Download Risks and Benefits Statins

EMIS NUG conference
September 2010
Warwick University
Julia Hippisley-Cox
Sessional GP
Epidemiologist
Director QResearch
Director ClinRisk Ltd
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EMIS & EMIS Practices contributing data
Many GPs & nurses for suggestions, piloting
University of Nottingham
Academic colleagues
ClinRisk Ltd (software)
THIN (validation data)
Oxford University – independent validation
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Update on QSurveillance
QFeedback
Update on QScores
◦ QIntervention
◦ QFracture
◦ Qcancer
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General discussion
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Real time infectious diseases surveillance system
Vaccine uptake reporting system
History
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2004 - Pilot study on QResearch in 2004
2005 - Upgraded to online QFLU
2006 – Separate Flu vaccine service
2007 – Separate Pneumo vaccine service
2007 – upgraded to QSurveillance Avon floods
Included prospective consent for data extraction in
emergency
Key part of HPA and DH emergency response
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Age/sex aggregated data
100-150 indicators
Infectious diseases
Vaccine uptake – flu, pneumo, MMR
Daily, weekly, monthly, quarterly, annual
reports
No patients can be identified
Counts < 5 suppressed
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JHC custodian & responsible to practices,
profession, ethics etc
No patients identifiable
Counts < 5 suppressed
Process for new indicators:
◦ Practice consent covers additional data extracted to
support emergency response
◦ consult with relevant agency re need, ethics and
advisory board (including NUG)
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Practice consent
Oversight board/review mechanism with NUG
representation
Robust safeguards in place to protect
patients and practices
Practices able to switch it on or off
Practice can access and benefit from data
extracted
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Incredibly busy with flu pandemic
Daily reporting over 10 months
Unexpectedly high demand across NHS
Detailed coverage by media
Under resourced
Need to ensure its scalable, resilient, properly
resourced.
Decision to industrialise it
Ensure practices can access and benefit from
data
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Population level
◦ Risk stratification
◦ Identification of rank ordered list of patients for
recall or reassurance
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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
Disease outcomes
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QRISK (CVD)
QDScore (diabetes)
QFracture
QKidney (CKD3b+)
Qcancer
Range of other
significant outcomes
Status
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published
published
published
published
completed
In progress
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Different approach needed
Assess baseline risk of outcomes
Then how they change with interventions
Use RCTs and meta analyses for benefits
Use database analyses for unintended effects
Starting with commonly used drugs e.g
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Statins
Antidepressants
HRT
Warfarin
Antipsychotics
NSAIDS
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Identify patients at high risk of vascular
disease
◦ CVD
◦ Diabetes
◦ Stage 3b,4, 5 Kidney Disease
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Assessment of individual’s risk profile
Risks and benefits of interventions
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Weight loss
Smoking cessation
BP control
Statins
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Risk of CVD & “Heart age”
Extensively reviewed and externally validated
Now included in
◦ QOF
◦ DH Vascular Guidance
◦ NICE
Widespread use across NHS
Nearly all GP systems, many pharmacies, some
hospitals, NHS Choices, Supermarkets, Occupational
Health etc
Also free Open Source and Closed Software
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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.
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Set of algorithms
◦ Identifies those at risk of
 CKD3b+
 End Stage Renal Failure
◦ Published BMC 2010
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So we can then
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Identify high risk
Modify risk factors
Avoid nephrotoxic drugs
Monitor more closely
Prevent deterioration
Improve outcomes
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Two recent papers:
◦ Unintended effects statins (BMJ, 2010)
◦ Individualising Risks & Benefits of Statins (Heart,
2010)
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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
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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
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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Osteoporosis major cause preventable morbidity &
mortality.
2 million women affected in E&W
180,000 osteoporosis fractures each year
30% women over 50 years will get vertebral fracture
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20% hip fracture patients die within 6/12
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50% hip fracture patients lose the ability to live
independently
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1.8 billion is cost of annual social and hospital care
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29
Not Worth Living
In Constant Pain
Daily Analgesia
Hard to Bend
Hard to Stand
Wake Early
0
20
40
60
80
% Patients
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Scane et al, Osteoporosis Int 1994; 4: 89-92.
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Effective interventions exist to reduce fracture risk
Challenge is better identification of high risk
patients likely to benefit
Avoiding over treatment in those unlikley to benefit
or who may be harmed
Some guidelines recommend BMD but high cost
and low specificity
Other guidelines recommend using 10 year risk of
fracture
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Cohort study using patient level QResearch
database
Similar methodology to QRISK
Published in BMJ 2009
Algorithm includes established risk factors
Undertook validation against FRAX
Developed risk calculator which can
- identify high risk patients for assessment
- show risk of fracture to patients
QFracture
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Primary care
Works better in EMIS
Open Source
No funding
Includes extra risk factors eg
◦ Falls
◦ CVD
◦ Type 2 diabetes
◦ Asthma
◦ Antidepressants
◦ Detail smoking/Alcohol
◦ HRT
FRAX
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Selected cohorts
Over-predicts in EMIS
Not published
Industry sponsored
NOGG guidance
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64 year old women
Heavy smoker
Non drinker
BMI 20.6
Asthma
On steroids
Rheumatoid
H/O falls
20
15
10
Before
After
5
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Vit D +
calcium
Bisphosph
Hip
HRT (out of
protectors
fashion)
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Need to quantify risks of interventions
Few large long term safety studies
Bisphosphonates may increase risk of
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Oesophageal cancer
Atrial fibrillation
Osteonecrosis of jaw
Atypical fracture
? Other outcomes
Key thing for my patient is
◦ Baseline risk of fracture
◦ Likely benefit of intervention
◦ risk of adverse effects of intervention
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What is the overall risk/benefit ratio?
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Tools to predict risk of range of common cancers
Risk stratification:
 Identify those who need regular screening
 Identify those who need ad hoc assessment
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Patient communication
 Background risk with family history – may be reassuring
 Risk of cancer with “alarm” symptoms
 Risks of cancer with smoking as decision aid for smoking
cessation
 Current
 Ex smoker
 Non smoker
Cancers
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Breast cancer
Prostate
Colorectal
Oesophageal
Renal/bladder
Lung
Ovary
Uterus
Alarm symptoms
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Breast lump
Prostatism
Rectal bleeding
Dysphagia
Haematuria
Haemoptysis
Abdo pain/distension
Post menopausal
bleeding
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Information about QResearch database
Academic papers
Technical & statistical documents
Open source software
Patient information
Clinician information
Power points presentations
Information on how to contribute to the
database (or email [email protected] )
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Questions
Comments
Suggestions
Feedback