The goal of OmopSketch is to characterise and visualise an OMOP CDM instance to asses if it meets the necessary criteria to answer a specific clinical question and conduct a certain study.
You can install the development version of OmopSketch from GitHub with:
# install.packages("remotes")
remotes::install_github("OHDSI/OmopSketch")Let’s start by creating a cdm object using the Eunomia mock dataset:
library(duckdb)
#> Loading required package: DBI
library(CDMConnector)
library(dplyr, warn.conflicts = FALSE)
library(OmopSketch)
con <- dbConnect(duckdb(), eunomia_dir())
cdm <- cdmFromCon(con = con, cdmSchema = "main", writeSchema = "main")
#> Note: method with signature 'DBIConnection#Id' chosen for function 'dbExistsTable',
#> target signature 'duckdb_connection#Id'.
#> "duckdb_connection#ANY" would also be valid
cdm
#>
#> ── # OMOP CDM reference (duckdb) of Synthea synthetic health database ──────────
#> • omop tables: person, observation_period, visit_occurrence, visit_detail,
#> condition_occurrence, drug_exposure, procedure_occurrence, device_exposure,
#> measurement, observation, death, note, note_nlp, specimen, fact_relationship,
#> location, care_site, provider, payer_plan_period, cost, drug_era, dose_era,
#> condition_era, metadata, cdm_source, concept, vocabulary, domain,
#> concept_class, concept_relationship, relationship, concept_synonym,
#> concept_ancestor, source_to_concept_map, drug_strength
#> • cohort tables: -
#> • achilles tables: -
#> • other tables: -We first create a snapshot of our database. This will allow us to track when the analysis has been conducted and capture details about the CDM version or the data release.
summariseOmopSnapshot(cdm) |>
tableOmopSnapshot(type = "flextable")
Once we have collected the snapshot information, we can start
characterising the clinical tables of the CDM. By using
summariseClinicalRecords() and
tableClinicalRecords(), we can easily visualise the main
characteristics of specific clinical tables.
summariseClinicalRecords(cdm, c("condition_occurrence", "drug_exposure")) |>
tableClinicalRecords(type = "flextable")
#> ℹ Adding variables of interest to condition_occurrence.
#> ℹ Summarising records per person in condition_occurrence.
#> ℹ Summarising condition_occurrence: `in_observation`, `standard_concept`,
#> `source_vocabulary`, `domain_id`, and `type_concept`.
#> ℹ Adding variables of interest to drug_exposure.
#> ℹ Summarising records per person in drug_exposure.
#> ℹ Summarising drug_exposure: `in_observation`, `standard_concept`,
#> `source_vocabulary`, `domain_id`, and `type_concept`.
We can also explore trends in the clinical table records over time.
summariseRecordCount(cdm, c("condition_occurrence", "drug_exposure"), interval = "years") |>
plotRecordCount(facet = "omop_table", colour = "cdm_name")
After visualising the main characteristics of our clinical tables, we can explore the observation period details. OmopSketch provides several functions to have an overview the dataset study period.
Using summariseInObservation() and
plotInObservation(), we can gather information on the
number of records per year.
summariseInObservation(cdm$observation_period, output = "records", interval = "years") |>
plotInObservation(colour = "cdm_name")
#> `result_id` is not present in result.
#> `result_id` is not present in result.
You can also visualise and explore the characteristics of the
observation period per each individual in the database using
summariseObservationPeriod().
summariseObservationPeriod(cdm$observation_period) |>
tableObservationPeriod(type = "flextable")
Or if visualisation is preferred, you can easily build a histogram to explore how many participants have more than one observation period.
summariseObservationPeriod(cdm$observation_period) |>
plotObservationPeriod(colour = "observation_period_ordinal")
OmopSketch also provides functions to explore some of (or all) the concepts in the dataset.
acetaminophen <- c(1125315, 1127433, 1127078)
summariseConceptSetCounts(cdm, conceptSet = list("acetaminophen" = acetaminophen)) |>
filter(variable_name == "Number records") |>
plotConceptSetCounts(colour = "codelist_name")
#> Warning: ! `codelist` contains numeric values, they are casted to integers.
#> ℹ Searching concepts from domain drug in drug_exposure.
#> ℹ Counting concepts
As seen, OmopSketch offers multiple functionalities to provide a general overview of a database. Additionally, it includes more tools and arguments that allow for deeper exploration, helping to assess the database’s suitability for specific research studies. For further information, please refer to the vignettes.