|Year : 2023 | Volume
| Issue : 1 | Page : 31-38
|Optimal positive airway pressure requirement and polysomnography indices of obstructive sleep apnea severity in the Saudi population
Ahmad A Bamagoos1, Shahad A Alshaynawi2, Atheer S Gari2, Atheer M Badawi2, Mudhawi H Alhiniah2, Asma A Alshahrani2, Renad R Rajab2, Reem K Bahaj2, Faris Alhejaili3, Siraj O Wali3
1 Department of Physiology, Faculty of Medicine in Rabigh, King Abdulaziz University; Sleep Medicine and Research Centre, King Abdulaziz University Hospital, Jeddah, Saudi Arabia
2 Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
3 Sleep Medicine and Research Centre, King Abdulaziz University Hospital, Jeddah, Saudi Arabia
|Date of Submission||11-May-2022|
|Date of Acceptance||06-Aug-2022|
|Date of Web Publication||25-Jan-2023|
Dr. Ahmad A Bamagoos
Department of Physiology, Faculty of Medicine in Rabigh, Sleep Medicine and Research Centre, King Abdulaziz University Hospital, King Abdulaziz University, Jeddah
Source of Support: None, Conflict of Interest: None
| Abstract|| |
CONTEXT: Positive airway pressure (PAP) is the first-line therapy for obstructive sleep apnea (OSA). Overnight PAP titration for determining optimal PAP requirements is expensive and often inconvenient. Prediction of optimal PAP requirements from diagnostic polysomnography via mathematical equations is possible but variable across populations.
AIMS: We aimed to (1) determine the optimal PAP requirement, (2) determine differences in optimal PAP requirements across OSA severity groups, (3) determine the relationship between optimal PAP requirement and diagnostic polysomnography measurements of OSA severity, and (4) develop a pilot equation to predict the optimal PAP requirement from diagnostic polysomnography in a sample from the Saudi population.
METHODS: We analyzed records pertaining to adult OSA patients (n = 215; 63% of males) who underwent standardized diagnostic and titration polysomnography in our sleep laboratory between 2015 and 2019. Demographic, anthropometric, and clinical information were also collected for the analysis. Inferential statistics were performed for comparisons between diagnostic and titration studies and between OSA severity groups. Regression analyses were also performed to determine the potential predictors of optimal PAP requirements. Data were presented as the mean (± standard deviation) or median (25th–75th quartiles) according to normality.
RESULTS: The median optimal PAP requirement was 13 (9–17) cmH2O. The optimal PAP requirement was significantly greater for male versus female participants (14 [10–17] vs. 12 [8–16] cmH2O) and for participants with severe OSA (16 [12–20] cmH2O, n = 119) versus those with moderate (11 [8–14] cmH2O, n = 63) or mild (9 [7–12] cmH2O, n = 33) OSA. When combined, nadir oxygen saturation, oxygen desaturation index, and arousal index could be used to predict the optimal PAP requirement (R2 = 0.39, F = 34.0, P < 0.001).
CONCLUSIONS: The optimal PAP requirement in the Saudi population is relatively high and directly correlated with OSA severity. Diagnostic polysomnography measurements of OSA severity predicted the optimal PAP requirement in this sample. Prospective validation is warranted.
Keywords: Automatic positive airway pressure (APAP), Continuous positive airway pressure (CPAP), effective pressure, therapeutic pressure
|How to cite this article:|
Bamagoos AA, Alshaynawi SA, Gari AS, Badawi AM, Alhiniah MH, Alshahrani AA, Rajab RR, Bahaj RK, Alhejaili F, Wali SO. Optimal positive airway pressure requirement and polysomnography indices of obstructive sleep apnea severity in the Saudi population. Ann Thorac Med 2023;18:31-8
|How to cite this URL:|
Bamagoos AA, Alshaynawi SA, Gari AS, Badawi AM, Alhiniah MH, Alshahrani AA, Rajab RR, Bahaj RK, Alhejaili F, Wali SO. Optimal positive airway pressure requirement and polysomnography indices of obstructive sleep apnea severity in the Saudi population. Ann Thorac Med [serial online] 2023 [cited 2023 Mar 20];18:31-8. Available from: https://www.thoracicmedicine.org/text.asp?2023/18/1/31/368494
Obstructive sleep apnea (OSA) is a condition characterized by transient episodes of airflow interruption during sleep despite continued breathing efforts. Patients with OSA suffer from loud snoring, hypoxemia, fragmented sleep, and excessive daytime sleepiness.,, OSA increases the risk of developing several comorbid conditions, including metabolic and cardiovascular disease.,, Therefore, long-term treatment of OSA is essential.
