2014 icbo courtot_meddra
DESCRIPTION
A presentation supporting discussion on (1) how could MedDRA benefit from an ontological representation and (2) how we can practically move forward in creating this formalization. Presented at the International Conference on Biomedical Ontology 2014 in Houston, TX: http://icbo14.com/sessions/meddra-and-ontology/TRANSCRIPT
MedDRA and Ontology
Dr. Mélanie Courtot, [email protected]
Brinkman lab, Simon Fraser University
Hsiao lab, BC Public Health Microbiology and Research Laboratory
Part I The Logic of Surveillance Guidelines: An Analysis of Vaccine Adverse Event Reports from an Ontological Perspective
Background and problem statement
• Surveillance of Adverse Events Following Immunization is important• Detection of issues with vaccine • Importance of vaccine-risk communication
• Analysis of AE reports is a subjective, time- and money costly process• Manual review of the textual reports
The Brighton collaboration
• https://brightoncollaboration.org• Provides case definitions and guidelines to standardize
reporting• Well established network (adopted as standard in Canada
2009)
Hypothesis
Encoding Brighton guidelines in OWL allows automated classification of adverse events at similar accuracy
Test case
• VAERS dataset• Vaccine Adverse Event Reporting System• 6032 reports: ~5800 negative, ~230 positive• Post H1N1 immunization 2009/2010• Manually classified for anaphylaxis
• MedDRA (Medical Dictionary of Regulatory Activities) is used to represent clinical findings
Example VAERS report
Automated Diagnosis workflow
Results
At best cut-off point: Sensitivity 57%Specificity 97%
Standardized MedDRA Queries
• SMQs are an existing MedDRA-based screening method
• Retrieval of documents based on Anaphylaxis SMQ alone only fair: 54% sensitivity, 97% specificity
• Idea: • Identify MedDRA terms that are significantly associated
with the diagnosis outcome using contingency tables• Augment the existing MedDRA SMQ with those terms
Cosine similarity method• Represent documents (query and report) as vectors of
terms• Compare the cosine measure of the angle they form
Cosine ~ 1Query ~ Report
Cosine ~ 0Query != Report
Example• Vector MEDDRA SMQ:
’Choking', 'Cough’, ’Oedema’, 'Rash’
• Vector REPORT#72:
’Oedema’, 'Rash’, ‘Vomiting’
• Vector REPORT#104:
‘Palpitations’, ‘Fatigue’, Neuropathy’
Results - Expanded MedDRA SMQ
At best cut-off point: Sensitivity 92%, Specificity 87%
Automate classification of adverse events
• Using a MedDRA- based mapping, I successfully encoded existing clinical guidelines for adverse events classification
• I also suggested ways to detect terms of importance when analyzing MedDRA annotated reports
• This allows for faster
and cheaper detection
of potential safety issues.
Publication
Acknowledgements
• Ryan Brinkman, BC Cancer Agency, Vancouver, Canada
• Alan Ruttenberg, University at Buffalo, New York, USA
• Julie Lafleche, Robert Pless, Barbara Law, Public Health Agency of Canada, Ottawa, Ontario
• Jan Bonhoeffer, Brighton Collaboration, Basel, Switzerland
• Patrick Revelle, MSSO
Part IISupport MedDRA with ontology
Current encoding of reports
MedDRA coding (1) done in second step, post report collection at the national level (2) by specifically trained coders (3) not in contact with the patient or physician who saw the patient
Definitions
• PHAC, User Guide to completion and submission of the AEFI reports, http://www.phac-aspc.gc.ca/im/pdf/AEFI-ug-gu-eng.pdf
• Abdominal pain: Sensation of discomfort or pain in the abdominal region. (BCCD: Vaccine 28 (2010) 4487-4498)
• Potential to use SNOMED mappings to start adding definitions
Decomposition of terms• Application site
• Perforated, atrophy, dermatitis, erythema, ulcer• HLT = Application and instillation site reactions• HLGT = Administration site reactions• SOC = General disorders and administration site conditions
• Gastric ulcer• “stomach” and “ulcer”• Ulcer = http://purl.bioontology.org/ontology/MEDDRA/10045285
Location• Abdominal pain = pain which occurs in the abdomen
Foundational Model of Anatomy
Foundational Model Explorer, http://fme.biostr.washington.edu/FME/index.html
Ability to classify according to different guidelines
