The Farr Institute Delivering new evidence and risk ... · The Farr Institute – Delivering new...

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@ Speaker twitter handle The Farr Institute Delivering new evidence and risk prediction tools through data analysis Professor Marion Bennie, Professor of Pharmacy / Chief Pharmacist, University of Strathclyde, Gasgow / NHS National Services Scotland, Edinburgh

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The Farr Institute – Delivering new evidence and risk prediction tools through data analysis Professor Marion Bennie, Professor of Pharmacy / Chief Pharmacist, University of Strathclyde, Gasgow / NHS National Services Scotland, Edinburgh

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OUTLINE

• Our data – the Scottish national datasets (individual level)

• Our network - Farr Institute of Health Informatics Research

• Our applications - pharmacoepidemiology exemplars – Predicting risk of healthcare associated infection

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Dornie Castle

A&E Maternity

BIRTH

DEATH

Child health surveillance

Immunisation

GP consultations

Outpatients Hospital Admissions

Mental Health

Prescribing Screening

Continuing care census

Cancer registry

Our Data - from cradle to grave

Dental inspection Scottish birth record

Substance misuse

Clinical audits

Plus

•Workforce

•Costs

•Experience

HAI

Scottish Prescription

Community

Health

Index

(CHI)

Patient’s details

Drug details

Prescriber details

Prescribing Information system

Alvarez-Madrazo S, McTaggart S, Nangle C, Nicholson E, Bennie M. Data Resource Profile: The Scottish National Prescribing Information System (PIS)Int. J Epidemiology, 2016, 1–8 doi: 10.1093/ije/dyw060

Primary care prescribing data

• Total population coverage - 5.3million • A unique patient identifier applied (completeness

raising from 88% in 2009 to 96% by 2014) • Over 345 million items dispensed from 2009 to

2014.

Alvarez-Madrazo S, McTaggart S, Nangle C, Nicholson E, Bennie M. Data Resource Profile: The Scottish National Prescribing Information System (PIS)Int. J Epidemiology, 2016, 1–8 doi: 10.1093/ije/dyw060

Farr Institute, London

UCL, LSHTM, Queen Mary, UCL

Partners, Public Health England

Farr Institute, Scotland

Dundee, Aberdeen, Edinburgh, Glasgow, St Andrews, Strathclyde, MRC HGU, NHS NSS

Farr Institute, CIPHER

Swansea, Brighton, Bristol, Cardiff, Exeter, Oxford, Sussex, NWIS, Public Health Wales

Farr Institute, HeRC Manchester, Bradford, Durham, Lancaster, Liverpool, Newcastle, Sheffield, York, AHSNs

Our Research Network – The UK Farr Institute

STRATHCLYDE INSTITUTE OF PHARMACY & BIOMEDICAL SCIENCES

New Medicines, Better Medicines, Better Use of Medicines

Research

New Medicines – Fundamentals

New Medicines – Drug Discovery

Better Medicines Better Use of

Medicines Teaching

UG: MPharm UG: Biomedical

Sciences Masters degrees Doctoral study Knowledge Exchange

New drugs New formulations New imaging – the

Centre for Biophotonics

Industrial links

Application of predictive modelling to

estimate the risk of community associated

Clostridium difficile infection using routine

health data

Jiafeng Pan, Kim Kavanagh, Chris Robertson, Charis Marwick, Peter Davey, Camilla Wiuff, Scott Bryson, Marion Bennie

Create prediction models for the risk of acquiring a healthcare associated infection (HAI)

Vision: real time data were available, could aid clinical decision making at point of GP consultation/hospital admission

Study aim

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• Around 2000 cases

per year in Scotland

• Previous work:

– 3 year data 2010-2013

– Quantify association

between CDI and

antibiotic prescribing

using linked individual

level data

C.difficile Infection (CDI)

in Scotland

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Data Linkage

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Up to 6 controls are

matched on the basis of

age, gender and location.

