# install.packages(c("CDMConnector", "dplyr", "tidyr", "DBI", "CodelistGenerator", "duckdb"))
library(CDMConnector)
library(dplyr)
library(tidyr)
library(DBI)
library(omopgenerics)
library(CodelistGenerator)
CDMConnector::requireEunomia("synpuf-1k", "5.3")
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")CodelistGenerator and PhenotypeR
CodelistGenerator and Phenotype R
Working with the OMOP CDM vocabulary tables & characterising our cohorts
Outline
CodelistGenerator
- Introduction to Vocabulary Tables
- Create Codelists Using CodelistGenerator
- Codelist Diagnostics
PhenotypeR
- Introduction to PhenotypeR
1. Introduction to Vocabulary Tables
1.1 Vocabulary Tables
In OMOP, vocabularies refer to a set of clinical terminologies used to represent health-related concepts in a consistent way across different database.
ATHENA is the official OHDSI web-based tool used to access the OMOP vocabularies.
1.1 Vocabulary Tables
Example: Asthma
Concept: Single clinical idea with unique combination of concept_id, concept_name, domain, and standard concept status. 
1.1 Vocabulary Tables
Example: Asthma
Concept: Single clinical idea with unique combination of concept_id, concept_name, domain, and standard concept status. 
1.1 Vocabulary Tables
Example: Asthma
Concept: Single clinical idea with unique combination of concept_id, concept_name, domain, and standard concept status. 
1.1 Vocabulary Tables
Example: Asthma
Concept relationships: Mapped to, has descendant, has ancestor, etc. 
1.2 CDM Vocabulary Tables
Where can we find all this information in our CDM?
Connect to a mock cdm:
cdm── # OMOP CDM reference (duckdb) of Eunomia Synpuf ───────────────────────────────────────────────────────────────────────────────• 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_definition, attribute_definition• cohort tables: -• achilles tables: achilles_analysis, achilles_results, achilles_results_dist• other tables: -1.2 CDM Vocabulary Tables
Concept table:
cdm$concept |> glimpse()Rows: ??
Columns: 10
Database: DuckDB v1.1.0 [martaa@Windows 10 x64:R 4.4.1/C:\Users\martaa\AppData\Local\Temp\RtmpwFCqVw\file78788cf5cd8.duckdb]
$ concept_id <int> 43674447, 43674448, 43674449, 43674450, 43674451, 43674452, 43674453, 43674454, 43674455, 43674456, 436…
$ concept_name <chr> "Benperidol 2 MG [Glianimon]", "Benperidol 2 MG Oral Tablet [Benperidol-Neuraxpharm] by Neuraxpharm", "…
$ domain_id <chr> "Drug", "Drug", "Drug", "Drug", "Drug", "Drug", "Drug", "Drug", "Drug", "Drug", "Drug", "Drug", "Drug",…
$ vocabulary_id <chr> "RxNorm Extension", "RxNorm Extension", "RxNorm Extension", "RxNorm Extension", "RxNorm Extension", "Rx…
$ concept_class_id <chr> "Branded Drug Comp", "Marketed Product", "Branded Drug", "Clinical Drug Box", "Clinical Drug Box", "Bra…
$ standard_concept <chr> "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", NA,…
$ concept_code <chr> "OMOP929237", "OMOP929240", "OMOP929249", "OMOP929272", "OMOP929273", "OMOP929308", "OMOP929324", "OMOP…
$ valid_start_date <date> 2017-08-24, 2017-08-24, 2017-08-24, 2017-08-24, 2017-08-24, 2017-08-24, 2017-08-24, 2017-08-24, 2017-0…
$ valid_end_date <date> 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-31, 2099-1…
$ invalid_reason <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "U", "U", NA, NA, NA, N…Example:
cdm$concept |>
filter(concept_name == "Asthma",
vocabulary_id == "SNOMED",
standard_concept == "S")# Source: SQL [?? x 10]
# Database: DuckDB v1.1.0 [martaa@Windows 10 x64:R 4.4.1/C:\Users\martaa\AppData\Local\Temp\RtmpwFCqVw\file78788cf5cd8.duckdb]
concept_id concept_name domain_id vocabulary_id concept_class_id standard_concept concept_code valid_start_date valid_end_date
<int> <chr> <chr> <chr> <chr> <chr> <chr> <date> <date>
1 317009 Asthma Condition SNOMED Clinical Finding S 195967001 2002-01-31 2099-12-31
# ℹ 1 more variable: invalid_reason <chr>1.2 CDM Vocabulary Tables
Concept ancestor table:
cdm$concept_ancestor |> glimpse()Rows: ??
