自我报告的阻塞性睡眠呼吸暂停与心脏窘迫有关吗?网络分析

IF 1 Q4 CARDIAC & CARDIOVASCULAR SYSTEMS
Michael R. Le Grande, Alison Beauchamp, Andrea Driscoll, Debra Kerr, Alun C Jackson
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引用次数: 0

摘要

阻塞性睡眠呼吸暂停(OSA)、肥胖、各种代谢变量和社会心理结果之间的关系错综复杂。目前还没有研究探讨这些预测因素与心脏病相关的特定疾病痛苦(心脏痛苦)之间的关系。我们的目的是利用网络分析框架研究 OSA 与心脏不适之间的关联。 这项对 2021 年进行的一项观察性横断面研究的二次分析包括来自澳大利亚和美国的 405 名医院和社区成人,他们报告在过去 12 个月中发生过急性冠状动脉事件(如心肌梗塞,或冠状动脉旁路移植手术或经皮冠状动脉介入治疗等)。对参与者的社会人口学变量、临床风险因素、合并症(包括事件发生后的时间、OSA、肥胖、糖尿病、高血压和高脂血症)和心脏窘迫(由心脏窘迫量表短表报告)进行了调查。对这些数据进行了引导探索性图表分析(EGA),以确定聚类在一起的变量的维度。通过多变量逻辑回归法,对 EGA 维度有贡献的变量被用来预测心脏窘迫。 EGA 确定了三个不同的维度:维度 1 - 临床风险因素和条件,包括 OSA;维度 2 - 与心脏事件相关的变量;维度 3 - 与心脏窘迫密切相关的变量。就维度 1 而言,在完全调整模型中,只有 OSA 是心脏骤停的重要预测因素(调整后的几率比 = 2.08,95% 置信区间 = 1.02-4.25,P = 0.044)。进一步分析表明,OSA 与身体挑战以及角色和人际关系的变化有关。 本研究发现,自我报告的 OSA 与心脏困扰有关,尤其是与身体挑战及角色和人际关系变化有关的困扰。这些研究结果表明,OSA 有可能会增加人际关系中的压力;然而,本研究仅从 OSA 患者的角度对其痛苦进行了评估。EGA 是一种有用的方法,可用于描述 OSA 和心脏不适等不同预测变量之间的复杂关联。由于数据是自我报告的,因此有必要进一步调查以确认研究结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Is Self-Reported Obstructive Sleep Apnea Associated with Cardiac Distress? A Network Analysis
The relationship between obstructive sleep apnea (OSA), obesity, various metabolic variables, and psychosocial outcomes is complex. No studies have examined the association between these predictors and disease-specific distress related to heart disease (cardiac distress). We aimed to study the association between OSA and cardiac distress using a network analysis framework. This secondary analysis of an observational cross-sectional study conducted in 2021 consisted of 405 hospital- and community-sourced adults from Australia and the United States who reported an acute coronary event (such as a myocardial infarction, or procedures such as coronary artery bypass graft surgery, or percutaneous coronary intervention) in the previous 12 months. Participants were surveyed in relation to sociodemographic variables, clinical risk factors, comorbidities (including time since event, OSA, obesity, diabetes, hypertension, and hyperlipidemia), and cardiac distress (reported by the Cardiac Distress Inventory Short-Form). These data were subjected to bootstrapped exploratory graph analysis (EGA), which identifies the dimensions of variables that cluster together. Variables that contributed to the EGA dimensions were used to predict cardiac distress using multivariable logistic regression. Three distinct dimensions were identified by the EGA: Dimension 1 – clinical risk factors and conditions including OSA, Dimension 2 – variables related to the heart event, and Dimension 3 – variables closely related to cardiac distress. For Dimension 1, only OSA was a significant predictor of cardiac distress in the fully adjusted model (adjusted odds ratio = 2.08, 95% confidence interval = 1.02–4.25, P = 0.044). Further analysis indicated that OSA was associated with physical challenges and changes in roles and relationships. This study identified that self-reported OSA is associated with cardiac distress, particularly distress that was associated with physical challenges and changes to roles and relationships. These findings imply that OSA could potentially increase stress in a relationship; however, distress was only assessed from the perspective of the participant with OSA in this study. EGA is a useful method for describing complex associations between diverse predictor variables such as OSA and cardiac distress. Owing to the self-reported aspect of the data, further investigation to confirm study outcomes is warranted.
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CiteScore
1.10
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