Presented at 2014 AMIA Joint Summits, April 9, 2014, San Francisco, CA BACKGROUND. Pancreatic cancer is one of the most common causes of cancer-related deaths in the United States, it is difficult to detect early and typically has a very poor prognosis. We present a novel method of large-scale clinical hypothesis generation based on phenome wide association study performed using Electronic Health Records (EHR) in a pancreatic cancer cohort. METHODS. The study population consisted of 1,154 patients diagnosed with malignant neoplasm of pancreas seen at The Froedtert & The Medical College of Wisconsin academic medical center between the years 2004 and 2013. We evaluated death of a patient as the primary clinical outcome and tested its association with the phenome, which consisted of over 2.5 million structured clinical observations extracted out of the EHR including labs, medications, phenotypes, diseases and procedures. The individual observations were encoded in the EHR using 6,617 unique ICD-9, CPT-4, LOINC, and RxNorm codes. We remapped this initial code set into UMLS concepts and then hierarchically expanded to support generalization into the final set of 10,164 clinical concepts, which formed the final phenome. We then tested all possible pairwise associations between any of the original 10,164 concepts and death as the primary outcome. RESULTS. After correcting for multiple testing and folding back (generalizing) child concepts were appropriate, we found 231 concepts to be significantly associated with death in the study population. CONCLUSIONS. With the abundance of structured EHR data, phenome wide association studies combined with knowledge engineering can be a viable method of rapid hypothesis generation.

1. EHR-based
Phenome Wide Association Study
in Pancreatic Cancer
Tomasz Adamusiak MD PhD
@7omasz

2. Conflict of interest disclosure
Tomasz Adamusiak has no real or apparent
conflicts of interest to report
2

3. Learning Objectives
• Recognize the value of structured clinical
information
• Identify computational and terminology
challenges in big data analytics
3

4. phe·no·type
n. Clinical Informatics
all clinically relevant features contained in
patient’s electronic health record
4

5. Phenome
5
0% 10% 20% 30% 40% 50% 60% 70%
Other
Labs
Medications
Procedures
Problems
Concepts (7k)
Observations (2M)

6. Re-transform data on demand
6

7. Focused on discrete data elements
Categorical variables:
• Ethnicity
• Problems
• Procedures
• Medications
• Clinical results
– laboratory tests
– vitals
7

8. Meaningful Use context
Categorical variables:
• Ethnicity
• Problems
• Procedures
• Medications
• Clinical results
– laboratory tests
– vitals
Clinical terminologies:
• OMBSNOMED CT
• ICD9/10
• HCPCS/CPT-4
• Medi-SpanRxNorm
• CPT-4
• LOINC
8

9. Pancreatic cancer has an extremely
poor prognosis
• Survival: For all stages combined,
• 1-year relative survival rates 25%
• 5-year relative survival rates 6%
Source: http://www.cancer.org/acs/groups/content/@nho/documents/document/acspc-024113.pdf
9

10. Test all associations with pancreatic
cancer and death as primary outcome
10
1298 patients
2 359 265
observations
2004 - 2013
ICD 9/10,
SNOMED CT,
CPT-4, LOINC,
RxNorm
6 617 codes
10 164 concepts
231 significant
associations

11. Single terminology
11

12. Add other terminologies to the mix
12

13. Use relations other than subsumption
(non-isa)
13

14. Use relations other than subsumption
(non-isa) to increase statistical power
14
Histamine H2
Antagonists
Cimetidine
Cimetidine 300
MG
Cimetidine 300
MG Oral Tablet
Cimetidine 400
MG
Cimetidine 400
MG Oral Tablet
constitutes
ingredient_of
isaRxNorm

15. Use meta-categorization
(UMLS Semantic Network)
15

16. Not all codes are created equal
16

17. Expansion in UMLS across MU sources
17
Diabetes mellitus without
mention of complication,
type II or unspecified
type, not stated as
uncontrolled
ICD-9
ICD-10
SNOMED CT
NDF-RT
Situation
with explicit
context
Metabolic
diseases
roots:

18. 6o of terminological Kevin Bacon
Acute myocardial infarction
Myocardial ischemia
Vascular Diseases
Disorder of soft tissue
Collagen Diseases
Connective Tissue Diseases
Epidermal and dermal conditions
Skin and subcutaneous tissue disorders
Dermatologic disorders
18

19. UMLS is ideal for integration of
heterogeneous clinical data
• Translational potential (OMIM, GO, NCIt)
• Single entry point to MU terminologies
• Cross-walk between MU terms
• Terminology-agnostic
• Text-mining
19

20. Extracting genetic information out of
EHR is a major challenge
Encounter due to genetic
counseling
Yes No
Outcome
Deceased 2 813
Alive 3 336
20
Background
reference
Methods:
• Chi-squared test
• Bonferroni correction
• RR estimate of effect size

21. Statistically significant highlights
Decreased Risk (RR < 1)
• sevoflurane Inhalant Solution
• Ionic iodinated contrast media
Increased Risk (RR > 1)
• cytopathology
• cimetidine
21
Resource
utilization

22. CORRELATION DOES NOT IMPLY
CAUSATION
Private traits and attributes are predictable from digital records of
human behavior. Kosinski M1, Stillwell D, Graepel T. PMID: 23479631
22
By Jono Winn (Flickr) [CC-BY-2.0], via Wikimedia Commons

23. Future work: cohort profiles
1. Malignant neoplasm of pancreas (C0346647)
2. Digestive System Neoplasms (C0012243)
3. Glucose test, blood by glucose monitoring device(s) cleared by the FDA
specifically for home use (C0373627)
4. Hepatic function panel This panel must include the following: Albumin (82040)
Bilirubin, total (82247) Bilirubin, direct (82248) Phosphatase, alkaline (84075)
Protein, total (84155) Transferase, alanine amino (ALT) (SGPT) (84460)
Transferase, aspartate amino (AST) (SGOT) (C0812554)
5. Basic metabolic panel (Calcium, total) This panel must include the following:
Calcium, total (82310) Carbon dioxide (bicarbonate) (82374) Chloride (82435)
Creatinine (82565) Glucose (82947) Potassium (84132) Sodium (84295) Urea
nitrogen (BUN) (84520) (C0519823)
6. Regular Insulin, Human 100 UNT/ML Injectable Solution (C0977794)
7. heparin sodium, porcine 10 UNT/ML Injectable Solution (C0977415)
8. Pancreatic Diseases (C0030286)
9. Dexamethasone 4 MG/ML Injectable Solution (C0976136)
10. Sodium Chloride 0.154 MEQ/ML Injectable Solution (C0980221)
23

24. Limitations
• Gaps in data
– Out of network
– Provider-related
– Terminology-related
24

25. Thank you
Co-authors:
Mary Shimoyama, PhD
tomasz@mcw.edu
@7omasz
25
Results
http://dx.doi.org/10.6084/m9.figshare.816958
For more background information
Next-generation phenotyping using the Unified
Medical Language System (UMLS). Adamusiak T,
Shimoyama N, Shimoyama M, JMIR Med Inform.
doi:10.2196/medinform.3172
Acknowledgements
We thank Stacy Zacher, Glenn Bushee, and Bradley
Taylor for their help.
This project was funded in part by the Advancing a
Healthier Wisconsin endowment at the Medical
College of Wisconsin and the National Center for
Research Resources and the National Center for
Advancing Translational Sciences, National Institutes
of Health, through grant UL1 RR031973.