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QCancer Scores –tools for earlier detection of cancer

Julia Hippisley-Cox, GP, Professor Epidemiology & Director ClinRisk LtdGP Lincoln Refresher Course18th May 2012

+Acknowledgements

Co-author Dr Carol Coupland

QResearch database

University of Nottingham

ClinRisk (software)

EMIS & contributing practices & User Group

BJGP and BMJ for publishing the work

Oxford University (independent validation)

cancer teams, DH + RCGP+ other academics with whom we are now working

+QResearch Database

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

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, QFracture.

Data linkage – deaths, deprivation, cancer, HES

+Clinical Research Cycle

Clinical practice &

benefit

Clinical questions

Research +

innovation

Integration clinical system

+QFeedback – integrated into EMIS

+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

+Early diagnosis of cancer: The problem

UK has relatively poor track record when compared with other European countries

Partly due to late diagnosis with estimated 7,500+ lives lost annually

Later diagnosis due to mixture of late presentation by patient (alack awareness) Late recognition by GP Delays in secondary care

+Example of Colon cancer

This is one of the most common cancers

Half of patients never have a NICE qualifying sympton

Only one quarter diagnosed via 2 week clinic

One quarter present as emergencies

Earlier diagnosis my result in stage sift or prevent some emergencies.

+Example of pancreatic cancer

11th most common cancer

< 20% patients suitable for surgery

84% dead within a year of diagnosis

Chances of survival better if diagnosis made at early stage

Very few established risk factors (smoking, chronic pancreatitis, alcohol) so screening programme unlikely

Challenge is to identify symptoms in primary care - particularly hard for pancreatic cancer

+Lung cancer

Commonest cause of death in UK

Very few diagnosed at operable stage

No screening tests currently but Chest xray useful

Vast majority present to GPs with symptoms.

+Currently Qcancer predicts risk 6 cancers

PancreasLung Kindey

Ovary Colorectal Gastro-oesoph

+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.

+QCancer scores – approach taken Maximise strengths of routinely collected data electronic databases

Large representative samples including rare cancers

Algorithms can be applied to the same setting eg general practice

Account for multiple symptoms

Adjustment for family history

Better definition of smoking status (non, ex, light, moderate, heavy)

Age – absolutely key as PPV varies hugely by age

updated to meet changing requirements, populations, recorded data

+Incidence of key symptoms vary by age and sex

+PPV of symptoms also vary by age in men (Jones et al BMJ 2007).

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+And PPV vary by age in women(Jones et al BMJ 2007).

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+Methods – development algorithm Huge representative sample from primary care aged 30-

84

Identify new alarm symptoms (eg rectal bleeding, haemoptysis) and other risk factors (eg age, COPD, smoking, family history)

Identify cancer outcome - all new diagnoses either on GP record or linked ONS deaths record in next 2 years

Established methods to develop risk prediction algorithm

Identify independent factors adjusted for other factors

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

+‘Red’ flag or alarm symptoms

Haemoptysis

Haematemesis

Dysphagia

Rectal bleeding

Postmenopausal bleeding

Haematuria

dysphagia

Constipation

Loss of appetite

Weight loss

Indigestion +/- heart burn

Abdominal pain

Abdominal swelling

Family history

Anaemia

cough

+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

+Methods - validation

Previous QScores validation – similar or better performance on external data

Once algorithms developed, tested performance separate sample of QResearch practices fully 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

+Discrimination QCancer scores

lung renal colorectal gastroes pancreas ovary0.76

0.78

0.8

0.82

0.84

0.86

0.88

0.9

0.92

0.94

ROC values for women

+Calibration - observed vs predicted risk for ovarian cancer

+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

+Symptom recording in ovarian cancer: cohort vs controls

QCancer BMJ (2012)

Hamilton BMJ (2009)

Abdominal pain 11.4% 8.7%

Abdominal distension

0.4% 0.6%

Loss appetite 0.5% 1.5%

Post menopausal bleeding

1.6% 1.1%

Rectal bleeding 2.2% 1.5%

Weight loss 1.2% Not reportedNote: different sample – QCancer national cohort 30-84 years Hamilton local sample age matched controls 40+

+Using QCancer in practice – v similar to QRISK2

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

+Clinical settings

Modelling done on primary care population

Intended for use in primary care setting ie GP consultation

Potential use in other clinical settings as with QRISK Pharmacy Supermarkets ‘health buses’ Secondary care

Potential use by patients - linked to inline access to health records.

+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

+

Thank you for listening

Any questions (if time)