+ qcancer: symptom based approach to early diagnosis of cancer julia hippisley-cox, gp, professor...
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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)
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
+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
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
+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
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
+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
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
+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
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
+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.
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
+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
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
+‘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
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
+Qcancer now predicts risk all major cancers including
PancreasLung Kidney Ovary
Colorectal Gastro Testis
Breast Prostate Blood
Cervix
Uterus
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
+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
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
+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
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
+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
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
+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.
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
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
This work by Julia Hippisley-Cox is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License