Clinical Predictors of Mixed Apneas in Patients with Obstructive Sleep Apnea (OSA)

Liu, Pengfei; Chen, Quanhui; Fang, Yuan; Zhang, Qingru; Zhang, Xiaoying; Xue, Chan; Wei, Yuqing; Wang, Yakun; Wang, Hanqiao

doi:10.2147/nss.s351946

Cited by 10 publications

(6 citation statements)

References 39 publications

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“…Moreover, the mild high altitude itself can pose health risks, such as the development of OSAHS (11)(12)(13). Several other measures, including SpO 2 and ODI values, are important when evaluating the impact of OSAHS on a patient's health (7)(8)(9)(10). In this study, we analyzed associations between AHI levels and ODI, LSpO 2 , MSpO 2 , and TST-SpO 2 <90% values by regression analysis.…”

Section: Discussionmentioning

confidence: 99%

“…These assess oxygen desaturation severity during sleep and correlate with AHI ( 7–9 ). Higher AHI often means lower MSaO 2 and LSaO 2 , higher TST-SpO 2 <90%, and higher ODI, indicating more severe OSAHS ( 7 , 10 ). However, there are currently few studies exploring these metrics in high-altitude areas, especially in mainland China.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the contribution of mild high-altitude exposure to obstructive sleep apnea-hypopnea syndrome comorbidities

Hao,

Peng,

Bian

et al. 2024

Front. Neurol.

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BackgroundObstructive sleep apnea-hypopnea syndrome (OSAHS) is a common sleep disorder. The lower atmospheric pressure and decreased oxygen levels of high-altitude areas can exacerbate the severity of OSAHS, but research into OSAHS in high-altitude areas remains limited. This study, from June 2015 to January 2020, involved 4,667 patients with suspected OSAHS and 38 healthy volunteers. The non-OSAHS group (AHI <5/h) had 395 patients, while the larger OSAHS group (AHI ≥5/h) comprised 4,272 patients. The significant size difference between the groups emphasized the study’s focus on OSAHS, using the non-OSAHS mainly for comparison.MethodsSleep technicians monitored the OSAHS patient group overnight by polysomnography (PSG), the apnea-hypopnea index (AHI), the mean oxygen saturation (MSpO2), lowest oxygen saturation (LSpO2), the oxygen desaturation index (ODI) and the total sleep time with oxygen saturation less than 90% (TST-SpO2 <90%). Healthy volunteers self-monitored sleep patterns at home, using the CONTEC RS01 respiration sleep monitor with a wristwatch sleep apnea screen meter. The RSO1 wristwatch-style device has already been studied for consistency and sensitivity with the Alice-6 standard multi-lead sleep monitor and can be used for OSAHS screening in this region.ResultsLSpO2 recordings from healthy volunteers (86.36 ± 3.57%) and non-OSAHS (AHI <5/h) cohort (78.59 ± 11.99%) were much lower than previously reported normal values. Regression analysis identified no correlations between AHI levels and MSpO2 or TST-SpO2 <90%, weak correlations between AHI levels and LSpO2 or MSpO2, and a strongly significant correlation between AHI levels and the ODI (r = 0.76, p < 0.05). The data also indicated that the appropriate clinical thresholds for OSAHS patients living at mild high altitude are classified as mild, moderate, or severe based on LSpO2 saturation criteria of 0.85–0.90, 0.65–0.84, or <0.65, respectively.ConclusionThe study findings suggest that individuals with an AHI score below 5 in OSAHS, who reside in high-altitude areas, also require closer monitoring due to the elevated risk of nocturnal hypoxia. Furthermore, the significant correlation between ODI values and the severity of OSAHS emphasizes the importance of considering treatment options. Additionally, the assessment of hypoxemia severity thresholds in OSAHS patients living in high-altitude regions provides valuable insights for refining diagnostic guidelines.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the contribution of mild high-altitude exposure to obstructive sleep apnea-hypopnea syndrome comorbidities

Hao,

Peng,

Bian

et al. 2024

Front. Neurol.

