Piloting a

Comprehensive

Knowledge Base for

Pharmacovigilance

Using Standardized

VocabulariesAuthors and contributors

Vojtech Huser, MD, PhD

Jeremy Jao

Jon Duke, MD, MS

Patrick B. Ryan, PHD

Scott D. Nelson, PharmD

Richard D. Boyce, PhD

Erica A. Voss, MPH

Michel Dumontier, PhD

Nicholas Tatonetti, PhD

Lee Evans

Majid Rastegar-Mojarad, MS

Abraham G. Hartzema, PhD

Johan Ellenius, PhD

Rave Harpaz, PhD

Magnus Wallberg, MSc

Christian Reich, MD, PhD

AMIA CRI 3/26/2015

2

Disclosures

• I disclose that neither I nor my wife have

relevant financial relationships with

commercial interests

3

Problem statement

• An overwhelming amount of information

relevant to drug safety-relevant is being

generated

– stored in a wide array of disparate

information sources

– using differing terminologies

– at a faster pace than ever before

The relevant evidence sourcesSpontaneous adverse

event data(FAERS, VigiBase™,

ClinicalTrials.gov)

Literature(PubMed, SemMed)

Product labeling(SPL, SPC)

Indications / Contraindications

(FDB™)

Observational healthcare data(claims + EHR)

FAERS – FDA Adverse Event Reporting System; SPL – Structured Produce Labeling; SPC – Summary of

Product Characteristics; FDB™ - First DatabankTM EHR – Electronic Health Record;

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Objective

• Synthesize adverse drug event evidence within a

standard framework for clinical research

– The Observational Health Data and Informatics

Initiative (OHDSI)

• A common data model and standard vocabulary

– Easy to adopt and used by numerous sites

• A suite of tools that improve the value of

observational clinical data

– data characterization, population- level estimation, patient-

level prediction,

– phenotyping, cohort and quality measure design

A new adverse event evidence

base built into OHDSI

Largescale Adverse Effects Related to Treatment

Evidence Standardization (Laertes)

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The pilot version of Laertes

• Merging sources into the OHDSI

standard vocabulary

• The data schema

• Current progress

Merging the sources

Drugs (RxNorm)

Conditions (SNOMED)

Spontaneous adverse event data

(FAERS, VigiBase™, ClinicalTrials.gov)

MedDRA

->

SNOMED

Freetext,

ATC

-> RxNorm

Literature(PubMed, SemMed)

MeSH, UMLS

-> SNOMED

MeSH,

UMLS

-> RxNorm

Product labeling(SPL, SPC)

Freetext ->

MedDRA®

->

SNOMED

SPL Set ID

-> RxNorm

Indications / Contraindications

(FDB™)

ICD-9-CM

->

SNOMED

NDC/GenS

eqNum

-> RxNorm

Observational healthcare data(claims + EHR)

ICD-9-CM,

ICD-10

->

SNOMED

NDC/GPI/ATC

-> RxNorm

Drug classifications

(ATC, NDF-RT)

Condition classifications(MedDRA®, Ontology of

Adverse Events)

Source to Drug

MappingSource to

HOI Mapping

Evidence

Sources

Current progress on evidence sources

Spontaneous adverse event data

(FAERS, VigiBase™, ClinicalTrials.gov)

Literature(PubMed, SemMed)

Product labeling(SPL, SPC)

Indications / Contraindications

(FDB™)

Observational healthcare data(claims + EHR)

Evidence

Sources

PubMed (Avillach et al.):

• Case reports: 84,181

• Clinical trials: 25,813

• Other: 1,146

SemMed (Kilicoglu et al)

• Case reports: 2,372

• Clinical trials: 1,169

Avillach P, Dufour JC, Diallo G, Salvo F, Joubert M, Thiessard F, Mougin F, Trifirò G, Fourrier-Réglat A, Pariente A, Fieschi M. Design and val idation of an automated

method to detect known adverse drug reactions in MEDLINE: a contribution from the EU-ADR project. J Am Med Inform Assoc. 2013 May 1;20(3):446-52

Kilicoglu H, Rosemblat G, Fiszman M, Rindflesch TC. Constructing a semantic predication gold standard from the biomedical literature. BMC Bioinformatics. 2011 Dec

20;12:48

Duke, Jon, Jeff Friedlin, and Patrick Ryan. "A quantitative analysis of adverse events and “overwarning” in drug labeling." Archives of internal medicine 171.10 (2011): 941-

954.

