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Psychiatric disorder

Presentation

We provide the PsychiatricDisorderFromICD10 class to extract visits or patients with ICD10 codes related to psychiatric disorders

Available disorders
  • Anxiety Disorders
  • Bipolar and Related Disorders
  • Depressive Disorders
  • Disruptive, Impulse Control and Conduct Disorders
  • Dissociative Disorders
  • Feeding and Eating Disorders
  • Mental Health Symptom
  • Miscellaneous
  • Obsessive-Compulsive and Related Disorders
  • Personality Disorders
  • Schizophrenia Spectrum and Other Psychotic Disorders
  • Sleep-Wake Disorders
  • Somatic Symptom and Related Disorders
  • Substance-Related and Addictive Disorders
  • Suicide or Self-Injury
  • Trauma and Stressor-Related Disorders

How it works

The algorithm works by looking for either DP, DR or DAS ICD10 codes associated with psychiatric disorder. The codes terminology comes from this article1 and is available under PsychiatricDisorderFromICD10.ICD10_CODES

Usage

By default, all cancer types mentionned above are extracted

from eds_scikit.io import HiveData
data = HiveData(DBNAME)
from eds_scikit.phenotype import PsychiatricDisorderFromICD10

psy = PsychiatricDisorderFromICD10(data)
data = psy.to_data()

To choose a subset of disorders, use the disorder_types argument:

psy = PsychiatricDisorderFromICD10(
    data,
    disorder_types=[
        "Anxiety Disorders",
        "Trauma and Stressor-Related Disorders",
    ],
)

The final phenotype DataFrame is then available at data.computed["PsychiatricDisorderFromICD10"]

Optional parameters

PARAMETER DESCRIPTION
data

A BaseData object

TYPE: BaseData

disorder_types

Optional list of disorder types to use for phenotyping

TYPE: Optional[List[str]] DEFAULT: None

level

On which level to do the aggregation, either "patient" or "visit"

TYPE: str DEFAULT: 'patient'

subphenotype

Whether the threshold should apply to the phenotype ("phenotype" column) of the subphenotype ("subphenotype" column)

TYPE: bool DEFAULT: True

threshold

Minimal number of events (which definition depends on the level value)

TYPE: int DEFAULT: 1

Citation

You can get the BibTex of the corresponding article1 by calling

cancer.cite()
@article{2022_covid_4CE,
    author = {Gutiérrez-Sacristán, Alba and Serret-Larmande, Arnaud and Hutch, Meghan R. and Sáez, Carlos and Aronow, Bruce J. and Bhatnagar, Surbhi and Bonzel, Clara-Lea and Cai, Tianxi and Devkota, Batsal and Hanauer, David A. and Loh, Ne Hooi Will and Luo, Yuan and Moal, Bertrand and Ahooyi, Taha Mohseni and Njoroge, Wanjikũ F. M. and Omenn, Gilbert S. and Sanchez-Pinto, L. Nelson and South, Andrew M. and Sperotto, Francesca and Tan, Amelia L. M. and Taylor, Deanne M. and Verdy, Guillaume and Visweswaran, Shyam and Xia, Zongqi and Zahner, Janet and Avillach, Paul and Bourgeois, Florence T. and Consortium for Clinical Characterization of COVID-19 by EHR (4CE)},
    title = "{Hospitalizations Associated With Mental Health Conditions Among Adolescents in the US and France During the COVID-19 Pandemic}",
    journal = {JAMA Network Open},
    volume = {5},
    number = {12},
    pages = {e2246548-e2246548},
    year = {2022},
    month = {12},
    abstract = "{The COVID-19 pandemic has been associated with an increase in mental health diagnoses among adolescents, though the extent of the increase, particularly for severe cases requiring hospitalization, has not been well characterized. Large-scale federated informatics approaches provide the ability to efficiently and securely query health care data sets to assess and monitor hospitalization patterns for mental health conditions among adolescents.To estimate changes in the proportion of hospitalizations associated with mental health conditions among adolescents following onset of the COVID-19 pandemic.This retrospective, multisite cohort study of adolescents 11 to 17 years of age who were hospitalized with at least 1 mental health condition diagnosis between February 1, 2019, and April 30, 2021, used patient-level data from electronic health records of 8 children’s hospitals in the US and France.Change in the monthly proportion of mental health condition–associated hospitalizations between the prepandemic (February 1, 2019, to March 31, 2020) and pandemic (April 1, 2020, to April 30, 2021) periods using interrupted time series analysis.There were 9696 adolescents hospitalized with a mental health condition during the prepandemic period (5966 [61.5\\%] female) and 11 101 during the pandemic period (7603 [68.5\\%] female). The mean (SD) age in the prepandemic cohort was 14.6 (1.9) years and in the pandemic cohort, 14.7 (1.8) years. The most prevalent diagnoses during the pandemic were anxiety (6066 [57.4\\%]), depression (5065 [48.0\\%]), and suicidality or self-injury (4673 [44.2\\%]). There was an increase in the proportions of monthly hospitalizations during the pandemic for anxiety (0.55\\%; 95\\% CI, 0.26\\%-0.84\\%), depression (0.50\\%; 95\\% CI, 0.19\\%-0.79\\%), and suicidality or self-injury (0.38\\%; 95\\% CI, 0.08\\%-0.68\\%). There was an estimated 0.60\\% increase (95\\% CI, 0.31\\%-0.89\\%) overall in the monthly proportion of mental health–associated hospitalizations following onset of the pandemic compared with the prepandemic period.In this cohort study, onset of the COVID-19 pandemic was associated with increased hospitalizations with mental health diagnoses among adolescents. These findings support the need for greater resources within children’s hospitals to care for adolescents with mental health conditions during the pandemic and beyond.}",
    issn = {2574-3805},
    doi = {10.1001/jamanetworkopen.2022.46548},
    url = {https://doi.org/10.1001/jamanetworkopen.2022.46548},
    eprint = {https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2799437/gutirrezsacristn\_2022\_oi\_221314\_1670339179.72376.pdf},
}

Reference

Check the code reference here for a more detailled look.


  1. Alba Gutiérrez-Sacristán, Arnaud Serret-Larmande, Meghan R. Hutch, Carlos Sáez, Bruce J. Aronow, Surbhi Bhatnagar, Clara-Lea Bonzel, Tianxi Cai, Batsal Devkota, David A. Hanauer, Ne Hooi Will Loh, Yuan Luo, Bertrand Moal, Taha Mohseni Ahooyi, Wanjikũ F. M. Njoroge, Gilbert S. Omenn, L. Nelson Sanchez-Pinto, Andrew M. South, Francesca Sperotto, Amelia L. M. Tan, Deanne M. Taylor, Guillaume Verdy, Shyam Visweswaran, Zongqi Xia, Janet Zahner, Paul Avillach, Florence T. Bourgeois, and Consortium for Clinical Characterization of COVID-19 by EHR (4CE). Hospitalizations Associated With Mental Health Conditions Among Adolescents in the US and France During the COVID-19 Pandemic. JAMA Network Open, 5(12):e2246548–e2246548, 12 2022. URL: https://doi.org/10.1001/jamanetworkopen.2022.46548, arXiv:https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2799437/gutirrezsacristn\_2022\_oi\_221314\_1670339179.72376.pdf, doi:10.1001/jamanetworkopen.2022.46548

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