Mathematical Equations to Predict Positive Airway Pressures for Obstructive Sleep Apnea: A Systematic Review

Factors Associated with the Underestimation of Manual CPAP Titration Pressure

During the SARS-CoV-2 pandemic, the use of in-laboratory positive airway pressure (PAP) titration studies was not routinely suggested. PAP pressure prediction calculations are emerging as alternative methods for the treatment of these patients. The underestimation of PAP titration pressure usually leads to unsatisfactory results for PAP therapy. This study aimed to evaluate the factors associated with the underestimation of PAP titration pressure when using PAP pressure prediction equations. Estimated PAP pressure formulas based on body mass index (BMI) and apnea-hypopnea index (AHI) were chosen to validate the accuracy of equations in the successful prediction of titration pressure. Among 341 adult patients diagnosed with obstructive sleep apnea (OSA) by overnight polysomnography (PSG) and who received overnight PAP titration in order to select a successful pressure, the mean age of the total subjects was 55.4 years old and 78.9% of patients were male. The average BMI and AHI scores were 27.1 ± 4.8 and 37 ± 21.7, respectively. After multivariate stepwise regression analysis, the odds ratio of participants with a pretitration AHI was 1.017 (95% CI: 1.005-1.030). Only the severity of OSA was significantly different between the underestimated group and the adequately assessed group. In conclusion, a high AHI, but not BMI, is associated with an underestimated CPAP titration pressure in adult patients with OSA.

Polysomnographic evaluation of obstructive sleep apnea treatment with fixed pressure CPAP determined by formula

PURPOSE

The use of continuous positive airway pressure (CPAP) is one of the therapeutic modalities for obstructive sleep apnea (OSA). Manual titration polysomnography and the 90th or 95th percentiles of pressure titrated by automatic CPAP (APAP) are the current standard for determining fixed pressure. Pressures programmed at an arbitrary fixed value, or at preset values based on body mass index (BMI) or by predictive formulas, are presented as alternative forms. This study aimed to evaluate the residual apnea-hypopnea index (r-AHI) in polysomnography with CPAP therapy using pressure determined by formula and assess its feasibility to start treatment.

METHODS

Patients referred for CPAP therapy were followed up in three outpatient assessments and underwent polysomnography study with pressure CPAP obtained by formula.

RESULTS

The study sample consisted of 80 patients, 41 women; age 58.6 ± 11.3 years, BMI 34.1 ± 7.5 kg/m² and cervical circumference 42.0 ± 4.2 cm. Most patients (74%) had severe OSA and Epworth sleepiness scale (ESS) of 12.0 ± 5.7 points. The calculated average pressure was 7.8 ± 2.1 cmH₂O. Polysomnography studies showed an r-AHI of 6.1 ± 5.2 events/h and reduction of 84% from baseline AHI. The r-AHI in the REM-supine was 8.4 ± 9.9 events/h. At 30- and 120-day follow-up assessment, adherence to CPAP was 78% and 75% and the ESS score was 6.9 and 6.1 points, respectively.

CONCLUSION

Results suggest that a formula provides an effective initial pressure in the majority of patients (73%). This simplified approach appears to be a viable alternative, with reductions in waiting lists and time from diagnosis to initiation of therapy.

Body weight and obstructive sleep apnea: a mathematical relationship between body mass index and apnea-hypopnea index in veterans

STUDY OBJECTIVES

A high body mass index (BMI) is a risk factor for obstructive sleep apnea. However, to our knowledge there is no reported equation that quantifies the relationship between weight, as measured by BMI, and apnea severity, as assessed by the apnea-hypopnea index (AHI). Our objective was to find a mathematical relationship between BMI and AHI.

METHODS

We prospectively recruited 434 veterans from our polysomnography laboratory. Veterans already undergoing a sleep study were approached, and those who consented were enrolled. The veterans who enrolled in our study also participated in their scheduled sleep study. This study was approved by our institutional review board.

RESULTS

We found a simple mathematical relationship between BMI and AHI: for every 1-point drop in BMI (corresponding to 5-8 pounds, depending on a person's height), AHI decreases by 6.2%. And limiting BMI to 25-40 kg/m2 (which includes about 80% of the BMIs), then AHI drops by 7.1%. Simply put as a rule of thumb: For every 7-pounds drop in weight, expect a 7% drop in AHI.

CONCLUSIONS

To our knowledge, this is the first simple mathematical equation that associates the severity of weight with the severity of apnea in veterans. This equation can be a practical rule of thumb that can be implemented in clinics to predict the amount of weight a patient needs to lose to decrease their apnea, which might help motivate patients to lose weight.