Positive airway pressure (PAP) is the standard therapy for patients with moderate-to-severe OSA., PAP therapy provides pressurized air into the nostrils to keep the airways open and prevent airflow interruption during sleep. Adherence to PAP therapy improves cardiovascular function, reduces excessive daytime sleepiness, and can alleviate OSA-related comorbid conditions., However, the prescription of PAP therapy requires patients to undergo an in-laboratory titration polysomnography for a manual determination of the optimal PAP requirement (the level of pressurized air at which all episodes of interrupted ventilation are corrected and optimal breathing is restored) in addition to diagnostic polysomnography., This process is time-consuming, labor-intensive, and inconvenient for most patients, such as people with a low socioeconomic status or those living in rural areas. Delays in treatment delivery may also occur as the prevalence of people diagnosed with OSA increases; the condition affects 50% of men and 24% of women worldwide and 58%–71% of the middle-aged people in Saudi Arabia., Although machines that perform automatic detection of optimal PAP requirement reduce the complexity of this process, they are contraindicated for many OSA patients with comorbid conditions such as congestive heart failure.
Recently, optimal PAP requirements have been linked to the severity of upper airway collapsibility (the threshold pressure of the pharynx at which the airways collapse despite continued breathing) when the measurements are performed in research laboratories. This suggests a possible relationship between the optimal PAP requirement and the OSA severity measured via polysomnography in clinical laboratories. Indeed, equations to predict optimal PAP requirements from diagnostic polysomnography measurements, together with demographic and clinical information, have been developed; however, they are widely variable and show inconsistent results. Several factors may explain this variability, including age, sex, body mass, and ethnic group.
To our knowledge, the optimal PAP requirement has never been explored or linked to OSA severity in a sample of patients from the Saudi population. Therefore, the overarching objective of this study was to explore the optimal PAP requirement in a sample of patients from the Saudi population who underwent manual PAP titration under in-laboratory polysomnography using standardized clinical methodologies., We particularly aimed to determine: (1) the average optimal PAP requirement; (2) the effects of OSA severity (i.e. mild, moderate, or severe,) on the optimal PAP requirement; and (3) the relationship between the optimal PAP requirement and diagnostic polysomnography measurements of OSA severity; and (4) we aimed to develop a pilot regression model to predict optimal PAP requirement from measurements of OSA severity.
| Methods|| |
Study design, population, and protocol
We performed a review of records to identify potential study participants who were adult patients with OSA (baseline apnea–hypopnea index [AHI] >5 events/h of sleep) and who underwent diagnostic and PAP titration polysomnography at our center between 2015 and 2019. Patients with predominant central sleep apnea, obesity hypoventilation syndrome, chronic obstructive pulmonary disease, chronic respiratory failure, and congestive heart failure were excluded from this study. After the initial screening, 269 patients with OSA met our selection criteria, of which 215 were included in the statistical analyses and 54 were excluded due to inconclusive measurements of optimal PAP requirements or inadequate sleep time (<2 h of sleep per night) during the diagnostic or PAP titration polysomnography. The data collated from the records included demographic details (age, sex, and ethnicity), anthropometric information (weight, height, and body mass index [BMI]), and clinical information (Epworth Sleepiness Scale [ESS] scores and STOP-BANG questionnaire scores), in addition to measurements of diagnostic and PAP titration polysomnography. Ethical approval was provided by the institutional review board.
The records included in this study pertained to participants who underwent diagnostic polysomnography using the equipment and technical specifications recommended by the American Academy of Sleep Medicine (AASM). Briefly, the participants were connected to an in-laboratory polysomnography system (SOMNOscreen plus; SOMNOmedics, Randersacker, Germany) via surface electrodes to enable electroencephalography, electrooculography, electromyography (submental and bilateral anterior tibialis muscles), and electrocardiography measurements. The participants were also connected to a nasal pressure sensor, nasal and oral airflow thermocouples, chest and abdominal impedance belts (for respiratory muscle movement), a pulse oximetry device (for blood oxygen saturation), a tracheal microphone (for snoring sound), and a body position sensor. The participants were reminded to remain in the supine position as much as possible during the sleep study. Polysomnography measurements were acquired and recorded on a personal computer using conventional software. Standard calibration procedures were conducted before the lights were turned off to ensure quality signal acquisition. The polysomnography recordings were scored and recorded manually by registered technologists according to the AASM Manual for the Scoring of Sleep and Associated Events., Quality control of the scoring was ensured by randomly selecting five cases per month for scoring by our laboratory technologists to audit interobserver reproducibility and accuracy.
