1. + QCancer Scores –tools for earlier detection of cancer Julia Hippisley-Cox, GP, Professor Epidemiology & Director ClinRisk Ltd Presentation to Mike Richards and Cancer Team, Department of Health, 17 th Jan 2012

2. + 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) Like to work with cancer teams, DH + RCGP+ other academics whose work we have built on.

3. + 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, QDScore. Data linkage – deaths, deprivation, cancer, HES

4. + The Research Cycle ‘clinically useful epidemiology - new knowledge & utilities to improve patient care’

5. + QFeedback – integrated into EMIS

6. + 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

7. + Current published & validated QScores scoresoutcomeWeb link QRISKCVDwww.qrisk.org QDScoreType 2 diabeteswww.qdsore.org QKidneyModerate/severe renal failurewww.qkidney.org QThrombosisVTEwww.qthrombosis.org QFractureOsteoporotic fracturewww.qfracture.org QinterventionRisks benefits interventions to lower CVD and diabetes risk www.qintervention.org QCancerDetection common cancerswww.qcancer.org

8. + Qcancer ‘diagnostic’ scores Currently 6 QCancer scores :  Colorectal  Lung  Gastro-oesophageal  Pancreas  Ovary  Renal

9. + 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.

10. + 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

11. + Incidence of key symptoms vary by age and sex

12. + PPV of symptoms also vary by age in men (Jones et al BMJ 2007).

13. + And PPV vary by age in women (Jones et al BMJ 2007).

14. + 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

15. + 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

16. + Results – the algorithms/predictors OutcomeRisk factorsSymptoms LungAge, 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 ColorectalAge, sex, alcohol, family history Rectal bleeding, appetite loss, weight loss, abdo pain, change bowel habit, anaemia PancreasAge, sex, type 2, chronic pancreatitis dysphagia, appetite loss, weight loss, abdo pain, abdo distension, constipation OvarianAge, family historyRectal bleeding, appetite loss, weight loss, abdo pain, abdo distension, PMB, anaemia RenalAge, sex, smoking status, prior cancer Haematuria, appetite loss, weight loss, abdo pain, anaemia

17. + Discrimination QCancer scores

18. + Calibration - observed vs predicted risk for ovarian cancer

19. + Sensitivity for top 5% and 10% of predicted risk Cut point Threshold top 10% Pick up rate for 10% Cut point threshold top 5% Pickup rate for 5% Colorectal0.5711.256 Gastro-oeso0.2770.465 Ovary0.2630.542 Pancreas0.2620.345 Renal0.1870.381 Lung0.4770.763

20. + Symptom recording in ovarian cancer: cohort vs controls QCancer BMJ (2012)Hamilton BMJ (2009) Abdominal pain11.4%8.7% Abdominal distension0.4%0.6% Loss appetite0.5%1.5% Post menopausal bleeding 1.6%1.1% Rectal bleeding2.2%1.5% Weight loss1.2%Not reported Note: different sample – QCancer national cohort 30-84 years Hamilton local sample age matched controls 40+

21. + Difference in age structure Exeter vs QResearch vs ONS

22. + Using QCancer in practice Standalone tools a. Web calculator www.qcancer.orgwww.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

23. + 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.

24. + Iphone/iPad

25. + GP systems integration Batch 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

26. + 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.

27. + 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

28. + Discussion and next steps