Transcript Qcancer Scores

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New tools to support decisions and diagnoses
Julia Hippisley-Cox,
GP, Professor Epidemiology & Director ClinRisk Ltd
EMIS NUG 12
06 Sept 2012
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Acknowledgements
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Co-authors
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QResearch database
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EMIS & contributing practices & User Group
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University of Nottingham
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ClinRisk (software)
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Oxford University (independent validation)
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Outline
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QSurveillance in EMIS Web
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QResearch data linkage project/Openpseudonymiser
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QFracture
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QCancer
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QDiabetes - Dr Tim Walter
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Work in progress
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Discussion
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QSurveillance live in EMIS Web
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Infectious diseases surveillance
to the HPA
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Automated vaccine returns DH
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QFeedback system
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Available all LV and EMIS Web
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For existing sites, check
activation EMAS manager
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If new, then email
[email protected]
g
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QSurveillance in Enquiry manager
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QFeedback in LV
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QFeedback for EMIS LV and Web
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QResearch Database
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Over 700 general practices across the UK, 14 million patients
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Joint venture between EMIS and University of Nottingham
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Patient level pseudonymised database for research
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Available for peer reviewed academic research where
outputs made publically available
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Open to all EMIS LV and Web practices including Scotland
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Data linkage – deaths, deprivation, cancer, HES
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QResearch Data Linkage Project
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QResearch database already linked to
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deprivation data
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cause of death data
Very useful for research
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better definition & capture of outcomes
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Health inequality analysis
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Improved performance of QRISK and similar scores
Planning additional linkages
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HES
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Cancer registries
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New approach pseudonymisation
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member of ECC of NIGB. s251 approvals for use of
identifiable data where public interest but consent not
possible and no practical alternative
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Need approach which doesn’t extract identifiable data but
still allows linkage
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Legal, ethical and NIGB approvals
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Secure, Scalable
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Reliable, Affordable
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Generates ID which are Unique to Project
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Applied within the heart of the clinical system
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Minimise disclosure
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www.openpseudonymiser.org
Scrambles NHS number BEFORE extraction from
clinical system
 Takes NHS number + project specific encrypted ‘salt
code’
 One way hashing algorithm (SHA2-256)
 Cant be reversed engineered
 Applied twice in to separate locations before data
leaves source
 Apply identical software to external dataset
 Allows two pseudonymised datasets to be linked
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Open Pseudonymiser
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Open P has been accepted as a standard by a number of major
organisations including
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NIGB
EMIS NUG
EMIS & other GP suppliers
BMA
NHS Information Centre
Office National Statistics
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EMIS is integrating it into so practices can ‘pseudonymised at
source’
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This is the ‘practical alternative’ to using identifiable data when
consent is impossible and helps protect patient confidentiality.
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“If in doubt, pseudonymise it!”
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Get switched on
all LV and Web practices
welcome to contribute to
both QResearch &
QSurveillance
Email [email protected]
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Clinical Research Cycle
Clinical
practice &
benefit
Integration
clinical
system
Clinical
questions
Research +
innovation
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QScores – new family of Risk Prediction
tools
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Individual assessment
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Who is most at risk of preventable disease?
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Who is likely to benefit from interventions?
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What is the balance of risks and benefits for my patient?
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Enable informed consent and shared decisions
Population level
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Risk stratification
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Identification of rank ordered list of patients for recall or reassurance
GP systems integration
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Allow updates tool over time, audit of impact on services and
outcomes
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Current published & validated
QScores
scores
outcome
Web link
QRISK
CVD
www.qrisk.org
QDiabetes
Type 2 diabetes
www.qdiabetes.org
QKidney
Moderate/severe renal failure
www.qkidney.org
QThrombosis
VTE
www.qthrombosis.org
QFracture
Osteoporotic fracture
www.qfracture.org
Qintervention
Risks benefits interventions to www.qintervention.org
lower CVD and diabetes risk
QCancer
Detection common cancers
www.qcancer.org
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Today we will cover three tools
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QFracture
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QCancer
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QDiabetes – Dr Tim Walters
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QFracture: Background
 Osteoporosis
major cause preventable morbidity &
mortality.
