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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).
haem
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haem
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dysp
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0
4
8
12
16
20 45-54 yrs55-64 yrs65-74 yrs75-84 yrs
+And PPV vary by age in women(Jones et al BMJ 2007).
haem
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haem
opty
sis
dysp
hagi
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leed
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12 45-54 yrs55-64 yrs65-74 yrs75-84 yrs
+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)