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Obstructive Sleep Apnea: New Perspectives

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A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Pulmonary".

Deadline for manuscript submissions: closed (10 June 2022) | Viewed by 29075

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Dr. Salim Surani

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Guest Editor

Department of Medicine, Section of Pulmonary, Critical Care and Sleep Medicine, Texas A&M University, College Station, TX, USA
Interests: critical care; quality improvement; pulmonary hypertension; IPF; sleep medicine; sepsis
Special Issues, Collections and Topics in MDPI journals

Dr. Pahnwat Taweesedt

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Co-Guest Editor

Department of Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78404, USA
Interests: sleep-disordered breathing; machine learning

Special Issue Information

Dear Colleagues,

Obstructive sleep apnea (OSA) or sleep-disordered breathing is one of the most common conditions affecting 17% of women and 34% of men in the United States. It is associated with increased risks of hypertension and cardiovascular disease. Its prevalence is rising with increasing obesity. The symptoms of OSA have been mentioned for more than 2000 years, but it was not until the 20^th century that clinicians started mentioning them as OSA. Aging, gender, craniofacial abnormalities, obesity, alcohol, and menopause have been described as the risk factors, to name a few. Several screening tools have been described as STOP-BANG, NAMES, Berlin Questionnaire. Innovations are now focused on artificial intelligence (AI) to help screen and predict the OSA without the expensive diagnosing modality as overnight nocturnal polysomnography (NPSG) and home sleep testing. Moreover, efforts are being made to utilize AI to help predict the positive airway pressure (PAP) among the patients who have been diagnosed with sleep-disordered breathing without the PAP titration in the laboratory. This book will focus on issues as they pertain to sleep breathing disorder from historical facts, epidemiology, global and financial toll, risk factors, presence in the different ethnic group, gender and race, diagnostic modalities, AI and its role, laboratory and genetic factors in diagnosis, the role of NPSG and home testing, craniofacial differences in OSA and its role, precision and patient-centered treatment of OSA, pharmacological, non-invasive positive pressure therapy, non-surgical and surgical treatment of sleep-related breathing, and the future in the diagnosis and treatment of OSA, to name a few. This book will help the clinicians know everything pertaining to sleep-related breathing from risk factors, diagnosis, and treatment.

Dr. Salim Surani
Dr. Pahnwat Taweesedt
Guest Editors

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Keywords

obstructive sleep apnea (OSA)
sleep disorders nocturnal polysomnography (NPSG)
positive air pressure (CPAP)
artificial intelligence (AI)
targeted treatment
medications

Published Papers (11 papers)

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Editorial

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3 pages, 237 KiB

Open AccessEditorial

Obstructive Sleep Apnea: New Perspective

by Salim Surani and Pahnwat Taweesedt

Medicina 2023, 59(1), 75; https://doi.org/10.3390/medicina59010075 - 29 Dec 2022

Cited by 3 | Viewed by 1654

Abstract

Obstructive sleep apnea (OSA) is one of the most common sleep disorders globally [...] Full article

(This article belongs to the Special Issue Obstructive Sleep Apnea: New Perspectives)

Research

Jump to: Editorial, Review

11 pages, 335 KiB

Open AccessArticle

Clinical and Polysomnographic Features Associated with Poor Sleep Quality in Patients with Obstructive Sleep Apnea

by Aleksander Kania, Kamil Polok, Natalia Celejewska-Wójcik, Paweł Nastałek, Andrzej Opaliński, Barbara Mrzygłód, Krzysztof Regulski, Mirosław Głowacki, Krzysztof Sładek and Grażyna Bochenek