Columns: 4
Database: DuckDB v1.1.0 [martaa@Windows 10 x64:R 4.4.1/C:\Users\martaa\AppData\Local\Temp\RtmpwFCqVw\file78788cf5cd8.duckdb]
$ ancestor_concept_id <int> 262, 8478, 8479, 8480, 8482, 8483, 8484, 8487, 8488, 8494, 8495, 8496, 8504, 8505, 8506, 8507, …
$ descendant_concept_id <int> 262, 8478, 8479, 8480, 8482, 8483, 8484, 8487, 8488, 8494, 8495, 8496, 8504, 8505, 8506, 8507, …
$ min_levels_of_separation <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,…
$ max_levels_of_separation <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,…Example:
cdm$concept_ancestor |>
filter(ancestor_concept_id == "317009") |>
inner_join(cdm$concept |>
select("descendant_concept_id" = "concept_id",
"descendant_concept_name" = "concept_name"),
by = "descendant_concept_id") |>
select("descendant_concept_name", "min_levels_of_separation")# Source: SQL [?? x 2]
# Database: DuckDB v1.1.0 [martaa@Windows 10 x64:R 4.4.1/C:\Users\martaa\AppData\Local\Temp\RtmpwFCqVw\file78788cf5cd8.duckdb]
descendant_concept_name min_levels_of_separation
<chr> <int>
1 Acute severe exacerbation of asthma co-occurrent with allergic rhinitis 4
2 Severe persistent allergic asthma 2
3 Acute severe exacerbation of severe persistent asthma co-occurrent with allergic rhinitis 4
4 Acute severe exacerbation of mild persistent allergic asthma co-occurrent with allergic rhinitis 4
5 Moderate persistent allergic asthma 2
6 Acute severe exacerbation of moderate persistent asthma co-occurrent with allergic rhinitis 4
7 Aspirin exacerbated respiratory disease 4
8 Chronic obstructive asthma co-occurrent with acute exacerbation of asthma 3
9 Severe persistent asthma co-occurrent with allergic rhinitis 3
10 Mild persistent asthma co-occurrent with allergic rhinitis 3
# ℹ more rows1.2 CDM Vocabulary Tables
Concept relationship table:
cdm$concept_relationship |> glimpse()Rows: ??
Columns: 6
Database: DuckDB v1.1.0 [martaa@Windows 10 x64:R 4.4.1/C:\Users\martaa\AppData\Local\Temp\RtmpwFCqVw\file78788cf5cd8.duckdb]
$ concept_id_1 <int> 5, 6, 9, 11, 14, 18, 20, 22, 26, 28, 37, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 233, 234, 262, 262…
$ concept_id_2 <int> 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 5, 6, 9, 11, 14, 18, 20, 22, 26, 28, 37, 44818955, 44818954…
$ relationship_id <chr> "Concept replaced by", "Concept replaced by", "Concept replaced by", "Concept replaced by", "Concept re…
$ valid_start_date <date> 2015-10-16, 2015-10-16, 2015-10-16, 2015-10-16, 2015-10-16, 2015-10-16, 2015-10-16, 2015-10-16, 2015-1…
$ valid_end_date <date> 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-31, 2099-1…
$ invalid_reason <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…Example:
cdm$concept_relationship |>
filter(concept_id_1 == "317009") |>
inner_join(cdm$concept |>
select("concept_id_2" = "concept_id",
"concept_name"),
by = "concept_id_2") |>
select("concept_name", "concept_id_2", "relationship_id")# Source: SQL [?? x 3]
# Database: DuckDB v1.1.0 [martaa@Windows 10 x64:R 4.4.1/C:\Users\martaa\AppData\Local\Temp\RtmpwFCqVw\file78788cf5cd8.duckdb]
concept_name concept_id_2 relationship_id
<chr> <int> <chr>
1 Steroid dependent asthma 46273635 Subsumes
2 Asthma-chronic obstructive pulmonary disease overlap syndrome 46274062 Subsumes
3 Asthma without status asthmaticus 4206340 Subsumes
4 Airway structure 4230487 Has finding site
5 Asthmatic bronchitis 4233784 Subsumes
6 Asthma 4264780 SNOMED - ind/CI
7 Bronchial Hyperreactivity 4267454 SNOMED - ind/CI
8 Asthma education 4293727 Focus of
9 Asthma confirmed 4293734 Asso finding of
10 Substance induced asthma 4312524 Subsumes
# ℹ more rows1.2 CDM Vocabulary Tables
Concept synonym table:
cdm$concept_synonym |> glimpse()Rows: ??
