{"title":"利用每小时呼吸暂停-低通气持续时间预测阻塞性睡眠呼吸暂停患者的低氧血症。","authors":"Changxiu Ma, Ying Zhang, Tingchao Tian, Ling Zheng, Jing Ye, Hui Liu, Dahai Zhao","doi":"10.2147/NSS.S452118","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To explore the role of the mean apnea-hypopnea duration (MAD) and apnea-hypopnea duration per hour (HAD) in hypoxemia and evaluate whether they can effectively predict the occurrence of hypoxemia among adults with OSA.</p><p><strong>Patients and methods: </strong>A total of 144 participants underwent basic information gathering and polysomnography (PSG). Logistic regression models were conducted to evaluate the best index in terms of hypoxemia. To construct the prediction model for hypoxemia, we randomly divided the participants into the training set (70%) and the validation set (30%).</p><p><strong>Results: </strong>The participants with hypoxemia tend to have higher levels of obesity, diabetes, AHI, MAD, and HAD compared with non-hypoxemia. The most relevant indicator of blood oxygen concentration is HAD (r = 0.73) among HAD, MAD, and apnea-hypopnea index (AHI). The fitness of HAD on hypoxemia showed the best. In the stage of establishing the prediction model, the area under the curve (AUC) values of both the training set and the validation set are 0.95. The increased HAD would elevate the risk of hypoxemia [odds ratio (OR): 1.30, 95% confidence interval (CI): 1.13-1.49].</p><p><strong>Conclusion: </strong>The potential role of HAD in predicting hypoxemia underscores the significance of leveraging comprehensive measures of respiratory disturbances during sleep to enhance the clinical management and prognostication of individuals with sleep-related breathing disorders.</p>","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11195681/pdf/","citationCount":"0","resultStr":"{\"title\":\"Using Apnea-Hypopnea Duration per Hour to Predict Hypoxemia Among Patients with Obstructive Sleep Apnea.\",\"authors\":\"Changxiu Ma, Ying Zhang, Tingchao Tian, Ling Zheng, Jing Ye, Hui Liu, Dahai Zhao\",\"doi\":\"10.2147/NSS.S452118\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To explore the role of the mean apnea-hypopnea duration (MAD) and apnea-hypopnea duration per hour (HAD) in hypoxemia and evaluate whether they can effectively predict the occurrence of hypoxemia among adults with OSA.</p><p><strong>Patients and methods: </strong>A total of 144 participants underwent basic information gathering and polysomnography (PSG). Logistic regression models were conducted to evaluate the best index in terms of hypoxemia. To construct the prediction model for hypoxemia, we randomly divided the participants into the training set (70%) and the validation set (30%).</p><p><strong>Results: </strong>The participants with hypoxemia tend to have higher levels of obesity, diabetes, AHI, MAD, and HAD compared with non-hypoxemia. The most relevant indicator of blood oxygen concentration is HAD (r = 0.73) among HAD, MAD, and apnea-hypopnea index (AHI). The fitness of HAD on hypoxemia showed the best. In the stage of establishing the prediction model, the area under the curve (AUC) values of both the training set and the validation set are 0.95. The increased HAD would elevate the risk of hypoxemia [odds ratio (OR): 1.30, 95% confidence interval (CI): 1.13-1.49].</p><p><strong>Conclusion: </strong>The potential role of HAD in predicting hypoxemia underscores the significance of leveraging comprehensive measures of respiratory disturbances during sleep to enhance the clinical management and prognostication of individuals with sleep-related breathing disorders.</p>\",\"PeriodicalId\":18896,\"journal\":{\"name\":\"Nature and Science of Sleep\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11195681/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature and Science of Sleep\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/NSS.S452118\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature and Science of Sleep","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/NSS.S452118","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
摘要
目的:探讨平均呼吸暂停-低通气持续时间(MAD)和每小时呼吸暂停-低通气持续时间(HAD)在低氧血症中的作用,并评估它们是否能有效预测成人 OSA 患者低氧血症的发生:共有 144 名参与者接受了基本信息收集和多导睡眠图检查(PSG)。采用逻辑回归模型评估低氧血症的最佳指数。为了建立低氧血症预测模型,我们将参与者随机分为训练集(70%)和验证集(30%):结果:与非低氧血症患者相比,低氧血症患者的肥胖、糖尿病、AHI、MAD 和 HAD 水平更高。在 HAD、MAD 和呼吸暂停-低通气指数(AHI)中,与血氧浓度最相关的指标是 HAD(r = 0.73)。HAD 对低氧血症的适应性最好。在建立预测模型阶段,训练集和验证集的曲线下面积(AUC)值均为 0.95。结论:HAD 的增加会提高低氧血症的风险[几率比(OR):1.30,95% 置信区间(CI):1.13-1.49]:HAD在预测低氧血症方面的潜在作用凸显了利用睡眠期间呼吸紊乱的综合措施来加强睡眠相关呼吸紊乱患者的临床管理和预后的重要性。
Using Apnea-Hypopnea Duration per Hour to Predict Hypoxemia Among Patients with Obstructive Sleep Apnea.
Purpose: To explore the role of the mean apnea-hypopnea duration (MAD) and apnea-hypopnea duration per hour (HAD) in hypoxemia and evaluate whether they can effectively predict the occurrence of hypoxemia among adults with OSA.
Patients and methods: A total of 144 participants underwent basic information gathering and polysomnography (PSG). Logistic regression models were conducted to evaluate the best index in terms of hypoxemia. To construct the prediction model for hypoxemia, we randomly divided the participants into the training set (70%) and the validation set (30%).
Results: The participants with hypoxemia tend to have higher levels of obesity, diabetes, AHI, MAD, and HAD compared with non-hypoxemia. The most relevant indicator of blood oxygen concentration is HAD (r = 0.73) among HAD, MAD, and apnea-hypopnea index (AHI). The fitness of HAD on hypoxemia showed the best. In the stage of establishing the prediction model, the area under the curve (AUC) values of both the training set and the validation set are 0.95. The increased HAD would elevate the risk of hypoxemia [odds ratio (OR): 1.30, 95% confidence interval (CI): 1.13-1.49].
Conclusion: The potential role of HAD in predicting hypoxemia underscores the significance of leveraging comprehensive measures of respiratory disturbances during sleep to enhance the clinical management and prognostication of individuals with sleep-related breathing disorders.
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.