Medicina 2022, 58(7), 907; https://doi.org/10.3390/medicina58070907 - 8 Jul 2022

Cited by 4 | Viewed by 2076

Abstract

Background and Objectives: Poor sleep quality in patients with obstructive sleep apnea (OSA) may be associated with different clinical and polysomnographic features. The aim of this study was to identify features associated with poor sleep quality in OSA patients. Materials and Methods: This was a cross-sectional study enrolling patients with OSA confirmed by polysomnography (PSG). In addition to gathering clinical data, patients were assessed using the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), and the Clinical Global Impression Scale. Univariate and multivariable analyses were performed to identify factors associated with an increased risk of poor sleep quality in this population. Results: Among 505 enrolled patients (mean age of 57.1 years, 69.7% male) poor quality of sleep (PSQI score ≥ 5) was confirmed in 68.9% of them. Multivariable analysis revealed the following factors associated with poor sleep quality: chronic heart failure (OR 3.111; 95% CI, 1.083–8.941, p = 0.035), male sex (OR 0.396; 95% CI, 0.199–0.787, p = 0.008), total ESS score (OR 1.193; 95% CI, 1.124–1.266, p < 0.001), minimal saturation during sleep (OR 1.034; 95% CI, 1.002–1.066, p = 0.036), and N3 percentage of total sleep time (OR 1.110; 95% CI, 1.027–1.200, p = 0.009). Conclusions: Our study suggests that both the female sex and coexistence of heart failure are independent risk factors for poor sleep quality. Moreover, we hypothesize that nocturnal hypoxia may lead to a misperception of sleep quality and may explain the counterintuitive association between a higher proportion of deep sleep and poor sleep quality. Full article

(This article belongs to the Special Issue Obstructive Sleep Apnea: New Perspectives)

8 pages, 1434 KiB

Open AccessArticle

Validation Study on Automated Sleep Stage Scoring Using a Deep Learning Algorithm

by Jae Hoon Cho, Ji Ho Choi, Ji Eun Moon, Young Jun Lee, Ho Dong Lee and Tae Kyoung Ha

Medicina 2022, 58(6), 779; https://doi.org/10.3390/medicina58060779 - 9 Jun 2022

Cited by 3 | Viewed by 2358

Abstract

Background and Objectives: Polysomnography is manually scored by sleep experts. However, manual scoring is a time-consuming and labor-intensive task. The goal of this study was to verify the accuracy of automated sleep-stage scoring based on a deep learning algorithm compared to manual sleep-stage scoring. Materials and Methods: A total of 602 polysomnography datasets from subjects (Male:Female = 397:205) aged 19 to 65 years (mean age, 43.8, standard deviation = 12.2) were included in the study. The performance of the proposed model was evaluated based on kappa value and bootstrapped point-estimate of median percent agreement with a 95% bootstrap confidence interval and R = 1000. The proposed model was trained using 482 datasets and validated using 48 datasets. For testing, 72 datasets were selected randomly. Results: The proposed model exhibited good concordance rates with manual scoring for stages W (94%), N1 (83.9%), N2 (89%), N3 (92%), and R (93%). The average kappa value was 0.84. For the bootstrap method, high overall agreement between the automated deep learning algorithm and manual scoring was observed in stages W (98%), N1 (94%), N2 (92%), N3 (99%), and R (98%) and total (96%). Conclusions: Automated sleep-stage scoring using the proposed model may be a reliable method for sleep-stage classification. Full article

(This article belongs to the Special Issue Obstructive Sleep Apnea: New Perspectives)

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11 pages, 736 KiB

Open AccessArticle

Ventricular Arrhythmias in Patients with Implanted Cardiac Devices at High Risk of Obstructive Sleep Apnea

