ArticleMedicine2026
Network analysis of symptoms and psychological distress in patients with sudden sensorineural hearing loss.
Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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Abstract
Sudden sensorineural hearing loss (SSNHL) is characterized by rapid-onset, unexplained hearing impairment, frequently accompanied by symptoms such as tinnitus, dizziness, and nausea. Patients with SSNHL often experience psychological distress, including anxiety and depression, which are interconnected, offering insights for holistic management. This study aims to elucidate these connections, with a focus on anxiety and depression, using network analysis to inform holistic management strategies. Network analysis was employed to model symptom-psychological interactions, identifying central nodes and their connectivity patterns. Key metrics included node strength, centrality, and edge weights to quantify associations between symptoms and psychological subscales. Network analysis revealed significant correlations between SSNHL symptoms (e.g., tinnitus, dizziness) and psychological subscales Hospital Anxiety and Depression Scale ([Hospital Anxiety and Depression Scale)-anxiety, Hospital Anxiety and Depression Scale-depression]). Key nodes with high centrality included anxiety, depression, and dizziness, demonstrating significant interconnections. Anxiety exhibited the highest node strength, indicating its pivotal role in the symptom-psychological network. Edge weights highlighted bidirectional relationships, particularly between anxiety and tinnitus, as well as depression and nausea. This study underscores the critical role of anxiety and depression in the symptom network of SSNHL, emphasizing their association with disease progression and patient well-being. It is important to note that, due to the cross-sectional design, these findings represent an exploratory model of associations rather than causal relationships. The application of network analysis has given us a deeper understanding of the multifaceted nature of SSNHL, enabling us to develop more personalized and effective treatment strategies.
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