Transcript Qcancer Scores

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Qcancer: symptom based approach to cancer risk
assessment
Julia Hippisley-Cox,
GP, Professor Epidemiology & Director ClinRisk Ltd
3rd cancer Care Congress
26 Sept 2012
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Acknowledgements
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Co-authors
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QResearch database
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EMIS & contributing practices & User Group
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University of Nottingham
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ClinRisk (software)
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Oxford University (independent validation)
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
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QResearch Database
www.qresearch.org
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Over 700 general practices across the UK, 14 million patients
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Joint venture between EMIS and University of Nottingham
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Patient level pseudonymised database for research
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Available for peer reviewed academic research where
outputs made publically available
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Data linkage – deaths, deprivation, cancer, HES
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
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QScores – new family of Risk Prediction
tools
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Individual assessment
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Who is most at risk of preventable disease?
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Who is likely to benefit from interventions?
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What is the balance of risks and benefits for my patient?
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Enable informed consent and shared decisions
Population level
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Risk stratification
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Identification of rank ordered list of patients for recall or reassurance
GP systems integration
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Allow updates tool over time, audit of impact on services and
outcomes
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Early diagnosis of cancer: The
problem
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UK has relatively poor track record when compared with
other European countries
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Partly due to late diagnosis with estimated 7,500+ lives lost
annually
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Later diagnosis due to mixture of
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late presentation by patient (alack awareness)
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Late recognition by GP
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Delays in secondary care
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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
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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 multiple symptoms
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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.
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
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Methods – development algorithm
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Huge representative sample from QResearch aged 30-84
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Identify new alarm symptoms (eg rectal bleeding,
haemoptysis) and other risk factors (eg age, COPD, 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 colorectal
cancer
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‘Red’ flag or alarm symptoms
(identified from studies including NICE guidelines 2005)
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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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Vaginal bleeding
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Abdominal swelling
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Haematuria
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Family history
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Anaemia
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Breast lump, pain, skin
tethering
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dysphagia
Constipation, cough
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
+ Qcancer now predicts risk all
major cancers including
Lung
Pancreas
Colorectal
Gastro
Testis
Breast
Prostate
Blood
Kidney
Ovary
Cervix
Uterus
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+ 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
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
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Methods - validation is crucial
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Essential to demonstrate the tools work and identify right
people in an efficient manner
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Tested performance
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separate sample of QResearch practices
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 – Positive predictive value, sensitivity
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Using QCancer in practice – v similar
to QRISK2
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Standalone tools
a.
Web calculator
www.qcancer.org/2013/female/php
www.qcancer.org/2013/male/php
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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
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
+ QCancer – women
http://qcancer.org/2013/female/index.php
PROFILE
64yr old woman,
Moderate smoker
Loss appetite
Abdo pain
Abdo swelling
72% risk of no cancer
28% risk any cancer
- ovarian = 20%
- colorectal = 1.5%
- pancreas =.16%
- Other 3.4%
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
+ QCancer – men
http://qcancer.org/2013/male/index.php
PROFILE
• 64yr old man,
• Heavy smoker
• FH GI cancer
• Loss appetite
• Recent VTE
• Weight loss
• Indigestion
• RESULTS
• 71% risk of no
cancer
• 29% risk any cancer
• Lung = 9%
• Pancreas =6%
• Prostate =2%
• Other =5%
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
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GP systems integration
Batch processing
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Similar to QRISK which is in 95% 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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+  Next steps - pilot work in clinical
practice supported by Macmillan& DH
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Thank you for listening
Questions & Discussion
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
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Comparison other cancer risk
tools
QCancer
The “RAT”
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Large UK sample with data until
2012
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20-40 Exeter practices; paper
records from 10 years ago
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Symptoms based approach
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Takes account of risk factors
including age, sex, smoking, FH
Focused on single symptoms
and pairs where enough data
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No adjustment for age
although cancer risk changes
with age
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Not validated
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Distributed as a mouse mat for
each cancer
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Independent external
validation by Oxford University
Can be updated and integrated
into computer systems into
workflow
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License