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Identifying adolescents at risk for depression: a prediction score performance in cohorts based in three different continents

Research output: Contribution to journalArticle

Thiago Botter-Maio Rocha, Helen Fisher, Arthur Caye, Luciana Anselmi, Louise Arseneault, Fernando C. Barros, Avshalom Caspi, Andrea Danese, Helen Gonçalves, HonaLee Harrington, Renate Houts, Ana Maria Batista Menezes, Terrie Edith Moffitt, Valeria Mondelli, Richie Poulton, Luis Augusto Rohde, Fernando Wehrmeister, Christian Kieling

Original languageEnglish
JournalJournal of the American Academy of Child and Adolescent Psychiatry
Publication statusAccepted/In press - 5 Dec 2019

King's Authors

Abstract

Objective: Prediction models have become frequent in the medical literature, but
most published studies are conducted in a single setting. Heterogeneity between
development and validation samples has been posited as a major obstacle for the generalization of models. We aimed to develop a multivariable prognostic model using sociodemographic variables easily obtainable from adolescents at age 15 to predict a depressive disorder diagnosis at age 18, and to evaluate its generalizability in two samples from diverse socioeconomic and cultural settings.

Methods: Data from the 1993 Pelotas Birth Cohort were used to develop the
prediction model, and its generalizability was evaluated in two representative cohort studies: the Environmental Risk (E-Risk) Longitudinal Twin Study and the Dunedin Multidisciplinary Health and Development Study.

Results: At age 15, 2,192 adolescents with no evidence of current or previous
depression were included (44.6% males). The apparent C-statistic of the models
derived in Pelotas ranged from 0.76 to 0.79, and the model obtained from a penalized logistic regression was selected for subsequent external evaluation. Major discrepancies between the samples were identified, impacting the external prognostic performance of the model (Dunedin and E-Risk C-statistics of 0.63 and 0.59, respectively). The implementation of recommended strategies to account for this heterogeneity among s samples.

Conclusion: An adolescent depression risk score comprising easily obtainable
predictors was developed with good prognostic performance in a Brazilian sample. Heterogeneity among settings was not trivial, but strategies to deal with sample diversity were identified as pivotal for providing better risk stratification across samples. Future efforts should focus on developing better methodological approaches for incorporating heterogeneity in prognostic research.

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