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AMIA Jt Summits Transl Sci Proc. 2014 Apr 7;2014:9-15. eCollection 2014.

EHR-based phenome wide association study in pancreatic cancer.

Author information

1
Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, WI.
2
Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, WI ; Department of Surgery, Medical College of Wisconsin, Milwaukee, WI.

Abstract

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.

PMID:
25717392
PMCID:
PMC4333703

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