Transcript Qcancer Scores - QResearch

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Towards personalised medicine – assessing risks and
benefits for individual patients
Prof Julia Hippisley-Cox, University of Nottingham,
Tony Mitchell Lecture
15th May 2013
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Acknowledgements
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Co-authors Drs Carol Coupland, Peter Brindle, John Robson
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QResearch database
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University of Nottingham
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EMIS & contributing practices & user group
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ClinRisk Ltd (software)
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Oxford University (independent validation, Prof Altman’s
team)
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Outline
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QResearch database +linked data
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General approach to risk prediction
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QRISK2
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QDiabetes
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QIntervention
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QFracture
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Any questions
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QResearch Database
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One of the worlds largest and richest research databases
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Over 700 general practices across the UK, 14 million patients
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Joint venture between EMIS (largest GP supplier > 55%
practices) and University of Nottingham
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Patient level pseudonymised database for research
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Available for peer reviewed academic research where
outputs made publically available
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Data from 1989 to present day.
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Information on QResearch – GP
derived data
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Demographic data – age, sex, ethnicity, SHA, deprivation
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Diagnoses
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Clinical values –blood pressure, body mass index
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Laboratory tests – FBC, U&E, LFTs etc
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Prescribed medication – drug, dose, duration, frequency,
route
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Referrals
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Consultations
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QResearch Data Linkage Project
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QResearch database already linked to
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deprivation data in 2002
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cause of death data in 2007
Very useful for research
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better definition & capture of outcomes
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Health inequality analysis
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Improved performance of QRISK2 and similar scores
Developed new technique for data linkage using
pseudonymised data
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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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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-
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Clinical Research Cycle
Clinical
practice &
benefit
Integration
into clinical
systems
Clinical
questions
Research +
innovation
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A new family of Risk Prediction tools
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Individual assessment
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Who is most at risk of preventable disease?
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Who is likely to benefit from interventions?
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What is the balance of risks and benefits for my patient?
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Enable informed consent and shared decisions
Population level
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Risk stratification
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Identification of rank ordered list of patients for recall or reassurance
GP systems integration
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Allow updates tool over time, audit of impact on services and
outcomes
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Criteria for choosing clinical
outcomes
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Major cause morbidity & mortality
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Represents real clinical need
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Related intervention which can be targeted
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Related to national priorities (ideally)
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Necessary data in clinical record
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Can be implemented into everyday clinical practice
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Change in research question
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Leads to
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Novel application of existing methods
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Development of new methods
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Better utilisation different data sources
Leads to
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Lively academic debate!
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Changes in policy and guidance
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New utilities to implement research findings
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(hopefully) Better patient care
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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 www.qintervention.org
lower CVD and diabetes risk
QCancer
Detection common cancers
www.qcancer.org
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Vascular Risk Engine: Requirements
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Identify patients at high risk of vascular disease
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CVD
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Diabetes
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Stage 3b,4, 5 Kidney Disease
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Assessment of individual’s risk profile
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Risks and benefits of interventions
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Weight loss
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Smoking cessation
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BP control
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Statins
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Why integrated tool CVD, diabetes,
CKD?
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Many of the risk factors over overlap
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Many of the interventions overlap
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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
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Help set individual priorities
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Development of personalised plans and achievable
target
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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
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Challenge: to develop a new CVD
risk score for use in UK
Aim for QRISK
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New cardiovascular disease risk score
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Calibrated to UK population
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Use routinely collected GP data
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Include additional known risk factors
(eg family history, deprivation)
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Better calibration and discrimination than Framingham
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Why a new CVD risk score?
 Framingham
limitations:
has many strengths but some
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
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QRisk1 risk factors
 Traditional
risk factors
Age, sex, smoking status
 Systolic blood pressure
 Ratio of total serum cholesterol/high density
lipoprotein (HDL) cholesterol
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 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
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Model Derivation
 Separate
 Cox
models in males and females
regression analysis
 Fractional
polynomials to model
non-linear risk relationships
 Multiple
imputation of missing values
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Derivation of QRISK2 Score
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Derivation cohort
 355
practices; 1,591,209 patients;
 96,709 events
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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
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Results
Hippisley-Cox J et al. BMJ 2008;336:1475-1482
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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
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Validation
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Separate sample of 176 QResearch practices; 750,232 patients;
43,396 events
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Validation statistics (for survival data)
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D statistic1 (discrimination)
R squared (% variation explained)
Predicted vs. observed CVD events
Clinical impact in terms of reclassification of patients into
high/low risk
Royston and Sauerbrei. A new measure of prognostic separation
in survival data. Stat Med 2004; 23: 723-748.
