A prognostic model for predicting functional impairment in youth mental health services.

Iorfino, F; Oliveira, R; Cripps, S; Marchant, R; Varidel, M; Capon, W; Crouse, JJ; Prodan, A; Scott, EM; Scott, J; Hickie, IB

A prognostic model for predicting functional impairment in youth mental health services.

Iorfino, F Oliveira, R Cripps, S

Marchant, R Varidel, M Capon, W Crouse, JJ Prodan, A Scott, EM Scott, J Hickie, IB

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Publisher:: CAMBRIDGE UNIV PRESS
Publication Type:: Journal Article
Citation:: Eur Psychiatry, 2024, 67, (1), pp. e87
Issue Date:: 2024-12-19

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Field	Value	Language
dc.contributor.author	Iorfino, F
dc.contributor.author	Oliveira, R
dc.contributor.author	Cripps, S https://orcid.org/0000-0003-3207-172X
dc.contributor.author	Marchant, R
dc.contributor.author	Varidel, M
dc.contributor.author	Capon, W
dc.contributor.author	Crouse, JJ
dc.contributor.author	Prodan, A
dc.contributor.author	Scott, EM
dc.contributor.author	Scott, J
dc.contributor.author	Hickie, IB
dc.date.accessioned	2025-01-28T05:42:33Z
dc.date.available	2025-01-28T05:42:33Z
dc.date.issued	2024-12-19
dc.identifier.citation	Eur Psychiatry, 2024, 67, (1), pp. e87
dc.identifier.issn	0924-9338
dc.identifier.issn	1778-3585
dc.identifier.uri	http://hdl.handle.net/10453/184372
dc.description.abstract	BACKGROUND: Functional impairment is a major concern among those presenting to youth mental health services and can have a profound impact on long-term outcomes. Early recognition and prevention for those at risk of functional impairment is essential to guide effective youth mental health care. Yet, identifying those at risk is challenging and impacts the appropriate allocation of indicated prevention and early intervention strategies. METHODS: We developed a prognostic model to predict a young person's social and occupational functional impairment trajectory over 3 months. The sample included 718 young people (12-25 years) engaged in youth mental health care. A Bayesian random effects model was designed using demographic and clinical factors and model performance was evaluated on held-out test data via 5-fold cross-validation. RESULTS: Eight factors were identified as the optimal set for prediction: employment, education, or training status; self-harm; psychotic-like experiences; physical health comorbidity; childhood-onset syndrome; illness type; clinical stage; and circadian disturbances. The model had an acceptable area under the curve (AUC) of 0.70 (95% CI, 0.56-0.81) overall, indicating its utility for predicting functional impairment over 3 months. For those with good baseline functioning, it showed excellent performance (AUC = 0.80, 0.67-0.79) for identifying individuals at risk of deterioration. CONCLUSIONS: We developed and validated a prognostic model for youth mental health services to predict functional impairment trajectories over a 3-month period. This model serves as a foundation for further tool development and demonstrates its potential to guide indicated prevention and early intervention for enhancing functional outcomes or preventing functional decline.
dc.format	Electronic
dc.language	eng
dc.publisher	CAMBRIDGE UNIV PRESS
dc.relation.ispartof	Eur Psychiatry
dc.relation.isbasedon	10.1192/j.eurpsy.2024.1787
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	11 Medical and Health Sciences, 17 Psychology and Cognitive Sciences
dc.subject.classification	Psychiatry
dc.subject.classification	3202 Clinical sciences
dc.subject.classification	5202 Biological psychology
dc.subject.classification	5203 Clinical and health psychology
dc.subject.mesh	Humans
dc.subject.mesh	Adolescent
dc.subject.mesh	Male
dc.subject.mesh	Female
dc.subject.mesh	Prognosis
dc.subject.mesh	Mental Health Services
dc.subject.mesh	Child
dc.subject.mesh	Young Adult
dc.subject.mesh	Adult
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	Mental Disorders
dc.subject.mesh	Adolescent Health Services
dc.subject.mesh	Humans
dc.subject.mesh	Prognosis
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	Mental Disorders
dc.subject.mesh	Mental Health Services
dc.subject.mesh	Adolescent
dc.subject.mesh	Adult
dc.subject.mesh	Child
dc.subject.mesh	Adolescent Health Services
dc.subject.mesh	Female
dc.subject.mesh	Male
dc.subject.mesh	Young Adult
dc.subject.mesh	Humans
dc.subject.mesh	Adolescent
dc.subject.mesh	Male
dc.subject.mesh	Female
dc.subject.mesh	Prognosis
dc.subject.mesh	Mental Health Services
dc.subject.mesh	Child
dc.subject.mesh	Young Adult
dc.subject.mesh	Adult
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	Mental Disorders
dc.subject.mesh	Adolescent Health Services
dc.title	A prognostic model for predicting functional impairment in youth mental health services.
dc.type	Journal Article
utslib.citation.volume	67
utslib.location.activity	England
utslib.for	11 Medical and Health Sciences
utslib.for	17 Psychology and Cognitive Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Provost
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated	2025-01-28T05:42:32Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	67
utslib.citation.issue	1

Abstract:

BACKGROUND: Functional impairment is a major concern among those presenting to youth mental health services and can have a profound impact on long-term outcomes. Early recognition and prevention for those at risk of functional impairment is essential to guide effective youth mental health care. Yet, identifying those at risk is challenging and impacts the appropriate allocation of indicated prevention and early intervention strategies. METHODS: We developed a prognostic model to predict a young person's social and occupational functional impairment trajectory over 3 months. The sample included 718 young people (12-25 years) engaged in youth mental health care. A Bayesian random effects model was designed using demographic and clinical factors and model performance was evaluated on held-out test data via 5-fold cross-validation. RESULTS: Eight factors were identified as the optimal set for prediction: employment, education, or training status; self-harm; psychotic-like experiences; physical health comorbidity; childhood-onset syndrome; illness type; clinical stage; and circadian disturbances. The model had an acceptable area under the curve (AUC) of 0.70 (95% CI, 0.56-0.81) overall, indicating its utility for predicting functional impairment over 3 months. For those with good baseline functioning, it showed excellent performance (AUC = 0.80, 0.67-0.79) for identifying individuals at risk of deterioration. CONCLUSIONS: We developed and validated a prognostic model for youth mental health services to predict functional impairment trajectories over a 3-month period. This model serves as a foundation for further tool development and demonstrates its potential to guide indicated prevention and early intervention for enhancing functional outcomes or preventing functional decline.

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http://hdl.handle.net/10453/184372