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Détail de l'auteur
Auteur Paul E. CROARKIN |
Documents disponibles écrits par cet auteur (2)
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Commentary: Treatment failure and success: a commentary on defining and treating pediatric treatment-resistant depression - reflections on Dwyer et al. (2020) / Jeffrey R. STRAWN in Journal of Child Psychology and Psychiatry, 61-3 (March 2020)
[article]
Titre : Commentary: Treatment failure and success: a commentary on defining and treating pediatric treatment-resistant depression - reflections on Dwyer et al. (2020) Type de document : Texte imprimé et/ou numérique Auteurs : Jeffrey R. STRAWN, Auteur ; Paul E. CROARKIN, Auteur Article en page(s) : p.333-335 Langues : Anglais (eng) Mots-clés : Depression major depressive disorder treatment-resistant depression Index. décimale : PER Périodiques Résumé : Nearly two in five youth with major depressive disorder fail to respond to first-line interventions. As such, treatment-resistant depression represents a formidable challenge for clinicians and researchers. In fact, even considering the diagnosis of treatment-resistant depression in children and adolescents requires (a) defining treatment-resistant depression and, by extension, treatment failure; (b) defining recovery; (c) understanding its developmental trajectory; and in addition to (d) understanding the evidence for treatment interventions in this population. Accumulating data suggest that treatment-resistant depression is heterogeneous and that this heterogeneity may inform interventions. Additionally, these data suggest that substantially more nuance is needed in evaluating the 'adequacy' of prior treatments whether they are psychotherapeutic or psychopharmacologic. Last, adjunctive interventions that focus on neuromodulation, glutamatergic, GABAergic, and inflammatory pathways remain poorly understood in youth with treatment-resistant depression despite very significant advances in adults with treatment-resistant depression. En ligne : http://dx.doi.org/10.1111/jcpp.13207 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=420
in Journal of Child Psychology and Psychiatry > 61-3 (March 2020) . - p.333-335[article] Commentary: Treatment failure and success: a commentary on defining and treating pediatric treatment-resistant depression - reflections on Dwyer et al. (2020) [Texte imprimé et/ou numérique] / Jeffrey R. STRAWN, Auteur ; Paul E. CROARKIN, Auteur . - p.333-335.
Langues : Anglais (eng)
in Journal of Child Psychology and Psychiatry > 61-3 (March 2020) . - p.333-335
Mots-clés : Depression major depressive disorder treatment-resistant depression Index. décimale : PER Périodiques Résumé : Nearly two in five youth with major depressive disorder fail to respond to first-line interventions. As such, treatment-resistant depression represents a formidable challenge for clinicians and researchers. In fact, even considering the diagnosis of treatment-resistant depression in children and adolescents requires (a) defining treatment-resistant depression and, by extension, treatment failure; (b) defining recovery; (c) understanding its developmental trajectory; and in addition to (d) understanding the evidence for treatment interventions in this population. Accumulating data suggest that treatment-resistant depression is heterogeneous and that this heterogeneity may inform interventions. Additionally, these data suggest that substantially more nuance is needed in evaluating the 'adequacy' of prior treatments whether they are psychotherapeutic or psychopharmacologic. Last, adjunctive interventions that focus on neuromodulation, glutamatergic, GABAergic, and inflammatory pathways remain poorly understood in youth with treatment-resistant depression despite very significant advances in adults with treatment-resistant depression. En ligne : http://dx.doi.org/10.1111/jcpp.13207 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=420 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)
[article]
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 et/ou numérique 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 D. 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
in Journal of Child Psychology and Psychiatry > 63-11 (November 2022) . - p.1347-1358[article] Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants [Texte imprimé et/ou numérique] / 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 D. 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