Polysomnography measurements of obstructive sleep apnea severity
The severity of OSA was defined during diagnostic polysomnography according to the AHI, which measures the average number of apnea and hypopnea episodes per hour of sleep at a single night.,, We used AASM cutoffs to categorize patients into mild (AHI = 5–15 events/h), moderate (AHI = 15–30 events/h), or severe (AHI >30 events/h) OSA groups for relevant analyses. Other polysomnography measurements commonly used in clinical practice to represent OSA severity were also selected for analyses in this study, including AHI during nonrapid and rapid eye movement sleep (NREM AHI and REM AHI, respectively), nadir oxygen saturation during sleep (MinO2Sat), oxygen desaturation index at 3% (ODI 3%: the average number of respiratory episodes coupled with a reduction in oxygen saturation ≥3% per hour of sleep), the sleep time spent with oxygen saturation under 90% (T90), the total arousal index (the average number of cortical arousal episodes per hour of sleep), the respiratory arousal index (the average number of cortical arousal episodes coupled with an obstructive respiratory event per hour of sleep), the maximum respiratory event duration, and the average respiratory event duration.
In addition to the standard diagnostic polysomnography setup, the participants were connected to either a continuous or a bilevel PAP machine (CPAP or BiPAP, respectively) via a full-face mask for the manual titration of therapeutic pressure requirements. A minority of participants (<3%) were fitted with a nasal mask or nasal pillows. The titration procedures followed the recommended AASM guidelines. After the participants were equipped with the required setting, a signal calibration was performed, and the CPAP/BiPAP machine was initially set to provide pressurized air at 4 cmH2O. The participants were reminded to remain in the supine position as much as possible during their titration polysomnography to ensure measurement consistency compared to their diagnostic polysomnography. After the participants fell asleep, the pressure was adjusted every 5 min in 1 cmH2O increment until normal breathing was visually observed (i.e., round peak inspiratory flow and O2 saturation > 90% for at least 15 continuous minutes), indicating an optimal PAP requirement for correcting OSA. The titration of PAP was continued during epochs of REM sleep, in addition to NREM sleep, to prevent an underestimation of the optimal PAP requirement. The scoring and reporting methodologies of the titration polysomnography sessions were identical to those used for the diagnostic studies described above.
Data presentation and statistical analyses
The Statistical Package for the Social Sciences (SPSS V23, IBM Corp., NY, USA) was used for the statistical analyses. Descriptive data are presented as the mean (±SD, standard deviation) or median (25th–75th quartiles), according to the normality distribution of the data (Shapiro–Wilk test). Paired Student's t-test (or the Mann–Whitney U test, according to the normality distribution) was performed to explore changes in polysomnography measurements between the diagnostic and PAP titration studies. One-way analysis of variance (ANOVA) (or the Kruskal–Wallis test, according to the normality distribution) was performed to evaluate the difference in optimal PAP requirements between the OSA severity groups. Dunn's post hoc test was performed to assess multiple comparisons between the groups. Pearson's (or Spearman's, according to the normality distribution) correlation coefficient was performed to assess the relationship between optimal PAP requirements and measurements of OSA severity. Linear regression analysis was performed to determine the ability of individual measurements acquired during diagnostic polysomnography to predict optimal PAP requirements. Multiple regression analysis was performed to build a pilot model for predicting the optimal PAP requirement from a combination of measurements that showed good performance in independently predicting the optimal PAP requirement. P < 0.05 was considered to indicate significance for the descriptive and comparative statistical testing. To avoid false-positive results, a more stringent P < 0.01 was considered to indicate significance in the correlation and prediction statistics.
| Results|| |
Participant characteristics at baseline
Participant characteristics and measurements from the diagnostic and PAP titration polysomnography are summarized in [Table 1]. The participants in this study were predominantly middle-aged males with central obesity, mild daytime sleepiness, and moderate-to-severe OSA. No systematic differences were noted between the participants in our sample and those excluded from the statistical analyses in terms of age (P = 0.12), BMI (P = 0.22), AHI (P = 0.28), MinO2Sat (P = 0.28), or ESS (P = 0.36), although the percentage of female patients was slightly higher in the excluded group (38% vs. 55%, respectively).
