Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents / Alice STEPHENS in Journal of Child Psychology and Psychiatry, 64-3 (March 2023)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 64-3 (March 2023) . - p.367-375 Titre : Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents Type de document : Texte imprimé et/ou numérique Auteurs : Alice STEPHENS, Auteur ; Judith ALLARDYCE, Auteur ; Bryony WEAVERS, Auteur ; Jessica LENNON, Auteur ; Rhys BEVAN JONES, Auteur ; Victoria POWELL, Auteur ; Olga EYRE, Auteur ; Robert POTTER, Auteur ; Valentina ESCOTT PRICE, Auteur ; David OSBORN, Auteur ; Anita THAPAR, Auteur ; Stephan COLLISHAW, Auteur ; Ajay K. THAPAR, Auteur ; Jon HERON, Auteur ; Frances RICE, Auteur Article en page(s) : p.367-375 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : 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=â’.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. En ligne : https://doi.org/10.1111/jcpp.13704 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=493 [article] Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents [Texte imprimé et/ou numérique] / Alice STEPHENS, Auteur ; Judith ALLARDYCE, Auteur ; Bryony WEAVERS, Auteur ; Jessica LENNON, Auteur ; Rhys BEVAN JONES, Auteur ; Victoria POWELL, Auteur ; Olga EYRE, Auteur ; Robert POTTER, Auteur ; Valentina ESCOTT PRICE, Auteur ; David OSBORN, Auteur ; Anita THAPAR, Auteur ; Stephan COLLISHAW, Auteur ; Ajay K. THAPAR, Auteur ; Jon HERON, Auteur ; Frances RICE, Auteur . - p.367-375. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 64-3 (March 2023) . - p.367-375 Index. décimale : PER Périodiques Résumé : 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=â’.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. En ligne : https://doi.org/10.1111/jcpp.13704 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=493

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