New tools to support decisions and diagnoses
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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. A cknowledgements. Co-authors QResearch database EMIS & contributing practices & User Group University of Nottingham ClinRisk (software )

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New tools to support decisions and diagnoses

New tools to support decisions and diagnoses

Julia Hippisley-Cox,

GP, Professor Epidemiology & Director ClinRisk Ltd

EMIS NUG 12

06 Sept 2012


A cknowledgements

Acknowledgements

  • Co-authors

  • QResearch database

  • EMIS & contributing practices & User Group

  • University of Nottingham

  • ClinRisk (software)

  • Oxford University (independent validation)


Outline

Outline

  • QSurveillance in EMIS Web

  • QResearch data linkage project/Openpseudonymiser

  • QFracture

  • QCancer

  • QDiabetes - Dr Tim Walter

  • Work in progress

  • Discussion


Qsurveillance live in emis web

QSurveillance live in EMIS Web

  • Infectious diseases surveillance to the HPA

  • Automated vaccine returns DH

  • QFeedback system

  • Available all LV and EMIS Web

  • For existing sites, check activation EMAS manager

  • If new, then email [email protected]


Qsurveillance in enquiry manager

QSurveillance in Enquiry manager


Qfeedback in lv

QFeedback in LV


Qfeedback for emis lv and web

QFeedback for EMIS LV and Web


Qresearch database

QResearch Database

  • 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

  • Open to all EMIS LV and Web practices including Scotland

  • Data linkage – deaths, deprivation, cancer, HES


Qresearch data linkage project

QResearch Data Linkage Project

  • QResearch database already linked to

    • deprivation data

    • cause of death data

  • Very useful for research

    • better definition & capture of outcomes

    • Health inequality analysis

    • Improved performance of QRISK and similar scores

  • Planning additional linkages

    • HES

    • Cancer registries


New approach pseudonymisation

New approach pseudonymisation

  • member of ECC of NIGB. s251 approvals for use of identifiable data where public interest but consent not possible and no practical alternative

  • Need approach which doesn’t extract identifiable data but still allows linkage

    • Legal, ethical and NIGB approvals

    • Secure, Scalable

    • Reliable, Affordable

    • Generates ID which are Unique to Project

    • Applied within the heart of the clinical system

    • Minimise disclosure


Www openpseudonymiser org

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


Open pseudonymiser

Open Pseudonymiser

  • Open P has been accepted as a standard by a number of major organisations including

    • NIGB

    • EMIS NUG

    • EMIS & other GP suppliers

    • BMA

    • NHS Information Centre

    • Office National Statistics

  • EMIS is integrating it into so practices can ‘pseudonymised at source’

  • This is the ‘practical alternative’ to using identifiable data when consent is impossible and helps protect patient confidentiality.

  • “If in doubt, pseudonymise it!”


Get switched on

all LV and Web practices welcome to contribute to both QResearch & QSurveillance

Email [email protected]

Get switched on


Clinical research cycle

Clinical Research Cycle


Qscores new family of r isk p rediction tools

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 qscores

Current published & validated QScores


Today we will cover three tools

Today we will cover three tools

  • QFracture

  • QCancer

  • QDiabetes – Dr Tim Walters


Qfracture background

QFracture: Background

  • Osteoporosis major cause preventablemorbidity & mortality.

  • 300,000osteoporosis 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


Qfracture challenge

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


Qfracture in national guidelines

QFracture in national guidelines

  • Published August 2012

  • Assess fracture risk all women 65+ and all men 75+

  • Assess fracture risk if risk factors

  • Estimate 10 year fracture risk using QFracture or FRAX

  • Consider use of medication to reduce fracture risk


Two new indicators recommended qof 2013 for rheumatoid arthritis

Two new indicators recommended QOF 2013 for Rheumatoid Arthritis

http://www.nice.org.uk/media/D76/FE/NICEQOFAdvisoryCommittee2012SummayRecommendations.pdf


Comparison of qfracture vs frax

Comparison of QFracture vs FRAX

QFracture

FRAX

  • Developed in UK primary care

  • Better identifies high risk

  • Less likely to over predict

  • Independent external validation

  • Risk over different time periods

  • Includes extra factors known to affect fracture risk eg

    • Antidepressants

    • Nursing home

    • Falls

  • Will be integrated EMIS Web

  • Mostly non-UK research cohorts

  • Industry sponsored

  • Over predicts leading to over treatment

  • Lack of independent validation

  • Not published and open to scrutiny


New tools to support decisions and diagnoses

QFracture Web calculator www.qfracture.org

  • Example:

  • 64 year old women

  • History of falls

  • Asthma

  • Rheumatoid arthritis

  • On steroids

  • 10% risk hip fracture

  • 20% risk of any fracture


Qscores on the app store

QScoreson the app store


Early diagnosis of cancer the problem

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


Symptoms based approach

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


Qcancer scores what they need to do

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.


Methods development algorithm

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


Red flag or alarm symptoms identified from studies including nice guidelines 2005

‘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


Qcancer now predicts risk all major cancers including

Qcancer now predicts risk all major cancers including

Lung

Pancreas

Kidney

Ovary

Colorectal

Testis

Gastro

Cervix

Breast

Prostate

Blood

Uterus


Results the algorithms predictors

Results – the algorithms/predictors


Methods validation is crucial

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


Using qcancer in practice v similar to qrisk2

Using QCancer in practice – v similar to QRISK2

  • Standalone tools

    • Web calculator

      www.qcancer.org/2013/female/php

      www.qcancer.org/2013/male/php

    • Windows desk top calculator

    • Iphone – simple calculator

  • Integrated into clinical system

    • Within consultation: GP with patients with symptoms

    • Batch: Run in batch mode to risk stratify entire practice or PCT population


  • Qcancer women http qcancer org 2013 female index php

    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

    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%


    Gp system integration within consultation

    GP system integration: Within consultation

    • Uses data already recorded (eg age, family history)

    • Use of alerts to prompt use of template

    • Automatic risk calculation in real time

    • Display risk enables shared decision making

    • Information stored in patients record and transmitted on referral letter/request for investigation

    • Allows automatic subsequent audit of process and clinical outcomes


    Gp systems integration batch processing

    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.


    Comparison other cancer risk tools

    Comparison other cancer risk tools

    QCancer

    The “RAT”

    • Large UK sample with data until 2012

    • Symptoms based approach

    • Takes account of risk factors including age, sex, smoking, FH

    • Independent external validation by Oxford university

    • Can be updated and integrated into computer systems into workflow

    • 30-40 Exeter practices; paper records from 10 yrs ago

    • Focused on single symptoms and pairs where enough data

    • Doesn’t adjust for age although cancer risk clearly changes with age

    • Not been validated (independently or by authors)

    • Distributed as a mouse mat for each cancer


    New tools to support decisions and diagnoses

    • Next steps - pilot work in clinical practice supported by DH


    Work in progress qadmissions

    Work in progress; QAdmissions

    • New tool to identify patients at risk of emergency admission “QAdmissions”

    • Based on pseudonymised linked primary and secondary care data on QResearch

    • Will predict overall admission risk but also top most common type of admission

      • cardiovascular

      • Asthma etc

    • So that interventions can be better targeted to prevent admission

    • In partnership with East London. Hear more at Kambiz Boomla session tomorrow


    Qdiabetes

    QDiabetes

    • 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

    Preventing type 2 diabetes - risk identification & interventions for individuals at high risk

    2012


    Thank you for listening questions discussion

    Thank you for listeningQuestions & Discussion


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