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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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Presentation on theme: "+ New tools to support decisions and diagnoses Julia Hippisley-Cox, GP, Professor Epidemiology & Director ClinRisk Ltd EMIS NUG 12 06 Sept 2012."— Presentation transcript:

1 + New tools to support decisions and diagnoses Julia Hippisley-Cox, GP, Professor Epidemiology & Director ClinRisk Ltd EMIS NUG 12 06 Sept 2012

2 + Acknowledgements Co-authors QResearch database EMIS & contributing practices & User Group University of Nottingham ClinRisk (software) Oxford University (independent validation)

3 + Outline QSurveillance in EMIS Web QResearch data linkage project/Openpseudonymiser QFracture QCancer QDiabetes - Dr Tim Walter Work in progress Discussion

4 + 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 qsurveillance@qsurveillance.or g qsurveillance@qsurveillance.or g


6 + QSurveillance in Enquiry manager

7 + QFeedback in LV

8 + QFeedback for EMIS LV and Web

9 + 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

10 + 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

11 + 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

12 + 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

13 + 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!”

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15 + Get switched on all LV and Web practices welcome to contribute to both QResearch & QSurveillance Email julia.hippisley-

16 + Clinical Research Cycle Clinical practice & benefit Clinical questions Research + innovation Integration clinical system

17 + 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

18 + Current published & validated QScores scoresoutcomeWeb link QDiabetesType 2 QKidneyModerate/severe renal QFractureOsteoporotic QinterventionRisks benefits interventions to lower CVD and diabetes risk QCancerDetection common

19 + Today we will cover three tools QFracture QCancer QDiabetes – Dr Tim Walters

20 + 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 QFracture: Background

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22 + 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: challenge

23 + 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

24 + Two new indicators recommended QOF 2013 for Rheumatoid Arthritis IDindicatorComments 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 with rheumatoid arthritis who have had fracture risk assessment using tool adjusted for RA in last 27 months NICE recommends QFracture

25 + Comparison of QFracture vs 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 QFracture FRAX

26 + Example: 64 year old women History of falls Asthma Rheumatoid arthritis On steroids 10% risk hip fracture 20% risk of any fracture QFracture Web calculator

27 + QScores on the app store

28 + 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

29 + 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

30 + 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.

31 + 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

32 + ‘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

33 + Qcancer now predicts risk all major cancers including PancreasLung KidneyOvary Colorectal Gastro Testis Breast Prostate Blood Cervix Uterus

34 + Results – the algorithms/predictors OutcomeRisk factorsSymptoms LungAge, 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 ColorectalAge, sex, alcohol, family history Rectal bleeding, appetite loss, weight loss, abdo pain, change bowel habit, anaemia PancreasAge, sex, type 2, chronic pancreatitis dysphagia, appetite loss, weight loss, abdo pain, abdo distension, constipation OvarianAge, family historyRectal bleeding, appetite loss, weight loss, abdo pain, abdo distension, PMB, anaemia RenalAge, sex, smoking status, prior cancer Haematuria, appetite loss, weight loss, abdo pain, anaemia

35 + 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

36 + Using QCancer in practice – v similar to QRISK2 Standalone tools a. Web calculator 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

37 + QCancer – women 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%

38 + QCancer – men 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%

39 + 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

40 + GP systems integration Batch 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.

41 + Comparison other cancer risk tools 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 QCancerThe “RAT”

42 + Next steps - pilot work in clinical practice supported by DH

43 + 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

44 + 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 Already integrated into EMIS Web Evaluation in London and Berkshire

45 + Thank you for listening Questions & Discussion

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