+ towards personalised medicine – assessing risks and benefits for individual patients prof julia...
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Towards personalised medicine – assessing risks and benefits for individual patientsProf Julia Hippisley-Cox, University of Nottingham, Tony Mitchell Lecture15th May 2013
+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)
+Outline
QResearch database +linked data
General approach to risk prediction
QRISK2
QDiabetes
QIntervention
QFracture
Any questions
+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.
+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 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
+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
+QResearch Database + data linked in 2013
Data source Time period data available
GP data 1989-
ONS cause of death 1997-
ONS cancer registration 1997-
HES Outpatient data 1997-
HES Inpatient data 1997-
HES A&E data 2007-
+Clinical Research Cycle
Clinical practice &
benefit
Clinical questions
Research +
innovation
Integration into clinical
systems
+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
+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
+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
+Published & validated scores
scores outcome Web link
QRISK2 CVD www.qrisk.org
QDiabetes Type 2 diabetes www.qdiabetes.org
QStroke Ischaemia stroke www.qstroke.org
QKidney Moderate/severe renal failure
www.qkidney.org
QThrombosis VTE www.qthrombosis.org
QFracture Osteoporotic fracture www.qfracture.org
QIntervention Risks benefits interventions to lower CVD and diabetes risk
www.qintervention.org
QCancer Detection common cancers www.qcancer.org
+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
+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
+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
+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
+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
+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
20
+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
+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
+Interactions
24
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
+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
25
1 Royston and Sauerbrei. A new measure of prognostic separation in survival data. Stat Med 2004; 23: 723-748.
+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
Validation statistics QRISK2 Framingham
Women
D statistic 1.80 1.63
R2 43.5% 38.9%
Men
D statistic 1.62 1.50
R2 38.4% 34.8%
27
Hippisley-Cox J et al. BMJ 2008;336:1475-1482
+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
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
External validation using THIN database
33
QRESEARCH:QRISK2
THIN:QRISK2
Women
ROC statistic 0.817 (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)
R2 statistic (95% CI) 43.5 (42.8 to 44.2) 39.5 (36.6 to 42.4)
Men
ROC statistic 0.792 (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)
R2 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
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.
+QRISK2 across the world source Google Analytics 8th May 2011-6th May 2013
Last 2 years 0.5 million
uses 169
countries
+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.
+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
+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
+Background to Benefits of Statins Intended benefits - reduction in CVD risk
Possible unintended benefits Thrombosis Rheumatoid arthritis Cancer Fractures Parkinson’s disease Dementia
+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
+
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
+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!
+
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
+
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
+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
+Two new indicators recommended QOF 2013 for Rheumatoid ArthritisID indicator Comments
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
+
• 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
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