Qayyum Nida T, Wallace C Hunter, Khayat Rami N, Grosberg Anna
Department of Chemical and Biomolecular Engineering, University of California, Irvine, Irvine, CA, United States.
UCI Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center (CIRC), University of California, Irvine, Irvine, CA, United States.
Front Physiol. 2023 Aug 3;14:1198132. doi: 10.3389/fphys.2023.1198132. eCollection 2023.
Obstructive sleep apnea (OSA) is a sleep disorder caused by periodic airway obstructions and has been associated with numerous health consequences, which are thought to result from tissue hypoxia. However, challenges in the direct measurement of tissue-level oxygenation make it difficult to analyze the hypoxia exposure pattern in patients. Furthermore, current clinical practice relies on the apnea-hypopnea index (AHI) and pulse oximetry to assess OSA severity, both of which have limitations. To overcome this, we developed a clinically deployable mathematical model, which outputs tissue-level oxygenation. The model incorporates spatial pulmonary oxygen uptake, considers dissolved oxygen, and can use time-dependent patient inputs. It was applied to explore a series of breathing patterns that are clinically differentiated. Supporting previous studies, the result of this analysis indicated that the AHI is an unreliable indicator of hypoxia burden. As a proof of principle, polysomnography data from two patients was analyzed with this model. The model showed greater sensitivity to breathing in comparison with pulse oximetry and provided systemic venous oxygenation, which is absent from clinical measurements. In addition, the dissolved oxygen output was used to calculate hypoxia burden scores for each patient and compared to the clinical assessment, highlighting the importance of event length and cumulative impact of obstructions. Furthermore, an intra-patient statistical analysis was used to underscore the significance of closely occurring obstructive events and to highlight the utility of the model for quantitative data processing. Looking ahead, our model can be used with polysomnography data to predict hypoxic burden on the tissues and help guide patient treatment decisions.
阻塞性睡眠呼吸暂停（OSA）是一种由周期性气道阻塞引起的睡眠障碍，与众多健康后果相关，这些后果被认为是由组织缺氧导致的。然而，直接测量组织水平氧合存在挑战，这使得分析患者的缺氧暴露模式变得困难。此外，目前的临床实践依赖于呼吸暂停低通气指数（AHI）和脉搏血氧饱和度测定来评估OSA的严重程度，这两者都有局限性。为了克服这一问题，我们开发了一种可临床应用的数学模型，该模型可输出组织水平的氧合情况。该模型纳入了肺部空间氧摄取，考虑了溶解氧，并且可以使用随时间变化的患者输入数据。它被用于探索一系列临床上有区别的呼吸模式。支持先前的研究，该分析结果表明AHI是缺氧负担的不可靠指标。作为原理验证，使用该模型分析了两名患者的多导睡眠图数据。与脉搏血氧饱和度测定相比，该模型对呼吸表现出更高的敏感性，并提供了临床测量中不存在的体静脉氧合情况。此外，利用溶解氧输出结果计算每位患者的缺氧负担评分，并与临床评估进行比较，突出了事件时长和阻塞累积影响的重要性。此外，还进行了患者内统计分析，以强调紧密发生的阻塞性事件的重要性，并突出该模型在定量数据处理方面的实用性。展望未来，我们的模型可与多导睡眠图数据一起使用，以预测组织的缺氧负担，并帮助指导患者的治疗决策。