Columns: 3
Database: DuckDB v1.1.0 [martaa@Windows 10 x64:R 4.4.1/C:\Users\martaa\AppData\Local\Temp\RtmpwFCqVw\file78788cf5cd8.duckdb]
$ concept_id <int> 33, 8689, 8691, 8715, 8715, 9173, 9174, 9176, 9176, 9177, 9181, 9188, 9189, 9189, 9189, 9190, 9190,…
$ concept_synonym_name <chr> "Provider specialty", "Flu due to unidentified influenza virus w oth resp manifest", "Maternal care…
$ language_concept_id <int> 4180186, 4180186, 4180186, 4180186, 4180186, 4180186, 4180186, 4180186, 4180186, 4180186, 4180186, …Example:
cdm$concept_synonym |>
filter(concept_id == "317009")# Source: SQL [?? x 3]
# Database: DuckDB v1.1.0 [martaa@Windows 10 x64:R 4.4.1/C:\Users\martaa\AppData\Local\Temp\RtmpwFCqVw\file78788cf5cd8.duckdb]
concept_id concept_synonym_name language_concept_id
<int> <chr> <int>
1 317009 Asthma (disorder) 4180186
2 317009 Bronchial hyperresponsiveness 4180186
3 317009 Airway hyperreactivity 4180186
4 317009 Bronchial hyperreactivity 4180186
5 317009 Bronchial asthma 4180186
6 317009 Bronchial hypersensitivity 4180186
7 317009 BHR - Bronchial hyperreactivity 4180186
8 317009 Hyperreactive airway disease 4180186
9 317009 Asthmatic 41801861.3 Exploring Vocabulary Tables with CodelistGenerator
Search results will be specific to the version of the vocabulary being used
getVocabVersion(cdm)[1] "v5.0 06-AUG-21"Which vocabularies are available?
getVocabularies(cdm = cdm) [1] "APC" "ATC" "BDPM" "CMS Place of Service" "Cohort"
[6] "Concept Class" "Condition Type" "CPT4" "Currency" "Death Type"
[11] "Device Type" "Domain" "DPD" "DRG" "Drug Type"
[16] "Ethnicity" "Gemscript" "Gender" "HCPCS" "HES Specialty"
[21] "ICD10" "ICD10CM" "ICD9CM" "ICD9Proc" "LOINC"
[26] "MDC" "Meas Type" "Multilex" "Multum" "NDC"
[31] "NDFRT" "None" "Note Type" "NUCC" "Obs Period Type"
[36] "Observation Type" "OPCS4" "OXMIS" "PCORNet" "Procedure Type"
[41] "Provider" "Race" "Read" "Relationship" "Revenue Code"
[46] "RxNorm" "RxNorm Extension" "SMQ" "SNOMED" "SPL"
[51] "Supplier" "UCUM" "VA Class" "VA Product" "Visit"
[56] "Visit Type" "Vocabulary" 1.3 Exploring Vocabulary Tables with CodelistGenerator
Which domains are present?
getDomains(cdm) [1] "Race" "Gender" "Device" "Drug" "Procedure"
[6] "Meas Value" "Ethnicity" "Provider" "Metadata" "Condition"
[11] "Visit" "Relationship" "Spec Disease Status" "Observation" "Unit"
[16] "Measurement" "Meas Value Operator" "Spec Anatomic Site" "Revenue Code" "Route"
[21] "Specimen" "Currency" What concept classes are present?
getConceptClassId(cdm,
standardConcept = "Standard",
domain = "Drug") [1] "Branded Drug" "Branded Drug Box" "Branded Drug Comp" "Branded Drug Form" "Branded Pack"
[6] "Branded Pack Box" "Clinical Drug" "Clinical Drug Box" "Clinical Drug Comp" "Clinical Drug Form"
[11] "Clinical Pack" "Clinical Pack Box" "CPT4" "CPT4 Modifier" "HCPCS"
[16] "Ingredient" "Marketed Product" "Quant Branded Box" "Quant Branded Drug" "Quant Clinical Box"
[21] "Quant Clinical Drug"1.3 Exploring Vocabulary Tables with CodelistGenerator
What relationships do we have between standard concepts?
getRelationshipId(cdm,
standardConcept1 = c("standard"),
standardConcept2 = c("standard"),
domains1 = "condition",
domains2 = "condition") [1] "Asso finding of" "Asso with finding" "Due to of" "Finding asso with" "Followed by" "Follows"
[7] "Has asso finding" "Has due to" "Has manifestation" "Is a" "Manifestation of" "Mapped from"
[13] "Maps to" "Occurs after" "Occurs before" "Subsumes" What relationships do we have between non-standard to standard concepts?