|Table 1: Participant characteristics at the diagnostic and positive airway pressure titration polysomnography studies|
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The numbers of participants who had mild, moderate, or severe OSA during diagnostic polysomnography were 33 (15%), 63 (30%), and 119 (55%), respectively. The severe OSA group had a greater proportion of male participants than female participants (70% vs. 30%, respectively, Pearson's χ2 = 6.7, P = 0.035). The mild and moderate OSA groups comprised approximately equal proportions of both sexes. Participants in the severe OSA group had a higher median BMI than those in the mild OSA group (30 [27–36] vs. 38 [34–48] kg/m2, respectively, Kruskal–Wallis test P = 0.011, Dunn's post hoc test P = 0.010). No difference in age distribution was noted across the OSA severity groups. [Table 2] summarizes participant characteristics as well as key polysomnography measurements across OSA severity groups.
|Table 2: Participant characteristics and key polysomnography measurements across obstructive sleep apnea severity groups|
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Optimal positive airway pressure requirement and measurements of obstructive sleep apnea severity
All participants were naïve to PAP therapy before the titration polysomnography sessions. The majority of polysomnography measurements improved with PAP therapy [Table 1]. The median optimal PAP requirement of participants in our sample was 13 [9–17] cmH2O. On average, compared to the female participants, the male participants required more air pressure to control their OSA (14 [10–17] vs. 12 [8–16] cmH2O, respectively, P = 0.011, Mann–Whitney U test). The optimal PAP requirement was not significantly different between older and younger participants (groups defined by median age: 50 [41–60] years) or between morbidly obese and less obese participants (groups defined by median BMI: 38 [32–46] kg/m2).
We found a significant difference in the optimal PAP requirement across OSA severity groups (Pearson's χ2 = 45.4, P < 0.001, Kruskal–Wallis test). Dunn's post hoc test revealed a significantly greater optimal PAP requirement in participants with severe OSA than in those with moderate or mild OSA [Figure 1]. No difference was noted in the optimal PAP requirement between participants with moderate and mild OSA (P = 0.55).
|Figure 1: The optimal continuous positive airway pressure requirement was significantly greater in participants who had severe obstructive sleep apnea (apnea–hypopnea index >30 events/h of sleep) compared with those who had moderate or mild OSA (16 [12–20] vs. 11 [8–14] vs. 9 [7–12] cmH2O, respectively; P < 0.001, Kruskal–Wallis test)|
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Predicting optimal positive airway pressure requirement from diagnostic measurements of obstructive sleep apnea severity
We found a significant correlation between optimal PAP requirements and several indices related to OSA severity. Spearman's correlation testing revealed that the optimal PAP requirement was moderately correlated with the total sleep time AHI (r = 0.51, P < 0.001), NREM AHI (r = 0.56, P < 0.001), ODI 3% (r = 0.58, P < 0.001), T90 (r = 0.50, P < 0.001), total arousal index (r = 0.53, P < 0.001), and respiratory arousal index (r = 0.50, P < 0.001) in a direct manner and with MinO2Sat (r = −0.46, P < 0.001) in an inverse manner. No correlation was found between the optimal PAP requirement and REM AHI. The optimal PAP requirement also showed a significant correlation with BMI at diagnostic polysomnography (P = 0.03), although this was statistically unimportant (r = 0.14).
Bivariate linear regression analyses revealed that several variables measured at the time of diagnostic polysomnography were able to independently predict the optimal PAP requirement. [Table 3] summarizes the regression output for these variables. The variables explained 22%–31% of the optimal PAP requirement variance. The prediction power was not attenuated after adjusting for key cofounders, including sex, age, BMI, and total sleep time (TST) (two-block method: first block for individual polysomnography predictors and second block for confounders). AHI during REM sleep did not show an adequate ability to predict the optimal PAP requirement.
|Table 3: Independent predictors of optimal positive airway pressure requirements (output from simple linear regression) before and after adjustment for common confounders*|
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Multivariable regression analysis (one-block backward elimination of the included potential predictors and key confounders) indicated that a combination of ODI 3%, MinO2Sat, and respiratory arousal index provided the best fit for the prediction of the optimal PAP requirement (R2 = 0.39, F = 34.0, P < 0.001). The prediction equation was: optimal PAP requirement = 19.27 + 0.05 ODI + 0.06 total arousal index −0.13 minO2Sat.
| Discussion|| |
To our knowledge, this study is the first to describe the relationship between optimal PAP requirements and OSA severity in the Saudi population. We report several key findings. First, the median optimal PAP requirement in our sample was 13 cmH2O. Second, the optimal PAP requirement was greater in patients with severe OSA (AHI >30 events/h of sleep) than in patients with mild or moderate forms of the disease. Third, the optimal PAP requirement showed a moderate correlation with and could be predicted from diagnostic polysomnography indices that commonly represent OSA severity in clinical practice, including the ODI 3%, MinO2Sat, and total arousal indices.
