The Farr Institute Delivering new evidence and risk prediction tools through data analysis

Transcription

1 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, Speaker twitter handle

2 @ Speaker twitter handle 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 HIMSS Europe GmbH 2

3 Dornie Castle

4 Our Data - from cradle to grave Plus Workforce Costs Experience Dental inspection Scottish birth record BIRTH Outpatients Substance misuse GP consultations Mental Health Hospital Admissions Continuing care census HAI Maternity Immunisation A&E Prescribing Screening Cancer registry DEATH Child health surveillance Clinical audits

5 Scottish Prescription Patient s details Drug details Community Health Index (CHI) Prescriber details

6 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: /ije/dyw060

7 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 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: /ije/dyw060

8 Our Research Network The UK Farr Institute 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 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

9 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

10 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

11 Study aim 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 11

12 C.difficile Infection (CDI) in Scotland Around 2000 cases per year in Scotland Previous work: 3 year data Quantify association between CDI and antibiotic prescribing using linked individual level data 12

13 Data Linkage ECOSS: Laboratory confirmed CDI cases SMR01: Hospital Admissions HA-CDI All CDI cases with linked hospital records CA-CDI All the cases and controls will be linked to : SMR01 comorbidity markers PIS: Prescriptions Antimicrobials Proton pump inhibitors/h2 antagonists Drug counts matched population based controls CA-CDI: tested in the community or tested within 48 hours of hospital admission & no admission in previous 3 months (n=1439) Up to 6 controls are matched on the basis of age, gender and location. (n=7945) Analysed with conditional logistic regression 13

14 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 ( ) < SIMD a 1: most deprived 1 - SIMD ( ) SIMD ( ) SIMD ( ) SIMD 5: least deprived 0.98 ( ) Charlson score Charlson score ( ) < Charlson score ( ) < Charlson score ( ) Charlson score ( ) Charlson score Unknown 0.80 ( ) Any hospital admission in previous year, No 1 - Any hospital admission in previous year, Yes 2.15 ( ) < Number different items dispensed in previous year 1.03 ( ) Care home residence, No 1 - Care home residence, Yes 1.15 ( ) PPI exposure, No 1 - PPI exposure, Yes 1.02 ( ) H2 antagonist exposure, No 1 - H2 antagonist exposure, Yes 1.41 ( )

15 Cumulative exposure in 6 months 4.4 (3.4, 5.6) 17.9 (7.6, 42.2) 9.2 (2.3, 37.1) 3.6 (2.8, 4.6) 7.2 (4.3, 12.3) 7.3 (2.3, 23.2) 2.1 (1.7, 2.7) 7.6 (5.1, 11.4) 10.1 (5.0, 20.4) 2.3 (1.9, 2.9) 4.6 (3.4, 6.2) 3.8 (2.4, 6.1) 2.2 (1.9, 2.6) 2.6 (2.3, 3.1) 15

16 Making predictions 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 16

17 Method 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 17

18 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 18

19 Cut point Sensitivity 65.8% Specificity 78.5% Correctly classified as CDI AUC Correctly classified as non case 19

20 Reduced Model Adjusted OR 95% CI p value Liver problems 4.16 (2.02, 8.55) Renal problems 2.84 (1.89, 4.27) < Metastatic cancer 7.02 (2.90, 17.02) < Inflammatory bowel disease 1.68 (1.14, 2.47) Total number of dispensed items last year (square root) 1.26 (1.23, 1.30) < Days of hospital stay in the previous year (log) 1.45 (1.33, 1.57) < DDDs of 4C exposure in the community last 6 months (log)* 1.85 (1.63, 2.09) < DDDs of non-4c antimicrobial exposure in the community last 6 months (log) 1.25 (1.17, 1.33) < *For every ~2.7 fold increase in DDD, odds of CDI increases by For every 2 fold increase in DDD, odds CDI increase by

21 Cut point Sensitivity 68.9% Specificity 75.6% Missed CDI cases Correctly classified as CDI AUC Correctly classified as non case 21

22 Missed CDI cases Younger mean age 53 vs. 72 Healthier 1.3 days in hospital vs different items vs 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) 22

23 Considering other modelling approaches. Machine Learning Data Prediction Random Forest 23

24 Comparing performance Random Forest AUC: 0.81 Sensitivity: 70% Specificity: 81% Logistic AUC: 0.79 Sensitivity: 69% Specificity: 76% 24

25 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 25

26 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 26

27 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 27

28 European Drug Utilization Research Group (EuroDURG) Conference 2017: Patients, Medicines, Bytes: Drug Utilization and E-health November 2017, Glasgow, UK

29 Thank you! Professor Marion Bennie, Professor of Pharmacy / Chief Pharmacist, University of Strathclyde, Gasgow / NHS National Services Scotland, Edinburgh Speaker twitter handle

30 The use of new oral anticoagulants in patients with atrial fibrillation in Scotland 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 T 1, Alvarez-Madrazo S 1, Robertson C 2, Bennie M 1,3 1 Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow 2 Department of Mathematics and Statistics, University of Strathclyde, Glasgow 3 Public Health and Intelligence Strategic Business Unit, NHS National Services Scotland, Edinburgh

31 ESPACOM framework Adherenc e Initiation First drug intake Implementation Continuous process Discontinuation Last drug intake First prescriptio n persistenc e Last prescriptio n Vrijens et al (2012). A new taxonomy for describing and defining adherence to medication. Br J Clin Pharmacol 73(5),

32 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).

33 Quantitative Work Stream Qualitative Work Stream

34 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

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