CITATION

Fattal D, Hester S, Wendt L. Body weight and obstructive sleep apnea: a mathematical relationship between body mass index and apnea-hypopnea index in veterans. J Clin Sleep Med. 2022;18(12):2723-2729.

A predictive model for optimal continuous positive airway pressure in the treatment of pure moderate to severe obstructive sleep apnea in China

Background

Numerous predictive formulas based on different ethnics have been developed to determine continuous positive airway pressure (CPAP) for patients with obstructive sleep apnea (OSA) without laboratory-based manual titrations. However, few studies have focused on patients with OSA in China. Therefore, this study aimed to develop a predictive equation for determining the optimal value of CPAP for patients with OSA in China.

Methods

526 pure moderate to severe OSA patients with attended CPAP titrations during overnight polysomnogram were spited into either formula derivation (419 patients) or validation (107 patients) group according to the treatment time. Predictive model was created in the derivation group, and the accuracy of the model was tested in the validation group.

Results

Apnea hypopnea index (AHI), body mass index (BMI), longest apnea time (LAT), and minimum percutaneous oxygen saturation (minSpO₂) were considered as independent predictors of optimal CPAP through correlation analysis and multiple stepwise regression analysis. The best equation to predict the optimal value of CPAP was: CPAPpred = 7.581 + 0.020*AHI + 0.101*BMI + 0.015*LAT-0.028*minSpO₂ (R² = 27.2%, p < 0.05).The correlation between predictive CPAP and laboratory-determined manual optimal CPAP was significant in the validation group (r = 0.706, p = 0.000). And the pressure determined by the predictive formula did not significantly differ from the manually titrated pressure in the validation cohort (10 ± 1 cmH₂O vs. 11 ± 3 cmH₂O, p = 0.766).

Conclusions

The predictive formula based on AHI, BMI, LAT, and minSpO₂ is useful in calculating the effective CPAP for patients with pure moderate to severe OSA in China to some extent.

Fraction of apnea is associated with the required continuous positive airway pressure level and reflects upper airway collapsibility in patients with obstructive sleep apnea

STUDY OBJECTIVES

We aimed to determine whether the fraction of apnea (Fapnea) could be used as an alternative index to reflect upper airway (UA) collapsibility.

METHODS

We retrospectively recruited 161 patients (16 women, mean age 47.8 years, body mass index [BMI] 28.0 kg/m², and apnea-hypopnea index [AHI] 46.4/h) with moderate to severe obstructive sleep apnea (OSA) who underwent nasal continuous positive airway pressure (CPAP) titration. The Fapnea was defined as the percentage of apneic events over the total apneic and hypopneic events during sleep in a supine position on diagnostic polysomnography. We randomly split the data (70/30) into the development and validation datasets. In the development dataset, we conducted a multiple regression analysis to assess the association of variables, including the age, sex, BMI, rapid eye movement (REM) supine AHI, and apnea with the CPAP level during supine REM sleep (REM_CPAP). Moreover, we developed an equation for predicting the CPAP level. Thereafter, we evaluated the correlation between the actual CPAP level and the value calculated using the model.

RESULTS

BMI and Fapnea were the only significant factors that predicted the REM_CPAP level (adjusted r=0.60, p<0.001) in the development dataset. The validation data revealed a significant correlation between the actual and predicted CPAP levels (r=0.69, p<0.0001). We observed similar associations during supine non-REM (NREM) sleep.

CONCLUSIONS

Fapnea could significantly predict the CPAP levels during both REM and NREM sleep, which likely reflects the UA collapsibility, independent of the BMI.

A Fuzzy Neural Network Model for Rapid Prediction of Optimal Positive Airway Pressures in OSAS Patients

Manual titration of positive airway pressure (PAP) is a gold standard to provide an optimal pressure for the treatment of obstructive sleep apnea-hypopnea syndrome (OSAS). Since manual titration studies were costly and time-consuming, many statistical models for predicting effective PAPs were reported. However, the prediction accuracies of the models associated with nocturnal parameters still remain low. This study proposes a fuzzy neural prediction network (FNPN) with input candidate variables, selected among easily available measurements (e.g., body mass index (BMI), waist circumstance (WC), and body composition) and OSAS related questionnaires, to rapidly predict an optimal PAP. The FNPN comprises fuzzy rules and is characterized with the ability of automatic rule growing and pruning from training data. A total of 147 participants from April 2018 to April 2019 were enrolled in Taichung Veterans General Hospital, Taiwan. After two selection processes for feature extraction, WC and BMI were the significant variables for entering the FNPN to predict optimal PAP. Experimental results showed that the average successful prediction rate of the proposed method was 71.8%. This study also found that Epworth sleepiness scales (ESS) and body composition, such as visceral fat area and percent body fat, were excluded in the final prediction model. Compared with existing models, the proposed prediction approach provided a rapid prediction of optimal PAP with higher accuracy.

