Use of RxNorm and NDF-RT to normalize and characterize participant-reported medications in an i2b2-based repository Colette Blach, DTMI, Duke University 1 I have no financial interests to disclose. 2 MURDOCK Community Registry 50,000 subjects, >10K to date • Patient reported medications, medical history, lifestyle data • Collect vital measurements and biospecimens • EHR data • Lab tests/omics • Annual follow up • Consent for recontact Cohort identification for follow-on studies 3 L. Kristin Newby, MD, MHS Rowena Dolor, MD, MHS Collection of Medication • Each participant was asked at enrollment and then annually for a list of current medications • 10,348 participants • Average 2 years follow-up • Free text entry of medications and reasons for taking • ~6 drugs reported per person each encounter • 158,738 total medication entries • 21,325 unique terms 4 Medication standardization “Prozac” “prozak” “fluoxetine” “Sarafem” Preferred Name: Fluoxetine ID: C0016365 CNS Meds Antidepressants SSRIs Fluoxetine MAOIs Sertraline Analgesics Tricyclics Opioid Paroxetine 5 Non-opioid NSAIDs RxNorm vs NDF-RT • RxNorm (complex ontology) • NDF-RT (hierarchy) 6 Medication standardization “Prozac” “prozak” “fluoxetine” “Sarafem” RxNorm API Preferred Name: Fluoxetine ID: C0016365 CNS Meds Antidepressants SSRIs Fluoxetine MAOIs Sertraline Analgesics Tricyclics Opioid Paroxetine 7 Non-opioid NSAIDs Medication standardization “Prozac” “prozak” “fluoxetine” “Sarafem” Preferred Name: Fluoxetine ID: C0016365 CNS Meds Antidepressants SSRIs Fluoxetine MAOIs Sertraline Tricyclics Modified NDF-RT Analgesics Opioid Paroxetine 8 Non-opioid NSAIDs What we had from MURDOCK participants What mapped directly to NDF-RT 9 Medication component objectives • Map free text drugs into coded terminology • Modify NDF-RT with RxNorm medication concepts (Brand names, ingredients and sets) • Translate coded terminology into leaves on modified NDF-RT hierarchy 10 Modified NDF-RT hierarchy • Medication input was largely brand name or ingredients only without dosage or route • NDF-RT branches were extended with ingredients or ingredient sets, then terminated with brand names. • NDF-RT mapping was limited to Drug Products by VA Class tree. • Mapping from NDF-RT to RxNorm entirely algorithm based. 11 NDF-RT maps directly from lowest class to [name-dose-route] Antidepressants SSRIs MAOIs Tricyclics Sertraline 100 MG oral tablet Sertraline 50 MG oral tablet Etc. Fluoxetine 60 MG oral tablet Fluoxetine 10 MG oral capsule Fluoxetine 4 MG/ML oral solution Etc. 12 Inserted drug name level- no dose, no route (b/c that’s what we asked participants to give) Antidepressants SSRIs Fluoxetine Tricyclics MAOIs Sertraline Paroxetine Sertraline 100 MG oral tablet Sertraline 50 MG oral tablet Etc. Fluoxetine 60 MG oral tablet Fluoxetine 10 MG oral capsule Fluoxetine 4 MG/ML oral solution 13 Constructing modified hierarchy • Reduce NDF-RT drug + route + dosage to drug-only concept ACETAMINOPHEN 325MG TAB [VA Product] ACETAMINOPHEN • Map brand names ingredients to the exact matching NDF-RT Product Components or sets of Product Components. Brand names with multiple ingredient are mapped first to avoid false partial matches. OXYCODONE HYDROCHLORIDE/ ACETAMINOPHEN PERCOCET • For some NDF-RT branches, drug route is required for algorithm to correctly identify tree branch. EPINEPHRINE is used for both Ophthalmic and Respiratory but Medihaler-Epi is not ophthalmic 14 Problems encountered • Common ingredients could be mismapped onto branches if handled singly and not as a only one of many concept components. ACETAMINOPHEN and OXYCODONE HCL/ACETAMINOPHEN • Multiple product components--problems mapping. Sometimes NDF-RT concepts list a set of product components that overlap but don't identically match RxNorm ingredient sets for what is visually the same concept. OCUVITE LUTEIN vs OCUVITE LUTEIN CAP NDF-RT ASCORBIC ACID RxNorm ASCORBIC ACID COPPER LUTEIN VITAMIN E ZINC CUPROUS OXIDE LUTEIN VITAMIN E ZINC OXIDE MURDOCK 15 Mapped Input Modified NDF-RT • Most mapped input linked directly to modified NDR-RT • Some input had dosage/route/timing, requiring transformations. Zolpidem Extended Release Tablet [Ambien] • RxCUI unmapped to modified NDF-RT tree are largely herbals, brand names, BCPs 17 Reason for Medication • Registry collected both drug names and reason for drug • Most common response: No Response • Some inconsistency with the drug and participant-entered reason for the drug, usually mis-mapping, secondary use or participant lack of understanding. Input: BENAPRIL HCL Reason: HIGH BLOOD PRESSURE Mapped to: BENADRYL (score = 50) (probably BENAZEPRIL?) Input: ROSUVASTATIN, CRESTOR Reason: BONE Mapped to: CRESTOR MURDOCK 18 Reason for Medication • Some of the participants were taking prescription drugs for a disorder but responded in medical history that they were never diagnosed with the disorder. 159 Participants Meds: CITALAPRAM, LEXAPRO, CELEXA, SERTRALINE, FLUOXETINE and others Reason: Depression Medical History Depression: No • Standardized entry of medication reasons would enhance medication data. 19 Researcher Queries Although mapping was highly usable, testing identified limitations. • Tool is not a substitute for researcher’s knowledge of medications. • Researchers cannot rely exclusively on medication category to identify cohort participants having a specific condition. • Fewer drugs exposed by only using “Drug Products by VA Class”. • Ideal implementation is based on specific use case. MURDOCK 20 Future Plans • Explore DrOn for more precise mapping? • Consider leverage reason taking in coding drug information. • Move to electronic input and drug dropdowns. MURDOCK 21 Acknowledgements • Lee Peters- NLM • Mike Lincoln- VA • John Carter- Apelon Co-Authors: • Guilherme Del Fiol • Chandel Dundee • Julie Frund • Rachel Richesson • Michelle Smerek • Anita Walden • Jessica D. Tenenbaum Funding from David H. Murdock and NIH UL1RR024128 • Russ Waitman • Nathan Graham • Matvey Palchuck 22
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