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Calculation of risk scores
 Risk
scores calculated in validation dataset
 Risk
score calculation:
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Used coefficients for risk factors obtained from Cox model
using multiple imputed data
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Combined these with patient characteristics in validation
data to give prognostic index
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Combined with baseline survival function estimated at 10
years to give estimated risk of CVD at 10 years for each
person
Validation statistics
QRISK2
Framingham
1.80
1.63
43.5%
38.9%
1.62
1.50
38.4%
34.8%
Women
D statistic
R2
Men
D statistic
R2
Hippisley-Cox J et al. BMJ 2008;336:1475-1482
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Reclassification
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112,156 patients (15.0%) classified as high risk (≥20%)
using Framingham
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78,024 patients (10.4%) classified as high risk (≥20%)
using QRISK2
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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%).
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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%).
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QRISK2 web
calculator:
www.qrisk.org
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QRISK2 web calculator
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QRISK2 web calculator
External validation using THIN database
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 Additional validation carried out using the THIN database
 Based on practices in UK using Vision system
 One validation carried out by QRISK authors
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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
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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
QRESEARCH:
QRISK2
THIN:
QRISK2
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)
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)
Women
Men
Collins GS, Altman DG. An independent and external validation of QRISK2
cardiovascular disease risk score: a prospective open cohort study. BMJ
2010;340:c2442
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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.
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QRISK2 in national guidelines
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QRISK2 in clinical settings
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QRISK2 across the world
source Google Analytics 8th May 2011-6th May 2013
Last 2 years
 0.5 million
uses
 169 countries
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QDiabetes– risk of Type 2 diabetes
www.qdiabetes.org
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Predicts risk of type 2 diabetes
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Published in BMJ (2009)
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Independent external validation by Oxford University
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Needed as epidemic of diabetes & obesity
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Evidence diabetes can be prevented
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Evidence that earlier diagnoses associated with better
prognosis.
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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
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Risks and Benefits of Statins
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Two recent papers:
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Unintended effects statins (Hippisley-Cox & Coupland, BMJ, 2010)
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Individualising Risks & Benefits of Statins (Hippisley-Cox &
Coupland, Heart, 2010)
Conclusions:
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New tools to quantify likely benefit from statins
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New tools to identify patients who might get rare adverse effects
eg myopathy for closer monitoring
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Background to Benefits of Statins
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Intended benefits - reduction in CVD risk
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Possible unintended benefits
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Thrombosis
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Rheumatoid arthritis
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Cancer
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Fractures
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Parkinson’s disease
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Dementia
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Statin - CVD benefit
 Three
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methods
Direct analysis of QR data change in CVD risk
Indirect analysis - changes in lipid levels
Synthesis of Clinical Trials
 Results
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All three methods broadly agree
20-30% reduction in risk
1st two methods can be individualised
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Statin – adverse effects
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Confirmed increased risk of
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Acute renal failure
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Liver dysfunction
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Serious myopathy
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Cataract
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Class effect
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Dose response for kidney failure & liver dysfunction
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Risk persists during Rx
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Highest risk in 1st year
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Resolves within a year of stopping
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So the task in the consultation
is to:
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Undertake clinical assessment
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Work out individual’s risk of disease
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Calculate expected risks and benefits from interventions
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Explain risks and benefits to an individual in a way they can
understand
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Draw some diagrams
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All within 10 minutes!
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Qintervention
www.qintervention.org
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QFracture: Background
 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
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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
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QFracture in national guidelines
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Published August 2012
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Assess fracture risk all women
65+ and all men 75+
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Assess fracture risk if risk
factors
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Estimate 10 year fracture risk
using QFracture or FRAX
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Consider use of medication to
reduce fracture risk
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Two new indicators recommended
QOF 2013 for Rheumatoid Arthritis
ID
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
NICE recommends QFracture
with rheumatoid arthritis who
have had fracture risk assessment
using tool adjusted for RA in last
27 months
http://www.nice.org.uk/media/D76/FE/NICEQOFAdvisoryCommittee2012SummayRecommendations.pdf
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www.qfracture.org
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Example:
64 year old women
History of falls
Asthma
Rheumatoid arthritis
On steroids
10% risk hip fracture
20% risk of any
fracture
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Our scores on the app store
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Thank you for listening
Questions & Discussion