getRelationshipId(cdm,
standardConcept1 = c("standard"),
standardConcept2 = c("non-standard"),
domains1 = "condition",
domains2 = "condition") [1] "Asso finding of" "Asso with finding" "Concept alt_to from" "Concept poss_eq from" "Concept replaces"
[6] "Concept same_as from" "Concept was_a from" "Due to of" "Finding asso with" "Followed by"
[11] "Follows" "Has asso morph" "Has due to" "Has interprets" "Has manifestation"
[16] "Is a" "Manifestation of" "Mapped from" "Occurs after" "Occurs before"
[21] "Subsumes" "Value mapped from" 1.3 Exploring Vocabulary Tables with CodelistGenerator
What dose forms do we have in the database?
getDoseForm(cdm) |> head(n = 20) [1] "Augmented Topical Cream" "Augmented Topical Gel" "Augmented Topical Lotion"
[4] "Augmented Topical Ointment" "Auto-Injector" "Bar Soap"
[7] "Bath Salts" "Buccal Film" "Buccal Tablet"
[10] "Cartridge" "Cement" "Chewable Bar"
[13] "Chewable Extended Release Oral Tablet" "Chewable Tablet" "Chewing Gum"
[16] "Collodion" "Delayed Release Oral Capsule" "Delayed Release Oral Granules"
[19] "Delayed Release Oral Tablet" "Dental Pin" What routes are available?
getRouteCategories(cdm) |> head(n = 20) [1] "implant" "inhalable" "injectable"
[4] "oral" "topical" "transdermal"
[7] "transmucosal" "transmucosal_buccal_or_sublingual" "transmucosal_nasal"
[10] "transmucosal_rectal" "transmucosal_vaginal" "unclassified_route" What dose units are present?
getDoseUnit(cdm) |> head(n = 20) [1] "50% cell culture infectious dose" "50% tissue culture infectious dose"
[3] "Actuation" "allergenic unit"
[5] "bacteria" "bioequivalent allergenic unit"
[7] "cells" "clinical unit"
[9] "colony forming unit" "focus forming unit"
[11] "homeopathic potency of centesimal series" "homeopathic potency of decimal series"
[13] "index of reactivity" "international unit"
[15] "limit of flocculation unit" "mega-international unit"
[17] "microgram" "microkatal"
[19] "millicurie" "milliequivalent" 1.4 ACHILLES Tables
- ACHILLES are additional tables that provide descriptive statistics of an OMOP CDM database.
- They are typically run after performing ETL.
cdm── # OMOP CDM reference (duckdb) of Eunomia Synpuf ───────────────────────────────────────────────────────────────────────────────• 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_definition, attribute_definition• cohort tables: -• achilles tables: achilles_analysis, achilles_results, achilles_results_dist• other tables: -1.4 ACHILLES Tables
The interesting part of CodelistGenerator:
1.4 ACHILLES Tables
Counts for asthma:
2. Generate Codelists Using CodelistGenerator
2.1 Systematic Search of Codes Using CodelistGenerator
- We are now going to create a codelist for asthma.
- We will first identify a set of concepts that may be relevant.
asthma_codes <- getCandidateCodes(
cdm = cdm,
keywords = "asthma",
domains = "Condition",
includeDescendants = TRUE)
asthma_codes %>% glimpse()Rows: 187
Columns: 6
$ concept_id <int> 46269770, 46269778, 46269789, 46270029, 46270082, 46273452, 46274124, 44810117, 4125022, 4141978, 41554…
$ found_from <chr> "From initial search", "From initial search", "From initial search", "From initial search", "From initi…
$ concept_name <chr> "Severe persistent allergic asthma", "Mild persistent asthma controlled", "Moderate persistent asthma u…
$ domain_id <chr> "Condition", "Condition", "Condition", "Condition", "Condition", "Condition", "Condition", "Condition",…
$ vocabulary_id <chr> "SNOMED", "SNOMED", "SNOMED", "SNOMED", "SNOMED", "SNOMED", "SNOMED", "SNOMED", "SNOMED", "SNOMED", "SN…
$ standard_concept <chr> "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S"…2.1 Systematic Search of Codes Using CodelistGenerator
# A tibble: 2 × 1
found_from
<chr>
1 From initial search
2 From descendants # A tibble: 1 × 1
domain_id
<chr>
1 Condition# A tibble: 1 × 1
standard_concept
<chr>
1 S 2.1 Systematic Search of Codes Using CodelistGenerator
- We are now going to create a codelist for asthma excluding the character child (includes child, childhood, children, etc…)
asthma_codes_with_exclusion <- getCandidateCodes(
cdm = cdm,
keywords = "asthma",
exclude = "child",
domains = "Condition",
includeDescendants = TRUE)- And compare both codelists:
compareCodelists(asthma_codes, asthma_codes_with_exclusion) |>
filter(codelist == "Only codelist 1")# A tibble: 2 × 5
concept_id concept_name codelist_1 codelist_2 codelist
<int> <chr> <dbl> <dbl> <chr>
1 4051466 Childhood asthma 1 NA Only codelist 1
2 45772073 Asthma in mother complicating childbirth 1 NA Only codelist 12.2 Generating Vocabulary Based Codelists for Medications
- We are now going to create a codelist based on the ingredient acetaminophen.
