A Prediction Nomogram of Severe Obstructive Sleep Apnea in Patients with Obesity Based on the Liver Stiffness and Abdominal Visceral Adipose Tissue Quantification.

IF 3 2区医学 Q2 CLINICAL NEUROLOGY

Nature and Science of Sleep Pub Date : 2024-09-27 eCollection Date: 2024-01-01 DOI:10.2147/NSS.S475534

Anbang Zhao, Bin Hao, Simin Liu, Xiaoyu Qiu, Xiaoping Ming, Xiuping Yang, Jie Cai, Zhen Li, Xiong Chen

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引用次数: 0

Abstract

Purpose: The diagnosis of severe OSA still relies on polysomnography, which causes a strong sense of restraint in patients with obesity. However, better prediction tools for severe OSA applicable to patients with obesity have not been developed.

Patients and methods: Relevant clinical data of 1008 patients with OSA who underwent bariatric surgery in our hospital were collected retrospectively. Patients were divided into training and test cohorts by machine learning. Univariate and multivariate logistic regression analysis was used to screen associations, including liver stiff measurement (LSM) and abdominal visceral tissue (aVAT), and to construct a severe OSA risk prediction nomogram. Then, we evaluated the effectiveness of our model and compared our model with the traditional Epworth Sleepiness Scale (ESS) model. Finally, our associations were used to explore the correlation with other indicators of OSA severity.

Results: Our study revealed that age, biological sex, BMI, LSM, aVAT, and LDL were independent risk factors for severe OSA in patients with obesity. A severe OSA risk prediction nomogram constructed by six indicators possessed high AUC (0.845), accuracy (77.6%), and relatively balanced specificity and sensitivity (72.4%, 82.8%). The Hosmer-Lemeshow test (P=0.296, 0.785), calibration curves, and DCA of the training and test cohorts suggested better calibration and more net clinical benefit. Compared with the traditional ESS model, our model had higher AUC (0.829 vs 0.545), sensitivity (78.9% vs 12.2%), PPV (77.9% vs 53.3%), and accuracy (75.4% vs 55.2%). In addition, the associations in our model were independently correlated with other indicators reflecting OSA severity.

Conclusion: We provided a simple, cheap, and non-invasive nomogram of severe OSA risk prediction for patients with obesity, which would be helpful for preventing further complications associated with severe OSA.

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基于肝脏硬度和腹部内脏脂肪组织定量的肥胖症患者严重阻塞性睡眠呼吸暂停预测提名图。

目的：严重 OSA 的诊断仍然依赖于多导睡眠图，这使肥胖患者产生强烈的束缚感。然而，适用于肥胖症患者的更好的重度 OSA 预测工具尚未开发出来：回顾性收集了在我院接受减肥手术的 1008 名 OSA 患者的相关临床数据。通过机器学习将患者分为训练组和测试组。使用单变量和多变量逻辑回归分析筛选相关性，包括肝硬度测量（LSM）和腹部内脏组织（aVAT），并构建严重 OSA 风险预测提名图。然后，我们评估了模型的有效性，并将模型与传统的埃普沃思嗜睡量表（ESS）模型进行了比较。最后，我们还利用我们的关联探讨了与其他 OSA 严重程度指标的相关性：我们的研究表明，年龄、生理性别、体重指数、LSM、aVAT 和 LDL 是肥胖患者发生严重 OSA 的独立风险因素。由六项指标构建的严重 OSA 风险预测提名图具有较高的 AUC（0.845）和准确性（77.6%），特异性和敏感性也相对均衡（72.4%、82.8%）。Hosmer-Lemeshow检验（P=0.296，0.785）、校准曲线以及训练队列和测试队列的DCA表明，校准效果更好，临床净效益更高。与传统的ESS模型相比，我们的模型具有更高的AUC（0.829 vs 0.545）、灵敏度（78.9% vs 12.2%）、PPV（77.9% vs 53.3%）和准确度（75.4% vs 55.2%）。此外，我们模型中的关联还与其他反映 OSA 严重程度的指标独立相关：我们为肥胖症患者提供了一个简单、廉价、无创的严重 OSA 风险预测提名图，这将有助于预防与严重 OSA 相关的更多并发症。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature and Science of Sleep Neuroscience-Behavioral Neuroscience

CiteScore

5.70

自引率

5.90%

发文量

245

审稿时长

16 weeks

期刊介绍： Nature and Science of Sleep is an international, peer-reviewed, open access journal covering all aspects of sleep science and sleep medicine, including the neurophysiology and functions of sleep, the genetics of sleep, sleep and society, biological rhythms, dreaming, sleep disorders and therapy, and strategies to optimize healthy sleep. Specific topics covered in the journal include: The functions of sleep in humans and other animals Physiological and neurophysiological changes with sleep The genetics of sleep and sleep differences The neurotransmitters, receptors and pathways involved in controlling both sleep and wakefulness Behavioral and pharmacological interventions aimed at improving sleep, and improving wakefulness Sleep changes with development and with age Sleep and reproduction (e.g., changes across the menstrual cycle, with pregnancy and menopause) The science and nature of dreams Sleep disorders Impact of sleep and sleep disorders on health, daytime function and quality of life Sleep problems secondary to clinical disorders Interaction of society with sleep (e.g., consequences of shift work, occupational health, public health) The microbiome and sleep Chronotherapy Impact of circadian rhythms on sleep, physiology, cognition and health Mechanisms controlling circadian rhythms, centrally and peripherally Impact of circadian rhythm disruptions (including night shift work, jet lag and social jet lag) on sleep, physiology, cognition and health Behavioral and pharmacological interventions aimed at reducing adverse effects of circadian-related sleep disruption Assessment of technologies and biomarkers for measuring sleep and/or circadian rhythms Epigenetic markers of sleep or circadian disruption.