The median optimal PAP requirement reported in this study tended to be higher than that reported in the literature., This may be attributed to the majority of participants in the study using oronasal masks during the PAP titration polysomnography., Nonetheless, we found a direct relationship between the optimal PAP requirement and OSA severity, as has been suggested in previous reports.,, Although not consistently supported by the literature, it is conceivable that the provision of a higher PAP is necessary for controlling respiratory events in people with more severe OSA.
Indeed, recent reports have linked the optimal PAP requirement to airway collapsibility,, a key pathophysiological contributor to OSA severity. Although some patients with moderate-to-severe OSA experience mild airway collapsibility and have low optimal PAP requirements, more collapsible airways often lead to more severe OSA., The participants in this study were predominantly males and had increased BMI. Both are risk factors for poor airway collapsibility and the development of severe OSA., Compared to the female participants, the male participants in this study had higher optimal PAP requirements. However, the effect of body mass on the optimal PAP requirement was nonsignificant in the current analysis. This is presumably attributable to the fact that the participants in this study were homogeneously obese (median BMI: 38 kg/m2). Ethnicity is another major contributing factor to the pathophysiology of OSA. Certain ethnicity groups have confined craniofacial structures which predispose them to increased airway collapsibility and increased OSA severity in response to minor increments in BMI. The Saudi population is characterized by a diversity of ethnic backgrounds, and many people are descendants of multiple racial groups. The effects of craniofacial structures and ethnicity on the optimal PAP requirement of the Saudi population have not been systematically explored in this or other studies. However, we emphasize that the ethnic backgrounds of participants may in part explain the higher optimal PAP requirement seen in the current analysis. The empirical measurement of airway collapsibility during PAP titration polysomnography may further elucidate the interrelationship between airway collapsibility, OSA severity, and optimal PAP requirements.
The current analysis demonstrates the ability of polysomnography measurements used for the evaluation of OSA severity in clinical practice to predict optimal PAP requirements. Previous studies have shown that this is possible, but the results have varied across groups. Miljeteig and Hoffstein were the first to describe an equation for predicting the optimal PAP requirement from polysomnography and clinical measurements. They proposed obesity and the number of respiratory events per hour of sleep as key predictors of optimal PAP requirements. Thereafter, several reports suggested similar results in Caucasian, Asian, and mixed populations.,,,,, In this study, the number of respiratory events per hour of NREM sleep was a predictor of the optimal PAP requirement when considered independently but not during multivariable analyses. Although the AHI has been used for decades to define OSA severity in research and clinical practice, it captures a single aspect of OSA severity (i.e., the number of respiratory events) and ignores many other aspects (e.g., the length and type of events), which renders its reliability questionable.,, In contrast, deoxygenation indices have been increasingly recognized as surrogate markers of OSA severity and the impact of OSA on cardiovascular health., Expectedly, greater optimal PAP is required for the correction of OSA in patients with poor deoxygenation indices. In the current study, deoxygenation indices allowed a better prediction of the optimal PAP requirement when multiple variables were analyzed, and this was also noted in earlier reports.,,,, The arousal index was selected for inclusion in our PAP prediction equation. We found no previous studies reporting the inclusion of indices of arousal disturbance during sleep in PAP prediction equations. OSA is a heterogeneous disease, and nonanatomical mechanisms such as frequent arousal from sleep can add to the severity of the condition, thereby increasing PAP therapy requirements. Frequent cortical arousals during sleep impair the body's ventilatory control system and reduce periods of stable sleep.,, A combination of hypoxic burden and sleep fragmentation appears to be essential for predicting the optimal PAP requirement in OSA.
The clinical value of these findings requires further elucidation. An accurate identification of therapeutic PAP requirements is necessary to maximize treatment effectiveness and minimize side effects and dropout rates. Although current equations may not replace titration polysomnography in predicting optimal PAP requirements, they show noninferiority to single-night PAP titration polysomnography., Predication equations can be used for the prescription of PAP therapy for patients who cannot access laboratory testing services, including those living in remote areas, those experiencing movement difficulties, and those who cannot afford payments for additional polysomnography testing. Prediction equations can also assist in minimizing the duration of the PAP titration process during laboratory-based polysomnography, thereby encouraging cost-effective split-night polysomnography studies, and in reducing the titration failure rate.
