QCancer - pancreas 27th June 2012

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QCancer Scores –a new approach to identifying
patients at risk of having cancer
Julia Hippisley-Cox,
GP, Professor Epidemiology & Director ClinRisk Ltd
Pancreatic cancer UK Summit 2012
27th June 2012
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Acknowledgements

Co-author Dr Carol Coupland

QResearch database

University of Nottingham

ClinRisk (software)
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EMIS & contributing practices & User Group
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BJGP and BMJ for publishing the work
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Oxford University (independent validation)

cancer teams, DH + RCGP+ other academics with whom we are
now working
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QResearch Database

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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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,
QFracture.

Data linkage – deaths, deprivation, cancer, HES
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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 for decision support
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

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
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Why pancreatic cancer?
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11th most common cancer
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< 20% patients suitable for surgery
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84% dead within a year of diagnosis
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Chances of survival better if diagnosis made at early stage
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Very few established risk factors (smoking, chronic
pancreatitis, alcohol) so screening programme unlikely
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Challenge is to identify symptoms in primary care particularly hard for pancreatic cancer
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Symptoms based approach
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Patients present with symptoms
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GPs need to decide which patients to investigate and refer
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Decision support tool must mirror setting where decisions made
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Symptoms based approach needed (rather than cancer based)
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Must account for multiple symptoms

Must have face clinical validity eg adjust for age, sex, smoking,
FH
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updated to meet changing requirements, populations, recorded
data
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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

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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Methods – development algorithm
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Huge representative sample from primary care aged 30-84
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Identify new alarm symptoms (eg appetite loss, weight loss,
abdo distension) and other risk factors (eg age, smoking,
smoking, family history)
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Identify cancer outcome - all new diagnoses either on GP
record or linked ONS deaths record in next 2 years
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Established methods to develop risk prediction algorithm
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Identify independent factors adjusted for other factors
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Measure of absolute risk of cancer. Eg 5% risk of pancreatic
cancer
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‘Red’ flag or alarm symptoms
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Haemoptysis
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Loss of appetite
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Haematemesis
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Weight loss
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Dysphagia
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Indigestion +/- heart burn
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Rectal bleeding
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Abdominal pain
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Postmenopausal bleeding
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Abdominal swelling
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Haematuria
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Family history
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dysphagia
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Anaemia
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Constipation
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cough
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Incidence of key symptoms vary
by age and sex
+ Currently Qcancer predicts risk 6
cancers
Lung
Pancreas
Ovary
Colorectal
Kindey
Gastro-oesoph
+ Results – the algorithms/predictors
Outcome
Risk factors
Symptoms
Lung
Age, sex, smoking,
deprivation, COPD,
prior cancers
Haemoptysis, appetite loss, weight loss,
cough, anaemia
Gastrooeso
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
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Methods - validation
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Previous QScores validation – similar or better performance
on external data
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Once algorithms developed, tested performance
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separate sample of QResearch practices
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fully 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
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Results of validation
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Explained 59-62% of variation R2
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ROC 0.84 (women) and 0.87 (men)
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D statistic high (2.44 for women and 2.61 men)
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Calibration – close predicted vs observed
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Good sensitivity : The 10% of patients with highest risk
accounted for 62% of all pancreatic cancers diagnosed in
next two years
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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%
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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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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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Thank you for listening
Any questions (if time)
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