QCancer Scores –a new approach to identifying patients at risk of having cancer

1. 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

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) • cancer teams, DH + RCGP+ other academics with whom we are now working

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, QDiabetes, QFracture. • Data linkage – deaths, deprivation, cancer, HES

4. Clinical Research Cycle

5. QScores – new family of Risk Prediction tools for decision support • 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

6. Why 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

7. Symptoms based approach • Patients present with symptoms • GPs need to decide which patients to investigate and refer • Decision support tool must mirror setting where decisions made • Symptoms based approach needed (rather than cancer based) • Must account for multiple symptoms • Must have face clinical validity eg adjust for age, sex, smoking, FH • updated to meet changing requirements, populations, recorded data

8. 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.

9. Methods – development algorithm • Huge representative sample from primary care aged 30-84 • Identify new alarm symptoms (egappetite loss, weight loss, abdo distension) and other risk factors (eg age, smoking, 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 pancreatic cancer

10. ‘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

11. Incidence of key symptoms vary by age and sex

12. Currently Qcancer predicts risk 6 cancers Lung Pancreas Kindey Colorectal Ovary Gastro-oesoph

13. Results – the algorithms/predictors

14. 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

15. Results of validation • Explained 59-62% of variation R2 • ROC 0.84 (women) and 0.87 (men) • D statistic high (2.44 for women and 2.61 men) • Calibration – close predicted vs observed • Good sensitivity : The 10% of patients with highest risk accounted for 62% of all pancreatic cancers diagnosed in next two years

16. 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%

17. 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.

18. 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

19. Thank you for listeningAny questions (if time)