 300,000
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
2
billion is cost of annual social and hospital care
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QFracture: challenge
 Effective
interventions exist to reduce fracture risk
 Challenge
is better identification of high risk
patients likely to benefit
 Avoid
over treatment in those unlikely to benefit or
who may be harmed
 Some
guidelines recommend BMD but expensive
and not very specific
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QFracture in national guidelines
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Published August 2012
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Assess fracture risk all women
65+ and all men 75+
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Assess fracture risk if risk
factors
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Estimate 10 year fracture risk
using QFracture or FRAX
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Consider use of medication to
reduce fracture risk
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Two new indicators recommended
QOF 2013 for Rheumatoid Arthritis
ID
indicator
Comments
NM56
% patients with RA 30-84 years
who have had a CVD risk
assessment using a CVD risk
assessment for RA in last 15/12
QRISK2 only CVD risk tool
- 30-84 yrs
- adjusted for RA
NM57
% of patients with RA 50-90yrs
NICE recommends QFracture
with rheumatoid arthritis who
have had fracture risk assessment
using tool adjusted for RA in last
27 months
http://www.nice.org.uk/media/D76/FE/NICEQOFAdvisoryCommittee2012SummayRecommendations.pdf
+ Comparison of QFracture vs FRAX
QFracture
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Developed in UK primary care
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Better identifies high risk
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Less likely to over predict
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Independent external validation
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Risk over different time periods
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Includes extra factors known to
affect fracture risk eg
 Antidepressants
 Nursing home
 Falls
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Will be integrated EMIS Web
FRAX
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Mostly non-UK research cohorts
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Industry sponsored
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Over predicts leading to over
treatment
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Lack of independent validation
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Not published and open to
scrutiny
+ QFracture Web calculator
www.qfracture.org
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Example:
64 year old women
History of falls
Asthma
Rheumatoid arthritis
On steroids
10% risk hip fracture
20% risk of any
fracture
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QScores on the app store
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Early diagnosis of cancer: The
problem
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UK has relatively poor track record when compared with
other European countries
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Partly due to late diagnosis with estimated 7,500+ lives lost
annually
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Later diagnosis due to mixture of
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late presentation by patient (alack awareness)
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Late recognition by GP
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Delays in secondary care
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Symptoms based approach
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Patients present with symptoms
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GPs need to decide which patients to investigate and refer
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Decision support tool must mirror setting where decisions made
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Symptoms based approach needed (rather than cancer based)
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Must account for multiple symptoms
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Must have face clinical validity eg adjust for age, sex, smoking,
FH
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updated to meet changing requirements, populations, recorded
data
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QCancer scores – what they need
to do
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Accurately predict level of risk for individual based on risk
factors and multiple symptoms
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Discriminate between patients with and without cancer
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Help guide decision on who to investigate or refer and
degree of urgency.
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Educational tool for sharing information with patient.
Sometimes will be reassurance.
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Methods – development algorithm
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Huge representative sample from QResearch aged 30-84
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Identify new alarm symptoms (eg rectal bleeding,
haemoptysis) and other risk factors (eg age, COPD, smoking,
family history)
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Identify cancer outcome - all new diagnoses either on GP
record or linked ONS deaths record in next 2 years
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Established methods to develop risk prediction algorithm
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Identify independent factors adjusted for other factors
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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)
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Haemoptysis
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Loss of appetite
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Haematemesis
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Weight loss
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Dysphagia
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Indigestion +/- heart burn
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Rectal bleeding
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Abdominal pain
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Vaginal bleeding
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Abdominal swelling
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Haematuria
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Family history
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Anaemia
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Breast lump, pain, skin
tethering
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dysphagia
Constipation, cough
+ Qcancer now predicts risk all
major cancers including
Lung
Pancreas
Colorectal
Gastro
Testis
Breast
Prostate
Blood
Kidney
Ovary
Cervix
Uterus
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Outcome
Risk factors
Symptoms
Lung
Age, sex, smoking,
deprivation, COPD,
prior cancers
Haemoptysis, appetite loss, weight loss,
cough, anaemia
Gastrooeso
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
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Essential to demonstrate the tools work and identify right
people in an efficient manner
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Tested performance
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separate sample of QResearch practices
external dataset (Vision practices) at Oxford University
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Measures of discrimination - identifying those who do and
don’t have cancer
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Measures of calibration - closeness of predicted risk to
observed risk
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Measure performance – Positive predictive value, sensitivity
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Using QCancer in practice – v similar
to QRISK2
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Standalone tools
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Web calculator
www.qcancer.org/2013/female/php
www.qcancer.org/2013/male/php
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b.