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“…Many studies have shown a strong correlation between age and OSA (Ancoli-Israel et al, 1991;Liu et al, 2022), as age increases, the muscles in the nasopharynx relax and tend to collapse. Kapsimalis and Kryger (2002) found that 61.2% of people aged < 55 years had OSA, 78% of people aged > 55 years had OSA, and the prevalence of OSA increased significantly after age > 60 years.…”

Section: Agementioning

confidence: 99%

Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apnea

et al. 2022

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BackgroundOSA is an independent risk factor for several systemic diseases. Compared with mild OSA, patients with moderate-to-severe OSA have more severe impairment in the function of all organs of the body. Due to the current limited medical condition, not every patient can be diagnosed and treated in time. To enable timely screening of patients with moderate-to-severe OSA, we selected easily accessible variables to establish a risk prediction model.MethodWe collected 492 patients who had polysomnography (PSG), and divided them into the disease-free mild OSA group (control group), and the moderate-to-severe OSA group according to the PSG results. Variables entering the model were identified by random forest plots, univariate analysis, multicollinearity test, and binary logistic regression method. Nomogram were created based on the binary logistic results, and the area under the ROC curve was used to evaluate the discriminative properties of the nomogram model. Bootstrap method was used to internally validate the nomogram model, and calibration curves were plotted after 1,000 replicate sampling of the original data, and the accuracy of the model was evaluated using the Hosmer-Lemeshow goodness-of-fit test. Finally, we performed decision curve analysis (DCA) of nomogram model, STOP-Bang questionnaire (SBQ), and NoSAS score to assess clinical utility.ResultsThere are 6 variables entering the final prediction model, namely BMI, Hypertension, Morning dry mouth, Suffocating awake at night, Witnessed apnea, and ESS total score. The AUC of this prediction model was 0.976 (95% CI: 0.962–0.990). Hosmer-Lemeshow goodness-of-fit test χ2 = 3.3222 (P = 0.1899 > 0.05), and the calibration curve was in general agreement with the ideal curve. The model has good consistency in predicting the actual occurrence of moderate-to-severe risk, and has good prediction accuracy. The DCA shows that the net benefit of the nomogram model is higher than that of SBQ and NoSAS, with has good clinical utility.ConclusionThe prediction model obtained in this study has good predictive power for moderate-to-severe OSA and is superior to other prediction models and questionnaires. It can be applied to the community population for screening and to the clinic for prioritization of treatment.

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“…Obstructive sleep apnea (OSA) is a prevalent condition characterized by repeated episodes of partial or complete upper airway obstruction during sleep, leading to intermittent hypoxia and fragmented sleep [ 1 , 2 , 3 , 4 , 5 ]. While the general population is at risk, pregnant women are particularly susceptible due to physiological changes that occur during pregnancy, such as weight gain and edema that can exacerbate airway collapsibility, as well as hormonal fluctuations that may affect respiratory drive and muscle tone [ 6 , 7 ]. In addition, the growing fetus and the associated increase in body mass can lead to mechanical pressure on the respiratory system [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Obstructive Sleep Apnea in Pregnancy: A Comprehensive Review of Maternal and Fetal Implications

Maniaci,

La Via,

Pecorino

et al. 2024

Neurology International

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Obstructive sleep apnea (OSA) is a prevalent yet underdiagnosed condition in pregnancy, associated with various maternal and fetal complications. This review synthesizes the current evidence on the epidemiology, pathophysiology, and neurological consequences of OSA in pregnancy, along with the potential management strategies. Articles were sourced from the PubMed, EMBASE, and Cochrane databases until 2023. Our comprehensive review highlights that the incidence of OSA increases during pregnancy due to physiological changes such as weight gain and hormonal fluctuations. OSA in pregnancy is linked with gestational hypertension, pre-eclampsia, gestational diabetes, and potential adverse fetal outcomes such as intrauterine growth restriction and preterm birth. Continuous positive airway pressure (CPAP) therapy remains the most effective management strategy for pregnant women with OSA. However, adherence to CPAP therapy is often suboptimal. This comprehensive review underscores the importance of the early recognition, timely diagnosis, and effective management of OSA in pregnancy to improve both maternal and fetal outcomes. Future research should focus on enhancing screening strategies and improving adherence to CPAP therapy in this population.

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Clinical Predictors of Mixed Apneas in Patients with Obstructive Sleep Apnea (OSA)

Cited by 10 publications

References 39 publications

Assessing the contribution of mild high-altitude exposure to obstructive sleep apnea-hypopnea syndrome comorbidities

Assessing the contribution of mild high-altitude exposure to obstructive sleep apnea-hypopnea syndrome comorbidities

Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apnea

Obstructive Sleep Apnea in Pregnancy: A Comprehensive Review of Maternal and Fetal Implications

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