1. QScores: Supporting Vascular Risk Assesessment
Julia Hippisley-Cox
01 July 2010
London

2. Acknowledgements Practices contributing data to QResearch database
Many GPs & nurses for suggestions, piloting
EMIS - QResearch database & piloting
Academic colleagues
THIN (validation data)
Oxford University – independent validation
ClinRisk Ltd (software)
NICE (stimulating debate!)

3. Outline Overview risk prediction in primary care Vascular Risk Engine
Cardiovascular disease - QRISK
Diabetes - QDScore
Chronic Kidney Disease - QKidney
Risks and benefits interventions - QIntervention

4. See www.qresearch.org for
Information about QResearch database
Academic papers
Technical & statistical documents
Open source software
Patient information
Clinician information
Power points presentations

5. New research paradigm Moving from ivory tower
I wonder whether ...
I have a hunch (“hypothesis”) to test
Moving into real world NHS
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

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

7. The Research Cycle ‘clinically useful epidemiology - new knowledge & utilities to improve patient care’

8. Risk prediction tools needed
Population level
Risk stratification
Identification of rank ordered list of patients for recall or reassurance
Individual assessment
Baseline risk of disease/outcome
Risks and benefits of interventions
Needed to obtain informed consent from patients

9. Our diverse UK population in 2010
Why develop new tools?
Framingham 1970’s
Our diverse UK population in 2010

10. Why develop in primary care?
Huge scope for risk prediction models in primary care
High levels of computerisation
Unselected, large representative cohorts
Good quality databases
Detailed event level data for outcomes &predictors
Longitudinal data > 15 years
Linked ONS mortality data
Huge potential for decision support

11. 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

12. NHS Health Checks

13. 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

14. Vascular Risk Engine: 10 Requirements
Scientifically sound –peer reviewed, validated
Appropriate for context & decade
Accurate identify higher & lower risk patient
Socially equitable
Clinically relevant – include relevant risk factors, obesity, family history, comorbidity

15. Vascular Risk Engine: 10 Requirements
6. Practical –used in busy clinics
7. Available stand alone & integrated into systems
8. Consistent - “same patient same score anywhere”
9. Updatable - to reflect changes in populations & data & advances in methodology
10. Adaptable - as NHS requirements change
10 Year risk
Life Time Risk
Measures of Relative Risk – Heart age etc

16. QRISK2 - www.qrisk.org Risk of CVD – 1-15 years & lifetime
Extensively reviewed and externally validated
Included in QOF, NICE, DH Vascular Guidance
Widespread use across NHS
Incorporated into nearly all GP systems, many pharmacies, some hospitals, NHS Choices, Occupational Health etc
Available as Open Source and Closed Software

18. QRISK – Lifetime Risk
NICE guidance based on absolute 10 year risk ie chance of having an event over next 10 years
QRISK model is very flexible
QRISK can calculate risk over any age or time period
QRISK Lifetime Risk is an alternative to 10 year risk
Example: If you are 35, your lifetime risk is the risk of developing CVD by age 99 assuming you live that long.

19. Life time risk - example
50 year old man
Heavy smoker
SBP=190
cholesterol ratio=7.8
10 year risk = 18%
Lifetime risk= 48%

20. Why Identify Chronic Kidney Disease?20

21. Growth in prevalent patients by RRT at the end of each year 1982-2007

22. Identifying people with or at risk of CKD
Identify who needs intervention to minimise CV risk
Identify progressive CKD and how to manage it
Identify who needs referral for specialist kidney care 22 22

23. QKidney-why focus high risk CKD3b+?
Kidney is twice as important as we have two (renal tsar!)
Optimise early management
Prevention of progression
Prevention of complications
Reduce late referral – cause of avoidable harm
Optimise use of resources in our Health Services
The right patient in the right place at the right time 23 23

24. So developed QKidneyScores
Set of algorithms
Identifies those at risk of developing CKD3b+
Those at risk of deteriorating
So we can then
Identify high risk
Modify risk factors
Avoid nephrotoxic drugs
Monitor more closely
Hopefully prevent deteriorating
Improve outcomes

25. QDScore – risk of Type 2 diabetes
Predicts risk of type 2 diabetes
Published in BMJ (2009)
Independent external validation by Oxford Uni
Needed as epidemic of diabetes & obesity
Evidence diabetes can be prevented
Evidence that earlier diagnoses associated with better prognosis.

26. QRISK CVD
Cholesterol ratio
FHCHD
Atrial Fibrillation
SBP
Diabetes
Rheumatoid
Common risk factors
Age
Deprivation
Ethnicity
Smoking
Hypertension
Body Mass index
QKidney – stage 3b,4,5
FH Kidney Disease
CCF
PVD
Rheumatoid
SLE
Kidney stones
NSAIDS
SBP
Diabetes
CVD
QDScore (Diabetes)
FH of Diabetes
Steroid tablets
CVD
Common interventions
Smoking cessation
Weight loss
BP control
Exercise
Lipid lowering
Etc

27. From risk of three diseases to risks/benefits interventions

28. Primary prevention CVD: (slide from NICE website)
Offer information about:
absolute risk of CVD
absolute benefits/harms of an
intervention over a 10-year period
Information should:
present individualised risk/benefit
scenarios
present absolute risk of events
numerically
use appropriate diagrams and text
NOTES FOR PRESENTERS:
Key points to raise:
Use everyday, jargon-free language to communicate information on risk - technical terms should be clearly explained.
Set aside adequate time during the consultation to provide information on risk assessment and to allow any questions to be answered.
Document the discussion relating to the consultation on risk assessment and the person’s decision.
Additional information:
Inform people that CVD risk equations can only provide an estimate of risk but that the likelihood of misclassification is reduced as the estimated CVD risk increases above the threshold of 20% risk over 10 years.
For those whose CVD risk is at a level that merits intervention but they decline treatment, advise them that their CVD risk should be considered again in the future.
Recommendation in full:
People should be offered information about their absolute risk of CVD and about the absolute benefits and harms of an intervention over a 10-year period. This information should be in a form that:
presents individualised risk and benefit scenarios
presents the absolute risk of events numerically
uses appropriate diagrams and text.
(See [NICE guideline 1.2.4]

29. But 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!

30. Risks and Benefits Statins
Two recent papers:
Unintended effects statins BMJ (2010)
Individualising Risks and Benefits of Statins 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

33. New approach, new models, new utilities
At population level
Risk stratification so identify and recall high risk patients CVD, diabetes, CKD3b+
Must be fit for purpose - scientifically robust, practical and automated
At patient level
Calculation of risk and benefits for the individual
Help inform decision making
Better more individualised informed care

34. QScores: Supporting Vascular Risk Assesessment
Julia Hippisley-Cox
01 July 2010
London