Obstructive Sleep Apnea and Pulmonary Hypertension: A Review of Literature

Shah, Farhan A; Moronta, Shaidy; Braford, Michalla; Greene, Nelson

doi:10.7759/cureus.14575

Cited by 7 publications

(10 citation statements)

References 29 publications

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“…There have been numerous reports on the association between sleep apnea and RH. However, it is worth noting that about half of primary HTN patients have obstructive sleep apnea, while about half of obstructive sleep apnea patients have primary HTN; these two conditions often coexist [31,32]. Uric acid is the main endogenous antioxidant in the human body as a final product of purine metabolism.…”

Section: Discussionmentioning

confidence: 99%

Predictive Modeling of Resistant Hypertension Risk: Incorporating the TyG Index and Clinical Factors

Yang,

Liu,

Yang

et al. 2023

Preprint

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BackgroundResistant hypertension (RH), a form of high blood pressure that remains uncontrolled despite maximum medication, poses a significant cardiovascular risk. This paper introduces a novel predictive model, combining the triglyceride-glucose (TyG) index with traditional clinical factors, to anticipate the development of RH in patients with newly diagnosed primary hypertension.MethodsThe study included hospitalized patients with newly diagnosed primary hypertension and stable blood pressure after medication treatment from August 2019 to early August 2021. After screening, a total of 1635 cases were finally included and divided into development and validation cohorts. The least absolute shrinkage and selection operator (LASSO) regression was applied to select potential risk factors. Multivariate Cox regression analysis was used to identify independent hazard factors constructed by the predictive nomogram. Receiver operating characteristic curve analysis (ROC), calibration curve, and C-index were used to evaluate the performance of the nomogram.ResultsA total of 1227 patients were assigned to the development queue, while 408 patients were assigned to the validation queue. The constructed column line chart includes five clinical variables: age, apnea-hypopnea index (AHI), uric acid, fasting blood glucose, and TyG index. Multivariate Cox regression analysis revealed that compared to the other four risk factors, TyG index (HR=3.97, 95% CI: 2.81 - 5.62, P < 0.01) was significantly associated with RH. ROC curve analysis showed prediction values of 0.895 and 0.837 for RH in the development cohort and prediction values of 0.854 and 0.832 in the validation cohort respectively. The C-index was found to be 0.76 in the development cohort and 0.66 in the validation cohort. Furthermore, Kaplan-Meier analysis indicated that compared to the low-risk group, there was a higher likelihood of developing RH in the high-risk group.ConclusionsBased on the TyG index and electronic health record data, a model can be constructed to accurately and reliably predict the occurrence of RH in patients with stable blood pressure after initial diagnosis of primary hypertension and drug treatment.

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Section: Discussionmentioning

confidence: 99%

Predictive Modeling of Resistant Hypertension Risk: Incorporating the TyG Index and Clinical Factors

Yang,

Liu,

Yang

et al. 2023

Preprint

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“…The development of cardiovascular consequences in OSA patients is believed to be associated with intermittent hypoxia ( Shah et al, 2021 ). Indeed, changes in tissue oxygenation due to airway obstruction affect the level of cellular reactive oxygen species (ROS), which may lead to vascular injury and remodeling ( Shah et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

“…Obstructive sleep apnea (OSA) is a sleep-related breathing disorder caused by repeated pharyngeal collapse, which leads to episodes of restricted breathing (hypopnea) or ceased breathing (apnea) ( Shah et al, 2021 ). Consequently, if left untreated, OSA hasbeen linked to health problems including hypertension, worsening cardiovascular disease outcomes, metabolic disorders, and cognitive dysfunction ( Lam et al, 2006 ; Bucks et al, 2013 ; Kendzerska et al, 2014 ; Wu et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

A mathematical model to serve as a clinical tool for assessing obstructive sleep apnea severity

Qayyum,

Wallace,

Khayat

et al. 2023

Front. Physiol.

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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.

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“…27 Patients with severe OSA are also estimated to have an approximately 20% prevalence of pulmonary hypertension, though this is typically mild in the absence of other cardiorespiratory disease. 28…”

Section: Cardiovascular Diseasementioning

confidence: 99%

“…The presence of sleep‐disordered breathing was also associated with a higher frequency of cardiac arrhythmias including atrial fibrillation (OR 4.02), non‐sustained ventricular tachycardia (OR 3.40), and complex ventricular ectopy (OR 1.74) in one retrospective study 27 . Patients with severe OSA are also estimated to have an approximately 20% prevalence of pulmonary hypertension, though this is typically mild in the absence of other cardiorespiratory disease 28 …”

Section: Complicationsmentioning

confidence: 99%

Obstructive sleep apnoea in obesity: A review

Tai,

Phillips,

Yee

et al. 2024

Clinical Obesity

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SummaryObstructive sleep apnea is a common comorbidity that occurs in individuals with obesity. It classically manifests with excessive daytime sleepiness, resulting in reduced quality of life, workplace productivity, and an increased risk of motor vehicle accidents. Weight gain plays an important role in its pathogenesis through worsening upper airway collapsibility, and current treatment options are targeted towards mechanically overcoming upper airway obstruction and weight loss. Continuous positive airway pressure therapy remains the most widely prescribed treatment for obstructive sleep apnea but poor tolerance is a common barrier to effective treatment. Sustainable weight loss is an important treatment option but can be difficult to achieve without bariatric surgery. The recent advances in incretin‐based pharmacotherapies represent a promising avenue not only in achieving long‐term weight loss but also in treating obstructive sleep apnoea and alleviating the burden of its symptoms and comorbidities.

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Obstructive Sleep Apnea and Pulmonary Hypertension: A Review of Literature

Cited by 7 publications

References 29 publications

Predictive Modeling of Resistant Hypertension Risk: Incorporating the TyG Index and Clinical Factors

Predictive Modeling of Resistant Hypertension Risk: Incorporating the TyG Index and Clinical Factors

A mathematical model to serve as a clinical tool for assessing obstructive sleep apnea severity

Obstructive sleep apnoea in obesity: A review

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