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. 2024 Dec 19;67(1):e87.
doi: 10.1192/j.eurpsy.2024.1787.

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

Frank Iorfino  1 , Rafael Oliveira  1   2 , Sally Cripps  3   4 , Roman Marchant  3   4 , Mathew Varidel  1 , William Capon  1 , Jacob J Crouse  1 , Ante Prodan  1   5   6 , Elizabeth M Scott  1 , Jan Scott  7 , Ian B Hickie  1
Affiliations

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

Frank Iorfino et al. Eur Psychiatry. .

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.

Keywords: early intervention; functional impairment; precision psychiatry; prediction; prognostic tools; youth mental health.

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Conflict of interest statement

I.B.H. is the Co-Director, Health and Policy at the Brain and Mind Centre (BMC), University of Sydney. The BMC operates an early-intervention youth services at Camperdown under contract to headspace. He is the Chief Scientific Advisor to, and a 3.2% equity shareholder in, Innow Well Pty Ltd, which supports the transformation of mental health services internationally through the use of innovative technologies.

E.S. is Principal Research Fellow at the Brain and Mind Centre, The University of Sydney. She is Discipline Leader of Adult Mental Health, School of Medicine, University of Notre Dame, and a Consultant Psychiatrist. She was the Medical Director, Young Adult Mental Health Unit, St Vincent’s Hospital Darlinghurst until January 2021. She has received honoraria for educational seminars related to the clinical management of depressive disorders supported by Servier, Janssen, and Eli-Lilly pharmaceuticals. She has participated in a national advisory board for the antidepressant compound Pristiq, manufactured by Pfizer. She was the National Coordinator of an antidepressant trial sponsored by Servier.

All other authors declare no financial or non-financial competing interests.

Figures

Figure 1.
Figure 1.
An overview of the predictive model. An individual’s clinical and demographic characteristics serve as inputs to the cluster assignment model, which predicts the probabilities of whether an individual’s score is going to remain the same, improve, or deteriorate. Combined with the cluster’s prediction, the individual’s initial score informs a trajectory prediction model which predicts an individual’s response over time. The magenta crosses indicate the actual scores given by clinicians. Note that the trajectory model is only informed of the individual’s initial score at baseline, and not the score at their second visit, which the model must predict.
Figure 2.
Figure 2.
Model performance metrics and AUC. Panel A presents results for the overall model predicting whether an individual’s SOFAS score would significantly drop by 10 points over the course of 3 months. Panel B presents results for the model predicting functional impairment at the next consultation for individuals with initially good functioning (SOFAS above 70). Abbreviations: NPV, negative predictive value; PPV, positive predictive value.
Figure 3.
Figure 3.
Model outputs for an individual who improved over the course of 3 months (Panel A), and for another individual who deteriorated over the course of 3 months (Panel B). The “cluster” graph shows the probability of each change cluster (“constant,” “up,” and “down”) for both individuals. The “predicted trajectories” graph shows the simulated trajectories based on the cluster model and the individuals initial score (~50 for the person in panel A and ~60 for the person in panel B). “Up” trajectories are shaded green, “constant” trajectories are shaded red, and “down” trajectories are shaded blue. The dotted black line and cross show the actual observed trajectory and SOFAS score for both individuals over the follow-up period.
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References

    1. Scott J, Fowler D, McGorry P, Birchwood M, Killackey E, Christensen H, et al. Adolescents and young adults who are not in employment, education, or training. BMJ. 2013;347:f5270. - PubMed
    1. Kieling C, Buchweitz C, Caye A, Silvani J, Ameis SH, Brunoni AR, et al. Worldwide prevalence and disability from mental disorders across childhood and adolescence: evidence from the global burden of disease study. JAMA Psychiatr [Internet]. 2024 Jan 31 [cited 2024 Mar 4]. Available from: 10.1001/jamapsychiatry.2023.5051. - DOI - PMC - PubMed
    1. McKnight PE, Kashdan TB. The importance of functional impairment to mental health outcomes: a case for reassessing our goals in depression treatment research. Clin Psychol Rev. 2009;29(3):243. - PMC - PubMed
    1. Iorfino F, Carpenter JS, Cross SP, Crouse J, Davenport TA, Hermens DF, et al. Social and occupational outcomes for young people who attend early intervention mental health services: a longitudinal study. Med J Aust. 2022;216(2):87–93. - PubMed
    1. KPMG Consulting. Evaluation of the national headspace program. Canberra, Australia: Australia government department of health, 2022; p. 1–347.

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