1. + Towards personalised medicine – assessing risks and benefits for individual patients Prof Julia Hippisley-Cox, University of Nottingham, Tony Mitchell Lecture 15 th May 2013

2. + Acknowledgements Co-authors Drs Carol Coupland, Peter Brindle, John Robson QResearch database University of Nottingham EMIS & contributing practices & user group ClinRisk Ltd (software) Oxford University (independent validation, Prof Altman’s team)

3. + Outline QResearch database +linked data General approach to risk prediction QRISK2 QDiabetes QIntervention QFracture Any questions

4. + QResearch Database One of the worlds largest and richest research databases Over 700 general practices across the UK, 14 million patients Joint venture between EMIS (largest GP supplier > 55% practices) and University of Nottingham Patient level pseudonymised database for research Available for peer reviewed academic research where outputs made publically available Data from 1989 to present day.

5. + 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

6. + 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 technique for data linkage using pseudonymised data QResearch Data Linkage Project

7. + 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

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9. + QResearch Database + data linked in 2013 Data sourceTime period data available GP data1989- ONS cause of death1997- ONS cancer registration1997- HES Outpatient data1997- HES Inpatient data1997- HES A&E data2007-

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

11. + A new family of Risk Prediction tools Individual assessment  Who is most at risk of 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 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

12. + 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 Can be implemented into everyday clinical practice

13. + Change in research question Leads to Novel application of existing methods Development of new methods Better utilisation different data sources Leads to Lively academic debate! Changes in policy and guidance New utilities to implement research findings (hopefully) Better patient care

14. + 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 www.qintervention.org QCancerDetection common cancerswww.qcancer.org

15. + Vascular Risk Engine: Requirements Identify patients at high risk of vascular disease CVD Diabetes Stage 3b,4, 5 Kidney Disease Assessment of individual’s risk profile Risks and benefits of interventions Weight loss Smoking cessation BP control Statins

16. + Why integrated tool CVD, diabetes, CKD? Many of the risk factors over overlap Many of the interventions overlap But different patients have different risk profiles Smoking biggest impact on CVD risk Obesity has biggest impact on diabetes risk Blood pressure biggest impact on CKD risk Help set individual priorities Development of personalised plans and achievable target

17. + Primary prevention CVD: (slide from NICE website) Offer information about: absolute risk of vascular disease absolute benefits/harms of an intervention Information should: present individualised risk/benefit scenarios present absolute risk of events numerically use appropriate diagrams and text

18. + Challenge: to develop a new CVD risk score for use in UK New cardiovascular disease risk score Calibrated to UK population Use routinely collected GP data Include additional known risk factors (eg family history, deprivation) Better calibration and discrimination than Framingham 18 Aim for QRISK

19. + Why a new CVD risk score? Framingham has many strengths but some limitations: Small cohort (5,000 patients) from one American town Almost entirely white Developed during peak incidence CVD in US Doesn’t include certain risk factors (body mass index, family history, blood pressure treatment, deprivation) Over predicts CVD risk by up to 50% in European populations Underestimates risk in patients from deprived areas 19

20. + QRisk1 risk factors Traditional risk factors Age, sex, smoking status Systolic blood pressure Ratio of total serum cholesterol/high density lipoprotein (HDL) cholesterol New risk factors Deprivation (Townsend score output area) Family history of premature CVD 1 st degree relative aged < 60 years Body mass index Blood pressure treatment 20

21. + Model Derivation Separate models in males and females Cox regression analysis Fractional polynomials to model non-linear risk relationships Multiple imputation of missing values 21

22. + Derivation of QRISK2 Score Derivation cohort 355 practices; 1,591,209 patients; 96,709 events Additional risk factors: ethnic group type 2 diabetes, treated hypertension, rheumatoid arthritis, renal disease, atrial fibrillation Interactions with age 22 J Hippisley-Cox, C Coupland, et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ 2008; 336: 1475-1482

23. + Results 23 Hippisley-Cox J et al. BMJ 2008;336:1475-1482

24. + Interactions 24 Fig 1Impact of age on hazard ratios for cardiovascular disease risk factors using the QRISK2 model. Hippisley-Cox J et al. BMJ 2008;336:1475-1482

25. + Validation Separate sample of 176 QResearch practices; 750,232 patients; 43,396 events Validation statistics (for survival data) D statistic 1 (discrimination) R squared (% variation explained) Predicted vs. observed CVD events Clinical impact in terms of reclassification of patients into high/low risk 25 1 Royston and Sauerbrei. A new measure of prognostic separation in survival data. Stat Med 2004; 23: 723-748.

26. + Calculation of risk scores Risk scores calculated in validation dataset Risk score calculation: Used coefficients for risk factors obtained from Cox model using multiple imputed data Combined these with patient characteristics in validation data to give prognostic index Combined with baseline survival function estimated at 10 years to give estimated risk of CVD at 10 years for each person 26

27. Validation statistics QRISK2Framingham Women D statistic1.801.63 R2R2 43.5%38.9% Men D statistic1.621.50 R2R2 38.4%34.8% 27 Hippisley-Cox J et al. BMJ 2008;336:1475-1482

28. + Reclassification 112,156 patients (15.0%) classified as high risk (≥20%) using Framingham 78,024 patients (10.4%) classified as high risk (≥20%) using QRISK2 41.1% of patients classified as high risk using Framingham would be classified as low risk using QRISK2. Their observed 10 year risk was 16.6% (95% CI 16.1% to 17.0%). 15.3% of patients classified as high risk using QRISK2 would be classified as low risk using Framingham. Their observed 10 year risk was 23.3% (95% CI 22.2% to 24.4%). 28

