Multiple studies have shown that Multisystemic Therapy (MST) is, at group level, an effective treatment for adolescents showing serious externalizing problem behavior. The current study expands previous research on MST by, first, examining whether subgroups of participants who respond differently to treatment could be identified. Second, we investigated if the different trajectories of change during MST could be predicted by individual (hostile attributions) and contextual (parental sense of parenting competence and deviant and prosocial peer involvement) pre-treatment factors. Participants were 147 adolescents (mean age = 15.91 years, 104 (71%) boys) and their parents who received MST. Pre-treatment assessment of the predictors and 5 monthly assessments of externalizing behavior during treatment took place using both adolescent and parents’ self-reports. Six distinct subgroups, showing different trajectories of change in externalizing problem behavior during MST, were identified. Two of the 6 trajectories of change showed a poor treatment response, as one class did not change in externalizing problem behavior and the other class even increased. The remaining 4 trajectories displayed a positive effect of MST, by showing a decrease in externalizing behavior. Most of these trajectories could be predicted by parental sense of parenting competence. Additionally, lower involvement with prosocial peers was a predictor of the group that appeared to be resistant to MST. Adolescents do respond differently to MST, which indicates the importance of personalizing treatment. Protective factors, such as parental sense of parenting competence and prosocial peers, seem to require additional attention in the first phase of MST.
Multiple studies have shown that Multisystemic Therapy (MST) is, at group level, an effective treatment for adolescents showing serious externalizing problem behavior. The current study expands previous research on MST by, first, examining whether subgroups of participants who respond differently to treatment could be identified. Second, we investigated if the different trajectories of change during MST could be predicted by individual (hostile attributions) and contextual (parental sense of parenting competence and deviant and prosocial peer involvement) pre-treatment factors. Participants were 147 adolescents (mean age = 15.91 years, 104 (71%) boys) and their parents who received MST. Pre-treatment assessment of the predictors and 5 monthly assessments of externalizing behavior during treatment took place using both adolescent and parents’ self-reports. Six distinct subgroups, showing different trajectories of change in externalizing problem behavior during MST, were identified. Two of the 6 trajectories of change showed a poor treatment response, as one class did not change in externalizing problem behavior and the other class even increased. The remaining 4 trajectories displayed a positive effect of MST, by showing a decrease in externalizing behavior. Most of these trajectories could be predicted by parental sense of parenting competence. Additionally, lower involvement with prosocial peers was a predictor of the group that appeared to be resistant to MST. Adolescents do respond differently to MST, which indicates the importance of personalizing treatment. Protective factors, such as parental sense of parenting competence and prosocial peers, seem to require additional attention in the first phase of MST.
Background & aims: Accurate diagnosis of sarcopenia requires evaluation of muscle quality, which refers to the amount of fat infiltration in muscle tissue. In this study, we aim to investigate whether we can independently predict mortality risk in transcatheter aortic valve implantation (TAVI) patients, using automatic deep learning algorithms to assess muscle quality on procedural computed tomography (CT) scans. Methods: This study included 1199 patients with severe aortic stenosis who underwent transcatheter aortic valve implantation (TAVI) between January 2010 and January 2020. A procedural CT scan was performed as part of the preprocedural-TAVI evaluation, and the scans were analyzed using deep-learning-based software to automatically determine skeletal muscle density (SMD) and intermuscular adipose tissue (IMAT). The association of SMD and IMAT with all-cause mortality was analyzed using a Cox regression model, adjusted for other known mortality predictors, including muscle mass. Results: The mean age of the participants was 80 ± 7 years, 53% were female. The median observation time was 1084 days, and the overall mortality rate was 39%. We found that the lowest tertile of muscle quality, as determined by SMD, was associated with an increased risk of mortality (HR 1.40 [95%CI: 1.15–1.70], p < 0.01). Similarly, low muscle quality as defined by high IMAT in the lowest tertile was also associated with increased mortality risk (HR 1.24 [95%CI: 1.01–1.52], p = 0.04). Conclusions: Our findings suggest that deep learning-assessed low muscle quality, as indicated by fat infiltration in muscle tissue, is a practical, useful and independent predictor of mortality after TAVI.
