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