• For example, the Brighton collaboration guidelines for adverse events following immunization
• Recognized need from the community: an SMQ for seizure according to the Brighton case definition has been added to MedDRA, others were in progress (early 2014)
Guideline classification• A MedDRA term “abdominal pain”, defined as “pain and
occurs in abdomen” can be reconciled with another term such as “abdominal symptoms” -> even though the latter wouldn’t be defined in MedDRA, a reasoner would automatically infer the correct hierarchy
Report #12345
Abdominal painRashFever
+
Clinical Guideline
AbdominalsymptomAndDermatologicalsymptom
Reasoner Diagnosis X according to clinical guideline
ReasonerDiagnosis X2 according to clinical guideline 2
Example: Brighton anaphylaxisLevel 1
Hierarchy depth• Constraint on the hierarchy depth: MedDRA has only 5
levels• System organ class (SOC)• High Level Group Term (HGLT)• High Level Term (HLT)• Preferred Term (PT)• Lowest Level Term (LLT)
• Relationship between LLTs and PTs not always well defined• LLTs (e.g., “Junctional bradycardia”, “Junctional tachycardia” and
“Reciprocating tachycardia”) that are not synonymous with each other or to their associated PT (e.g., “Nodal arrhythmia”).
Richesson 2008, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2575842/
Formalization of relations between terms
• Relations between LLTs (and PTs) could be formally defined
(1) Terms can have synonyms (“alternative terms” in the OBO Foundry)
(2) Terms can be related using SKOS relations such as “broader synonyms” or “narrower synonyms”, though ultimately we would like to have different classes created
(3) OWL supports multiple languages for terms via lang attribute
Subsumption
• VAERS 370841;positive for anaphylaxis
• "As reported by mother, at 11 AM child screamed with abdominal and stomach pain. Mother called Drs. office and talked with nurse who said the symptoms should go away and if they didn't bring child in. Pain subsided in 30 minutes. No more complaints until 11/25/09 and child reported stomach pain again and at this time had difficulty swallowing solid food. Could drink liquids without difficulty. On 11/26/09 through 11/28/09 child demonstrated no symptoms other than when asked the child would state his stomach hurt. On 11/29/09 child would not eat. Mom called Drs. office and talked to the nurse, relating symptoms and telling nurse childs lips were purplish. Nurse said to take child to the ER. Blood and urine tests, as well as X-Rays done at ER. Mother states Dr. said he couldn't find anything wrong and child should not be having these symptoms. Sent home from ER. On 11/30/09 mother called Drs. office and nurse said to bring child in. Dr. ran some more tests which came out normal. Mother states Dr. called the drug (H1N1) company and was told they had not had any such symptoms reported. Dr. talked with another Dr. in office and decided it was an allergic reaction to the H1N1 vaccination received on 11/20/09. Prednisolone 15 mg. per ml. take 7.5 ml. per day po prescribed for child. On 12/01/09 mother states child still cannot eat but not screaming with stomach pain. 12/10/11/21/2009 ED records for 11/29/2009, c/o's abdominal pain, lack of appetite, dysphagia, cyanotic around mouth and lips 12/10/2009”
• Abdominal pain;10000081;Abdominal pain upper;10000087;Barium swallow normal;10004126;Blood glucose increased;10005557;Blood potassium decreased;10005724;
SMQsStandardized MedDRA Queries
Ability to add new SMQs• Based on different guidelines (e.g., Brighton)• Could be developed by users using the MedDRA terms
directly• Would be easier to maintain using the subsumption
hierarchy • if one term is added under one existing terms, it would get
automatically included• Annotating with “abdominal pain upper” would automatically imply
“abdominal pain”
Other applications• Add temporal descriptors, and provide time related
relation• “sudden onset”• “rapid progression”• “is preceded by”
• Check consistency at data entry time?
In summary
1. Add definitions to PTs
2. Add relations such as location of the adverse event
3. Have a reasoner classify the resulting ontology
4. In each anatomical system group, check and add relations between PTs (e.g. abdominal pain lower is_a abdominal pain)
5. Repeat for LLTs