New Risk Prediction Tools – generating clinical benefits from clinical data

1. New Risk Prediction Tools – generating clinical benefits from clinical data Julia Hippisley-Cox, GP, Professor Epidemiology & Director ClinRisk Ltd Primary Health Information 2012 24 April 2012

2. Acknowledgements • Co-author Dr Carol Coupland • QResearch database • University of Nottingham • ClinRisk (software) • EMIS & contributing practices & EMIS User Group • BJGP and BMJ for publishing the work • Oxford University (independent validation)

3. About me • Inner city GP • Clinical epidemiologist University Nottingham • Director QResearch (NFP partnership UoN and EMIS) • Director ClinRisk Ltd (Medical research & software) • Member Ethics & ConfidentilityCommittee NIGB

4. QResearch Databasewww.qresearch.org • 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 • Data linkage – deaths, deprivation, cancer, HES • 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.

5. QFeedback – integrated into EMIS

6. Clinical Research Cycle

7. 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

8. Current published & validated QScores

9. Today we will cover two types of tools • Prognostic tool – QFracture • Diagnostic tool - QCancer

10. QFracture: Background • Osteoporosis major cause preventablemorbidity & mortality. • 2 million women affected in E&W • 180,000 osteoporosis fractures each year • 30% women over 50 years will get vertebral fracture • 20% hip fracture patients die within 6/12 • 50% hip fracture patients lose the ability to live independently • 1.8 billion is cost of annual social and hospital care

12. QFracture: challenge • Effective interventions exist to reduce fracture risk • Challenge is better identification of high risk patients likely to benefit • Avoiding over treatment in those unlikely to benefit or who may be harmed • Draft NICE guideline (2012) recommend using 10 year risk of fracture either using QFracture or FRAX • QFracture also being piloted for QOF indicator

13. QFracture: development • Cohort study using patient level QResearch database • Similar methodology to QRISK • Published in BMJ 2009 • Algorithm includes established risk factors • Developed risk calculator which can • - identify high risk patients for assessment • - show risk of fracture to patients

14. Advantages QFracture vs FRAX • Published & validated • More accurate in UK primary care • Can be updated annually • Independent of pharma industry • Includes extra risk factors eg • Falls • CVD • Type 2 diabetes • Asthma • Antidepressants • Detail smoking/Alcohol • HRT

15. QFracture: Clinical example • 64 year old women • Heavy smoker • Non drinker • BMI 20.6 • Asthma • On steroids • Rheumatoid • H/O falls

17. QFracture + other QScores on the app store

18. QScores for systems integration Possible to integrate QFracture (and the other QScores) into any clinical computer system • Software libraries in Java or .NET • Test harness • Documentation • Support • For details see www.qfracture.org

19. QCancer – the problem • UK has poor track record in cancer diagnosis cf Europe • Partly due to late diagnosis • Late diagnosis might be late presentation or non-recognition by GPs or both • Earlier diagnosis may lead to more Rx options and better prognosis • Problem is that cancer symptoms can be diffuse and non-specific so need better ways to quantify cancer risk to help prioritise investigation

20. 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. • Symptom based approach rather than cancer based approach

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

22. Methods – development • Huge sample from primary care aged 30-84 • Identify • new alarm symptoms (eg rectal bleeding, haemoptysis, weight loss, appetite loss, abdominal pain, rectal bleeding) and • other risk factors (eg age, COPD, smoking, family history) • Identify patient with cancers • Identify independent factors which predict cancers • Measure of absolute risk of cancer. Eg 5% risk of colorectal cancer

23. Methods - validation • Once algorithms developed, tested performance • separate sample of QResearch practices • 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

24. Results – the algorithms/predictors

25. Sensitivity for top10% of predicted cancer risk

26. Using QCancer in practice • 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

27. 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.

28. Iphone/iPad

29. 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

30. 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

31. Next steps - pilot work in clinical practice supported by DH

32. Thank you for listeningAny questions (if time)