qcancer scores tools for earlier detection of cancer
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QCancer Scores –tools for earlier detection of cancer. Julia Hippisley-Cox, GP, Professor Epidemiology & Director ClinRisk Ltd GP Lincoln Refresher Course 18 th May 2012. A cknowledgements. Co-author Dr Carol Coupland QResearch database University of Nottingham ClinRisk (software )

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qcancer scores tools for earlier detection of cancer

QCancer Scores –tools for earlier detection of cancer

Julia Hippisley-Cox,

GP, Professor Epidemiology & Director ClinRisk Ltd

GP Lincoln Refresher Course

18th May 2012

a cknowledgements
  • 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
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
qscores new family of r isk p rediction tools
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
early diagnosis of cancer the problem
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
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
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
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
Currently Qcancer predicts risk 6 cancers







qcancer scores what they need to do
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
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
methods development algorithm
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
‘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
methods validation
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
symptom recording in ovarian cancer cohort vs controls
Symptom recording in ovarian cancer: cohort vs controls

Note: different sample – QCancer national cohort 30-84 years

Hamilton local sample age matched controls 40+

using qcancer in practice v similar to qrisk2
Using QCancer in practice – v similar to QRISK2
  • Standalone tools
      • Web calculator www.qcancer.org
      • Windows desk top calculator
      • Iphone – simple calculator
  • Integrated into clinical system
      • Within consultation: GP with patients with symptoms
      • Batch: Run in batch mode to risk stratify entire practice or PCT population
gp system integration within consultation
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 integration batch processing
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
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
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