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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.

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Presentation on theme: "+ QCancer Scores –tools for earlier detection of cancer Julia Hippisley-Cox, GP, Professor Epidemiology & Director ClinRisk Ltd GP Lincoln Refresher Course."— Presentation transcript:

1 + 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

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

4 + Clinical Research Cycle Clinical practice & benefit Clinical questions Research + innovation Integration clinical system

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 QDiabetesType 2 diabeteswww.qdiabetes.org QKidneyModerate/severe renal failurewww.qkidney.org QThrombosisVTEwww.qthrombosis.org QFractureOsteoporotic fracturewww.qfracture.org QinterventionRisks benefits interventions to lower CVD and diabetes risk QCancerDetection common cancerswww.qcancer.org

8 + 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

9 + 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.

10 + Example of pancreatic cancer 11 th 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

11 + 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.

12 + Currently Qcancer predicts risk 6 cancers PancreasLung Kindey Ovary Colorectal Gastro-oesoph

13 + 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.

14 + 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

15 + Incidence of key symptoms vary by age and sex

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

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

18 + Methods – development algorithm Huge representative sample from primary care aged 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

19 + ‘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

20 + 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

21 + 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

22 + Discrimination QCancer scores

23 + Calibration - observed vs predicted risk for ovarian cancer

24 + Sensitivity for top 10% of predicted cancer risk Cut point Threshold top 10% Pick up rate for 10% Colorectal0.571 Gastro- oesophageal Ovary0.263 Pancreas0.262 Renal0.187 Lung0.477

25 + 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 years Hamilton local sample age matched controls 40+

26 + Using QCancer in practice – v similar to QRISK2 Standalone tools a. Web calculator 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

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 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

30 + 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.

31 + 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

32 + Next steps - pilot work in clinical practice supported by DH

33 + Thank you for listening Any questions (if time)


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