Comorbidity network characteristics of depressive and anxiety symptoms and their associations with quality of life in patients with spinal cord injury: a cross-sectional study.

Objective: To examine the topological structure of the comorbidity network of depressive and anxiety symptoms in patients with subacute spinal cord injury (SCI), identify core and bridge symptoms, and explore the independent associations between specific symptoms and quality-of-life outcomes. Methods: A total of 316 patients with subacute SCI were assessed using the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7), and the World Health Organization Quality of Life Brief Version (WHOQOL-BREF). A Gaussian graphical model (GGM) was estimated to construct the depression-anxiety symptom network. Expected influence (EI) and bridge expected influence (bEI) were calculated to identify core and bridge nodes. Flow network analyses were performed to evaluate independent conditional associations between symptoms and quality-of-life outcomes. Network comparison tests (NCTs) were used to examine network invariance across key clinical subgroups. Ordinal PHQ-9 and GAD-7 items were handled using cor_auto-derived polychoric correlations where appropriate, and sensitivity analyses examined EBIC tuning, no-jitter estimation, data-driven communities, and covariate-adjusted flow networks. Results: Centrality analyses showed that guilt (PHQ.6, EI = 1.257), irritability (GAD.6, EI = 1.096), and psychomotor changes (PHQ.8, EI = 1.006) were among the most influential symptoms in the network, whereas depressed mood (PHQ.2, bEI = 1.241), concentration difficulties (PHQ.7, bEI = 0.972), irritability (GAD.6, bEI = 0.954), and psychomotor changes (PHQ.8, bEI = 0.942) showed the greatest bridge effects across the depression and anxiety communities. The strongest edges in the network were PHQ.9-PHQ.8 (weight = 0.396), PHQ.5-PHQ.4 (weight = 0.395), and PHQ.8-GAD.5 (weight = 0.384). Flow network analyses indicated that suicidal ideation, depressed mood, and restlessness were more strongly negatively associated with general health, whereas concentration difficulties and psychomotor changes were more strongly negatively associated with overall quality of life. NCT results showed no significant differences in network structure or global strength across sex or injury severity subgroups (P > 0.05). Bootstrap analyses supported acceptable network stability. All bivariate symptom-QOL correlations were negative after verification of scoring direction; positive symptom-QOL partial edges were therefore interpreted cautiously as conditional suppression effects rather than as evidence that symptoms were associated with better QOL. Conclusion: The depression-anxiety comorbidity network in patients with SCI appears to be organized around a limited number of highly influential symptoms, particularly guilt, irritability, and psychomotor abnormalities. Moving beyond total questionnaire scores, prioritizing symptoms with high centrality, strong bridge effects, and close links to quality-of-life outcomes may help refine psychological assessment and intervention planning in SCI rehabilitation. All edges should be interpreted as undirected conditional associations, not causal pathways.