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

  1. Overview

    Phenotype Type
    Disease or syndrome
    Sex
    Both
    Valid Event Date Range
    01/01/1999 - 01/07/2016
    Coding System
    Read codes v2Med codesICD10 codes
    Collections
    CALIBERPhenotype Library
    Tags
    No data
  2. Definition

    Use MODIFIED CALIBER 'Diabetes' phenotyping algorithm for

    1. T1DM,

    2. T2DM,

    3. '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 type

    ELSE 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 type

    ELSE classify as no 'Diabetes'

  3. Implementation

  4. Clinical Code List

  5. 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

    No data
  6. API

    To Export Phenotype Details:

    FormatAPI
    JSON site_root/api/v1/phenotypes/PH152/version/304/detail/?format=json
    R Package

    # Download here

    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

    # Download here

    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:

    FormatAPI
    JSON site_root/api/v1/phenotypes/PH152/version/304/export/codes/?format=json
    CSV site_root/phenotypes/PH152/version/304/export/codes/
    R Package

    # Download here

    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

    # Download here

    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
    )

  7. Version History

    Version IDNameOwnerPublish date
    304 Diabetes ieuan.scanlon2021-10-06currently shown