by Akram Khan, Ryan D. Clay, Asha Singh, Chitra Lal and Larisa G. Tereshchenko

Medicina 2022, 58(6), 757; https://doi.org/10.3390/medicina58060757 - 2 Jun 2022

Cited by 2 | Viewed by 2324

Abstract

Background and Objectives: Patients with pre-existing cardiac disease have a higher prevalence of Obstructive Sleep Apnea (OSA). OSA has been associated with an increased risk of supraventricular and ventricular arrhythmia. We screened subjects with implanted pacemakers and automated implantable cardioverter defibrillators (AICD) for OSA with the Berlin Questionnaire and compared the incidence of ventricular arrhythmias and automated implantable cardioverter defibrillator (AICD) firing between high and low OSA risk groups. Materials and Methods: We contacted 648 consecutive patients from our arrhythmia clinic to participate in the study and performed final analyses on 171 subjects who consented and had follow-up data. Data were abstracted from the electronic health record for the incidence of non-sustained ventricular tachycardia (NSVT), ventricular tachycardia (VT), ventricular fibrillation (VF) and AICD firing and then compared between those at high versus low risk of OSA using the Berlin Questionnaire and multivariate negative binomial regression. Results: The average follow-up period was 24.2 ± 4.4 months. After adjusting for age, gender and history of heart failure, those subjects at high risk of OSA had a higher burden of NSVT vs. those with a low risk of OSA (33.4 ± 96.2 vs. 5.82 ± 17.1 episodes, p = 0.003). A predetermined subgroup analysis of AICD recipients also demonstrated a significantly higher burden of NSVT in the high vs. low OSA risk groups (66.2 ± 128.6 vs. 18.9 ± 36.7 episodes, p = 0.033). There were significant differences in the rates of VT, VF or AICD shock burden between the high and low OSA risk groups and in the AICD subgroup analysis. Conclusions: There was increased ventricular ectopy among pacemaker and AICD recipients at high risk of OSA, but the prevalence of VT, VF or AICD shocks was similar to those with low risk of OSA. Full article

(This article belongs to the Special Issue Obstructive Sleep Apnea: New Perspectives)

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8 pages, 1455 KiB

Open AccessArticle

Variations in Polysomnographic Indices of Obstructive Sleep Apnea following Lingual Tonsil Hypertrophy Excision: Is the Difference Significant?

by Ashraf Wahba, Khaled Abdelaal, Ayman Yehia, Ahmed Alsheikh, Randa Abdallah, Zakaria Ahmed, Alaa Elmazny and Mohamed Shams Eldin

Medicina 2022, 58(5), 573; https://doi.org/10.3390/medicina58050573 - 22 Apr 2022

Cited by 1 | Viewed by 2073

Abstract

Background and Objectives: Obstructive sleep apnea (OSA) is a sleep-related respiratory disorder that affects between 5% and 20% of the population. In obstructive sleep apnea, lingual tonsillar hypertrophy (LTH) has been suggested as a contributing factor to airway blockage. Objectives: The aim of this work is to demonstrate the polysomnographic indices and their values in OSA patients with LTH before and after the surgical intervention. Materials and Methods: The study was conducted on eighteen patients endoscopically diagnosed as having LTH, with the main complaints being snoring, sleep apnea, and/or sleep disturbance. Clinical examination, grading of LTH, body mass index (BMI), endoscopic assessment using Muller’s maneuver, and sleep endoscopy were recorded for all patients. The Epworth Sleepiness Scale (ESS) and overnight sleep polysomnography (PSG) were conducted before and after the surgical removal of LTH. All data were submitted for statistical analysis. Results: The mean ± SD of the AHI decreased from 33.89 ± 26.8 to 20.9 ± 19.14 postoperatively, and this decrease was of insignificant statistical value. The average SpO2 (%) mean ± SD was 91.14 ± 5.96, while the mean ± SD of the desaturation index was 34.64 ± 34.2. Following surgery, these indices changed to 96.5 ± 1.47 and 9.36 ± 7.58, respectively. The mean ± SD of the ESS was changed after the surgery, from 17.27 ± 6.48 to 7.16 ± 3.56. The mean ± SD of sleep efficacy was 71.2 ± 16.8 and the snoring index mean ± SD was 277.6 ± 192.37, and both improved postoperatively, to become 88.17 ± 9.1 and 62.167 ± 40.01, respectively. Conclusions: The AHI after lingual tonsillectomy showed no statistically significant change. The changes in the average SpO2 (%), desaturation index, sleep efficiency, snoring index, and Epworth Sleepiness Scale following the surgery were statistically significant. Full article

(This article belongs to the Special Issue Obstructive Sleep Apnea: New Perspectives)

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Review

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10 pages, 325 KiB

Open AccessReview

Sleep and Safety among Healthcare Workers: The Effect of Obstructive Sleep Apnea and Sleep Deprivation on Safety

by Likhita Shaik, Mustafa S. Cheema, Shyam Subramanian, Rahul Kashyap and Salim R. Surani

Medicina 2022, 58(12), 1723; https://doi.org/10.3390/medicina58121723 - 24 Nov 2022

