Use of Healthcare Data for Drug Safety Monitoring

Cynthia Sung, PhD FCPHealth Services & Systems Research

The views and opinions expressed in this presentation are those

of the individual presenter and should not be attributed to the

Health Sciences Authority.

Disclaimer

•Clinical trials• Size of trials• Inclusion/Exclusion criteria• Limited data on Asians

•Adulterated and counterfeit products•Economic burden of Adverse Drug Reactions (ADRs)

• 8.1% of adult, non-elective admissions caused by ADRs (SGH)• Median hospital stay – 4 days• Hospital bed utilization

Importance of Drug Safety Monitoring

ChanSL,etal.BrJClin Pharmacol.2016Dec;82(6):1636-1646

•Doctors and manufacturers submit reports to local drug regulatory authority

•Singapore Critical Medical Information Store (CMIS) – 20,000 reports annually

•WHO Vigibase• 110 countries• 10 million records

Traditional surveillance

Statistical MethodsReporting Odds Ratio (ROR)

Bayesian Confidence Propagation Neural Network (BCPNN) – WHO

Gamma Poisson Shrinker (GPS) – FDA

Sequential Probability Ratio Test (SPRT)

Data mining of Spontaneous Reporting Systems

Significantsignalsofdisproportionatereporting(1993– 2013)by4dataminingtoolsandtheir

inter-relationships(nottoscale)

Legend:BCPNN BayesianConfidencePropagationNeuralNetworkGPS GammaPoissonShrinkerROR reportingoddsratioSPRT SequentialProbabilityRatioTest

Ang PS,etal.ExpertOpin DrugSafety.2017May101(5):667-674ChanCL,etal.DrugSafety.2017Aug40(8):703-713

•Under-reporting, Stimulated reporting•Basic information only

• Often missing data on dosage, concomitant medications• Missing information on patient history, co-morbidities etc

•Lack of data on drug usage – unable to calculate incidence

Limitations of Spontaneous Reporting Systems

Enhancement with Big Healthcare Data

7

PassiveADRreportingprogram

ActiveSurveillanceSystem

CourtesyofPeiSanAng

EMR Data Types

8

Structureddata UnstructureddataCharacteristics • Codedwithcontrolled

categories& vocabulary• Consistentandorganizedformat

• Easiertoidentifyout-of-rangeresults

Examples Demographics,lab testresults,prescriptions

Characteristics • Freetext• Difficulttocode

Examples Dischargesummaries,nursingnotes,clinicalimages,drawings

Context LOW,RANegation:KIVOHGAHep Bvaccine

Big Data for Drug Safety Monitoring - USA

CourtesyofJeffBrown

• Is angioedema risk higher with ACE inhibitors vs ARBs

• 3.9 million users of antihypertensives

• Study completed in 5 months, ~$250,000

• Conventional Epi study: 2 years, $2 millionToh,SetalArchInternMed2012,172:1582-9

Big Data for Drug Safety Monitoring - UK

•ClinicalPracticeResearchDatalink (CPRD) runbyMHRAandNationalInstituteofHealthResearch

•>25yearsofprimarycarerecordsfromgeneralpractitionersintheUKwithlinkstootherdataresourcessuchascancer/deathregistries,andhospitaleventstatistics

•Example:Humanpapillomavirus(HPV)vaccineandchronicfatiguesyndrome

Big Data for Drug Safety Monitoring - OHDSI

•ObservationalHealthDataSciences&InformaticsProgram

•Public-privatepartnership:academic,insuranceandinformaticscompanies

•20countriesincl.US,UK,Japan,Korea•CommonDataModel(CDM)•OpenSourceAnalyticTools•682millionpatients

Hripcsak etalPNAS2016,113(27):7329-36

Roden Detal,ClinicalPharmTher 2008,84(3):362-369

As of 2016• 2.58 million subjects• 19 million ICD-9 codes• 121 million laboratory results• 122 million drug prescriptions• 26 million clinical notes

• 235,000 subjects with banked DNA• Enables PheWAS

EMERGE Network – Vanderbilt UniversityElectronic Medical Records & Genomics

Big Data for Drug Safety Monitoring - Singapore

•SurveillanceAndPharmacogenomicsforAdverseDrugReactions

•A*STARfundedcollaborativeprogramamongHSA,GIS,TLGM,NUSandhospitals

•Pilotonde-identifiedrecordsfromNUH2000-2013•Aimtobuildinfrastructuretowardsestablishinganactivedrugsafetysurveillancesystemtocomplementtheexistingnationalspontaneousreportingsystem

Comparison on Extreme Laboratory Test (CERT)

Statin-Induced Myopathy

CK≥4xULNMyopathy/Rhabdo

CK<4xULNNomyopathy

Algorithm Development

Lab

CK>4xULN

Nohightroponins orCKMB:CKratio

AlgorithmsA&D

AlgorithmA,CandD

D/Csummaries

TextsearchPresenceofatleast1‘statin terms’&1

‘myo terms’

Dx

TextsearchContains‘statin’+(‘myo’

or‘rhabdo’)

eRx

Nostatin ond/cfromadmissionwithevent

• Algorithmwasdevelopedusingone6-monthperiod,validatedontwoother6-monthperiods

• Detected3xmorecasesofrhabdomyolysisthanspontaneousadverseeventreportstoHSA

• Incidenceof statinmyopathy0.18%

Text Mining of Discharge Summaries

• CollaboratewithInstituteforInfocomm Research(I2R), A*STARtodeveloptextminingalgorithm

• CreatedrugandAEgazetteers•Developrulesfordrug-AErelationships

• Evaluateperformancewithmanuallycuratedrecords

• 50records<3minbymachine,2hr16minbyhuman

Aspirin plavix

metformin

Aspirin

steroid

Synacthen

hydrocortisone

mitomicin

contrast

DM

nephropathy

Iatrogeniccushings

allergy

glaucoma

nephropathy

Synacthen

Ang PS,etalBigDataResearch.2016Jun;4:37-43.

•Data silos

•Legacy systems and frequently evolving coding systems (ICD-9CM, ICD-10, SNOMED)

•Different data structures at different clusters•Missing private GP data•Missing data on medication purchases at GPs & commercial pharmacies

•Job opportunities for those with data analytic skills who also understand pharmacy practice and clinical care

Challenges and Opportunities

PlatformforqueryingEMRdatafrompublichospitalsandpolyclinics

atanationallevel

• Develop algorithms and interventions to reduce incidence of ADRs• Deploy on a national level• Dashboard with visualizations for real-time active surveillance

Future Plans

HSA-SAPhIRE team

• Health Sciences Authority • Genome Institute of Singapore• Translational Laboratory for Genetic Medicine• Institute for Infocomm Research• National University Hospital, Academic Information Office• Khoo Teck Puat Hospital• School of Computing, NUS• Saw Swee Hock School of Public Health, NUS• London School of Hygiene and Tropical Medicine• Ajou University, South Korea

Multi-institutional, Multi-disciplinary

Thetransformationofhealthcarethroughbigdataisuponus