Predicting anxiety treatment outcome in community mental health services using linked health administrative data.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
04 09 2024
04 09 2024
Historique:
received:
17
05
2024
accepted:
29
08
2024
medline:
5
9
2024
pubmed:
5
9
2024
entrez:
4
9
2024
Statut:
epublish
Résumé
Anxiety disorders is ranked as the most common class of mental illness disorders globally, affecting hundreds of millions of people and significantly impacting daily life. Developing reliable predictive models for anxiety treatment outcomes holds immense potential to help guide the development of personalised care, optimise resource allocation and improve patient outcomes. This research investigates whether community mental health treatment for anxiety disorder is associated with reliable changes in Kessler psychological distress scale (K10) scores and whether pre-treatment K10 scores and past health service interactions can accurately predict reliable change (improvement). The K10 assessment was administered to 46,938 public patients in a community setting within the Western Australia dataset in 2005-2022; of whom 3794 in 4067 episodes of care were reassessed at least twice for anxiety disorders, obsessive-compulsive disorder, or reaction to severe stress and adjustment disorders (ICD-10 codes F40-F43). Reliable change on the K10 was calculated and used with the post-treatment score as the outcome variables. Machine learning models were developed using features from a large health service administrative linked dataset that includes the pre-treatment K10 assessment as well as community mental health episodes of care, emergency department presentations, and inpatient admissions for prediction. The classification model achieved an area under the receiver operating characteristic curve of 0.76 as well as an F1 score, precision and recall of 0.69, and the regression model achieved an R
Identifiants
pubmed: 39232215
doi: 10.1038/s41598-024-71557-2
pii: 10.1038/s41598-024-71557-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
20559Informations de copyright
© 2024. The Author(s).
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