29. + QRISK2 web calculator: www.qrisk.org www.qrisk.org 29

30. + 30 QRISK2 web calculator

31. + 31 QRISK2 web calculator

32. External validation using THIN database 32 Additional validation carried out using the THIN database Based on practices in UK using Vision system One validation carried out by QRISK authors Hippisley-Cox J et al. The performance of the QRISK cardiovascular risk prediction algorithm in an independent UK sample of patients from general practice: a validation study. Heart 2007:hrt.2007.134890. An independent validation carried out by a separate group Collins GS, Altman DG. An independent and external validation of QRISK2 cardiovascular disease risk score: a prospective open cohort study. BMJ 2010;340:c2442

33. External validation using THIN database 33 QRESEARCH: QRISK2 THIN: QRISK2 Women ROC statistic0.817 (0.814 to 0.820)0.801 D statistic (95% CI)1.795 (1.769 to 1.820)1.66 (1.56 to 1.76) R 2 statistic (95% CI)43.5 (42.8 to 44.2)39.5 (36.6 to 42.4) Men ROC statistic0.792 (0.789 to 0.794)0.773 D statistic (95% CI)1.615 (1.594 to 1.637)1.45 (1.31 to 1.59) R 2 statistic (95% CI)38.4 (37.8 to 39.0)33.3 (28.9 to 37.8) Collins GS, Altman DG. An independent and external validation of QRISK2 cardiovascular disease risk score: a prospective open cohort study. BMJ 2010;340:c2442

34. Annual updates to QRISK2 34 Reasoning: Changes in population characteristics – e.g. incidence of cardiovascular disease is falling; obesity is rising; smoking rates are falling Improvements in data quality - recording of predictors and clinical outcomes becomes more complete over time (e.g. ethnic group now 50%). Inclusion of new risk factors Changes in requirements for how the risk prediction scores can be used - e.g. changes in age ranges.

35. + QRISK2 in national guidelines

36. + QRISK2 in clinical settings

37. + QRISK2 across the world source Google Analytics 8 th May 2011-6 th May 2013 Last 2 years  0.5 million uses  169 countries

38. + QDiabetes– risk of Type 2 diabetes www.qdiabetes.org www.qdiabetes.org Predicts risk of type 2 diabetes Published in BMJ (2009) Independent external validation by Oxford University Needed as epidemic of diabetes & obesity Evidence diabetes can be prevented Evidence that earlier diagnoses associated with better prognosis.

39. + QDiabetes in NICE (2012) Preventing type 2 diabetes - risk identification & interventions for individuals at high risk 2012 Risk assessment recommended include QDiabetes Individual assessment and also batch processing Includes deprivation & ethnicity Ages 25-84 Efficient as 2 extra questions on top of QRISK www.qintervention.org Integrated into EMIS Web Evaluation in London and Berkshire

40. + Risks and Benefits of Statins Two recent papers: Unintended effects statins (Hippisley-Cox & Coupland, BMJ, 2010) Individualising Risks & Benefits of Statins (Hippisley-Cox & Coupland, Heart, 2010) Conclusions: New tools to quantify likely benefit from statins New tools to identify patients who might get rare adverse effects eg myopathy for closer monitoring

41. + Background to Benefits of Statins Intended benefits - reduction in CVD risk Possible unintended benefits Thrombosis Rheumatoid arthritis Cancer Fractures Parkinson’s disease Dementia

42. + Statin - CVD benefit Three methods Direct analysis of QR data change in CVD risk Indirect analysis - changes in lipid levels Synthesis of Clinical Trials Results All three methods broadly agree 20-30% reduction in risk 1 st two methods can be individualised

43. + Statin – adverse effects Confirmed increased risk of Acute renal failure Liver dysfunction Serious myopathy Cataract Class effect Dose response for kidney failure & liver dysfunction Risk persists during Rx Highest risk in 1 st year Resolves within a year of stopping

44. + So the task in the consultation is to: Undertake clinical assessment Work out individual’s risk of disease Calculate expected risks and benefits from interventions Explain risks and benefits to an individual in a way they can understand Draw some diagrams All within 10 minutes!

45. + Qintervention www.qintervention.org www.qintervention.org

46. + Osteoporosis major cause preventable morbidity & mortality. 300,000 osteoporosis fractures each year 30% women over 50 years will get vertebral fracture 20% hip fracture patients die within 6/12 50% hip fracture patients lose the ability to live independently 2 billion is cost of annual social and hospital care QFracture: Background

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48. + Effective interventions exist to reduce fracture risk Challenge is better identification of high risk patients likely to benefit Avoid over treatment in those unlikely to benefit or who may be harmed Some guidelines recommend BMD but expensive and not very specific QFracture: challenge

49. + QFracture in national guidelines Published August 2012 Assess fracture risk all women 65+ and all men 75+ Assess fracture risk if risk factors Estimate 10 year fracture risk using QFracture or FRAX Consider use of medication to reduce fracture risk

50. + Two new indicators recommended QOF 2013 for Rheumatoid Arthritis IDindicatorComments NM56% patients with RA 30-84 years who have had a CVD risk assessment using a CVD risk assessment for RA in last 15/12 QRISK2 only CVD risk tool - 30-84 yrs - adjusted for RA NM57% of patients with RA 50-90yrs with rheumatoid arthritis who have had fracture risk assessment using tool adjusted for RA in last 27 months NICE recommends QFracture http://www.nice.org.uk/media/D76/FE/NICEQOFAdvisoryCommittee2012SummayRecommendations.pdf

51. + Example: 64 year old women History of falls Asthma Rheumatoid arthritis On steroids 10% risk hip fracture 20% risk of any fracture QFracture Web calculator www.qfracture.org www.qfracture.org

52. + Our scores on the app store

53. + Thank you for listening Questions & Discussion