Windows desk top calculator
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Iphone – simple calculator
Integrated into clinical system
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Within consultation: GP with patients with symptoms
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Batch: Run in batch mode to risk stratify entire practice or
PCT population
+ QCancer – women
http://qcancer.org/2013/female/index.php
PROFILE
64yr old woman,
Moderate smoker
Loss appetite
Abdo pain
Abdo swelling
72% risk of no cancer
28% risk any cancer
- ovarian = 20%
- colorectal = 1.5%
- pancreas =.16%
- Other 3.4%
+ QCancer – men
http://qcancer.org/2013/male/index.php
PROFILE
• 64yr old man,
• Heavy smoker
• FH GI cancer
• Loss appetite
• Recent VTE
• Weight loss
• Indigestion
• RESULTS
• 71% risk of no
cancer
• 29% risk any cancer
• Lung = 9%
• Pancreas =6%
• Prostate =2%
• Other =5%
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GP system integration:
Within consultation
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Uses data already recorded (eg age, family history)
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Use of alerts to prompt use of template
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Automatic risk calculation in real time
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Display risk enables shared decision making
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Information stored in patients record and transmitted on
referral letter/request for investigation
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Allows automatic subsequent audit of process and clinical
outcomes
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GP systems integration
Batch processing
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Similar to QRISK which is in 95% of GP practices– automatic
daily calculation of risk for all patients in practice based on
existing data.
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Identify patients with symptoms/adverse risk profile without
follow up/diagnosis
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Enables systematic recall or further investigation
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Systematic approach - prioritise by level of risk.
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Comparison other cancer risk
tools
QCancer
The “RAT”
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Large UK sample with data
until 2012
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30-40 Exeter practices; paper
records from 10 yrs ago
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Symptoms based approach
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Takes account of risk factors
including age, sex, smoking,
FH
Focused on single symptoms
and pairs where enough data
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Independent external
validation by Oxford
university
Doesn’t adjust for age
although cancer risk clearly
changes with age
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Not been validated
(independently or by authors)
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Distributed as a mouse mat for
each cancer
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Can be updated and
integrated into computer
systems into workflow
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practice supported by DH
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Work in progress; QAdmissions
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New tool to identify patients at risk of emergency admission
“QAdmissions”
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Based on pseudonymised linked primary and secondary care
data on QResearch
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Will predict overall admission risk but also top most common
type of admission
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cardiovascular
Asthma etc
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So that interventions can be better targeted to prevent
admission
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In partnership with East London. Hear more at Kambiz Boomla
session tomorrow
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QDiabetes
Preventing type 2
diabetes - risk
identification &
interventions for
individuals at high risk
2012
• Type 2 diabetes epidemic
• Potential for prevention
• Risk assessment using validated
risk tools including QDiabetes
• Individual assessment and also
batch processing
• QDiabetes is UK & fully validated
• Includes deprivation & ethnicity
• Ages 25-84
• Efficient as 2 extra questions on top
of QRISK
• www.qintervention.org
• Already integrated into EMIS Web
• Evaluation in London and Berkshire
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Thank you for listening
Questions & Discussion