Impact of upper airway configuration on CPAP titration assessed by CT during Müller's maneuver in OSA patients

PURPOSE

Continuous positive airway pressure (CPAP) is the current gold-standard treatment for moderate to severe obstructive sleep apnea (OSA), and upper airway anatomy plays an increasingly important role in evaluating the efficacy of CPAP therapy. The aim of this observational study was to investigate the influence of upper airway anatomy on CPAP titration in OSA patients assessed by computed tomography (CT) during Müller's maneuver.

METHODS

Consecutive patients under investigation for OSA by undergoing polysomnography and CT scan of the upper airway while awake were enrolled. Successful full-night manual titration was performed to determine the optimal CPAP pressure level for OSA patients in supine position using a nasal mask.

RESULTS

A total of 157 subjects (134 males and 23 females) were included. Both apnea-hypopnea index (AHI) and LaSO2 significantly correlated with CPAP titration level, upper airway length (UAL), distance from mandibular plane to hyoid bone (MPH), and neck circumference (all p < 0.05). There were significant positive correlations between CPAP titration level and UAL (r = 0.348, p = 0.000) and MPH (r = 0.313, p = 0.002). Stepwise multiple linear regression analyses were performed to evaluate the independent predictors of AHI, LaSO2, and CPAP titration level. CPAP titration level was identified as an independent explanatory variable for AHI and LaSO2 after adjustment for confounders. Multiple linear regression analyses also indicated that body mass index (BMI) and UAL were independently associated with CPAP titration level (all p < 0.05).

CONCLUSIONS

Upper airway abnormalities combined with anthropometric parameters play important roles in CPAP titration for OSA patients, providing additional insight into the factors influencing OSA treatment strategies. UAL and BMI should be taken into consideration when choosing CPAP titration level to improve CPAP compliance.

Role of rhinomanometry in the prediction of therapeutic positive airway pressure for obstructive sleep apnea

Background

This study was conducted to evaluate the relationship between nasal resistance in different posture and optimal positive airway pressure (PAP) level. Other potential factors were also assessed for possible influence on PAP pressure.

Methods

Forty- three patients diagnosed with obstructive sleep apnea (OSA) were prospectively recruited in this study. Nasal resistance was assessed by active anterior rhinomanometry in a seated position and then in a supine position at pressures of 75, 150, and 300 pascal. The factors correlating with PAP pressure were analyzed, including nasal resistance and patients’ clinical data.

Results

Univariate analysis revealed that PAP pressure was correlated to nasal resistance in the supine position at 75 and 150 pascal (SupineNR75 and SupineNR150) (P = 0.019 and P = 0.004 in Spearman’s correlation coefficient analysis), but not correlated to nasal resistance in the seated position at different pressures or in the supine position at 300 pascal. The multiple linear regression analysis revealed that both SupineNR150 and body mass index (BMI) significantly predicted PAP pressure (β = 0.308, p = 0.044; β = 0.727, p = 0.006). The final PAP pressure predictive model was:

PAP pressure = 0.29 BMI + 2.65 SupineNR150 + 2.11.

Conclusions

Nasal resistance in the supine position measured at 150 pascal may provide valuable information regarding optimal PAP pressure. Rhinomanometry should be included in the treatment algorithm of OSA patients when PAP therapy is considered.

Predictive equation for optimal continuous positive airway pressure in children with obstructive sleep apnoea

Aim

A subgroup of children with obstructive sleep apnoea (OSA) requires treatment with continuous positive airway pressure (CPAP). This study's aims were: 1) to determine if the optimal CPAP for the treatment of OSA in children correlates with body mass index (BMI); 2) to determine the correlation between polysomnographic variables and optimal CPAP in children with OSA; and 3) to develop a CPAP predictive equation for children with OSA.

Methods

This was a retrospective study of children with OSA who underwent CPAP titration studies. Patients with craniofacial abnormalities (except Down syndrome) and neuromuscular diseases were excluded. Polysomnograms were done using Sandman Elite. Correlations between optimal CPAP, clinical and polysomnographic variables were analysed. A multivariable linear regression model for optimal CPAP was developed.

