Codelist diagnostics

In PhenotypeR: Assess Study Cohorts Using a Common Data Model

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>", message = FALSE, warning = FALSE,
  fig.width = 7
)

library(CDMConnector)
if (Sys.getenv("EUNOMIA_DATA_FOLDER") == "") Sys.setenv("EUNOMIA_DATA_FOLDER" = tempdir())
if (!dir.exists(Sys.getenv("EUNOMIA_DATA_FOLDER"))) dir.create(Sys.getenv("EUNOMIA_DATA_FOLDER"))
if (!eunomiaIsAvailable()) downloadEunomiaData(datasetName = "synpuf-1k")

Introduction

In this example we're going to summarise the characteristics of individuals with an ankle sprain, ankle fracture, forearm fracture, a hip fracture and different measurements using the Eunomia synthetic data.

We'll begin by creating our study cohorts.

library(CDMConnector)
library(CohortConstructor)
library(CodelistGenerator)
library(PhenotypeR)
library(MeasurementDiagnostics)
library(dplyr)
library(ggplot2)

con <- DBI::dbConnect(duckdb::duckdb(), 
                      CDMConnector::eunomiaDir("synpuf-1k", "5.3"))
cdm <- CDMConnector::cdmFromCon(con = con, 
                                cdmName = "Eunomia Synpuf",
                                cdmSchema   = "main",
                                writeSchema = "main", 
                                achillesSchema = "main")

cdm$injuries <- conceptCohort(cdm = cdm,
  conceptSet = list(
    "ankle_sprain" = 81151,
    "ankle_fracture" = 4059173,
    "forearm_fracture" = 4278672,
    "hip_fracture" = 4230399,
    "measurements_cohort" = c(40660437L, 2617206L, 4034850L,  2617239L, 4098179L)
  ),
  name = "injuries")
cdm$injuries |> 
  glimpse()

Summarising code use

To get a good understanding of the codes we've used to define our cohorts we can use the codelistDiagnostics() function.

code_diag <- codelistDiagnostics(cdm$injuries)

Codelist diagnostics builds on CodelistGenerator and MeasurementDiagnostics R packages to perform the following analyses:

• Achilles code use: Which summarises the counts of our codes in our database based on achilles results using summariseAchillesCodeUse().
• Orphan code use: Orphan codes refer to codes that we did not include in our cohort definition, but that have any relationship with the codes in our codelist. So, although many can be false positives, we may identify some codes that we may want to use in our cohort definitions. This analysis uses summariseOrphanCodes().
• Cohort code use: Summarises the cohort code use in our cohort using summariseCohortCodeUse().
• Measurement diagnostics: If any of the concepts used in our codelist is a measurement, it summarises its code use using summariseCohortMeasurementUse().

The output of a function is a summarised result table.

Add codelist attribute

Some cohorts that may be created manually may not have the codelists recorded in the cohort_codelist attribute. The package has a utility function to record a codelist in a cohort_table object:

cohortCodelist(cdm$injuries, cohortId = 1)
cdm$injuries <- cdm$injuries |>
  addCodelistAttribute(codelist = list(new_codelist = c(1L, 2L)), cohortName = "ankle_fracture")
cohortCodelist(cdm$injuries, cohortId = 1)

Visualise the results

We will now use different functions to visualise the results generated by CohortDiagnostics. Notice that these functions are from CodelistGenerator and MeasurementDiagnostics R packages packages.

Achilles code use

tableAchillesCodeUse(code_diag)

Orphan code use

tableOrphanCodes(code_diag)

Cohort code use

tableCohortCodeUse(code_diag)

Measurement timings

tableMeasurementTimings(code_diag)

plotMeasurementTimings(code_diag)

Measurement value as concept

tableMeasurementValueAsConcept(code_diag)

plotMeasurementValueAsConcept(code_diag)

Measurement value as numeric

tableMeasurementValueAsNumeric(code_diag)

plotMeasurementValueAsNumeric(code_diag)

PhenotypeR documentation built on Aug. 8, 2025, 6:30 p.m.