A presentation of a new adverse drug event evidence base (Laertes - http://goo.gl/nZSqVw) within a standard framework for clinical research (OHDSI - www.ohdsi.org) made at the American Medical Informatics Association Joint Summits on Translational Research on 3/26/2015

1. Piloting a
Comprehensive
Knowledge Base for
Pharmacovigilance
Using Standardized
Vocabularies
Authors 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. 2
Disclosures
• I disclose that neither I nor my wife have
relevant financial relationships with
commercial interests

3. 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

4. The relevant 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)
FAERS – FDA Adverse Event Reporting System; SPL – Structured Produce Labeling; SPC – Summary of
Product Characteristics; FDB™ - First DatabankTM EHR – Electronic Health Record;

5. 5
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

6. A new adverse event evidence
base built into OHDSI
Largescale Adverse Effects Related to Treatment
Evidence Standardization (Laertes)

7. 7
The pilot version of Laertes
• Merging sources into the OHDSI
standard vocabulary
• The data schema
• Current progress

8. 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
Mapping
Source to
HOI Mapping
Evidence
Sources

9. 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

10. 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 …

11. More details on the schema

12. 12
Lets look at two example uses of
Laertes
• Finding and reviewing evidence
• Using Laertes and other OHDSI tools to
address quality improvement

13. 13
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.

14. The query
select
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;

15. 15
Lisinopril - Overview

16. Clinical trial with evidence on lisinopril-
angioedema

17. 17
Lisinopril - Overview

18. Case reports with evidence on Lisinopril-
angioedema

19. 19
Lisinopril - Overview

20. Case reports with evidence on lisinopril-
aplastic anemia

21. 21
Lisinopril - Overview

22. Structured product label with evidence
on lisinopril-aplastic anemia

23. 23
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?

24. 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

25. 25
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

26. 26
Acknowledgements
• Funding: The American taxpayers via:
– National Library of Medicine (1R01LM011838-01)
– National Institute of Aging (K01AG044433-01)

27. 27
Discussion

28. 28
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