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QPrediction Scores – new developmentsProfessor Julia Hippisley-CoxProfessor of Clinical EpidemiologyDirector ClinRisk LtdDirector QResearch@juliahcox
Acknowledgements
Co-authors QResearch database - EMIS
practices, EMIS, Nottingham University
EMIS NUG (including screencasts) ClinRisk Ltd (development &
software) Office National Statistics (mortality
data) HSCIC (pseudonymised HES data)
Embargoed until publication
Overview
QBleed Algorithm QBleed + QStroke Update on tools integrated into EMIS
Web
QPrediction tools Individual assessment
Who is most at risk of current or 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 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
QBleed algorithm
QBleed: background
Anticoagulants used in prevention & treatment of VTE To reduce risk ischaemic stroke with AF
Although use of anticoagulants in AF is in QOF uptake is low
Legitimate concerns around safety particularly risk of major bleeds
Need to quantify absolute risk of bleed to help make informed decision on risk/benefit
http://www.nice.org.uk/guidance/cg180/chapter/1-recommendations
NICE guideline recommendationBleeding Risk section 1.4
When discussing benefits and risk of anticoagulation in AF explain that For most people benefit exceeds risk Except for those with increased bleeding
risk where careful monitoring required Discuss options and base choice on
their clinical features and preferences
Only treat after informed discussion on risks & benefits
NICE Guide on AF June 2014
Currently recommends HAS-BLED score
Scoring system major bleed in AF Derived from 3978 hospital based
patients Not externally validated Risk factors for HAS-BLED v similar
to CHADS stroke Simple scoring system not measure
of absolute risk
Embargoed until publication
HAS-BLED – variables included Hypertension Renal disease Liver disease Prior Stroke Prior bleed or predisposition Age 65 (yes/no) Medication (antiplatelets, NSAID) Alcohol (> 8 units/week) Labile INR – but supposed to be for new
users so INR wont be available!
Embargoed until publication
QBleed: Aim
Develop new risk algorithm which Predict 1yr & 5yr absolute risk of GI and
intracranial bleed new users anticoagulants c.f. non-use Includes clinically relevant variables
ameliorable to change Can be implemented in routine GP systems Can be shared with patient to help inform
decision making Can be updated regularly
Embargoed until publication
QResearch – data source
Developed using QResearch database Very large validated GP database Derived from EMIS (largest GP supplier) Representative ethnically diverse
population
Linked to Hospital Episode Statistics Linked to ONS cause of death data
Embargoed until publication
QBleed - method
Design: Cohort study Study period: 2008-2013 Patients: 4.4 million aged 21-99 years Baseline: assessment of predictive
factors focused on clinically relevant variables primary care
Outcome: GI bleed or intracranial bleed on linked mortality or hospital data
Intracranial bleed & upper GI bleed
Upper GI bleed 21,614 cases on QResearch linked hospital or mortality records
Intracranial bleed 9,040 cases on QResearch linked hospital or mortality records
Largest ever such study. Increases reliability of results and generalisability of findings
QBleed: predictors
Age, sex, BMI Ethnicity Deprivation Smoking & alcohol Abnormal platelets Medication
Antiplatelets NSAIDS Steroids Antidepressants Anticonvulsants
Atrial fibrillation Heart Failure Treated hypertension Cancer Liver
disease/pancreatitis Oesophageal varices VTE Prior bleed (GI, brain,
haematuria,haemoptysis)
QBleed: Validation
Gold standard to test performance of risk tool on separate population
We used 2 validation samples Different practices in QResearch (from
EMIS) Different practices in CPRD (from Vision
Practices)
QBleed :Discrimination
Women Men
Upper GI bleed
ROC 0.77 0.75
R2 40.7 36.9
D statistic 1.7 1.57
Intracranial bleed
ROC 0.85 0.81
R2 58 53.3
D statistic 2.4 2.2
Higher values indicates better discrimination
Similar results CPRD and QResearch
Fig 3 Mean predicted risks and observed risks at five years by 10th of predicted risk applying QBleed risk prediction scores to all patients in QResearch validation cohort.
Hippisley-Cox J , and Coupland C BMJ 2014;349:bmj.g4606
©2014 by British Medical Journal Publishing Group
QBleed :performance for top 10% at highest risk
Cut off 5 year
risk (%)
Sensitivity (%)
Observed risk (%)
Upper GI bleed
1.4% 38% 2.7%
Intracranial bleed
0.7% 51% 1.5% For example, using threshold of top 10% at risk will
correctly identify 38% of those who get upper GI bleed 51% of those who get intracranial bleed
ComparisonQBleed vs HAS-BLED
QBLEED
4.4 million GP patients
30,681 events 2 clear outcomes Followed over 5 years Absolute risk Includes more
clinically relevant factors
Externally validated Easy to update over
time
HAS-BLED
4,000 hospital patients 53 events Unclear what ‘major
bleed’ is Followed over 1 year Simple count only Includes INR which wont
have prior to Rx Not externally validated Unclear about updates
“This is among the largest of the outpatient derivation cohorts used in this specialty to date and provides extra power to develop more robust predictive models using more candidate covariates than other scores”.
“Such a model represents a change in our approach to assessing bleeding risk, from simple, point based scores, to a more inclusive, complex model”.
“While there may be implications for implementation, this progression may make sense clinically—there are often patient subtleties and characteristics that inevitably increase the risk of bleeding but are not captured in simpler scores”.
“While calculating bleeding risk is no longer “simple,” neither is the decision to use long term anticoagulation”.
“A more comprehensive model may adjust for these factors, giving doctors and their patients a more refined estimate of absolute risk”.
Questions remaining
How should GPs use risk estimates when making decisions about bleeding?
What risk is too high? Is threshold same for every
patient & every indication? Are there patients for whom
extra risk is negligible compared with underling stroke risk?
Embargoed until publication
QStroke: www.qstroke.org
Estimates risk of ischaemic stroke over 1-10 years Includes age, sex, ethnicity, deprivation Smoking, diabetes, AF, CCF, CVD Rheumatoid, chronic renal disease Valvular heart disease Treated hypertension and FH CHD SBP, cholesterol, BMI
Integrated into EMIS WEB
http://qbleed.org/plus-qstroke 75yr old man with AF, light smoker, heavy alcohol, NSAIDS
QPrediction Scores & EMISWeb
ALREADY IN EMIS WEB
QRISk2 QDiabetes QStroke QFracture (QAdmissions)
IN PLANNING PHASES
QCancer (release 4.11)
QKidney QThrombosis QBleed QIntervention
http://bmjopen.bmj.com/content/4/8/e005809.abstract
New validation of QScores on CPRD
http://www.emisnug.org.uk/
Using QPrediction scores in templates & batch mode
http://emisnug.org.uk/video/adding-calculation-template-emis-web
http://emisnug.org.uk/video/running-calculation-eg-qrisk-group-patients-batch-add
EMIS NUG screen casts courtesy of Dr Geoff Schrecker
& EMIS NUG
www.qresearch.org
Contributing data to QResearch
Currently around 800 practices contributing
Would like around 1000 Pseudonymised data with no strong
identifiers IG approved EMIS NUG, REC, BMA,
RCGP Only used for research All research peer reviewed and
published Need to activate QResearch in EMIS
Web even if sharing data for many years via LV
Two more screen casts from EMIS NUG
http://emisnug.org.uk/video/add-rbac-activity-emis-web-user-profile
http://emisnug.org.uk/video/enabling-sharing-agreements-qsurveillance-and-qresearch
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