Exploring the structure of psychological distress and its symptoms: A psychometric network analysis.

• DOI: 10.1200/jco.2024.42.16_suppl.12112
• Publication Date: 2024-06-13T14:43:02Z
• Authors: Alessandro Rossi; Maria Marconi; Stefania Mannarini; Andrea Calcaterra; Luigi De Cicco; Claudio Verusio; Giuseppe Di Lucca
• Keywords: 03 medical and health sciences; 0302 clinical medicine

12112 Background: Psychological research is currently surging in oncological settings, with the construct of psychological distress gaining significant attention. Several studies have demonstrated that psychological distress can have profound adverse effects on adherence, therapy compliance, medical treatments, and overall quality of life (NCCN, 2015). However, it remains a challenging endeavor to attain a clear understanding of the implications of psychological distress in the daily lives of oncology patients. To address this gap, our study aimed to identify the central symptoms of psychological distress and elucidate their relationships. Methods: Oncological outpatients (N = 504; meanage = 67.15, SD = 10.16; 50% females) were recruited at the Presidio Ospedaliero of Saronno, ASST Valle Olona, Italy. The Psychological Distress Inventory-Revised (PDI-R; Rossi et al., 2022), utilized to measure internal, external, and general distress, demonstrated high internal consistency in assessing patients' psychological distress. As a preliminary step before conducting confirmatory factor analysis, we employed an analysis to assess the structural validity of the PDI-R. Subsequently, we performed a Psychometric Network Analysis with the EBICglasso algorithm (5000 bootstrap) to uncover the network structure of psychological distress and identify nodes with greater strength and expected influence. Results: The preliminary analysis indicated that the factorial structure demonstrates excellent fit indices [RMSEA = 0.055; CFI = 0.997; SRMR = 0.041]. Additionally, the Psychometric Network Analysis showed a high accuracy (CS-coefficient = 0.75). Psychological distress symptoms spontaneously clustered into internal and external distress, as per the model, with all symptoms showing noteworthy interconnections. Notably, the strongest connections were observed within the 'external distress' facet. On one hand, the Network Analysis revealed that item #7 (External distress: 'diminished interest in the world'; z = 1.480), item #2 (Internal distress: 'moments of dejection or depression'; z = 1.141), and item #8 (External distress: 'the illness has negatively influenced your relationships with others'; z = 0.542) were identified as nodes with both the highest strength and expected influence. Conclusions: These findings have meaningful implications for clinical practice, as clinical interventions should prioritize addressing symptoms that exhibit the strongest connections within the network. Given that these central nodes have the potential to influence all other connected symptoms, they represent crucial focal points for clinicians. Tailoring more targeted and efficient therapeutic approaches to address these central symptoms can have beneficial effects on medical treatments and enhance the overall quality of life for patients.