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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 CycleClinical

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 QScoresscores outcome Web linkQRISK CVD www.qrisk.org QDiabetes Type 2 diabetes www.qdiabetes.orgQKidney Moderate/severe renal

failurewww.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 71Gastro-oesophageal

0.2 77

Ovary 0.2 63Pancreas 0.2 62Renal 0.1 87Lung 0.4 77

+Using QCancer in practice

Standalone tools a. Web calculator www.qcancer.org b. Windows desk top calculatorc. Iphone – simple calculator

Integrated into clinical systema. 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)