(n=7945)

CA-CDI: tested in the

community or tested

within 48 hours of

hospital admission

& no admission in previous

3 months (n=1439)

All CDI cases with

linked hospital records

ECOSS: Laboratory

confirmed CDI cases

SMR01: Hospital

Admissions

All the cases and controls will be linked to :

• SMR01 – comorbidity markers

• PIS: Prescriptions

• Antimicrobials

• Proton pump inhibitors/H2 antagonists

• Drug counts

CA-CDI

matched

population

based controls

Analysed with

conditional logistic

regression

HA-CDI

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Adjusted

OR (95% CI) p-value

Exposed to antibiotics in the previous 6 months, No 1 -

Exposed to antibiotics in the previous 6 months, Yes 2.80 (2.41- 3.25) <0.0001

SIMDa 1: most deprived 1 -

SIMD 2 0.85 (0.67-1.08) 0.178

SIMD 3 0.88 (0.68-1.14) 0.329

SIMD 4 0.95 (0.72-1.24) 0.696

SIMD 5: least deprived 0.98 (0.73-1.31) 0.872

Charlson score 0 1 -

Charlson score 1 2.42 (1.82-3.21) <0.0001

Charlson score 2 2.60 (1.89-3.57) <0.0001

Charlson score 3 2.23 (1.33-3.74) 0.002

Charlson score 4+ 2.83 (1.48-5.44) 0.002

Charlson score Unknown 0.80 (0.67-0.96) 0.016

Any hospital admission in previous year, No 1 -

Any hospital admission in previous year, Yes 2.15 (1.80-2.56) <0.0001

Number different items dispensed in previous year 1.03 (1.01-1.04) 0.002

Care home residence, No 1 -

Care home residence, Yes 1.15 (0.89-1.48) 0.283

PPI exposure, No 1 -

PPI exposure, Yes 1.02 (0.86-1.21) 0.819

H2 antagonist exposure, No 1 -

H2 antagonist exposure, Yes 1.41 (1.02-1.96) 0.036

Cumulative exposure in 6 months

17.9 (7.6, 42.2) 4.4 (3.4, 5.6) 9.2 (2.3, 37.1)

2.3 (1.9, 2.9)

2.1 (1.7, 2.7)

3.6 (2.8, 4.6) 7.2 (4.3, 12.3)

7.6 (5.1, 11.4)

2.2 (1.9, 2.6)

7.3 (2.3, 23.2)

3.8 (2.4, 6.1)

2.6 (2.3, 3.1)

4.6 (3.4, 6.2)

10.1 (5.0, 20.4)

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• Use existing matched (on age, gender) case-control study focusing on community acquired cases

• Data are split into training (2/3) and test sets (1/3) maintaining matches

• Prediction models are built using conditional logistic regression on training data

• Performance assessed on test data – Area Under Curve (AUC), sensitivity and specificity of model

Making predictions

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• Measures of cumulative exposure modelled rather than categorised – i.e. trend fitted to antibiotic DDD exposure rather

than groups

– Trend assessed using fractional polynomials to define trend and then explicitly defined in model

• Use individual risk factors rather than Charlson score

Method

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Full model Health care variables • Total number of dispensed items last year (square root)

• Total number of different dispensed items last year (square root)

• Number of hospital admission in the previous year (inverse square root)

• Number of emergency hospital admission in the previous year (inverse)

• Days of hospital stay in the previous year (log+1)

Prescribing variables • PPI in the community last 6 month (y/n)

• H2 antagonist in the community last 6 month (y/n)

• DDDs of 4C exposure in the community last 6 month (log)

• DDDs of non-4C antimicrobial exposure in the community last 6 month (log)

• DDDs of FQ exposure in the community last 6 month (log)

Demographic variables • SIMD (socioeconomic quintile)

• Resident in care home

• (age, gender, location matched)

Comorbidities • Congestive heart failure, cardiomyopathy

• Atherosclerosis, aortic aneurysm, vascular disease

• Stroke

• Dementia

• Bronchitis, pneumoconiosis

• Gout, lupus, rheumatoid arthritis

• Gastro ulcers

• Liver problems

• Renal problems

• Cancer

• Alcohol-related liver failure

• Metastatic cancer

• Inflammatory bowel disease

• Diabetes

• Diabetes with complications

• Hemiplegia, paraplegia

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Sensitivity 65.8%

Specificity 78.5%

AUC

0.789

Correctly classified as non case

Correctly

classified as CDI

Cut point

0.869

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Reduced Model

*For every ~2.7 fold increase in DDD, odds of CDI increases by 1.85. For every 2 fold increase in DDD, odds CDI

increase by 1.53.

