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 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] / 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

Identifying the neurodevelopmental and psychiatric signatures of genomic disorders associated with intellectual disability: a machine learning approach / Adam CUNNINGHAM ; Sergio Marco SALAS ; Matthew BRACHER-SMITH ; Samuel CHAWNER ; Jan STOCHL ; Tamsin FORD ; F. Lucy RAYMOND ; Valentina ESCOTT PRICE ; Marianne B.M. VAN DEN BREE in Molecular Autism, 14 (2023)  

Public ISBD [article]  inMolecular Autism > 14 (2023) . - 19 p. Titre : Identifying the neurodevelopmental and psychiatric signatures of genomic disorders associated with intellectual disability: a machine learning approach Type de document : texte imprimé Auteurs : Adam CUNNINGHAM, Auteur ; Sergio Marco SALAS, Auteur ; Matthew BRACHER-SMITH, Auteur ; Samuel CHAWNER, Auteur ; Jan STOCHL, Auteur ; Tamsin FORD, Auteur ; F. Lucy RAYMOND, Auteur ; Valentina ESCOTT PRICE, Auteur ; Marianne B.M. VAN DEN BREE, Auteur Article en page(s) : 19 p. Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : BACKGROUND: Genomic conditions can be associated with developmental delay, intellectual disability, autism spectrum disorder, and physical and mental health symptoms. They are individually rare and highly variable in presentation, which limits the use of standard clinical guidelines for diagnosis and treatment. A simple screening tool to identify young people with genomic conditions associated with neurodevelopmental disorders (ND-GCs) who could benefit from further support would be of considerable value. We used machine learning approaches to address this question. METHOD: A total of 493 individuals were included: 389 with a ND-GC, mean age=9.01, 66% male) and 104 siblings without known genomic conditions (controls, mean age=10.23, 53% male). Primary carers completed assessments of behavioural, neurodevelopmental and psychiatric symptoms and physical health and development. Machine learning techniques (penalised logistic regression, random forests, support vector machines and artificial neural networks) were used to develop classifiers of ND-GC status and identified limited sets of variables that gave the best classification performance. Exploratory graph analysis was used to understand associations within the final variable set. RESULTS: All machine learning methods identified variable sets giving high classification accuracy (AUROC between 0.883 and 0.915). We identified a subset of 30 variables best discriminating between individuals with ND-GCs and controls which formed 5 dimensions: conduct, separation anxiety, situational anxiety, communication and motor development. LIMITATIONS: This study used cross-sectional data from a cohort study which was imbalanced with respect to ND-GC status. Our model requires validation in independent datasets and with longitudinal follow-up data for validation before clinical application. CONCLUSIONS: In this study, we developed models that identified a compact set of psychiatric and physical health measures that differentiate individuals with a ND-GC from controls and highlight higher-order structure within these measures. This work is a step towards developing a screening instrument to identify young people with ND-GCs who might benefit from further specialist assessment. En ligne : http://dx.doi.org/10.1186/s13229-023-00549-2 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=513 [article] Identifying the neurodevelopmental and psychiatric signatures of genomic disorders associated with intellectual disability: a machine learning approach [texte imprimé] / Adam CUNNINGHAM, Auteur ; Sergio Marco SALAS, Auteur ; Matthew BRACHER-SMITH, Auteur ; Samuel CHAWNER, Auteur ; Jan STOCHL, Auteur ; Tamsin FORD, Auteur ; F. Lucy RAYMOND, Auteur ; Valentina ESCOTT PRICE, Auteur ; Marianne B.M. VAN DEN BREE, Auteur . - 19 p. Langues : Anglais (eng) in Molecular Autism > 14 (2023) . - 19 p. Index. décimale : PER Périodiques Résumé : BACKGROUND: Genomic conditions can be associated with developmental delay, intellectual disability, autism spectrum disorder, and physical and mental health symptoms. They are individually rare and highly variable in presentation, which limits the use of standard clinical guidelines for diagnosis and treatment. A simple screening tool to identify young people with genomic conditions associated with neurodevelopmental disorders (ND-GCs) who could benefit from further support would be of considerable value. We used machine learning approaches to address this question. METHOD: A total of 493 individuals were included: 389 with a ND-GC, mean age=9.01, 66% male) and 104 siblings without known genomic conditions (controls, mean age=10.23, 53% male). Primary carers completed assessments of behavioural, neurodevelopmental and psychiatric symptoms and physical health and development. Machine learning techniques (penalised logistic regression, random forests, support vector machines and artificial neural networks) were used to develop classifiers of ND-GC status and identified limited sets of variables that gave the best classification performance. Exploratory graph analysis was used to understand associations within the final variable set. RESULTS: All machine learning methods identified variable sets giving high classification accuracy (AUROC between 0.883 and 0.915). We identified a subset of 30 variables best discriminating between individuals with ND-GCs and controls which formed 5 dimensions: conduct, separation anxiety, situational anxiety, communication and motor development. LIMITATIONS: This study used cross-sectional data from a cohort study which was imbalanced with respect to ND-GC status. Our model requires validation in independent datasets and with longitudinal follow-up data for validation before clinical application. CONCLUSIONS: In this study, we developed models that identified a compact set of psychiatric and physical health measures that differentiate individuals with a ND-GC from controls and highlight higher-order structure within these measures. This work is a step towards developing a screening instrument to identify young people with ND-GCs who might benefit from further specialist assessment. En ligne : http://dx.doi.org/10.1186/s13229-023-00549-2 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=513

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