Clinical Course for Patients With Trisomy 13 and 18 Pursuing Life-Prolonging Therapies Versus Comfort-Directed Care

Background: Care for infants with Trisomy 13 and 18 is evolving with more children being offered medical and surgical interventions. Parents and clinicians of children diagnosed with trisomy 13 and 18 would benefit from understanding how parental goals of care correlate with the subsequent clinical course of children with these conditions. Objective: To describe and compare parental goals of care (GOC) and clinical course in infants with trisomy 13 and 18. Design: Single center, retrospective (2013-19) analysis of electronic health record repository at a birthing center and a tertiary care hospital. Measurements: ICD-9/10 codes were used to identify patients with trisomy 13 or 18 born between 2013-2019. Their records were abstracted for their diagnosis, hospitalization days, interventions, GOC, death location and length of life. Result: Twenty-eight total patients were identified; trisomy 13, mosaic trisomy 13 and trisomy 18 were diagnosed in 9, 2 and 17 patients respectively. Among the 26 patients with complete trisomy 13 or 18, 8 had life-prolonging and 18 had comfort care goals at birth/diagnosis. Life-prolonging goals were not associated with longer life (p = 0.36) but were associated with more mean hospital days (70 vs. 12, p = 0.01), ICU days (66 vs. 9, p = 0.009), intubation (7/8 vs 7/18, p = 0.04), and death in ICU (7/7 vs. 10/17, p = 0.02). Zero patients underwent cardiac surgery. Conclusion: Parental GOC did not affect length of life in children with complete trisomy, but did alter treatment intensity. This may inform decision making for patients with trisomy 13 or 18.

You Will Attain My Goal

This paper deals with parental goals for children, that is, goals parents want their children to attain by the time they reach adulthood. Presenting an adaption of the German Aspirations Index to assess parental goals for children (AI-PG), factorial structure and psychometric properties in a nonclinical sample of German parents of children between 10 and 15 years of age were investigated. Parents (NT1 = 948; NT2 = 670) rated the importance of different parental goals for children. The AI-PG structure was first examined in a subsample of mothers using confirmatory factor analyses. Main results provided support for the hypothesized hierarchical factorial structure. Accordingly, the given 21 items can be assigned to seven first-order factors that reflect goal-contents, and two second-order factors that reflect goals’ motivational orientation as either intrinsic or extrinsic. The questionnaire shows good psychometric properties (i.e., homogeneity, model-based reliability, and stability). Multiple-group analyses confirmed measurement invariance across informants (mothers and fathers), gender of target children (daughters and sons), and measurement time points. Small but significant relationships of the scales with need-threatened environment, parenting practices, and child internalizing behavior problems added to the scale’s construct validity. Results are discussed in terms of the fruitfulness of a concept of parental goals for children within self-determination theory.

Predicting Model for Aggressive Directiveness in Light of Tadeusz Tomaszewski's Theory of Action: Structural and Data Mining Approach

The study focused on verifying the relationship between the ability to meet parental goals, parental difficulty, the child's representation in the parent's mind, and aggressive directiveness. The project refers to Tomaszewski’s theory of action as well as to Gurycka's theory of parental mistakes, in which the inability to achieve parental goals is treated as the main cause of experienced parental difficulties. The analyses were performed on data collected from 158 mothers of preschool children. The analyses were performed using structural equations as well as associative algorithms and artificial intelligence algorithms: cluster analysis and artificial neural networks. The structural model revealed strong relations between variables. The cluster analysis revealed three characteristic profiles in the maternal population that are distinguished by the level of analyzed variables. The artificial neural network revealed that, on the basis of the variables included in the model, the parents’ results in aggressive directiveness can be predicted.