acetaminophen <- getDrugIngredientCodes(cdm = cdm,
name = "acetaminophen",
nameStyle = "{concept_name}",
doseUnit = "milligram",
routeCategory = NULL,
doseForm = NULL,
ingredientRange = c(1,Inf))
acetaminophen
- acetaminophen (22256 codes)2.2 Generating Vocabulary Based Codelists for Medications
- Now we are going to stratify the codelist into different route categories:
acetaminophen_stratified <- stratifyByRouteCategory(acetaminophen,
cdm = cdm,
keepOriginal = TRUE)
acetaminophen_stratified
- acetaminophen (22256 codes)
- acetaminophen_inhalable (3 codes)
- acetaminophen_injectable (689 codes)
- acetaminophen_oral (17219 codes)
- acetaminophen_topical (6 codes)
- acetaminophen_transmucosal_rectal (1459 codes)
along with 1 more codelists- Other stratification functions:
acetaminophen_by_concept <- stratifyByConcept(cdm)
acetaminophen_by_route <- stratifyByDoseUnit(cdm)2.2 Generating Vocabulary Based Codelists for Medications
- Subset only to codes with a specific route:
acetaminophen_oral <- subsetOnRouteCategory(acetaminophen,
cdm = cdm,
routeCategory = "oral")
acetaminophen_oral |> stratifyByRouteCategory(cdm)
- acetaminophen_oral (17219 codes)- Subset only to codes without a specific route:
acetaminophen_no_oral <- subsetOnRouteCategory(acetaminophen,
cdm = cdm,
routeCategory = "oral",
negate = TRUE)
acetaminophen_no_oral |> stratifyByRouteCategory(cdm)
- acetaminophen_inhalable (3 codes)
- acetaminophen_injectable (689 codes)
- acetaminophen_topical (6 codes)
- acetaminophen_transmucosal_rectal (1459 codes)- Other functions to subset a codelist:
2.3 Generating a Codelist Based on ATC Codes
atc <- getATCCodes(cdm = cdm, level = "ATC 1st")
atc
- A_alimentary_tract_and_metabolism (211265 codes)
- B_blood_and_blood_forming_organs (83485 codes)
- C_cardiovascular_system (233725 codes)
- D_dermatologicals (118870 codes)
- G_genito_urinary_system_and_sex_hormones (64486 codes)
- H_systemic_hormonal_preparations_excl_sex_hormones_and_insulins (36165 codes)
along with 8 more codelists2.4 Generating a Codelist Based on ICD10 Chapters
- We can generate codelists based on ICD10 chapters and subchapters
- Four different levels of granularity:
availableICD10(cdm, level = "ICD10 Chapter") |>
glimpse() chr [1:22] "Certain infectious and parasitic diseases" "Neoplasms" ...availableICD10(cdm, level = "ICD10 SubChapter") |>
glimpse() chr [1:274] "Intestinal infectious diseases" "Tuberculosis" "Certain zoonotic bacterial diseases" "Other bacterial diseases" ...availableICD10(cdm, level = "ICD10 Hierarchy") |>
glimpse() chr [1:2093] "Other salmonella infections" "Other bacterial intestinal infections" "Amoebiasis" "Brucellosis" "Other sepsis" ...availableICD10(cdm, level = "ICD10 Code") |>
glimpse() chr [1:14130] "Enteropathogenic Escherichia coli infection" "Enteroinvasive Escherichia coli infection" ...2.4 Generating a Codelist Based on ICD10 Chapters
icd_chapters <- getICD10StandardCodes(cdm = cdm,
name = NULL,
level = c("ICD10 Chapter"),
includeDescendants = TRUE)
icd_chapters
- i_certain_infectious_and_parasitic_diseases (65191 codes)
- ii_neoplasms (16262 codes)
- iii_diseases_of_the_blood_and_blood_forming_organs_and_certain_disorders_involving_the_immune_mechanism (6604 codes)
- iv_endocrine_nutritional_and_metabolic_diseases (13483 codes)
- ix_diseases_of_the_circulatory_system (38407 codes)
- v_mental_and_behavioural_disorders (4602 codes)
along with 16 more codelistsmental_and_behavioural_disorders <- getICD10StandardCodes(cdm = cdm,
name = "Mental and behavioural disorders",
level = "ICD10 Chapter",
includeDescendants = TRUE)
mental_and_behavioural_disorders
- v_mental_and_behavioural_disorders (4602 codes)2.5 SQL Server Practical
