In version 2 of Capr, we have introduced a whole new way of building concept sets. Some highlights of this new system are, we no longer need a database connection, only need OMOP concept ids, and we provide functionality to improve how we map to the vocabulary (i.e. find descendants or exclude). The new interface makes it much easier to build and read information about a concept set. In this vignette we will demonstrate how the new AI works!
Defining a concept set
The new function cs is a binder function that collects
integer OMOP ids into a set. The idea is for the function to look like
c() which in R is use to formulate vectors. Say we found
several ingredients that are all ace inhibitors. We can combine those
OMOP concept ids into a set as follows:
ace1 <- cs(1335471, #benazepril
1340128, #captopril
1341927, #enalapril
1308216, #lisinopril
1363749, #fosinopril
name = "aceInhibitors")
ace1
#> -- <Capr Concept Set> aceInhibitors --------------------------------------------
#> # A tibble: 5 x 9
#> conceptId conceptCode conceptName domainId vocabularyId standardConcept
#> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1335471 "" "" "" "" ""
#> 2 1340128 "" "" "" "" ""
#> 3 1341927 "" "" "" "" ""
#> 4 1308216 "" "" "" "" ""
#> 5 1363749 "" "" "" "" ""
#> # i 3 more variables: includeDescendants <lgl>, isExcluded <lgl>,
#> # includeMapped <lgl>Including Descendants
If you were only add the OMOP id for the ingredient, our query would
only look at whether that specific ID is among the drug concepts for a
set of patients. With OMOP, we often don’t want just the ingredient ID
but also its descendants. Descendants are child concepts that map to the
adult concepts. For example a brand name or specific dosage of
lisinopril would have a different OMOP concept Id but it would map to
the ingredient term via the vocabulary hierarchy. This helps us include
all variations of linisopril that could be seen in the data. In Capr we
can easily add this logic using the descendants command
ace2 <- cs(descendants(1335471, 1340128, 1341927, 1363749, 1308216),
name = "aceInhibitors")
ace2
#> -- <Capr Concept Set> aceInhibitors --------------------------------------------
#> # A tibble: 5 x 9
#> conceptId conceptCode conceptName domainId vocabularyId standardConcept
#> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1335471 "" "" "" "" ""
#> 2 1340128 "" "" "" "" ""
#> 3 1341927 "" "" "" "" ""
#> 4 1363749 "" "" "" "" ""
#> 5 1308216 "" "" "" "" ""
#> # i 3 more variables: includeDescendants <lgl>, isExcluded <lgl>,
#> # includeMapped <lgl>Toggling other logic
A similar strategy can be used to exclude concepts from a set or
include mapped. We can also combine this within the cs
function, as shown below
ace3 <- cs(descendants(1335471, 1340128, 1341927, 1363749), exclude(1308216),
name = "aceInhibitors")
ace3
#> -- <Capr Concept Set> aceInhibitors --------------------------------------------
#> # A tibble: 5 x 9
#> conceptId conceptCode conceptName domainId vocabularyId standardConcept
#> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1335471 "" "" "" "" ""
#> 2 1340128 "" "" "" "" ""
#> 3 1341927 "" "" "" "" ""
#> 4 1363749 "" "" "" "" ""
#> 5 1308216 "" "" "" "" ""
#> # i 3 more variables: includeDescendants <lgl>, isExcluded <lgl>,
#> # includeMapped <lgl>Fill in missing concept set details
By default, Capr puts in minimal information for the
concept set, the concept id. However it is often helpful to fill out the
remaining information of the concept using the OMOP vocabularies. To
fill out the remaining information, a user needs to connect to a OMOP
CDM to access the vocabulary information for the concept in the concept
set. First we show a regular concept set, and the json it renders.
diclofenac <- cs(descendants(1124300), name = "diclofenac")
cat(as.json(diclofenac))Now we add in the CDM connection to get the remaining information:
con <- DatabaseConnector::connect(Eunomia::getEunomiaConnectionDetails())
diclofenac <- getConceptSetDetails(diclofenac, con, vocabularyDatabaseSchema = "main")
cat(as.json(diclofenac))