Cited by 5 | Viewed by 3288

Abstract

Almost one billion people worldwide are affected by Obstructive Sleep Apnea (OSA). Affected individuals experience disordered breathing patterns during sleep, which results in fatigue, daytime drowsiness, and/or sleep deprivation. Working under the influence of these symptoms significantly impairs work productivity and leads to occupational accidents and errors. This impact is seen in healthcare workers (HCWs) who are not immune to these conditions. However, poorly controlled OSA in this subset of individuals takes a heavy toll on patient care due to the increased risk of medical errors and can also alter the mental and physical well-being of the affected HCW in various ways. OSA and safety issues have been recognized and mitigated among the airline and transport industries; however, the healthcare industry lags in addressing these concerns. This article reviews hypersomnolence and sleep disorder as key clinical features of OSA and their effect on HCW safety. Full article

(This article belongs to the Special Issue Obstructive Sleep Apnea: New Perspectives)

10 pages, 707 KiB

Open AccessReview

Review of Application of Machine Learning as a Screening Tool for Diagnosis of Obstructive Sleep Apnea

by Ishan Aiyer, Likhita Shaik, Alaa Sheta and Salim Surani

Medicina 2022, 58(11), 1574; https://doi.org/10.3390/medicina58111574 - 1 Nov 2022

Cited by 7 | Viewed by 2202

Abstract

Obstructive sleep apnea syndrome (OSAS) is a pervasive disorder with an incidence estimated at 5–14 percent among adults aged 30–70 years. It carries significant morbidity and mortality risk from cardiovascular disease, including ischemic heart disease, atrial fibrillation, and cerebrovascular disease, and risks related to excessive daytime sleepiness. The gold standard for diagnosis of OSAS is the polysomnography (PSG) test which requires overnight evaluation in a sleep laboratory and expensive infrastructure, which renders it unsuitable for mass screening and diagnosis. Alternatives such as home sleep testing need patients to wear diagnostic instruments overnight, but accuracy continues to be suboptimal while access continues to be a barrier for many. Hence, there is a continued significant underdiagnosis and under-recognition of sleep apnea in the community, with at least one study suggesting that 80–90% of middle-aged adults with moderate to severe sleep apnea remain undiagnosed. Recently, we have seen a surge in applications of artificial intelligence and neural networks in healthcare diagnostics. Several studies have attempted to examine its application in the diagnosis of OSAS. Signals included in data analytics include Electrocardiogram (ECG), photo-pletysmography (PPG), peripheral oxygen saturation (SpO2), and audio signals. A different approach is to study the application of machine learning to use demographic and standard clinical variables and physical findings to try and synthesize predictive models with high accuracy in assisting in the triage of high-risk patients for sleep testing. The current paper will review this latter approach and identify knowledge gaps that may serve as potential avenues for future research. Full article

(This article belongs to the Special Issue Obstructive Sleep Apnea: New Perspectives)

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12 pages, 812 KiB

Open AccessReview

Cardiovascular Complications of Obstructive Sleep Apnea in the Intensive Care Unit and Beyond

by Abdul Wahab, Arnab Chowdhury, Nitesh Kumar Jain, Salim Surani, Hisham Mushtaq, Anwar Khedr, Mikael Mir, Abbas Bashir Jama, Ibtisam Rauf, Shikha Jain, Aishwarya Reddy Korsapati, Mantravadi Srinivasa Chandramouli, Sydney Boike, Noura Attallah, Esraa Hassan, Mool Chand, Hasnain Saifee Bawaadam and Syed Anjum Khan

Medicina 2022, 58(10), 1390; https://doi.org/10.3390/medicina58101390 - 3 Oct 2022

Cited by 1 | Viewed by 2696

Abstract

Obstructive sleep apnea (OSA) is a common disease with a high degree of association with and possible etiological factor for several cardiovascular diseases. Patients who are admitted to the Intensive Care Unit (ICU) are incredibly sick, have multiple co-morbidities, and are at substantial risk for mortality. A study of cardiovascular manifestations and disease processes in patients with OSA admitted to the ICU is very intriguing, and its impact is likely significant. Although much is known about these cardiovascular complications associated with OSA, there is still a paucity of high-quality evidence trying to establish causality between the two. Studies exploring the potential impact of therapeutic interventions, such as positive airway pressure therapy (PAP), on cardiovascular complications in ICU patients are also needed and should be encouraged. This study reviewed the literature currently available on this topic and potential future research directions of this clinically significant relationship between OSA and cardiovascular disease processes in the ICU and beyond. Full article