Comments on design and methodologies
Several design methodologies strengthened the findings of this report. To our knowledge, we are the first authors to report the relationship between optimal PAP requirements and polysomnography indices of OSA severity in a sample of patients from the Saudi population. Although we had hoped for a larger sample to maximize the study's generalizability and calculation power, the number of participants in our sample exceeded that of most similar investigations. Statistically, the sample appears to be free of exclusion bias, despite a greater proportion of females being excluded from the study. Analyses of the subgroups, such as participants with REM-predominant or apnea-predominant OSA, were challenging due to missing data. Nonetheless, our findings are intuitive and align well with the pathophysiological mechanisms of OSA. The study's retrospective design and the predominance of participants with moderate-to-severe OSA may limit the generalizability of the findings. Prospective validation of our findings in a larger sample that includes patients from multiple centers who have a diverse spectrum of OSA severities can improve the interpretation.
| Conclusions|| |
This study demonstrates a high optimal PAP requirement in a sample of patients from the Saudi population and a direct correlation between the optimal PAP requirement and polysomnography measurements representing OSA severity. When combined, nadir oxygen saturation, oxygen desaturation index, and arousal index showed potential for predicting optimal PAP requirements from diagnostic polysomnography measurements. These findings may be attributed – in addition to OSA severity – to participant characteristics such as ethnicity, obesity, and male gender. Further prospective validation in a larger sample that includes patients from multiple centers who have a diverse spectrum of OSA severities may improve the generalizability of our findings.
Financial support and sponsorship
This research work was funded by Institutional Fund Projects under grant no. (IFPDP-258-22). Therefore, the authors gratefully acknowledge technical and financial support from Ministry of Education and Deanship of Scientific Research (DSR), King Abdulaziz University (KAU), Jeddah, Saudi Arabia.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Sleep-related breathing disorders in adults: Recommendations for syndrome definition and measurement techniques in clinical research. The report of an American academy of sleep medicine task force. Sleep 1999;22:667-89.
Whyte KF, Allen MB, Jeffrey AA, Gould GA, Douglas NJ. Clinical features of the sleep apnoea/hypopnoea syndrome. Q J Med 1989;72:659-66.
Berthon-Jones M, Sullivan CE. Ventilatory and arousal responses to hypoxia in sleeping humans. Am Rev Respir Dis 1982;125:632-9.
Gottlieb DJ, Whitney CW, Bonekat WH, Iber C, James GD, Lebowitz M, et al.
Relation of sleepiness to respiratory disturbance index: The sleep heart health study. Am J Respir Crit Care Med 1999;159:502-7.
Baaisharah SS, Bamagoos AA, Altaib HO, Alamaa MN, Aljammali KW, Bafakeer BS, et al.
Prevalence of depression, anxiety, and stress among obstructive sleep apnea patients in Saudi Arabia. Saudi J Intern Med 2017;7:5-12.
Dewan NA, Nieto FJ, Somers VK. Intermittent hypoxemia and OSA: Implications for comorbidities. Chest 2015;147:266-74.
Stepnowsky C, Sarmiento KF, Bujanover S, Villa KF, Li VW, Flores NM. Comorbidities, health-related quality of life, and work productivity among people with obstructive sleep apnea with excessive sleepiness: Findings from the 2016 us national health and wellness survey. J Clin Sleep Med 2019;15:235-43.
Kushida CA, Littner MR, Hirshkowitz M, Morgenthaler TI, Alessi CA, Bailey D, et al.
Practice parameters for the use of continuous and bilevel positive airway pressure devices to treat adult patients with sleep-related breathing disorders. Sleep 2006;29:375-80.
Epstein LJ, Kristo D, Strollo PJ Jr., Friedman N, Malhotra A, Patil SP, et al.
Clinical guideline for the evaluation, management and long-term care of obstructive sleep apnea in adults. J Clin Sleep Med 2009;5:263-76.
Sullivan CE, Issa FG, Berthon-Jones M, Eves L. Reversal of obstructive sleep apnoea by continuous positive airway pressure applied through the nares. Lancet 1981;1:862-5.
Gay P, Weaver T, Loube D, Iber C, Positive Airway Pressure Task Force, Standards of Practice Committee, et al.
Evaluation of positive airway pressure treatment for sleep related breathing disorders in adults. Sleep 2006;29:381-401.
Patel SR, White DP, Malhotra A, Stanchina ML, Ayas NT. Continuous positive airway pressure therapy for treating sleepiness in a diverse population with obstructive sleep apnea: Results of a meta-analysis. Arch Intern Med 2003;163:565-71.
Heinzer R, Vat S, Marques-Vidal P, Marti-Soler H, Andries D, Tobback N, et al.
Prevalence of sleep-disordered breathing in the general population: The hypnolaus study. Lancet Respir Med 2015;3:310-8.
Wali SO, Abalkhail B, Krayem A. Prevalence and risk factors of obstructive sleep apnea syndrome in a Saudi Arabian population. Ann Thorac Med 2017;12:88-94.
