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New Risk Prediction Tools – generating clinical benefits from clinical data
Julia Hippisley-Cox,
GP, Professor Epidemiology & Director ClinRisk Ltd
Primary Health Information 2012
24 April 2012

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Acknowledgements
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Co-author Dr Carol Coupland
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QResearch database
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University of Nottingham
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ClinRisk (software)
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EMIS & contributing practices & EMIS User Group
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BJGP and BMJ for publishing the work
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Oxford University (independent validation)

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About me
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Inner city GP
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Clinical epidemiologist University Nottingham
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Director QResearch (NFP partnership UoN and EMIS)
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Director ClinRisk Ltd (Medical research & software)
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Member Ethics & Confidentility Committee NIGB

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QResearch Database www.qresearch.org
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Over 700 general practices across the UK, 14 million patients
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Joint not for profit venture University of Nottingham and EMIS
(supplier > 55% GP practices)
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Validated database – used to develop many risk tools
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Data linkage – deaths, deprivation, cancer, HES
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Available for peer reviewed academic research where outputs made publically available
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Practices not paid for contribution but get integrated
QFeedback tool and utilities eg QRISK, QDiabetes.

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QFeedback – integrated into EMIS

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Clinical Research Cycle
Clinical practice & benefit
Integration clinical system
Clinical questions
Research + innovation

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QScores – new family of Risk Prediction tools
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Individual assessment
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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
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Population level
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Risk stratification
Identification of rank ordered list of patients for recall or reassurance
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GP systems integration
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Allow updates tool over time, audit of impact on services and outcomes

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Current published & validated
QScores scores
QRISK
QDiabetes
QKidney outcome Web link
CVD www.qrisk.org
Type 2 diabetes www.qdiabetes.org
Moderate/severe renal failure www.qkidney.org
QThrombosis VTE
QFracture Osteoporotic fracture
Qintervention Risks benefits interventions to lower CVD and diabetes risk
QCancer Detection common cancers www.qthrombosis.org
www.qfracture.org
www.qintervention.org
www.qcancer.org

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Today we will cover two types of tools
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Prognostic tool – QFracture
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Diagnostic tool - QCancer

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QFracture: Background
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Osteoporosis major cause preventable morbidity & mortality.
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2 million women affected in E&W
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180,000 osteoporosis fractures each year
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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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QFracture: challenge
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Effective interventions exist to reduce fracture risk
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Challenge is better identification of high risk patients likely to benefit
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Avoiding over treatment in those unlikely to benefit or who may be harmed
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Draft NICE guideline (2012) recommend using 10 year risk of fracture either using QFracture or
FRAX
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QFracture also being piloted for QOF indicator

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QFracture: development
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Cohort study using patient level QResearch database
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Similar methodology to QRISK
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Published in BMJ 2009
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Algorithm includes established risk factors
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Developed risk calculator which can
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- identify high risk patients for assessment
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- show risk of fracture to patients

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Advantages QFracture vs FRAX
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Published & validated
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More accurate in UK primary care
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Can be updated annually
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Independent of pharma industry
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Includes extra risk factors eg
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Falls
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CVD
Type 2 diabetes
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Asthma
Antidepressants
Detail smoking/Alcohol
HRT

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QFracture: Clinical example
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64 year old women
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Heavy smoker
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Non drinker
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BMI 20.6
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Asthma
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On steroids
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Rheumatoid
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H/O falls

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QFracture + other QScores on the app store

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QScores for systems integration
Possible to integrate QFracture (and the other QScores) into any clinical computer system
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Software libraries in Java or .NET
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Test harness
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Documentation
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Support
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For details see www.qfracture.org

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QCancer – the problem
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UK has poor track record in cancer diagnosis cf Europe
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Partly due to late diagnosis
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Late diagnosis might be late presentation or non-recognition by GPs or both
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Earlier diagnosis may lead to more Rx options and better prognosis
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Problem is that cancer symptoms can be diffuse and nonspecific so need better ways to quantify cancer risk to help prioritise investigation

