Observational Health Data Sciences and Informatics

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Proposal by: Vojtech Huser

Use Case: ingredients like neomycin can be taken oraly and localy (otic, ophtalmic). Such drug eras are medically distinct.

Changes prposed: Modify DRUG_ERA table and add route_concept_id column

Table DRUG_EXPOSURE has such column http://www.ohdsi.org/web/wiki/doku.php?id=documentation:cdm:drug_exposure

route_concept_id (integer) A foreign key to a predefined concept in the Standardized Vocabularies reflecting the route of administration

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Background

A 2016 poster analyzed era data (ingredients) in relationship Analysis of drug use by dose form in large healthcare databases: Data granularity issues and CDM considerations

Pasted result section: CCAE version 5 dataset within IMEDS cloud lab contains data for over 165 million patients for the period of January 2003 till March 2015. The OMOP terminology used by our project was ‘v5.0 3-Apr-2015’. To simplify the implementation (at our site and also possibly for other sites repeating our analysis), we structured the analysis to utilize Achilles pre-computed views for drug data (analysis ‘704: Number of persons with at least one drug exposure, by drug_concept_id by calendar year by gender by age decile’ that we further aggregated to only year level). To account for changing number of patients captured by the CCAE database, we divided raw drug prescription patient counts by the database population for a given year (utilizing again Achilles analysis ‘109: Number of persons with continuous observation in each year’). We stratified the data views supporting the consumer drug searches by year. The data presented below are for year 2014. The drug_exposure data extended with inferred dose form contained a total of 74 valid dose forms (out of 108 possible dose forms defined in the 2016-05 release of RxNorm). To improve final data display, we further grouped the number of dose form into categories. For example, ‘otic’ category grouped dose forms of ‘Otic Solution’ and ‘Otic Suspension’. Although RxNorm provides its own groupings of dose forms, we found it not suitable for our purpose and have used our own manually defined categorization. Because this was only a demonstration pilot, our custom categorization was not comprehensive and did not try to cover all possible RxNorm dose forms but rather tried to merely reduce the number of dose forms presented to the consumer. Figure 1 shows a selected subset of ingredients with y axis representing the percentage of ingredient by dose form category. For example, a consumer may arrive at the acyclovir ingredient and see that 79% of its use is oral and 21% is topical. The source code for our analysis (in SQL RedShift dialect and R) and selected outputs are available at https://github.com/vojtechhuser/OHDSI-drug-route. Our pilot experiment demonstrates the value of route information (or inferred dose form) at the ingredient level. We acknowledge that the drug view we designed may only be beneficial to consumers since clinicians and healthcare researchers are typically intimately familiar with drug route options for a particular ingredient of their interest. An additional motivation for our research, beyond the consumer focus, is the ability to exclude drug events involving non-systemic drugs. The preliminary analysis of the extended ingredient view shows that majority of non-systemic use can be identified from inferred dose form data with the exception of some ingredients where delineation of systemic/non-systemic effect would have to done at clinical drug level (e.g., certain topical or rectal ingredients/dose forms).