  (QAdmissions) IN PLANNING PHASES  QCancer (release 4.11)  QKidney  QThrombosis  QBleed  QIntervention

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26  calculation-template-emis-web calculation-template-emis-web  calculation-eg-qrisk-group-patients-batch- add calculation-eg-qrisk-group-patients-batch- add EMIS NUG screen casts courtesy of Dr Geoff Schrecker & EMIS NUG

27  Currently around 800 practices contributing  Would like around 1000  Pseudonymised data with no strong identifiers  IG approved EMIS NUG, REC, BMA, RCGP  Only used for research  All research peer reviewed and published  Need to activate QResearch in EMIS Web even if sharing data for many years via LV

28  activity-emis-web-user-profile activity-emis-web-user-profile  sharing-agreements-qsurveillance-and- qresearch sharing-agreements-qsurveillance-and- qresearch


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