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QCancer scores – what they need to do
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Accurately predict level of risk for individual based on risk factors and symptoms
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Discriminate between patients with and without cancer
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Help guide decision on who to investigate or refer and degree of urgency.
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Educational tool for sharing information with patient.
Sometimes will be reassurance.
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Symptom based approach rather than cancer based approach

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Currently Qcancer predicts risk 6 cancers
Lung Pancreas Kindey
Ovary
Colorectal
Gastro-oesoph

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Methods – development
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Huge sample from primary care aged 30-84
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Identify
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 new alarm symptoms (eg rectal bleeding, haemoptysis, weight loss, appetite loss, abdominal pain, rectal bleeding) and other risk factors (eg age, COPD, smoking, family history)
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Identify patient with cancers
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Identify independent factors which predict cancers
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Measure of absolute risk of cancer. Eg 5% risk of colorectal cancer

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Methods - validation
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Once algorithms developed, tested performance
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 separate sample of QResearch practices external dataset (Vision practices) at Oxford University
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Measures of discrimination - identifying those who do and don’t have cancer
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Measures of calibration - closeness of predicted risk to observed risk
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Measure performance – PPV, sensitivity, ROC etc

+ Results – the algorithms/predictors
Outcome Risk factors Symptoms
Lung Age, sex, smoking, deprivation, COPD, prior cancers
Gastrooeso
Age, sex, smoking status
Colorectal Age, sex, alcohol, family history
Haemoptysis, appetite loss, weight loss , cough, anaemia
Haematemsis, appetite loss, weight loss, abdo pain, dysphagia
Rectal bleeding , appetite loss, weight loss, abdo pain , change bowel habit, anaemia
Pancreas Age, sex, type 2, chronic pancreatitis dysphagia, appetite loss, weight loss, abdo pain , abdo distension, constipation
Ovarian Age, family history Rectal bleeding, appetite loss, weight loss, abdo pain, abdo distension, PMB, anaemia
Renal Age, sex, smoking status, prior cancer
Haematuria, abdo pain , appetite loss, anaemia weight loss,

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Sensitivity for top 10% of predicted cancer risk
Colorectal
Gastrooesophageal
Ovary
Pancreas
Renal
Lung
Cut point
Threshold top
10%
0.5
0.2
Pick up rate for
10%
71
77
0.2
0.2
0.1
0.4
63
62
87
77

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Using QCancer in practice
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Standalone tools a.
Web calculator www.qcancer.org
b.
c.
Windows desk top calculator
Iphone – simple calculator
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Integrated into clinical system a.
Within consultation: GP with patients with symptoms b.
Batch: Run in batch mode to risk stratify entire practice or
PCT population

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GP system integration:
Within consultation
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Uses data already recorded (eg age, family history)
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Stimulate better recording of positive and negative symptoms
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Automatic risk calculation in real time
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Display risk enables shared decision making between doctor and patient
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Information stored in patients record and transmitted on referral letter/request for investigation
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Allows automatic subsequent audit of process and clinical outcomes
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Improves data quality leading to refined future algorithms.

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Iphone/iPad

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GP systems integration
Batch processing
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Similar to QRISK which is in 90% of GP practices– automatic daily calculation of risk for all patients in practice based on existing data.
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Identify patients with symptoms/adverse risk profile without follow up/diagnosis
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Enables systematic recall or further investigation
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Systematic approach - prioritise by level of risk.
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Integration means software can be rigorously tested so ‘one patient, one score, anywhere’
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Cheaper to distribute updates

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Summary key points
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Individualised level of risk - including age, FH, multiple symptoms
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Electronic validated tool using proven methods which can be implemented into clinical systems
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Standalone or integrated.
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If integrated into computer systems,
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 improve recording of symptoms and data quality ensure accuracy calculations help support decisions & shared decision making with patient enable future audit and assessment of impact on services and outcomes

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Next steps - pilot work in clinical practice supported by DH

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Thank you for listening
Any questions (if time)