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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 ) - PowerPoint PPT Presentation
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+
QCancer Scores –tools for earlier detection of cancer
Julia Hippisley-Cox, GP, Professor Epidemiology & Director ClinRisk LtdGP Lincoln Refresher Course18th May 2012
+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
+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
+Clinical Research CycleClinical
practice & benefit
Clinical questions
Research +
innovation
Integration clinical system
+QFeedback – integrated into EMIS
+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
+Current published & validated QScoresscores outcome Web linkQRISK CVD www.qrisk.org QDiabetes Type 2 diabetes www.qdiabetes.orgQKidney Moderate/severe renal
failurewww.qkidney.org
QThrombosis VTE www.qthrombosis.org QFracture Osteoporotic fracture www.qfracture.org
Qintervention Risks benefits interventions to lower CVD and diabetes risk
www.qintervention.org
QCancer Detection common cancers www.qcancer.org
+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
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
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
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
PancreasLung Kindey
Ovary Colorectal Gastro-oesoph
+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 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
+Incidence of key symptoms vary by age and sex
+PPV of symptoms also vary by age in men (Jones et al BMJ 2007).
haem
aturia
haem
optys
is
dysph
agia
rectal
blee
ding
048
121620 45-54 yrs
55-64 yrs65-74 yrs75-84 yrs
+And PPV vary by age in women(Jones et al BMJ 2007).
haem
aturia
haem
optys
is
dysph
agia
rectal
blee
ding
02468
1012 45-54 yrs
55-64 yrs65-74 yrs75-84 yrs
+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
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
+Results – the algorithms/predictorsOutcom
eRisk factors Symptoms
Lung Age, 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
Colorectal
Age, sex, alcohol, family history
Rectal bleeding, appetite loss, weight loss, abdo pain, change bowel habit, anaemia
Pancreas Age, sex, type 2, chronic pancreatitis
dysphagia, appetite loss, weight loss, abdo pain, abdo distension, constipation
Ovarian Age, family history Rectal bleeding, appetite loss, weight loss, abdo pain, abdo distension, PMB, anaemia
Renal Age, sex, smoking status, prior cancer
Haematuria, appetite loss, weight loss, abdo pain, anaemia
+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
+Discrimination QCancer scores
lung renal colorectal gastroes pancreas ovary0.760.780.8
0.820.840.860.880.9
0.920.94
ROC values for women
+Calibration - observed vs predicted risk for ovarian cancer
+Sensitivity for top 10% of predicted cancer risk
Cut point Threshold top 10%
Pick up rate for 10%
Colorectal 0.5 71Gastro-oesophageal
0.2 77
Ovary 0.2 63Pancreas 0.2 62Renal 0.1 87Lung 0.4 77
+Symptom recording in ovarian cancer: cohort vs controls
QCancer BMJ (2012)
Hamilton BMJ (2009)
Abdominal pain 11.4% 8.7%Abdominal distension
0.4% 0.6%
Loss appetite 0.5% 1.5%Post menopausal bleeding
1.6% 1.1%
Rectal bleeding 2.2% 1.5%Weight loss 1.2% Not reportedNote: different sample – QCancer national cohort 30-84 years Hamilton local sample age matched controls 40+
+Using QCancer in practice – v similar to QRISK2
Standalone tools a. Web calculator www.qcancer.org b. Windows desk top calculatorc. Iphone – simple calculator
Integrated into clinical systema. Within consultation: GP with patients with symptoms b. Batch: Run in batch mode to risk stratify entire
practice or PCT population
+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.
+Iphone/iPad
+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
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
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
+Next steps - pilot work in clinical practice supported by DH
+
Thank you for listening
Any questions (if time)