3 Codelist Diagnostics
3.1 Code Counts Using ACHILLES Tables
- Let’s now characterise our asthma codelist obtained using
getCandidateCodes() - First, let’s turn our data table into a codelist object:
class(asthma_codes)[1] "tbl_df" "tbl" "data.frame"asthma <- newCodelist(list("asthma" = asthma_codes$concept_id))
class(asthma)[1] "codelist" "list" - Then, let’s summarise the number of counts using ACHILLES tables:
asthma_achilles <- summariseAchillesCodeUse(asthma,
cdm = cdm,
countBy = c("record", "person"))3.1 Code Counts Using ACHILLES Tables
tableAchillesCodeUse(asthma_achilles)
Database name
|
|||||||
|---|---|---|---|---|---|---|---|
Eunomia Synpuf
|
|||||||
| Codelist name | Domain ID | Standard concept name | Standard concept ID | Standard concept | Vocabulary ID |
Estimate name
|
|
| Record count | Person count | ||||||
| asthma | condition | Intrinsic asthma without status asthmaticus | 252658 | standard | SNOMED | 39 | 25 |
| Asthmatic pulmonary eosinophilia | 252942 | standard | SNOMED | 72 | 59 | ||
| Chronic asthmatic bronchitis | 256448 | standard | SNOMED | 74 | 63 | ||
| Exacerbation of asthma | 257581 | standard | SNOMED | 49 | 40 | ||
| Allergic bronchopulmonary aspergillosis | 257583 | standard | SNOMED | 5 | 5 | ||
| IgE-mediated allergic asthma | 312950 | standard | SNOMED | 67 | 45 | ||
| Cough variant asthma | 313236 | standard | SNOMED | 11 | 10 | ||
| Asthma | 317009 | standard | SNOMED | 274 | 183 | ||
| Acute exacerbation of chronic asthmatic bronchitis | 40481763 | standard | SNOMED | 33 | 29 | ||
| Exercise-induced asthma | 443801 | standard | SNOMED | 8 | 8 | ||
| Acute severe exacerbation of asthma | 45769438 | standard | SNOMED | 11 | 10 | ||
| Acute exacerbation of allergic asthma | 45769441 | standard | SNOMED | 7 | 7 | ||
| Acute severe exacerbation of immunoglobin E-mediated allergic asthma | 45769442 | standard | SNOMED | 12 | 12 | ||
| Acute severe exacerbation of intrinsic asthma | 45769443 | standard | SNOMED | 7 | 7 | ||
| Acute exacerbation of intrinsic asthma | 45773005 | standard | SNOMED | 18 | 13 | ||
3.2 Code Counts Using Patient-Level Data
- Let’s now count the number of records and person based on the database counts.
asthma_code_use <- summariseCodeUse(asthma,
cdm = cdm,
countBy = c("record", "person"),
byYear = FALSE,
bySex = TRUE,
ageGroup = NULL,
dateRange = as.Date(c(NA,NA)))3.2 Code Counts Using Patient-Level Data
tableCodeUse(asthma_code_use, groupColumn = "sex")
Database name
|
||||||||
|---|---|---|---|---|---|---|---|---|
Eunomia Synpuf
|
||||||||
| Codelist name | Standard concept name | Standard concept ID | Source concept name | Source concept ID | Source concept value | Domain ID |
Estimate name
|
|
| Record count | Person count | |||||||
| overall | ||||||||
| asthma | overall | - | NA | NA | NA | NA | 687 | 301 |
| Acute severe exacerbation of asthma | 45769438 | Asthma, unspecified type, with status asthmaticus | 44832424 | 49391 | condition | 11 | 10 | |
| Acute severe exacerbation of immunoglobin E-mediated allergic asthma | 45769442 | Extrinsic asthma with status asthmaticus | 44824288 | 49301 | condition | 12 | 12 | |
| Intrinsic asthma without status asthmaticus | 252658 | Intrinsic asthma, unspecified | 44824289 | 49310 | condition | 39 | 25 | |
| Acute exacerbation of chronic asthmatic bronchitis | 40481763 | Chronic obstructive asthma with (acute) exacerbation | 44837136 | 49322 | condition | 33 | 29 | |
| Acute severe exacerbation of intrinsic asthma | 45769443 | Intrinsic asthma with status asthmaticus | 44834769 | 49311 | condition | 7 | 7 | |
| Acute exacerbation of intrinsic asthma | 45773005 | Intrinsic asthma with (acute) exacerbation | 44837135 | 49312 | condition | 18 | 13 | |
