Predictors and Moderators Two Treatments of Oppositional Defiant Disorder in Children

Dedousis-Wallace, Anna; Drysdale, Sophia A. O.; McAloon, John; Murrihy, Rachael C.; Greene, Ross W.; Ollendick, Thomas H.

doi:10.1080/15374416.2022.2127102

Cited by 5 publications

(1 citation statement)

References 54 publications

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“…Similarly, child clinical research has also found racial/ethnic differences in parental etiological explanations for child problems [ 28 , 29 ]. This is particularly important given the research showing that parent attributions about the child’s problem predicts help-seeking attitudes, treatment attendance and completion, and treatment outcomes (e.g., [ 30 , 31 ]). Not only have etiological explanations been linked to service utilization, but they have been identified as a potential contributor to ethnic disparities in mental health service utilization (e.g., [ 32 ]), and parental etiological explanations partially mediate the relation between race/ethnicity and service utilization [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Parent Explanatory Model Personalization as a Method of Reducing Risk for Poor Engagement and Outcomes in PCIT among Culturally Diverse Families

Zerr,

McCabe,

Zhang

et al. 2024

JCM

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Background/Objectives: Evidence supports the efficacy of Behavioral Parent Training (BPT) interventions such as Parent–Child Interaction Therapy (PCIT) for treating child behavior problems; however, treatment engagement and outcomes vary across ethnic groups. Risk for poor treatment engagement and outcomes may be attributed in part to misalignment between parent explanatory model components (PEMs) and the traditional BPT model, including treatment expectations, etiological explanations, parenting styles, and family support for treatment. The present study aims to examine whether personalized treatment adaptations addressing these PEM–BPT misalignments reduce risk for poor treatment engagement and outcomes. Methods: The authors previously utilized the PersIn framework to develop a personalized version of PCIT (MY PCIT) that assesses these PEMs in order to identify families at risk for poor treatment engagement and outcomes. Families were identified as high risk (due to PEM–BPT misalignment) and low risk (meaning those without identified PEM–BPT misalignment) for specific PEMs. Families at elevated risk then received tailored treatment materials designed to improve alignment between the parental explanatory model and the PCIT treatment explanatory model. A recent pilot trial of MY PCIT demonstrated positive treatment outcomes; however, the extent to which adaptations were successful in reducing the underlying risk factors has not yet been examined. Results: Findings demonstrate that the personalization approach was effective in reducing indicators of risk, and that families who were initially at high and low risk during pre-treatment reported similar levels of treatment engagement and outcomes by post-treatment. Conclusions: The findings suggest that this personalized approach has the potential to reduce risk associated with poor treatment engagement and outcomes for culturally diverse families.

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Section: Introductionmentioning

confidence: 99%

Parent Explanatory Model Personalization as a Method of Reducing Risk for Poor Engagement and Outcomes in PCIT among Culturally Diverse Families

Zerr,

McCabe,

Zhang

et al. 2024

JCM

View full text Add to dashboard Cite

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Community-Delivered Collaborative and Proactive Solutions and Parent Management Training for Oppositional Youth: A Randomized Trial

Murrihy

Drysdale²,

Dedousis-Wallace³

et al. 2023

Behavior Therapy

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Identifying selective predictors of ADHD, Oppositional Defiant and Conduct Disorder onset in early adolescence with optimized deep learning

de Lacy,

Ramshaw

2023

Preprint

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IntroductionThe externalizing disorders of ADHD, Oppositional Defiant Disorder (ODD) and Conduct Disorder (CD) exhibit a strong uptick in incidence in late childhood to become some of the most common mental health conditions in adolescence and strong predictors of adult psychopathology. While treatable, substantial diagnostic overlap exists among the externalizing disorders, complicating intervention planning. Thus, early adolescence is a period of considerable interest in understanding which factors predict the onset of externalizing disorders and disambiguating those that may differentially predict the development of ADD versus (vs) ODD and CD.Materials and MethodsHere, we analyzed 5,777 multimodal candidate predictors collected from children age 9-10 yrs and their parents in the ABCD cohort spanning demographics; developmental and medical history; physiologic function; academic performance; social, physical and cultural environment; activities of everyday life, substance use and cortical and subcortical brain structure, volumetrics, connectivity and function to predict the future onset of ADHD, ODD and CD at 2-year follow-up. We used deep learning optimized with an innovative AI algorithm that jointly optimizes model training and performs automated feature selection to construct prospective, individual-level predictions of illness onset in this high-dimension data. Additional experiments furnished predictive models of all prevailing cases at 11-12 yrs and examined relative predictive performance when candidate predictors were restricted to only neural metrics derived from MRI.ResultsMultimodal models achieved strong, consistent performance with ∼86-97% accuracy, 0.919-0.996 AUROC and ∼82-97% precision and recall in testing in held-out, unseen data. In neural-only models, predictive performance dropped substantially but nonetheless accuracy and AUROC of ∼80% were achieved. Parent aggressive and externalizing traits uniquely differentiated the onset of ODD while structural MRI metrics in the limbic system specifically predicted the onset of CD. Psychosocial measures of sleep disorders, parent mental health and behavioral traits and school performance proved valuable across all disorders but cognitive and non-neural physiologic metrics were never selected. In neural-only models, structural and functional MRI metrics in subcortical regions and cortical-subcortical connectivity were emphasized over task fMRI or diffusion measures. Overall, we identified a strong correlation between accuracy and final predictor importance.ConclusionsDeep learning optimized with AI can generate highly accurate individual-level predictions of the onset of early adolescent externalizing disorders using multimodal features. Analysis of 5,777 multimodal candidate predictors highlighted psychosocial predictors related to sleep disorders, school performance and parent mental health and behavioral traits over other feature types. While externalizing disorders are frequently co-morbid in adolescents, certain predictors appeared specific to the onset of ODD or CD vs ADHD with structural MRI metrics in the limbic system offering particular promise in identifying children at risk for the onset of CD, a highly disabling disorder. The strong observed correlation between predictive accuracy and final predictor importance suggests that principled, data-driven searches for impactful predictors may facilitate the construction of robust, individual-level models in high-dimension data. To our knowledge, this is the first machine learning study to predict the onset of all three major adolescent externalizing disorders with the same design and participant cohort to enable direct comparisons, analyze >200 multimodal features and include as many types of neuroimaging metrics. Future work to test our observations in external validation data will help further test the generalizability of these findings.

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Predictors and Moderators Two Treatments of Oppositional Defiant Disorder in Children

Cited by 5 publications

References 54 publications

Parent Explanatory Model Personalization as a Method of Reducing Risk for Poor Engagement and Outcomes in PCIT among Culturally Diverse Families

Parent Explanatory Model Personalization as a Method of Reducing Risk for Poor Engagement and Outcomes in PCIT among Culturally Diverse Families

Community-Delivered Collaborative and Proactive Solutions and Parent Management Training for Oppositional Youth: A Randomized Trial

Identifying selective predictors of ADHD, Oppositional Defiant and Conduct Disorder onset in early adolescence with optimized deep learning

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