An Exploratory Analysis of the Impact of Family Functioning on Treatment for Depression in Adolescents / Norah C. FEENY in Journal of Clinical Child & Adolescent Psychology, 38-6 (November-December 2009)  

Public ISBD [article]  inJournal of Clinical Child & Adolescent Psychology > 38-6 (November-December 2009) . - p.814-825 Titre : An Exploratory Analysis of the Impact of Family Functioning on Treatment for Depression in Adolescents Type de document : texte imprimé Auteurs : Norah C. FEENY, Auteur ; John S. MARCH, Auteur ; Anne D. SIMONS, Auteur ; Diane E. MAY, Auteur ; Paul ROHDE, Auteur ; Robert L. FINDLING, Auteur ; Steven MCNULTY, Auteur ; David R. ROSENBERG, Auteur ; Sanjeev PATHAK, Auteur ; Christopher J. KRATOCHVIL, Auteur ; Betsy KENNARD, Auteur ; Susan G. SILVA, Auteur ; Golda S. GINSBURG, Auteur ; Mark A. REINECKE, Auteur ; John F. CURRY, Auteur ; Karen WELLS, Auteur ; Michele ROBINS, Auteur Année de publication : 2009 Article en page(s) : p.814-825 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : This article explores aspects of family environment and parent-child conflict that may predict or moderate response to acute treatments among depressed adolescents (N = 439) randomly assigned to fluoxetine, cognitive behavioral therapy, their combination, or placebo. Outcomes were Week 12 scores on measures of depression and global impairment. Of 20 candidate variables, one predictor emerged: Across treatments, adolescents with mothers who reported less parent-child conflict were more likely to benefit than their counterparts. When family functioning moderated outcome, adolescents who endorsed more negative environments were more likely to benefit from fluoxetine. Similarly, when moderating effects were seen on cognitive behavioral therapy conditions, they were in the direction of being less effective among teens reporting poorer family environments. En ligne : http://dx.doi.org/10.1080/15374410903297148 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=881 [article] An Exploratory Analysis of the Impact of Family Functioning on Treatment for Depression in Adolescents [texte imprimé] / Norah C. FEENY, Auteur ; John S. MARCH, Auteur ; Anne D. SIMONS, Auteur ; Diane E. MAY, Auteur ; Paul ROHDE, Auteur ; Robert L. FINDLING, Auteur ; Steven MCNULTY, Auteur ; David R. ROSENBERG, Auteur ; Sanjeev PATHAK, Auteur ; Christopher J. KRATOCHVIL, Auteur ; Betsy KENNARD, Auteur ; Susan G. SILVA, Auteur ; Golda S. GINSBURG, Auteur ; Mark A. REINECKE, Auteur ; John F. CURRY, Auteur ; Karen WELLS, Auteur ; Michele ROBINS, Auteur . - 2009 . - p.814-825. Langues : Anglais (eng) in Journal of Clinical Child & Adolescent Psychology > 38-6 (November-December 2009) . - p.814-825 Index. décimale : PER Périodiques Résumé : This article explores aspects of family environment and parent-child conflict that may predict or moderate response to acute treatments among depressed adolescents (N = 439) randomly assigned to fluoxetine, cognitive behavioral therapy, their combination, or placebo. Outcomes were Week 12 scores on measures of depression and global impairment. Of 20 candidate variables, one predictor emerged: Across treatments, adolescents with mothers who reported less parent-child conflict were more likely to benefit than their counterparts. When family functioning moderated outcome, adolescents who endorsed more negative environments were more likely to benefit from fluoxetine. Similarly, when moderating effects were seen on cognitive behavioral therapy conditions, they were in the direction of being less effective among teens reporting poorer family environments. En ligne : http://dx.doi.org/10.1080/15374410903297148 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=881

Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants / Arjun P. ATHREYA in Journal of Child Psychology and Psychiatry, 63-11 (November 2022)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 63-11 (November 2022) . - p.1347-1358 Titre : Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants Type de document : texte imprimé Auteurs : Arjun P. ATHREYA, Auteur ; Jennifer L. VANDE VOORT, Auteur ; Julia SHEKUNOV, Auteur ; Sandra J. RACKLEY, Auteur ; Jarrod M. LEFFLER, Auteur ; Alastair J. MCKEAN, Auteur ; Magdalena ROMANOWICZ, Auteur ; Betsy KENNARD, Auteur ; Graham J. EMSLIE, Auteur ; Taryn MAYES, Auteur ; Madhukar TRIVEDI, Auteur ; Liewei WANG, Auteur ; Richard M. WEINSHILBOUM, Auteur ; William V. BOBO, Auteur ; Paul E. CROARKIN, Auteur Article en page(s) : p.1347-1358 Langues : Anglais (eng) Mots-clés : Child  Humans  Adolescent  Fluoxetine/therapeutic use  Depressive Disorder, Major/therapy  Duloxetine Hydrochloride/therapeutic use  Artificial Intelligence  Double-Blind Method  Antidepressive Agents  Treatment Outcome  Machine Learning  Depression  adolescents  decision support tools Index. décimale : PER Périodiques Résumé : BACKGROUND: The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo. METHODS: The study samples included training datasets (N=271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N=255) or placebo (N=265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment. RESULTS: Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self-esteem, and depressed feelings) assessed with the Children's Depression Rating Scale-Revised at 4-6 weeks predicted treatment outcomes with fluoxetine at 10-12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10-12 week outcomes at 4-6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo-treated patients with accuracies of 67%. In placebo-treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants. CONCLUSIONS: PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression. En ligne : http://dx.doi.org/10.1111/jcpp.13580 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=490 [article] Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants [texte imprimé] / Arjun P. ATHREYA, Auteur ; Jennifer L. VANDE VOORT, Auteur ; Julia SHEKUNOV, Auteur ; Sandra J. RACKLEY, Auteur ; Jarrod M. LEFFLER, Auteur ; Alastair J. MCKEAN, Auteur ; Magdalena ROMANOWICZ, Auteur ; Betsy KENNARD, Auteur ; Graham J. EMSLIE, Auteur ; Taryn MAYES, Auteur ; Madhukar TRIVEDI, Auteur ; Liewei WANG, Auteur ; Richard M. WEINSHILBOUM, Auteur ; William V. BOBO, Auteur ; Paul E. CROARKIN, Auteur . - p.1347-1358. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 63-11 (November 2022) . - p.1347-1358 Mots-clés : Child  Humans  Adolescent  Fluoxetine/therapeutic use  Depressive Disorder, Major/therapy  Duloxetine Hydrochloride/therapeutic use  Artificial Intelligence  Double-Blind Method  Antidepressive Agents  Treatment Outcome  Machine Learning  Depression  adolescents  decision support tools Index. décimale : PER Périodiques Résumé : BACKGROUND: The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo. METHODS: The study samples included training datasets (N=271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N=255) or placebo (N=265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment. RESULTS: Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self-esteem, and depressed feelings) assessed with the Children's Depression Rating Scale-Revised at 4-6 weeks predicted treatment outcomes with fluoxetine at 10-12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10-12 week outcomes at 4-6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo-treated patients with accuracies of 67%. In placebo-treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants. CONCLUSIONS: PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression. En ligne : http://dx.doi.org/10.1111/jcpp.13580 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=490

The Dysfunctional Attitudes Scale: Psychometric Properties in Depressed Adolescents / Gregory M. ROGERS in Journal of Clinical Child & Adolescent Psychology, 38-6 (November-December 2009)  

Public ISBD [article]  inJournal of Clinical Child & Adolescent Psychology > 38-6 (November-December 2009) . - p.781-789 Titre : The Dysfunctional Attitudes Scale: Psychometric Properties in Depressed Adolescents Type de document : texte imprimé Auteurs : Gregory M. ROGERS, Auteur ; John S. MARCH, Auteur ; David R. ROSENBERG, Auteur ; Sanjeev PATHAK, Auteur ; Christopher J. KRATOCHVIL, Auteur ; Betsy KENNARD, Auteur ; Norah C. FEENY, Auteur ; Rick H. HOYLE, Auteur ; Susan G. SILVA, Auteur ; Mark A. REINECKE, Auteur ; Marjorie H. KLEIN, Auteur ; Marilyn J. ESSEX, Auteur ; Jong-Hyo PARK, Auteur ; John F. CURRY, Auteur ; Elizabeth B. WELLER, Auteur Année de publication : 2009 Article en page(s) : p.781-789 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : The psychometric properties and factor structure of the Dysfunctional Attitudes Scale were examined in a sample of 422 male and female adolescents (ages 12-17) with current major depressive disorder. The scale demonstrated high internal consistency ( = .93) and correlated significantly with self-report and interview-based measures of depression. Confirmatory factor analysis indicated that a correlated 2-factor model, with scales corresponding to perfectionism and need for social approval, provided a satisfactory fit to the data. The goodness-of-fit was equivalent across sexes and age groups. The findings support the use of the Dysfunctional Attitudes Scale and its subscales in the assessment of clinically depressed adolescents. En ligne : http://dx.doi.org/10.1080/15374410903259007 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=880 [article] The Dysfunctional Attitudes Scale: Psychometric Properties in Depressed Adolescents [texte imprimé] / Gregory M. ROGERS, Auteur ; John S. MARCH, Auteur ; David R. ROSENBERG, Auteur ; Sanjeev PATHAK, Auteur ; Christopher J. KRATOCHVIL, Auteur ; Betsy KENNARD, Auteur ; Norah C. FEENY, Auteur ; Rick H. HOYLE, Auteur ; Susan G. SILVA, Auteur ; Mark A. REINECKE, Auteur ; Marjorie H. KLEIN, Auteur ; Marilyn J. ESSEX, Auteur ; Jong-Hyo PARK, Auteur ; John F. CURRY, Auteur ; Elizabeth B. WELLER, Auteur . - 2009 . - p.781-789. Langues : Anglais (eng) in Journal of Clinical Child & Adolescent Psychology > 38-6 (November-December 2009) . - p.781-789 Index. décimale : PER Périodiques Résumé : The psychometric properties and factor structure of the Dysfunctional Attitudes Scale were examined in a sample of 422 male and female adolescents (ages 12-17) with current major depressive disorder. The scale demonstrated high internal consistency ( = .93) and correlated significantly with self-report and interview-based measures of depression. Confirmatory factor analysis indicated that a correlated 2-factor model, with scales corresponding to perfectionism and need for social approval, provided a satisfactory fit to the data. The goodness-of-fit was equivalent across sexes and age groups. The findings support the use of the Dysfunctional Attitudes Scale and its subscales in the assessment of clinically depressed adolescents. En ligne : http://dx.doi.org/10.1080/15374410903259007 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=880

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