Clarity Medication Mapping to NDF-RT Design and Current Status

Outline

• • • • • • Brief Tour of RxNorm Tables Used Design Current Status (Results) Next Steps Code Walkthrough Discussion

RxNorm Concept Names and Sources (rxnconso)

http://www.nlm.nih.gov/research/umls/rxnorm/docs/2012/rxnorm_doco_full_2012-3.html

(starting section 12.4) • • • • • “Primary” table – consists of all RxNorm Concepts – Example: A medication and synonyms - there may be several rows for a single concept.

– Disulfiram (generic) and Antabuse (brand name) are both the same concept and have the same RxCUI.

RxCUI: Concept Unique Identifier (unique per concept, may be many rows with the same CUI) RxAUI: Atom Unique Identifier (unique per entry in the table) SCUI: Source-asserted Concept Identifier – The identifier as provided by the source (NDDF, NDFRT, RXNORM, etc.) TTY: Term Type (preferred term, synonym, ingredient, etc.)

RxNorm Tables: Others

• • • • Simple Concept and Atom Attributes (RXNSAT) – Example: Used to match NDC and find VA Class types Related Concepts (RXNREL) – Example: Parent/Child relationships of VA classes, “ingredient_of”, etc.

Source Information (RXNSAB) – Source abbreviation/full name (NDFRT/National Drug File), version, etc.

Documentation for Abbreviated Values (RXNDOC) – Full name for abbreviations used in other tables

RxNav: Relationships

http://rxnav.nlm.nih.gov

VA Class Ontology

http://bioportal.bioontology.org/ontologies/47101/?p=terms&conceptid=N0000029067

Map Clarity Medications to RxCUI:

GCN/NDC • • • Clarity Medication List – clarity.clarity_medication

GCN (Generic Code Sequence Number - First Databank Inc.) – clarity.rx_med_gcnseqno

– rxnorm.rxnconso (code column when sab = NDDF, and tty != ‘IN’) NDC (National Drug Code) – clarity.clarity_ndc_codes

– rxnorm.rxnsat (atv column where atn = NDC)

The Leftovers: Match with MedEx NLP

http://knowledgemap.mc.vanderbilt.edu/research/content/medex-tool-finding-medication-information • • For the medications that don’t match using GCN/NDC, use MedEx (NLP) – map directly to RxCUI via the drug name in clarity – “NAME” (arbitrarily preferred) – “GENERIC_NAME” Issues – Closed source (though, open source soon as per authors) – Windows Only right now (Linux binaries won’t run with our current configuration on our servers) – Not integrated into our ETL (“manual technical-debt”) – Linking results with input is problematic

Map to Drug Form and VA Class

• • Map Medications to Semantic Clinical Drug and Form (SCDF) or Semantic Branded Drug and Form (SBDF) – Example Clarity Medication: “ANTABUSE 250 MG PO TAB” – Example SBDF: “Disulfiram Oral Tablet” Map Medications to Veterans Administration class (VA Class) • Example: “[AD100] ALCOHOL DETERRENTS”

Resulting I2B2 Hierarchy

The Leftovers:

No SCDF, SBDF, or VA Class! • Some medications didn’t map directly to SCDF, SBDF, or VA Class – Sometimes, it was because the drug mapped to an ingredient. – Example: “CEFAZOLIN INJ 1GM IVP” (medication id 210319, MedEx mapped to RxCui 2180 “CEFAZOLIN” an “ingredient”)

The Leftovers:

Map via “ingredient” relationships • • • • Use “ingredient_of” and “constitutes” relationships Use “isa” relationships to get SCDF/SBDF Help! Results in 21.7 Million results from 20,354 Medications!

– A huge number of components, packs, and associated SCDFs/SBDFs Reduce this by mapping to the SCDF/SBDFs we already have mapped from direct links – Is there a better way?

RxNav (Cefazolin)

Putting Relationships Together

i2b2 Ontology • Use prior mappings (Medications to SCDF/SBDF and Medications to VA Class) to then map the SCDF/SBDF to VA class.

• Create table with parent/child relationships – Use these relationships to build i2b2 compatible ontology

Resulting I2B2 Hierarchy

Results

Based on June 2012 data (Cimarron) • • “Round 1”: – – GCN + NDC Mapping 89.4% of medication observations covered (100,395,527 total facts, 10,636,780 missing facts) “Round 2”: – Added MedEx NLP – linking missing medications to SCDF/SBDF via "ingredient_of" relationship.

– 94.39% of medication observations (100,395,527 total facts, 5,630,904 missing facts)

Next Steps

• • • • Peer review of the code!

Manual mapping of some top concepts – Problem children thus far: http://informatics.kumc.edu/work/attachment/ticket/1246/unmapped_meds_20120823.csv

Review in more detail code from Dustin Key from Group Health (ghc.org) – Basic approach is the same as per overview How to test/validate?

References

RxNorm documentation http://www.nlm.nih.gov/research/umls/rxnorm/docs/2012/rxnorm_doco_full_2012-3.html

KUMC Work Ticket http://informatics.kumc.edu/work/ticket/1246 UMLS Reference Manual http://www.ncbi.nlm.nih.gov/books/NBK9676/ RxNav http://rxnav.nlm.nih.gov/ BioOntology.org

http://bioportal.bioontology.org

Paper: “Enabling Hierarchical View of RxNorm with NDF-RT Drug Classes” http://www.ncbi.nlm.nih.gov/pubmed/21347044 MedEx http://knowledgemap.mc.vanderbilt.edu/research/content/medex-tool-finding-medication-information

Code Walkthrough!

epic_med_mapping.sql