Towards personalised medicine – assessing risks and benefits for individual patients

1. Towards personalised medicine – assessing risks and benefits for individual patients Prof Julia Hippisley-Cox, University of Nottingham, Tony Mitchell Lecture 15th 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 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 technique for data linkage using pseudonymised data

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

9. QResearch Database + data linked in 2013

10. Clinical Research Cycle

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

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 Aim for QRISK • 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

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

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 1st degree relative aged < 60 years • Body mass index • Blood pressure treatment

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

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

24. Interactions Fig 1 Impact 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 statistic1 (discrimination) • R squared (% variation explained) • Predicted vs. observed CVD events • Clinical impact in terms of reclassification of patients into high/low risk 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

27. Validation statistics 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%).

29. QRISK2 web calculator: www.qrisk.org

30. QRISK2 web calculator

31. QRISK2 web calculator

32. External validation using THIN database • 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 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 • 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 8th May 2011-6th May 2013 • Last 2 years • 0.5 million uses • 169 countries

38. QDiabetes– risk of Type 2 diabeteswww.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) • 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 Preventing type 2 diabetes - risk identification & interventions for individuals at high risk 2012

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 • 1st 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 1st 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. Qinterventionwww.qintervention.org

46. QFracture: Background • Osteoporosis major cause preventablemorbidity & 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

48. QFracture: challenge • 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

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 http://www.nice.org.uk/media/D76/FE/NICEQOFAdvisoryCommittee2012SummayRecommendations.pdf