+

Qcancer: symptom based approach to early diagnosis of cancerJulia Hippisley-Cox, GP, Professor Epidemiology & Director ClinRisk LtdYorkshire Cancer Network8th November 2012

+Acknowledgements

Co-authors

QResearch database

EMIS & contributing practices & User Group

University of Nottingham

ClinRisk (software)

Oxford University (independent validation)

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+QResearch Database www.qresearch.org Over 700 general practices across the UK, 14 million

patients

Joint venture between EMIS and University of Nottingham

Patient level pseudonymised database for research

Available for peer reviewed academic research where outputs made publically available

Data linkage – deaths, deprivation, cancer, HES

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+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

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+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

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+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

+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

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+QCancer scores – what they need to do

Accurately predict level of risk for individual based on risk factors and multiple 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.

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+Methods – development algorithm Huge representative sample from QResearch 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

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+‘Red’ flag or alarm symptoms (identified from studies including NICE guidelines 2005)

Haemoptysis

Haematemesis

Dysphagia

Rectal bleeding

Vaginal bleeding

Haematuria

dysphagia

Constipation, cough

Loss of appetite

Weight loss

Indigestion +/- heart burn

Abdominal pain

Abdominal swelling

Family history

Anaemia

Breast lump, pain, skin tethering

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+Qcancer now predicts risk all major cancers including

PancreasLung Kidney Ovary

Colorectal Gastro Testis

Breast Prostate Blood

Cervix

Uterus

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+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

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+Methods - validation is crucial Essential to demonstrate the tools work and identify right

people in an efficient manner

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 – Positive predictive value, sensitivity

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+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%

+Using QCancer in practice – v similar to QRISK2

Standalone tools

a. Web calculator

www.qcancer.org/2013/female/php

www.qcancer.org/2013/male/php

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

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+GP systems integrationBatch processing

Similar to QRISK which is in 95% 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.

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Project update

November 2012

What are cancer decision support tools?

•Cancer decision support tools (CDS) are an aid to clinical decision-making, to assist GPs in their decisions about whether or not to refer or request further diagnostic investigation, in patients where they believe there is a risk of cancer

•The tools in this project display the risk of a patient having a specific type of cancer

•This risk is founded on analysis of historic population cohorts and their risk of having cancer based on a range of factors including symptoms, medical history and demographic data.

•In this project we are focusing on rollout of two existing high profile tools:1. QCancer developed by Professor Julia Hippisley-Cox2. The Risk Assessment Tool (RAT), developed by Professor Willie

Hamilton

November December January February March April May

2012 2013

Assimilation of QCancer and RAT into

IT platform

June

Development of training materials

Cancer network training sessions

July August

CDS project phase one timeline

Phase one end

Engagement with GP IT providers

Phase one tool in useGP training

rollout

Data gathering for evaluationDefine evaluation

methodology

GP practices recruited

Load software onto GP systems

Who is involved?

•This project will focus on the QCancer and RAT tools

•Macmillan Cancer Support will lead the project, with an evaluation overseen by Cancer Research UK, and with part-funding from the Department of Health. The project forms part of the National Awareness and Early Diagnosis Initiative (NAEDI)

•In phase one of the project, thirteen English cancer networks will lead the rollout at a local level, working with over 650 GP practices

•Practices in Wales and Scotland are also planning to take part

•In phase one, the tool used in practice will be developed by BMJ Informatica, though it will work across a wide range of native GP IT software

•In parallel, the project will engage with GP software providers, to promote understanding and ensure widespread use and rollout after phase one

What will the tool look like?

•Cancer decision support tools are designed to assess the risk of a patient having an existing, but as yet undiagnosed cancer, by calculating a risk based on factors such as symptoms, medical history and demographic profile

•GPs will use in everyday practice an IT-based cancer decision support tool with a simple user interface which can display scores for either RAT or QCancer

•The tool will work in three ways: i. Reactive prompt ii. Symptom checker iii. Audit function

(i) Reactive prompt

Working automatically in the background using READ-coded information, the tool will calculate a risk of having cancer for every patient seen in consultation. If the risk is above a certain level, a prompt will appear on screen letting the GP know that they might like to consider whether the patient might warrant a referral or investigation for a suspected cancer.

(ii) Symptom checker

Used in consultation where READ codes are not already known, a symptom checker can be called up, which allows the GP to enter relevant symptoms, calculate a risk, and then re-enter observed symptoms into the patient’s record as a READ-coded entry.

(iii) Audit function

An audit function will show calculated risk levels of all registered patients on a practice’s list. This can be sorted to show those calculated to have the highest risk, and used to consider whether any further action should be taken for these patients.

+

Thank you for listening

Questions & Discussion

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