ArticleJournal of child psychology and psychiatry, and allied disciplines2023
Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents.
Article in Journal of child psychology and psychiatry, and allied disciplines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 12 citations in OpenAlex.
- 'It is just a prediction; it's, like, not fact': youth attitudes towards risk prediction tools and personalised preventive interventions for depression and anxiety.BMJ mental health · 2026Article
- Longitudinal Prediction of Adolescent Depression from Environmental and Polygenic Risk Scores.medRxiv : the preprint server for health sciences · 2025Article
- Article
- Assessing a prediction model for depression risk using an early adolescent sample with self-reported depression.JCPP advances · 2025Article
- No prediction without prevention: A global qualitative study of attitudes toward using a prediction tool for risk of developing depression during adolescence.Global mental health (Cambridge, England) · 2024Article
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Authors and funding
15 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundParental 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.
methodsUsing 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.
resultsThe 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).
conclusionsThe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.