Results

198 children (mean±sd age 13.1±3.6 years) were studied. Optimal CPAP had a significant positive correlation with age (rho=0.216, p=0.002), obstructive apnoea-hypopnoea index (rho=0.421, p<0.001), 3% oxygen desaturation index (rho=0.417, p<0.001), rapid eye movement respiratory disturbance index (rho=0.378, p<0.001) and BMI z-score (rho=0.160, p=0.024); and a significant negative correlation with arterial oxygen saturation measured by pulse oximetry nadir (rho= −0.333, p<0.001). The predictive equation derived was:

Optimal CPAP (cmH₂O)=6.486+0.273·age (years)−0.664·adenotonsillectomy

(no=1, yes=0)+2.120·Down syndrome (yes=1, no=0)+0.280·BMI z-score.

Conclusion

The equation developed may help to predict optimal CPAP in children with OSA. Further studies are required to validate this equation and to determine its applicability in different populations.

A predictive equation may help derive the optimal CPAP for the treatment of obstructive sleep apnoea in childrenhttp://bit.ly/2wTf6Bw

Continuous Positive Airway Pressure Use for Obstructive Sleep Apnea in Acute, Traumatic Tetraplegia

OBJECTIVE

To describe continuous positive airway pressure (CPAP) use for treatment of obstructive sleep apnea (OSA) in acute tetraplegia, including adherence rates and associated factors.

DESIGN

Secondary analysis of CPAP data from a multinational randomized controlled trial.

SETTING

Inpatient rehabilitation units of 11 spinal cord injury centers.

PARTICIPANTS

People with acute, traumatic tetraplegia and OSA (N=79).

INTERVENTIONS

Autotitrating CPAP for OSA for 3 months.

MAIN OUTCOME MEASURES

Adherence measured as mean daily hours of use. Adherent (yes/no) was defined as an average of at least 4 hours a night throughout the study. Regression analyses determined associations between baseline factors and adherence. CPAP device pressure and leak data were analyzed descriptively.

RESULTS

A total of 79 participants from 10 spinal units (91% men; mean age ± SD, 46±16; 78±64d postinjury) completed the study in the treatment arm and 33% were adherent. Mean daily CPAP use ± SD was 2.9±2.3 hours. Better adherence was associated with more severe OSA (P=.04) and greater CPAP use in the first week (P<.01). Average 95th percentile pressure was low (9.3±1.7 cmH₂O) and 95th percentile leak was high (27.1±13.4 L/min).

CONCLUSION

Adherence to CPAP after acute, traumatic tetraplegia is low. Early acceptance of therapy and more severe OSA predict CPAP use over 3 months. People with acute tetraplegia require less pressure to treat their OSA than the nondisabled; however, air leak is high. These findings highlight the need for further investigation of OSA treatment in acute tetraplegia.

Air Pollutants Are Associated With Obstructive Sleep Apnea Severity in Non-Rapid Eye Movement Sleep

STUDY OBJECTIVES

The relationship between seasonal variation of obstructive sleep apnea and ambient temperature and pollutants has been inconsistent in previous studies. It is also unknown whether the seasonal variation in apnea-hypopnea index influences continuous positive airway pressure treatment dose. This study aims to examine the seasonality of obstructive sleep apnea and continuous positive airway pressure treatment, and the association between air pollutants and apnea-hypopnea index in adults with different sleep apnea severity during different sleep stages.

METHODS

Polysomnography of 5,413 patients referred to one sleep center during 2008-2015 were examined retrospectively. Ambient conditions and air pollutants levels were collected from the official air condition surveillance database. Cosinor analysis was used to examine seasonal variances. The general linear model was used to examine associations between air conditions and apnea-hypopnea index adjusted for seasonality. Models for apnea-hypopnea index in different sleep stages, sex groups, and obstructive sleep apnea severity groups were analyzed separately.

RESULTS

Seasonal variations for continuous positive airway pressure treatment were not significant. Particulate matter less than or equal to 10 μm, ozone, sulfur dioxide, and relative humidity were associated with apnea-hypopnea index only in patients with severe obstructive sleep apnea. The association was significant only in non-rapid eye movement sleep.

CONCLUSIONS

An adjustment for continuous positive airway treatment dose by season is not warranted. Protection for air pollutant-vulnerable groups should be provided. The exact mechanism of the associations between apnea-hypopnea index and air conditions only in non-rapid eye movement sleep must be clarified.

