Psychosocial assessment of families caring for a child with acute lymphoblastic leukemia, epilepsy or asthma: Psychosocial risk as network of interacting symptoms

Abstract

The purpose of this study is to assess psychosocial risk across several pediatric medical conditions and test the hypothesis that different severe or chronic pediatric illnesses are characterized by disease specific enhanced psychosocial risk and that risk is driven by disease specific connectivity and interdependencies among various domains of psychosocial function using the Psychosocial Assessment Tool (PAT). In a multicenter prospective cohort study of 195 patients, aged 5-12, 90 diagnosed with acute lymphoblastic leukemia (ALL), 42 with epilepsy and 63 with asthma, parents completed the PAT2.0 or the PAT2.0 generic version. Multivariate analysis was performed with disease as factor and age as covariate. Graph theory and network analysis was employed to study the connectivity and interdependencies among subscales of the PAT while data-driven cluster analysis was used to test whether common patterns of risk exist among the various diseases. Using a network modelling approach analysis, we observed unique patterns of interconnected domains of psychosocial factors. Each pathology was characterized by different interdependencies among the most central and most connected domains. Furthermore, data-driven cluster analysis resulted in two clusters: patients with ALL (89%) mostly belonged to cluster 1, while patients with epilepsy and asthma belonged primarily to cluster 2 (83% and 82% respectively). In sum, implementing a network approach improves our comprehension concerning the character of the problems central to the development of psychosocial difficulties. Therapy

Families following pediatric traumatic medical events: identifying psychosocial risk profiles using latent profile analysis

“…Together with other more traditional approaches, LPA may provide a new approach for exploring and understanding the impact of pediatric severe and chronic conditions on the needs of families (Colliva et al, 2020 ). In light of these findings, we should try to develop and implement interventions that directly target the most significant domains, while expecting improvements in the other domains as well.…”

“…Thus, it is often not clear what drives overall risk; diverse symptomatic manifestation may not be captured; and substantial information may be lost (Kotov et al, 2017 ). Using an approach that allows the study of relationships among individual difficulties and family factors, and illustrates the dynamic nature of the interaction between components, can be useful for improving our understanding of the characteristics of the problems and the domains central to the development of psychosocial difficulties (Colliva et al, 2020 ). For example, according to a recent study, epilepsy and asthma both have the same overall PAT score, but different domains have different interrelationships with each other that influence the risk for other domains (Colliva et al, 2020 ).…”

The Use of Psychotropic Medication in Pediatric Oncology for Acute Psychological and Psychiatric Problems: Balancing Risks and Benefits

“…The NNT provided a way to assess the impact of therapy by estimating the number of patients that need to be treated to have an effect on one patient. Especially in this vulnerable patient group, this concept might be helpful, as we know that not everyone is helped by just medicine but might need concomitant psychotherapeutic interventions as well [ 19 , 39 ]. In other words, some children might benefit, while some might have no benefit, but we must avoid that some are harmed at all costs.…”

Digital Phenotyping and Dynamic Monitoring of Adolescents Treated for Cancer to Guide Intervention: Embracing a New Era

“…Network analysis helps to determine if one domain or function is more important than others, if changes in one domain, factor, or symptom dynamically influence the function of others, and finally, what factor or domain is driving the network. In sum, network analysis will not only indicate which domains and factors mostly define individual vulnerability but will have important implications for clinical practice motivating personalized strategies in the prevention of mood, behavioral, and cognitive problems (46,(54)(55)(56)(57).…”

“…Therefore, combining network science and dynamic system theory with a toolbox containing digital phenotyping and biomarkers permits the integration of data across diverse levels of analysis and capture the nature of their dynamic relationship over time, both for patients and treatment modalities ( 52 – 57 ) ( Figure 1 ).…”