Diabetes
Kuan V, Denaxas S, Gonzalez-Izquierdo A, Direk K, Bhatti O, Husain S, Sutaria S, Hingorani M, Nitsch D, Parisinos C, Lumbers T, Mathur R, Sofat R, Casas JP, Wong I, Hemingway H, Hingorani A
PH152 / 304 Clinical-Coded Phenotype
Overview
Phenotype TypeDisease or syndromeSexBothValid Event Date Range01/01/1999 - 01/07/2016Coding SystemRead codes v2Med codesICD10 codesData SourcesCollectionsCALIBERPhenotype LibraryTagsNo dataDefinition
Use MODIFIED CALIBER 'Diabetes' phenotyping algorithm for
T1DM,
T2DM,
'Diabetes' other or uncertain type:
IF there is at least one record for code for type 2 'Diabetes' (diabdiag_gprd = 4)
and no record for type 1 'Diabetes' (no record with diabdiag_gprd = 3) then classify the patient as type 2 'Diabetes'ELSE if there is at least one record for code for type I 'Diabetes' (diabdiag_gprd = 3)
and no record for type 2 'Diabetes' (no record with diabdiag_gprd = 4) then classify the patient as type 1 'Diabetes'ELSE if there is at least one record of type 1 'Diabetes' (diabdiag_gprd = 3)
and type 2 'Diabetes' (diabdiag_gprd = 4) then classify as 'Diabetes' other or uncertain typeELSE if there are no diabdiag_gprd records for this patient:
If there is at least one record for Non-insulin-dependent 'Diabetes' mellitus (NIDDM) (dm_gprd = 4 or dm_hes = 4) and no record for IDDM (no record with dm_gprd = 3 or dm_hes = 3) then classify the patient as type 2 'Diabetes' ELSE if there is at least one record for Insulin-dependent 'Diabetes' mellitus (IDDM) (dm_gprd = 3 or dm_hes = 3) and no record for NIDDM (no record with dm_gprd = 4 or dm_hes = 4) then classify the patient as type 1 'Diabetes' ELSE if there is at least one record of 'Diabetes' (dm_gprd or dm_hes category 3, 4, 5 or 6) then classify as 'Diabetes' other or uncertain typeELSE classify as no 'Diabetes'
Implementation
Phenoflow IDImplementation
Clinical Code List
PUBLISHED - 135 Codes
PUBLISHED - 135 Codes
PUBLISHED - 15 Codes
Publication
Kuan V., Denaxas S., Gonzalez-Izquierdo A. et al. A chronological map of 308 physical and mental health conditions from 4 million individuals in the National Health Service. The Lancet Digital Health - DOI 10.1016/S2589-7500(19)30012-3
(DOI:10.1016/S2589-7500(19)30012-3)
Citation Requirements
API
To Export Phenotype Details:
Format API JSON site_root/api/v1/phenotypes/PH152/version/304/detail/?format=json R Package library(ConceptLibraryClient)
# Connect to API
client = ConceptLibraryClient::Connection$new(public=TRUE)
# Get details of phenotype
phenotype_details = client$phenotypes$get_detail(
'PH152',
version_id=304
)Py Package from pyconceptlibraryclient import Client
# Connect to API
client = Client(public=True)
# Get codelist of phenotype
phenotype_codelist = client.phenotypes.get_detail(
'PH152',
version_id=304
)To Export Phenotype Code List:
Format API JSON site_root/api/v1/phenotypes/PH152/version/304/export/codes/?format=json CSV site_root/phenotypes/PH152/version/304/export/codes/ R Package library(ConceptLibraryClient)
# Connect to API
client = ConceptLibraryClient::Connection$new(public=TRUE)
# Get codelist of phenotype
phenotype_codelist = client$phenotypes$get_codelist(
'PH152',
version_id=304
)Py Package from pyconceptlibraryclient import Client
# Connect to API
client = Client(public=True)
# Get codelist of phenotype
phenotype_codelist = client.phenotypes.get_codelist(
'PH152',
version_id=304
)Version History