measurementCohort {CohortConstructor}R Documentation

Create measurement-based cohorts

Description

measurementCohort() creates cohorts based on patient records contained in the measurement table. This function extends the conceptCohort() as it allows for measurement values associated with the records to be specified.

Usage

measurementCohort(
  cdm,
  conceptSet,
  name,
  valueAsConcept = NULL,
  valueAsNumber = NULL,
  table = c("measurement", "observation"),
  inObservation = TRUE
)

Arguments

cdm

A cdm reference.

conceptSet

A conceptSet, which can either be a codelist or a conceptSetExpression.

name

Name of the new cohort table created in the cdm object.

valueAsConcept

A vector of cohort IDs used to filter measurements. Only measurements with these values in the value_as_concept_id column of the measurement table will be included. If NULL all entries independent of their value as concept will be considered.

valueAsNumber

A list indicating the range of values and the unit they correspond to, as follows: list("unit_concept_id" = c(rangeValue1, rangeValue2)). If no name is supplied in the list, no requirement on unit concept id will be applied. If NULL, all entries independent of their value as number will be included.

table

Name of OMOP tables to search for records of the concepts provided. Options are "measurement" and/or "observation".

inObservation

If TRUE, only records in observation will be used. If FALSE, records before the start of observation period will be considered, with startdate the start of observation.

Value

A cohort table

Examples


library(CohortConstructor)
cdm <- mockCohortConstructor(con = NULL)
cdm$concept <- cdm$concept |>
  dplyr::union_all(
    dplyr::tibble(
      concept_id = c(4326744, 4298393, 45770407, 8876, 4124457),
      concept_name = c("Blood pressure", "Systemic blood pressure",
                       "Baseline blood pressure", "millimeter mercury column",
                       "Normal range"),
      domain_id = "Measurement",
      vocabulary_id = c("SNOMED", "SNOMED", "SNOMED", "UCUM", "SNOMED"),
      standard_concept = "S",
      concept_class_id = c("Observable Entity", "Observable Entity",
                           "Observable Entity", "Unit", "Qualifier Value"),
      concept_code = NA,
      valid_start_date = NA,
      valid_end_date = NA,
      invalid_reason = NA
    )
  )
cdm$measurement <- dplyr::tibble(
  measurement_id = 1:4,
  person_id = c(1, 1, 2, 3),
  measurement_concept_id = c(4326744, 4298393, 4298393, 45770407),
  measurement_date = as.Date(c("2000-07-01", "2000-12-11", "2002-09-08",
  "2015-02-19")),
  measurement_type_concept_id = NA,
  value_as_number = c(100, 125, NA, NA),
  value_as_concept_id = c(0, 0, 0, 4124457),
  unit_concept_id = c(8876, 8876, 0, 0)
)
cdm <- CDMConnector::copyCdmTo(
  con = DBI::dbConnect(duckdb::duckdb()),
  cdm = cdm, schema = "main")

cdm$cohort <- measurementCohort(
  cdm = cdm,
  name = "cohort",
  conceptSet = list("normal_blood_pressure" = c(4326744, 4298393, 45770407)),
  valueAsConcept = c(4124457),
  valueAsNumber = list("8876" = c(70, 120)),
  inObservation = TRUE
)

cdm$cohort

# You can also create multiple measurement cohorts, and include records
# outside the observation period.

cdm$cohort2 <- measurementCohort(
  cdm = cdm,
  name = "cohort2",
  conceptSet = list("normal_blood_pressure" = c(4326744, 4298393, 45770407),
                  "high_blood_pressure" = c(4326744, 4298393, 45770407)),
  valueAsConcept = c(4124457),
  valueAsNumber = list("8876" = c(70, 120),
                       "8876" = c(121, 200)),
  inObservation = FALSE
)

cdm$cohort2



[Package CohortConstructor version 0.4.0 Index]