FAERS :

• Subset with counts, EB05

and EBGM: 301,332

ClinicalTrials.gov: In process

VigiBase™: In process

US SPLs (Duke et al.):

• Adverse Drug Reactions:

2,411,943

EU SPCs (PREDICT):

• Adverse Drug Reactions:

42,767

In process

Can be done on local

installations

• Public data pending

The schema supports two use cases

Example association: Drug X – Renal Failure

Summary

Drill down

Spontaneous

reporting

EHR

Data

Scientific

Literature

Product

Labeling

Other

evidence

EB05 OR Count Count …

More details on the schema

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Lets look at two example uses of

Laertes

• Finding and reviewing evidence

• Using Laertes and other OHDSI tools to

address quality improvement

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Pharmacovigilance example

• HOIs associated with Lisinopril

– An ACE inhibitor that treats high blood

pressure and heart failure (WebMD)

– The blood pressure lowering effect might

help reduce the risk of diabetes

nephropathy

• Better understanding adverse events associated

with diabetes is a top priority (DHHS 2014)

WebMD - http://www.webmd.com/diabetes/tc/diabetic-nephropathy-treatment-overview

U.S. Department of Health and Human Services, Office of Disease Prevention and Health Promotion.

(2014). National Action Plan for Adverse Drug Event Prevention. Washington, DC: Author.

The queryselect

s.ingredient,

s.hoi,

s.ct_count as clintrials,

v.medline_mesh_clin_trial_link,

s.case_count,

v.medline_mesh_case_report_link,

s.splicer_count as label_count

from laertes_summary s

join drug_hoi_evidence_view v on s.hoi_id=v.hoi and

v.drug=ingredient_id

where ingredient_id = 1308216

and report_name='Stratified by ingredient and HOI'

and coalesce(ct_count, case_count, other_count, splicer_count) is

not null

and case_count is not null

order by case_count desc

limit 100;

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Lisinopril - Overview

Clinical trial with evidence on lisinopril-

angioedema

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Lisinopril - Overview

Case reports with evidence on Lisinopril-

angioedema

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Lisinopril - Overview

Case reports with evidence on lisinopril-

aplastic anemia

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Lisinopril - Overview

Structured product label with evidence

on lisinopril-aplastic anemia

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Opportunity - Quality improvement

in the nursing home setting

• The prevalence of anemia in 5 nursing

homes is 36% affecting quality of life.

• The health system is interested in

identifying potential interventions.

– Could prescribing be better optimized to

reduce this potential adverse event?

Quality improvement in

the nursing home setting

What drugs have

evidence for an

association with anemia?

• Laertes

Which kinds of anemia?

• Standard vocabulary

• Cohort definition (Circe)

• Phenotyping

What is the prevalence of

exposure to those drugs in my

facilities?

• Cohort characterization

(Heracles)

Are exposed patients at risk?

• OHDSI Methods library

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Summary

• Laertes is a new adverse event evidence

base built into clinical research

framework

– Enables summary and drill down evidence

search

– Can be integrated into other clinical

research workflows

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Acknowledgements

• Funding: The American taxpayers via:

– National Library of Medicine (1R01LM011838-01)

– National Institute of Aging (K01AG044433-01)

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Discussion

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How to get Involved

• Learn about OHDSI:http://www.ohdsi.org/

• Wiki: http://www.ohdsi.org/web/wiki/doku.php?id=pr

ojects:workgroups:kb-wg

• GitHub:https://github.com/OHDSI/KnowledgeBase