| Allergic bronchopulmonary aspergillosis | 257583 | Allergic bronchopulmonary aspergillosis | 44831284 | 5186 | condition | 5 | 5 | |
| Cough variant asthma | 313236 | Cough variant asthma | 44833611 | 49382 | condition | 11 | 10 | |
| Asthmatic pulmonary eosinophilia | 252942 | Pulmonary eosinophilia | 44827830 | 5183 | condition | 72 | 59 | |
| IgE-mediated allergic asthma | 312950 | Extrinsic asthma, unspecified | 44824287 | 49300 | condition | 67 | 45 | |
| Chronic asthmatic bronchitis | 256448 | Chronic obstructive asthma with status asthmaticus | 44820889 | 49321 | condition | 12 | 12 | |
| Acute exacerbation of allergic asthma | 45769441 | Extrinsic asthma with (acute) exacerbation | 44831278 | 49302 | condition | 7 | 7 | |
| Exacerbation of asthma | 257581 | Asthma, unspecified type, with (acute) exacerbation | 44823144 | 49392 | condition | 49 | 40 | |
| Exercise-induced asthma | 443801 | Exercise induced bronchospasm | 44826678 | 49381 | condition | 8 | 8 | |
| Asthma | 317009 | Asthma, unspecified type, unspecified | 44821988 | 49390 | condition | 274 | 183 | |
| Chronic asthmatic bronchitis | 256448 | Chronic obstructive asthma, unspecified | 44831280 | 49320 | condition | 62 | 52 | |
| Female | ||||||||
| asthma | overall | - | NA | NA | NA | NA | 402 | 164 |
| Acute severe exacerbation of intrinsic asthma | 45769443 | Intrinsic asthma with status asthmaticus | 44834769 | 49311 | condition | 4 | 4 | |
| Asthma | 317009 | Asthma, unspecified type, unspecified | 44821988 | 49390 | condition | 156 | 98 | |
| Acute exacerbation of allergic asthma | 45769441 | Extrinsic asthma with (acute) exacerbation | 44831278 | 49302 | condition | 4 | 4 | |
| Acute exacerbation of chronic asthmatic bronchitis | 40481763 | Chronic obstructive asthma with (acute) exacerbation | 44837136 | 49322 | condition | 23 | 19 | |
| Intrinsic asthma without status asthmaticus | 252658 | Intrinsic asthma, unspecified | 44824289 | 49310 | condition | 15 | 14 | |
| Asthmatic pulmonary eosinophilia | 252942 | Pulmonary eosinophilia | 44827830 | 5183 | condition | 38 | 37 | |
| IgE-mediated allergic asthma | 312950 | Extrinsic asthma, unspecified | 44824287 | 49300 | condition | 48 | 28 | |
| Acute exacerbation of intrinsic asthma | 45773005 | Intrinsic asthma with (acute) exacerbation | 44837135 | 49312 | condition | 12 | 7 | |
| Chronic asthmatic bronchitis | 256448 | Chronic obstructive asthma with status asthmaticus | 44820889 | 49321 | condition | 8 | 8 | |
| Exercise-induced asthma | 443801 | Exercise induced bronchospasm | 44826678 | 49381 | condition | 6 | 6 | |
| Acute severe exacerbation of immunoglobin E-mediated allergic asthma | 45769442 | Extrinsic asthma with status asthmaticus | 44824288 | 49301 | condition | 4 | 4 | |
| Exacerbation of asthma | 257581 | Asthma, unspecified type, with (acute) exacerbation | 44823144 | 49392 | condition | 30 | 23 | |
| Acute severe exacerbation of asthma | 45769438 | Asthma, unspecified type, with status asthmaticus | 44832424 | 49391 | condition | 8 | 7 | |
| Cough variant asthma | 313236 | Cough variant asthma | 44833611 | 49382 | condition | 9 | 8 | |
| Chronic asthmatic bronchitis | 256448 | Chronic obstructive asthma, unspecified | 44831280 | 49320 | condition | 37 | 29 | |
| Male | ||||||||
| asthma | overall | - | NA | NA | NA | NA | 285 | 137 |
| Cough variant asthma | 313236 | Cough variant asthma | 44833611 | 49382 | condition | 2 | 2 | |
| Intrinsic asthma without status asthmaticus | 252658 | Intrinsic asthma, unspecified | 44824289 | 49310 | condition | 24 | 11 | |
