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QCancer Scores –a new approach to identifying patients at risk of having cancerJulia Hippisley-Cox, GP, Professor Epidemiology & Director ClinRisk LtdPancreatic cancer UK Summit 201227th June 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, QDiabetes, QFracture.

Data linkage – deaths, deprivation, cancer, HES

+Clinical Research Cycle

Clinical practice &

benefit

Clinical questions

Research +

innovation

Integration clinical system

+QScores – new family of Risk Prediction tools for decision support

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

+Why 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

+Symptoms based approach

Patients present with symptoms

GPs need to decide which patients to investigate and refer

Decision support tool must mirror setting where decisions made

Symptoms based approach needed (rather than cancer based)

Must account for multiple symptoms

Must have face clinical validity eg adjust for age, sex, smoking, FH

updated to meet changing requirements, populations, recorded data

+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.

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

84

Identify new alarm symptoms (eg appetite loss, weight loss, abdo distension) and other risk factors (eg age, smoking, 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 pancreatic 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

+Incidence of key symptoms vary by age and sex

+Currently Qcancer predicts risk 6 cancers

PancreasLung Kindey

Ovary Colorectal Gastro-oesoph

+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, dyspepsia/hearburn

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, dyspepsia/heartburn

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

+Results of validation

Explained 59-62% of variation R2

ROC 0.84 (women) and 0.87 (men)

D statistic high (2.44 for women and 2.61 men)

Calibration – close predicted vs observed

Good sensitivity : The 10% of patients with highest risk accounted for 62% of all pancreatic cancers diagnosed in next two years

+Qcancer.org web calculator

PROFILE• 64 yr woman• non smoker• 3+unit alcohol• type2 diabetes• chronic pancreatitis• Loss appetite and weight• Indigestion• Anaemia

RISKS• Pancreatic cancer 12%• Gastrooesophageal 7%• Colorectal 4%• Ovarian cancer 2%• Renal cancer 1%• Lung cancer 2%

+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.

+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

+

Thank you for listening

Any questions (if time)