Network Analysis of the Association Between Frailty and Depression in Patients with Gastrointestinal Cancer: A Cross-Sectional Study

Objective To explore core symptoms and nursing interventions in the frailty-depression network of patients with gastrointestinal cancer Using Network Analysis Models to Inform Precision Intervention and Care. Design: A cross-sectional survey was conducted among patients with gastrointestinal cancer undergoing treatment in the oncology department of a tertiary general hospital in Shandong Province from February to June 2023, with concurrent assessments of frailty and depression symptoms. Methods The Fried Frailty Scale and Patient Health Questionnaire were used to assess frailty and depression status, respectively. A total of 238 patients with gastrointestinal cancer completed the questionnaires. Network analysis of the relationship was conducted using R software, including network relationship analysis, core symptom analysis, and evaluation of network structure accuracy and stability. Results Network analysis revealed the strongest associations between “FP2 (slowed gait) and FP4 (low physical activity),” “PHQ8 (bradykinesia/agitation) and PHQ9 (suicidal ideation)”, and “PHQ8 (bradykinesia/agitation) and PHQ6 (guilt)” which exhibited the strongest correlations. PHQ4 (lack of energy) exhibited the highest predictability and expected impact. Conclusion This study employed symptom network analysis to explore the frailty and depression relationship network among patients with gastrointestinal cancer.“PHQ4 (lack of energy)” was the most central node. Additionally, gender differences should be considered to develop scientifically grounded psychological interventions that improve patients' psychological well-being and mitigate their frailty and depression. Impact: This study employed network analysis to explore the association between frailty and depression in patients with gastrointestinal cancer. This methodology overcomes the limitations of traditional univariate analyses by visualizing and quantifying complex relationships among symptoms. It identifies the most central and influential symptoms and connection pathways within the network, offering a novel perspective and specific targets for precise clinical intervention