| Acute severe exacerbation of immunoglobin E-mediated allergic asthma | 45769442 | Extrinsic asthma with status asthmaticus | 44824288 | 49301 | condition | 8 | 8 | |
| Chronic asthmatic bronchitis | 256448 | Chronic obstructive asthma with status asthmaticus | 44820889 | 49321 | condition | 4 | 4 | |
| Acute severe exacerbation of intrinsic asthma | 45769443 | Intrinsic asthma with status asthmaticus | 44834769 | 49311 | condition | 3 | 3 | |
| Acute severe exacerbation of asthma | 45769438 | Asthma, unspecified type, with status asthmaticus | 44832424 | 49391 | condition | 3 | 3 | |
| Asthmatic pulmonary eosinophilia | 252942 | Pulmonary eosinophilia | 44827830 | 5183 | condition | 34 | 22 | |
| Asthma | 317009 | Asthma, unspecified type, unspecified | 44821988 | 49390 | condition | 118 | 85 | |
| Exercise-induced asthma | 443801 | Exercise induced bronchospasm | 44826678 | 49381 | condition | 2 | 2 | |
| IgE-mediated allergic asthma | 312950 | Extrinsic asthma, unspecified | 44824287 | 49300 | condition | 19 | 17 | |
| Acute exacerbation of allergic asthma | 45769441 | Extrinsic asthma with (acute) exacerbation | 44831278 | 49302 | condition | 3 | 3 | |
| Acute exacerbation of chronic asthmatic bronchitis | 40481763 | Chronic obstructive asthma with (acute) exacerbation | 44837136 | 49322 | condition | 10 | 10 | |
| Allergic bronchopulmonary aspergillosis | 257583 | Allergic bronchopulmonary aspergillosis | 44831284 | 5186 | condition | 5 | 5 | |
| Chronic asthmatic bronchitis | 256448 | Chronic obstructive asthma, unspecified | 44831280 | 49320 | condition | 25 | 23 | |
| Acute exacerbation of intrinsic asthma | 45773005 | Intrinsic asthma with (acute) exacerbation | 44837135 | 49312 | condition | 6 | 6 | |
| Exacerbation of asthma | 257581 | Asthma, unspecified type, with (acute) exacerbation | 44823144 | 49392 | condition | 19 | 17 | |
3.3 Identify Orphan Codes
- Orphan codes are concepts that might be related to our codelist but that have not been included.
- It looks for descendants (via concept_descendants table), ancestors (via concept_ancestor table), and concepts related to the codes included in the codelist (via concept_relationship)
- ACHILLES tables are used.
orphan <- summariseOrphanCodes(asthma,
cdm)3.3 Identify Orphan Codes
tableOrphanCodes(orphan)
Database name
|
|||||||
|---|---|---|---|---|---|---|---|
Eunomia Synpuf
|
|||||||
| Codelist name | Domain ID | Standard concept name | Standard concept ID | Standard concept | Vocabulary ID |
Estimate name
|
|
| Record count | Person count | ||||||
| asthma | condition | Chronic obstructive lung disease | 255573 | standard | SNOMED | 1,222 | 457 |
| Chronic bronchitis | 255841 | standard | SNOMED | 97 | 79 | ||
| Bronchitis | 256451 | standard | SNOMED | 146 | 104 | ||
| Acute exacerbation of chronic obstructive airways disease | 257004 | standard | SNOMED | 327 | 155 | ||
| Allergic rhinitis | 257007 | standard | SNOMED | 370 | 232 | ||
| Wheezing | 314754 | standard | SNOMED | 81 | 64 | ||
| Disorder of respiratory system | 320136 | standard | SNOMED | 79 | 75 | ||
| Respiratory finding | 4024567 | standard | SNOMED | 5 | 5 | ||
| At risk - finding | 4085075 | standard | SNOMED | 26 | 25 | ||
| Disorder of the nose | 4229909 | standard | SNOMED | 40 | 36 | ||
| Disorder characterized by eosinophilia | 4302954 | standard | SNOMED | 8 | 7 | ||
| Disorder due to infection | 432250 | standard | SNOMED | 1 | 1 | ||
| Poisoning by drug AND/OR medicinal substance | 438028 | standard | SNOMED | 17 | 13 | ||
| Left heart failure | 439846 | standard | SNOMED | 109 | 72 | ||
| Disorder of immune function | 440371 | standard | SNOMED | 1 | 1 | ||
| Poisoning | 442562 | standard | SNOMED | 1 | 1 | ||
Phenotype R