] [Full text]
Morgenthaler TI, Aurora RN, Brown T, Zak R, Alessi C, Boehlecke B, et al.
Practice parameters for the use of autotitrating continuous positive airway pressure devices for titrating pressures and treating adult patients with obstructive sleep apnea syndrome: An update for 2007. An American academy of sleep medicine report. Sleep 2008;31:141-7.
Landry SA, Joosten SA, Eckert DJ, Jordan AS, Sands SA, White DP, et al
. Therapeutic CPAP level predicts upper airway collapsibility in patients with obstructive sleep apnea. Sleep 2017;40:zsx056.
Camacho M, Riaz M, Tahoori A, Certal V, Kushida CA. Mathematical equations to predict positive airway pressures for obstructive sleep apnea: A systematic review. Sleep Disord 2015;2015:293868.
Berry RB, Budhiraja R, Gottlieb DJ, Gozal D, Iber C, Kapur VK, et al.
Rules for scoring respiratory events in sleep: Update of the 2007 AASM manual for the scoring of sleep and associated events. Deliberations of the sleep apnea definitions task force of the American academy of sleep medicine. J Clin Sleep Med 2012;8:597-619.
Sateia MJ. International classification of sleep disorders-third edition: Highlights and modifications. Chest 2014;146:1387-94.
Kushida CA, Chediak A, Berry RB, Brown LK, Gozal D, Iber C, et al.
Clinical guidelines for the manual titration of positive airway pressure in patients with obstructive sleep apnea. J Clin Sleep Med 2008;4:157-71.
BaHammam AS, Alassiri SS, Al-Adab AH, Alsadhan IM, Altheyab AM, Alrayes AH, et al.
Long-term compliance with continuous positive airway pressure in Saudi patients with obstructive sleep apnea. A prospective cohort study. Saudi Med J 2015;36:911-9.
Kim H, Lee M, Hwangbo Y, Yang KI. Automatic derivation of continuous positive airway pressure settings: Comparison with in-laboratory titration. J Clin Neurol 2020;16:314-20.
Genta PR, Kaminska M, Edwards BA, Ebben MR, Krieger AC, Tamisier R, et al.
The importance of mask selection on continuous positive airway pressure outcomes for obstructive sleep apnea. An official American thoracic society workshop report. Ann Am Thorac Soc 2020;17:1177-85.
Duarte RL, Mendes BA, Oliveira-E-Sá TS, Magalhães-da-Silveira FJ, Gozal D. Nasal versus oronasal mask in patients under auto-adjusting continuous positive airway pressure titration: A real-life study. Eur Arch Otorhinolaryngol 2020;277:3507-12.
Nino-Murcia G, McCann CC, Bliwise DL, Guilleminault C, Dement WC. Compliance and side effects in sleep apnea patients treated with nasal continuous positive airway pressure. West J Med 1989;150:165-9.
Miljeteig H, Hoffstein V. Determinants of continuous positive airway pressure level for treatment of obstructive sleep apnea. Am Rev Respir Dis 1993;147:1526-30.
Pevernagie DA, Shepard JW Jr. Relations between sleep stage, posture and effective nasal CPAP levels in OSA. Sleep 1992;15:162-7.
Oksenberg A, Arons E, Froom P. Does the severity of obstructive sleep apnea predict patients requiring high continuous positive airway pressure? Laryngoscope 2006;116:951-5.
Bamagoos AA, Eckert DJ, Sutherland K, Ngiam J, Cistulli PA. Dose-dependent effects of mandibular advancement on optimal positive airway pressure requirements in obstructive sleep apnoea. Sleep Breath 2020;24:961-9.
Eckert DJ. Phenotypic approaches to obstructive sleep apnoea – New pathways for targeted therapy. Sleep Med Rev 2018;37:45-59.
Bosi M, Incerti Parenti S, Fiordelli A, Poletti V, Alessandri-Bonetti G. Upper airway collapsibility in patients with OSA treated with continuous positive airway pressure: A retrospective preliminary study. J Clin Sleep Med 2020;16:1839-46.
Gharibeh T, Mehra R. Obstructive sleep apnea syndrome: Natural history, diagnosis, and emerging treatment options. Nat Sci Sleep 2010;2:233-55.
Mohsenin V. Effects of gender on upper airway collapsibility and severity of obstructive sleep apnea. Sleep Med 2003;4:523-9.
Young T, Shahar E, Nieto FJ, Redline S, Newman AB, Gottlieb DJ, et al.
Predictors of sleep-disordered breathing in community-dwelling adults: The sleep heart health study. Arch Intern Med 2002;162:893-900.