4DDD OR=2.4, 8 DDD OR=3.6, 16 DDD OR=5.5, 32 DDD OR=8.4

Adjusted OR 95% CI p value

Liver problems 4.16 (2.02, 8.55) 0.0001

Renal problems 2.84 (1.89, 4.27) <0.0001

Metastatic cancer 7.02 (2.90, 17.02) <0.0001

Inflammatory bowel disease 1.68 (1.14, 2.47) 0.009

Total number of dispensed items last year (square root) 1.26 (1.23, 1.30) <0.0001

Days of hospital stay in the previous year (log) 1.45 (1.33, 1.57) <0.0001

DDDs of 4C exposure in the community last 6 months

(log)* 1.85 (1.63, 2.09) <0.0001

DDDs of non-4C antimicrobial exposure in the

community last 6 months (log) 1.25 (1.17, 1.33) <0.0001

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Sensitivity 68.9%

Specificity 75.6%

AUC

0.791

Cut point

0.880

Missed CDI cases

Correctly

classified as CDI

Correctly classified as non case 21

Missed CDI cases

• Younger – mean age 53 vs. 72

• Healthier – 1.3 days in hospital vs. 13.8

– 5.8 different items vs. 16.3

• Less antibiotic exposure – 3.1 DDDs (sd 6.3) vs. 30 DDDs (sd 40.3)

– 67% no antibiotic prescribing vs. 23%

• 39% of missed cases no risk factors (care-home, PPI, H2, Antibiotics, no hospital admission, no unknown comorbidity)

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Considering other modelling

approaches….

• Machine Learning

• Random Forest

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Data Prediction

Comparing performance

• Random Forest – AUC: 0.81

– Sensitivity: 70%

– Specificity: 81%

• Logistic – AUC: 0.79

– Sensitivity: 69%

– Specificity: 76%

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Summary

• Models perform well but miss “healthy” CDI cases which have few risk factors recorded

• The risk models are good for population stratification – Putting individuals into groups for the

management of their screening/treatment

– Not so good if used to predict if a specified individual will get a disease or not

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Future work

• Recalibrate the model with more recent

data

• Create a realistic (based on data

availability at point of care) v.s. optimal

versions of the model

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Other Applications

Examination of phenotype characteristics on drug use and clinical outcome in

• DOACs (new cardiovascular drugs) in Atrial Fibrillation

• Cancer therapies for prostate and melanoma

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European Drug Utilization Research Group (EuroDURG) Conference 2017:

Patients, Medicines, Bytes: Drug Utilization and E-health

15 – 17 November 2017, Glasgow, UK

www.EuroDURG2017.com

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Thank you!

Professor Marion Bennie, Professor of Pharmacy / Chief Pharmacist, University of Strathclyde, Gasgow / NHS National Services Scotland, Edinburgh [email protected]

Aim: To increase the evidence from clinical practice regarding these new oral anticoagulants (NOACs)

Objectives: To analyse the use and prescribing trends over time of traditional and new anticoagulants in Scotland

To determine the clinical effectiveness and adverse events associated with different treatment options in AF patients with variable phenotype characteristics

Mueller T1, Alvarez-Madrazo S1, Robertson C2, Bennie M1,3 1Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow 2Department of Mathematics and Statistics, University of Strathclyde, Glasgow 3Public Health and Intelligence Strategic Business Unit, NHS National Services Scotland, Edinburgh

The use of new oral anticoagulants in patients with

atrial fibrillation in Scotland

Initiation

•First drug intake

Implementation

•Continuous process

Discontinuation

•Last drug intake

First prescription

Last prescription

Adherenc

e

persistence

Vrijens et al (2012). A new taxonomy for describing and defining adherence to medication. Br J Clin Pharmacol 73(5), 691-705

ESPACOM framework

NHS Scotland Cancer Medicines

Outcome Programme - Study Aims

• To co-ordinate an incremental program of planned studies to test the connectivity and linkage of current and evolving local and national datasets to determine clinical outcome data for cancer medicines.

• To test the feasibility of collecting and analysing quality of life data from clinical practice, aligned to the early exemplar studies, to inform a potential enhanced data strategy for collection and analysis of patient reported outcome measures (PROMs).

Qu

anti

tati

ve W

ork

Str

eam

Qu

alitative W

ork Stream

Year one exemplar projects

Prostate Cancer

- Abiraterone and Enzalutamide

- Baseline characteristics including PS,

co-morbidities

- Outcomes: duration of therapy, stop

reason, OS, time to PSA progression,

time to chemo/radio, Opiate prescribing

- PROMs

Melanoma

- BRAF inhibitors/MEK inhibitors and

immunotherapy

- Baseline characteristics including BRAF

status, depcat score, prognostic scores,

co-morbidities

- Outcomes: duration of therapy, OS, stop

reason, toxicities including supportive

meds and hospital admissions