Luo Xue, Li Shuangyan, Wu Qianyun, Xu Yan, Fang Ruichen, Cheng Yihong, Zhang Bin
Department of Psychiatry Sleep Medical Center, Nanfang Hospital Southern Medical University, No. 1838 North Guangzhou Avenue, Guangzhou, 510515, China.
Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangzhou, China.
BMC Psychiatry. 2025 Jan 28;25(1):77. doi: 10.1186/s12888-025-06532-w.
Patients with obstructive sleep apnea (OSA) frequently experience sleep disturbance and psychological distress, such as depression and anxiety, which may have a negative impact on their health status and functional abilities. To gain a more comprehensive understanding of the symptoms of depression, anxiety, and sleep disturbance in patients with OSA, the current study utilized network analysis to examine the interconnections among these symptoms.
Depressive and anxiety symptoms were evaluated using the Hospital Anxiety and Depression Scale (HADS), and sleep disturbance symptoms were evaluated using the Pittsburgh Sleep Quality Index (PSQI). A total of 621 patients with OSA completed the questionnaires. The indices 'Expected influence' and 'Bridge expected influence' were used as centrality measures in the symptom network. The Least Absolute Shrinkage and Selection Operator (LASSO) technique and the Extended Bayesian Information Criterion (EBIC) were utilized to estimate the network structure of depressive, anxiety, and sleep disturbance symptoms. A Network Comparison Test (NCT) was performed to evaluate the differences between the mild to moderate OSA and severe OSA networks.
Network analysis revealed that A6 ("Getting sudden feelings of panic") had the highest expected influence value and D6 ("Feeling being slowed down") had the highest bridge expected influence values in the networks. The NCT results revealed that the edge weights significantly differed between patients with mild to moderate OSA and those with severe OSA (M = 0.263, p = 0.008). There was no significant difference in global strength variation between the two networks (S = 0.185, p = 0.773).
Our results suggest that the highest expected influence value and bridge symptoms (e.g., A6 and D6) can be prioritized as potential targets for intervention and treatment in patients with OSA.
阻塞性睡眠呼吸暂停（OSA）患者经常经历睡眠障碍和心理困扰，如抑郁和焦虑，这可能对他们的健康状况和功能能力产生负面影响。为了更全面地了解OSA患者的抑郁、焦虑和睡眠障碍症状，本研究利用网络分析来检查这些症状之间的相互联系。
使用医院焦虑抑郁量表（HADS）评估抑郁和焦虑症状，使用匹兹堡睡眠质量指数（PSQI）评估睡眠障碍症状。共有621名OSA患者完成了问卷调查。指数“预期影响”和“桥梁预期影响”被用作症状网络中的中心性度量。采用最小绝对收缩和选择算子（LASSO）技术和扩展贝叶斯信息准则（EBIC）来估计抑郁、焦虑和睡眠障碍症状的网络结构。进行网络比较测试（NCT）以评估轻度至中度OSA网络和重度OSA网络之间的差异。
网络分析显示，在网络中，A6（“突然感到恐慌”）的预期影响值最高，D6（“感觉行动迟缓”）的桥梁预期影响值最高。NCT结果显示，轻度至中度OSA患者和重度OSA患者之间的边权重存在显著差异（M = 0.263，p = 0.008）。两个网络之间的全局强度变化没有显著差异（S = 0.185，p = 0.773）。
我们的结果表明，最高预期影响值和桥梁症状（如A6和D6）可被优先作为OSA患者干预和治疗的潜在目标。
