Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents

Stephens, Alice, Allardyce, Judith ORCID: https://orcid.org/0000-0003-4094-552X, Weavers, Bryony ORCID: https://orcid.org/0000-0001-9654-3939, Lennon, Jessica, Bevan-Jones, Rhys ORCID: https://orcid.org/0000-0001-8976-9825, Powell, Victoria, Eyre, Olga, Potter, Robert, Escott-Price, Valentina ORCID: https://orcid.org/0000-0003-1784-5483, Osborn, David, Thapar, Anita ORCID: https://orcid.org/0000-0002-3689-737X, Collishaw, Stephan ORCID: https://orcid.org/0000-0002-4296-820X, Thapar, Ajay ORCID: https://orcid.org/0000-0002-3689-737X, Heron, Jon and Rice, Frances ORCID: https://orcid.org/0000-0002-9484-1729 2023. Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents. Journal of Child Psychology and Psychiatry 64 (3) , pp. 367-375. 10.1111/jcpp.13704

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Abstract

Background: Parental depression is common and is a major risk factor for depression in adolescents. Early identification of adolescents at elevated risk of developing major depressive disorder (MDD) in this group could improve early access to preventive interventions. Methods: Using longitudinal data from 337 adolescents at high familial risk of depression, we developed a risk prediction model for adolescent MDD. The model was externally validated in an independent cohort of 1,384 adolescents at high familial risk. We assessed predictors at baseline and MDD at follow‐up (a median of 2–3 years later). We compared the risk prediction model to a simple comparison model based on screening for depressive symptoms. Decision curve analysis was used to identify which model‐predicted risk score thresholds were associated with the greatest clinical benefit. Results: The MDD risk prediction model discriminated between those adolescents who did and did not develop MDD in the development (C‐statistic = .783, IQR (interquartile range) = .779, .778) and the validation samples (C‐statistic = .722, IQR = −.694, .741). Calibration in the validation sample was good to excellent (calibration intercept = .011, C‐slope = .851). The MDD risk prediction model was superior to the simple comparison model where discrimination was no better than chance (C‐statistic = .544, IQR = .536, .572). Decision curve analysis found that the highest clinical utility was at the lowest risk score thresholds (0.01–0.05). Conclusions: The developed risk prediction model successfully discriminated adolescents who developed MDD from those who did not. In practice, this model could be further developed with user involvement into a tool to target individuals for low‐intensity, selective preventive intervention.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG) Medicine
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/
Publisher:	Wiley
ISSN:	0021-9630
Funders:	MRC, Wellcome Trust
Date of First Compliant Deposit:	13 September 2022
Date of Acceptance:	24 August 2022
Last Modified:	16 Jul 2024 09:56
URI:	https://orca.cardiff.ac.uk/id/eprint/152541

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