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New Risk Prediction Tools – generating clinical benefits from clinical dataJulia Hippisley-Cox, GP, Professor Epidemiology & Director ClinRisk LtdPrimary Health Information 201224 April 2012

+Acknowledgements

Co-author Dr Carol Coupland

QResearch database

University of Nottingham

ClinRisk (software)

EMIS & contributing practices & EMIS User Group

BJGP and BMJ for publishing the work

Oxford University (independent validation)

+About me

Inner city GP

Clinical epidemiologist University Nottingham

Director QResearch (NFP partnership UoN and EMIS)

Director ClinRisk Ltd (Medical research & software)

Member Ethics & Confidentility Committee NIGB

+QResearch Databasewww.qresearch.org

Over 700 general practices across the UK, 14 million patients

Joint not for profit venture University of Nottingham and EMIS (supplier > 55% GP practices)

Validated database – used to develop many risk tools

Data linkage – deaths, deprivation, cancer, HES

Available for peer reviewed academic research where outputs made publically available

Practices not paid for contribution but get integrated QFeedback tool and utilities eg QRISK, QDiabetes.

+QFeedback – integrated into EMIS

+Clinical Research Cycle

Clinical practice &

benefit

Clinical questions

Research + innovation

Integration clinical system

+QScores – 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

+Current published & validated QScores

scores outcome Web link

QRISK CVD www.qrisk.org

QDiabetes Type 2 diabetes www.qdiabetes.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

+Today we will cover two types of tools

Prognostic tool – QFracture

Diagnostic tool - QCancer

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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 20% hip fracture patients die within 6/12 50% hip fracture patients lose the ability to live

independently 1.8 billion is cost of annual social and hospital care

QFracture: Background

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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 unlikely to benefit or who may be harmed

Draft NICE guideline (2012) recommend using 10 year risk of fracture either using QFracture or FRAX

QFracture also being piloted for QOF indicator

QFracture: challenge

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Cohort study using patient level QResearch database

Similar methodology to QRISK

Published in BMJ 2009

Algorithm includes established risk factors

Developed risk calculator which can

- identify high risk patients for assessment

- show risk of fracture to patients

QFracture: development

+Advantages QFracture vs FRAX

Published & validated

More accurate in UK primary care

Can be updated annually

Independent of pharma industry

Includes extra risk factors eg Falls CVD Type 2 diabetes Asthma Antidepressants Detail smoking/Alcohol HRT

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64 year old women

Heavy smoker

Non drinker

BMI 20.6

Asthma

On steroids

Rheumatoid

H/O falls

QFracture: Clinical example

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+QFracture + other QScores on the app store

+QScores for systems integration

Possible to integrate QFracture (and the other QScores) into any clinical computer system

Software libraries in Java or .NET

Test harness

Documentation

Support

For details see www.qfracture.org

+QCancer – the problem

UK has poor track record in cancer diagnosis cf Europe

Partly due to late diagnosis

Late diagnosis might be late presentation or non-recognition by GPs or both

Earlier diagnosis may lead to more Rx options and better prognosis

Problem is that cancer symptoms can be diffuse and non-specific so need better ways to quantify cancer risk to help prioritise investigation

+QCancer scores – what they need to do

Accurately predict level of risk for individual based on risk factors and symptoms

Discriminate between patients with and without cancer

Help guide decision on who to investigate or refer and degree of urgency.

Educational tool for sharing information with patient. Sometimes will be reassurance.

Symptom based approach rather than cancer based approach

+Currently Qcancer predicts risk 6 cancers

PancreasLung Kindey

Ovary Colorectal Gastro-oesoph

+Methods – development

Huge sample from primary care aged 30-84

Identify 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)

Identify patient with cancers

Identify independent factors which predict cancers

Measure of absolute risk of cancer. Eg 5% risk of colorectal cancer

+Methods - validation

Once algorithms developed, tested performance separate sample of QResearch practices external dataset (Vision practices) at Oxford University

Measures of discrimination - identifying those who do and don’t have cancer

Measures of calibration - closeness of predicted risk to observed risk

Measure performance – PPV, sensitivity, ROC etc

+Results – the algorithms/predictorsOutcom

eRisk factors Symptoms

Lung Age, sex, smoking, deprivation, COPD, prior cancers

Haemoptysis, appetite loss, weight loss, cough, anaemia

Gastro-oeso

Age, sex, smoking status

Haematemsis, appetite loss, weight loss, abdo pain, dysphagia

Colorectal

Age, sex, alcohol, family history

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, appetite loss, weight loss, abdo pain, anaemia

+Sensitivity for top 10% of predicted cancer risk

Cut point Threshold top 10%

Pick up rate for 10%

Colorectal 0.5 71

Gastro-oesophageal

0.2 77

Ovary 0.2 63

Pancreas 0.2 62

Renal 0.1 87

Lung 0.4 77

+Using QCancer in practice

Standalone tools

a. Web calculator www.qcancer.org

b. Windows desk top calculator

c. Iphone – simple calculator

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

+GP system integration: Within consultation

Uses data already recorded (eg age, family history)

Stimulate better recording of positive and negative symptoms

Automatic risk calculation in real time

Display risk enables shared decision making between doctor and patient

Information stored in patients record and transmitted on referral letter/request for investigation

Allows automatic subsequent audit of process and clinical outcomes

Improves data quality leading to refined future algorithms.

+Iphone/iPad

+GP systems integrationBatch processing

Similar to QRISK which is in 90% of GP practices– automatic daily calculation of risk for all patients in practice based on existing data.

Identify patients with symptoms/adverse risk profile without follow up/diagnosis

Enables systematic recall or further investigation

Systematic approach - prioritise by level of risk.

Integration means software can be rigorously tested so ‘one patient, one score, anywhere’

Cheaper to distribute updates

+Summary key points

Individualised level of risk - including age, FH, multiple symptoms

Electronic validated tool using proven methods which can be implemented into clinical systems

Standalone or integrated.

If integrated into computer systems, 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

+Next steps - pilot work in clinical practice supported by DH

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Thank you for listening

Any questions (if time)