Sutherland K, Lee RW, Cistulli PA. Obesity and craniofacial structure as risk factors for obstructive sleep apnoea: Impact of ethnicity. Respirology 2012;17:213-22.
Torre-Bouscoulet L, Castorena-Maldonado A, López-Escárcega E, Vázquez-García JC, Pérez-Padilla R. Agreement between 95th
percentile pressure based on a 7-night auto-adjusting positive airway pressure trial versus. Equation-based predictions in sleep apnea. J Clin Sleep Med 2009;5:311-6.
El Solh AA, Aldik Z, Alnabhan M, Grant B. Predicting effective continuous positive airway pressure in sleep apnea using an artificial neural network. Sleep Med 2007;8:471-7.
Ito E, Tsuiki S, Namba K, Takise Y, Inoue Y. Upper airway anatomical balance contributes to optimal continuous positive airway pressure for Japanese patients with obstructive sleep apnea syndrome. J Clin Sleep Med 2014;10:137-42.
Schiza SE, Bouloukaki I, Mermigkis C, Panagou P, Tzanakis N, Moniaki V, et al.
Utility of formulas predicting the optimal nasal continuous positive airway pressure in a Greek population. Sleep Breath 2011;15:417-23.
Saiphoklang N, Leelasittikul K, Pugongchai A. Prediction of optimal continuous positive airway pressure in Thai patients with obstructive sleep apnea. Sci Rep 2021;11:13935.
Sarma L, Putti N, Alias K, Chilana M. Determination of equation for estimating continuous positive airway pressure in patients with obstructive sleep apnea for the Indian population. Lung India 2020;37:411-4.
] [Full text]
Kingshott RN, Vennelle M, Hoy CJ, Engleman HM, Deary IJ, Douglas NJ. Predictors of improvements in daytime function outcomes with CPAP therapy. Am J Respir Crit Care Med 2000;161:866-71.
Redline S, Kapur VK, Sanders MH, Quan SF, Gottlieb DJ, Rapoport DM, et al.
Effects of varying approaches for identifying respiratory disturbances on sleep apnea assessment. Am J Respir Crit Care Med 2000;161:369-74.
Wali SO, Manzar MD, Abdelaziz MM, Alshomrani R, Alhejaili F, Al-Mughales J, et al.
Putative associations between inflammatory biomarkers, obesity, and obstructive sleep apnea. Ann Thorac Med 2021;16:329-36. [Full text]
Tkacova R, McNicholas WT, Javorsky M, Fietze I, Sliwinski P, Parati G, et al.
Nocturnal intermittent hypoxia predicts prevalent hypertension in the European sleep apnoea database cohort study. Eur Respir J 2014;44:931-41.
Pevernagie DA, Gnidovec-Strazisar B, Grote L, Heinzer R, McNicholas WT, Penzel T, et al.
On the rise and fall of the apnea-hypopnea index: A historical review and critical appraisal. J Sleep Res 2020;29:e13066.
Akashiba T, Kosaka N, Yamamoto H, Ito D, Saito O, Horie T. Optimal continuous positive airway pressure in patients with obstructive sleep apnoea: Role of craniofacial structure. Respir Med 2001;95:393-7.
Stradling JR, Hardinge M, Paxton J, Smith DM. Relative accuracy of algorithm-based prescription of nasal CPAP in OSA. Respir Med 2004;98:152-4.
Basoglu OK, Tasbakan MS. Determination of new prediction formula for nasal continuous positive airway pressure in Turkish patients with obstructive sleep apnea syndrome. Sleep Breath 2012;16:1121-7.
Suzuki M, Ogawa H, Okabe S, Horiuchi A, Okubo M, Ikeda K, et al.
Digital recording and analysis of esophageal pressure for patients with obstructive sleep apnea-hypopnea syndrome. Sleep Breath 2005;9:64-72.
Younes M. Role of arousals in the pathogenesis of obstructive sleep apnea. Am J Respir Crit Care Med 2004;169:623-33.
Edwards BA, Eckert DJ, Jordan AS. Obstructive sleep apnoea pathogenesis from mild to severe: Is it all the same? Respirology 2017;22:33-42.
Stradling JR, Hardinge M, Smith DM. A novel, simplified approach to starting nasal CPAP therapy in OSA. Respir Med 2004;98:155-8.
Loredo JS, Berry C, Nelesen RA, Dimsdale JE. Prediction of continuous positive airway pressure in obstructive sleep apnea. Sleep Breath 2007;11:45-51.
[Table 1], [Table 2], [Table 3]