Antidepressant

Hayley C Gorton, Roger T Webb, Mathew J Carr, Marcos Delpozo-Banos, Ann John, Darren M Ashcroft

PH463 / 926 Clinical-Coded Phenotype

  1. Overview

    Phenotype Type
    Drug
    Phenotype UUID
    k9LnuZRRZceVZFyqsVBMGx
    Sex
    Both
    Valid Event Date Range
    01/01/1998 - 31/03/2014
    Coding System
    Read codes v2
    Data Sources
    Collections
    ClinicalCodes RepositoryPhenotype Library
    Tags
    No data
  2. Definition

    Importance:

    People with epilepsy are at increased risk of mortality, but, to date, the cause-specific risks of all unnatural causes have not been reported.

    Objective:

    To estimate cause-specific unnatural mortality risks in people with epilepsy and to identify the medication types involved in poisoning deaths.

    Design, Setting, and Participants:

    This population-based cohort study used 2 electronic primary care data sets linked to hospitalization and mortality records, the Clinical Practice Research Datalink (CPRD) in England (from January 1, 1998, to March 31, 2014) and the Secure Anonymised Information Linkage (SAIL) Databank in Wales (from January 1, 2001, to December 31, 2014). Each person with epilepsy was matched on age (within 2 years), sex, and general practice with up to 20 individuals without epilepsy. Unnatural mortality was determined using International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes V01 through Y98 in the Office for National Statistics mortality records. Hazard ratios (HRs) were estimated in each data set using a stratified Cox proportional hazards model, and meta-analyses were conducted using DerSimonian and Laird random-effects models. The analysis was performed from January 5, 2016, to November 16, 2017.

    Exposures:

    People with epilepsy were identified using primary care epilepsy diagnoses and associated antiepileptic drug prescriptions.

    Main Outcomes and Measures:

    Hazard ratios (HRs) for unnatural mortality and the frequency of each involved medication type estimated as a percentage of all medication poisoning deaths.

    Results:

    In total, 44678 individuals in the CPRD and 14 051 individuals in the SAIL Databank were identified in the prevalent epilepsy cohorts, and 891 429 (CPRD) and 279 365 (SAIL) individuals were identified in the comparison cohorts. In both data sets, 51% of the epilepsy and comparison cohorts were male, and the median age at entry was 40 years (interquartile range, 25-60 years) in the CPRD cohorts and 43 years (interquartile range, 24-64 years) in the SAIL cohorts. People with epilepsy were significantly more likely to die of any unnatural cause (HR, 2.77; 95% CI, 2.43-3.16), unintentional injury or poisoning (HR, 2.97; 95% CI, 2.54-3.48) or suicide (HR, 2.15; 95% CI, 1.51-3.07) than people in the comparison cohort. Particularly large risk increases were observed in the epilepsy cohorts for unintentional medication poisoning (HR, 4.99; 95% CI, 3.22-7.74) and intentional self-poisoning with medication (HR, 3.55; 95% CI, 1.01-12.53). Opioids (56.5% [95% CI, 43.3%-69.0%]) and psychotropic medication (32.3% [95% CI, 20.9%-45.3%)] were more commonly involved than antiepileptic drugs (9.7% [95% CI, 3.6%-19.9%]) in poisoning deaths in people with epilepsy.

    Conclusions and Relevance:

    Compared with people without epilepsy, people with epilepsy are at increased risk of unnatural death and thus should be adequately advised about unintentional injury prevention and monitored for suicidal ideation, thoughts, and behaviors. The suitability and toxicity of concomitant medication should be considered when prescribing for comorbid conditions.

  3. Implementation

    Implementation

    No data
  4. Clinical Code List

  5. Publication

    • Hayley C Gorton, Roger T Webb, Mathew J Carr, Marcos Delpozo-Banos, Ann John, Darren M Ashcroft, Risk of Unnatural Mortality in People With Epilepsy. JAMA Neurology, 75(8), 929-038, 2018.

    Citation Example

    Hayley C Gorton, Roger T Webb, Mathew J Carr, Marcos Delpozo-Banos, Ann John, Darren M Ashcroft. PH463 / 926 - Antidepressant. Phenotype Library [Online]. 06 October 2021. Available from: http://phenotypes.healthdatagateway.org/phenotypes/PH463/version/926/detail/. [Accessed 25 July 2024]

  6. API

    To Export Phenotype Details:

    FormatAPI
    JSON site_root/api/v1/phenotypes/PH463/version/926/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(
     'PH463',
     version_id=926
    )

    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(
     'PH463',
     version_id=926
    )

    To Export Phenotype Code List:

    FormatAPI
    JSON site_root/api/v1/phenotypes/PH463/version/926/export/codes/?format=json
    CSV site_root/phenotypes/PH463/version/926/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(
     'PH463',
     version_id=926
    )

    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(
     'PH463',
     version_id=926
    )

  7. Version History

    Version IDNameOwnerPublish date
    926 Antidepressant ieuan.scanlon2021-10-06currently shown