(This article belongs to the Special Issue Obstructive Sleep Apnea: New Perspectives)

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8 pages, 303 KiB

Open AccessReview

Patient-Centered Therapy for Obstructive Sleep Apnea: A Review

by Pahnwat Taweesedt, Hala Najeeb and Salim Surani

Medicina 2022, 58(10), 1338; https://doi.org/10.3390/medicina58101338 - 23 Sep 2022

Cited by 1 | Viewed by 1806

Abstract

Obstructive sleep apnea (OSA) is one of the most common sleep problems defined by cessation or decreased airflow despite breathing efforts. It is known to be related to multiple adverse health consequences. Positive airway pressure (PAP) is considered an effective treatment that is widely used. Various modes of PAP and other emerging treatment options are now available. A multidisciplinary approach, understanding diverse phenotypes of OSA, and shared decision-making are necessary for successful OSA treatment. Patient-centered care is an essential modality to support patient care that can be utilized in patients with OSA to help improve outcomes, treatment adherence, and patient satisfaction. Full article

(This article belongs to the Special Issue Obstructive Sleep Apnea: New Perspectives)

15 pages, 353 KiB

Open AccessReview

Overview of the Role of Pharmacological Management of Obstructive Sleep Apnea

by Enrique Arredondo, Monica DeLeon, Ishimwe Masozera, Ladan Panahi, George Udeani, Nhan Tran, Chi K. Nguyen, Chairat Atphaisit, Brooke de la Sota, Gabriel Gonzalez Jr., Eileen Liou, Zack Mayo, Jennifer Nwosu and Tori L. Shiver

Medicina 2022, 58(2), 225; https://doi.org/10.3390/medicina58020225 - 2 Feb 2022

Cited by 7 | Viewed by 4236

Abstract

Obstructive sleep apnea (OSA) remains a prominent disease state characterized by the recurrent collapse of the upper airway while sleeping. To date, current treatment may include continuous positive airway pressure (CPAP), lifestyle changes, behavioral modification, mandibular advancement devices, and surgical treatment. However, due to the desire for a more convenient mode of management, pharmacological treatment has been thoroughly investigated as a means for a potential alternative in OSA treatment. OSA can be distinguished into various endotypic or phenotypic classes, allowing pharmacological treatment to better target the root cause or symptoms of OSA. Some medications available for use include antidepressants, CNS stimulants, nasal decongestants, carbonic anhydrase inhibitors, and potassium channel blockers. This review will cover the findings of currently available and future study medications that could potentially play a role in OSA therapy. Full article

(This article belongs to the Special Issue Obstructive Sleep Apnea: New Perspectives)

12 pages, 349 KiB

Open AccessReview

Review of the Management of Obstructive Sleep Apnea and Pharmacological Symptom Management

by Ladan Panahi, George Udeani, Steven Ho, Brett Knox and Jason Maille

Medicina 2021, 57(11), 1173; https://doi.org/10.3390/medicina57111173 - 28 Oct 2021

Cited by 6 | Viewed by 3018

Abstract

Nearly a billion adults around the world are affected by a disease that is characterized by upper airway collapse while sleeping called obstructive sleep apnea or OSA. The progression and lasting effects of untreated OSA include an increased risk of diabetes mellitus, hypertension, stroke, and heart failure. There is often a decrease in quality-of-life scores and an increased rate of mortality in these patients. The most common and effective treatments for OSA include continuous positive airway pressure (CPAP), surgical treatment, behavior modification, changes in lifestyle, and mandibular advancement devices. There are currently no pharmacological options approved for the standard treatment of OSA. There are, however, some pharmacological treatments for daytime sleepiness caused by OSA. Identifying and treating obstructive sleep apnea early is important to reduce the risks of future complications. Full article

(This article belongs to the Special Issue Obstructive Sleep Apnea: New Perspectives)

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