Thirty-five alternatives to positive airway pressure therapy for obstructive sleep apnea: an overview of meta-analyses

INTRODUCTION

Positive airway pressure (PAP) devices are generally considered to be the first-line treatment of choice for most adults with obstructive sleep apnea (OSA). However, there are several alternatives. It is important for patients and their sleep providers to be aware of the most up-to-date information regarding the current international literature. Areas covered: The objective is to provide an overview of the meta-analyses evaluating non-PAP treatments for OSA. Four authors searched four databases, including PubMed/MEDLINE through 30 November 2017, for meta-analyses evaluating non-PAP therapies as treatment for OSA. Thirty-five non-PAP treatments were identified and were categorized based on the following anatomical subsites: (1) nose, (2) palate and oropharynx, (3) tongue, (4) skeletal surgery and jaw repositioning, and (5) other surgical and medical interventions. Treatments identified included surgeries, drugs, behavior modifications, nonsurgical weight loss, medical devices, body positioning, and oxygen treatment. Expert commentary: The 35 treatments described vary in their effectiveness in treating OSA in adults. In general, isolated nasal treatments are the least effective, whereas treatments that bypass the upper airway, significantly open the upper airway, and/or address multiple levels of the upper airway are more effective in improving apnea-hypopnea index and lowest oxygen saturation.

Oral appliance treatment outcome can be predicted by continuous positive airway pressure in moderate to severe obstructive sleep apnea

Background

Studies show that the therapeutic CPAP pressure is associated with oral appliance (OA) treatment outcome in obstructive sleep apnea (OSA) patients. However, these studies included either CPAP adherent patients using fixed pressure devices, or partly CPAP non-adherent patients using fixed pressure or auto-adjusting (auto-CPAP) devices. In many countries, auto-CPAP is predominately used, and only those non-adherent to therapy need a change to OA. Therefore, studies examining the relationship between CPAP pressures and OA treatment outcome should focus on patients non-adherent to auto-CPAP.

Purpose

The purpose of this paper is to assess if CPAP pressures predicted OA treatment outcome in patients non-adherent to auto-CPAP therapy.

Methods

The OA treatment responders and non-responders were defined by two success criteria ((1) AHI < 5; (2) 5 ≤ AHI < 10 and > 50% AHI reduction). Logistic regression analyses were performed for CPAP pressures and baseline variables. ROC curve analyses were used to identify CPAP pressure cutoff values, alone and combined with other explanatory variables, predicting the OA treatment outcome.

Results

Eighty-seven patients with moderate or severe OSA were included. Maximum CPAP pressures (CPAPmax) were higher in non-responders by both criteria and were, together with baseline AHI, associated with the OA treatment outcome in multivariate regression analyses. ROC curves identified an optimal CPAPmax cutoff of 12 cm H₂O, corresponding to a positive predictive value (PPV) of 0.85 in predicting non-response using criterion 1. A prediction model combining CPAPmax > 12 and baseline AHI ≥ 30 had a PPV of 1.0 for non-response by both criteria.

Conclusions

Maximum CPAP pressure was a moderate predictor of OA treatment outcome, but combined with baseline AHI, the ability to identify OA non-responders was high.

A new predictive model for continuous positive airway pressure in the treatment of obstructive sleep apnea

BACKGROUND

Numerous mathematical formulas have been developed to determine continuous positive airway pressure (CPAP) without an in-laboratory titration study. Recent studies have shown that style of CPAP mask can affect the optimal pressure requirement. However, none of the current models take mask style into account. Therefore, the goal of this study was to develop new predictive models of CPAP that take into account the style of mask interface.

METHODS

Data from 200 subjects with attended CPAP titrations during overnight polysomnograms using nasal masks and 132 subjects using oronasal masks were randomized and split into either a model development or validation group. Predictive models were then created in each model development group and the accuracy of the models was then tested in the model validation groups.

RESULTS

The correlation between our new oronasal model and laboratory determined optimal CPAP was significant, r = 0.61, p < 0.001. Our nasal formula was also significantly related to laboratory determined optimal CPAP, r = 0.35, p < 0.001. The oronasal model created in our study significantly outperformed the original CPAP predictive model developed by Miljeteig and Hoffstein, z = 1.99, p < 0.05. The predictive performance of our new nasal model did not differ significantly from Miljeteig and Hoffstein's original model, z = -0.16, p < 0.90. The best predictors for the nasal mask group were AHI, lowest SaO2, and neck size, whereas the top predictors in the oronasal group were AHI and lowest SaO2.

CONCLUSION

Our data show that predictive models of CPAP that take into account mask style can significantly improve the formula's accuracy. Most of the past models likely focused on model development with nasal masks (mask style used for model development was not typically reported in previous investigations) and are not well suited for patients using an oronasal interface. Our new oronasal CPAP prediction equation produced significantly improved performance compared to the well-known Miljeteig and Hoffstein formula in patients titrated on CPAP with an oronasal mask and was also significantly related to laboratory determined optimal CPAP.

When continuous positive airway pressure (CPAP) fails

Obstructive sleep apnoea (OSA) is increasingly prevalent, particularly in the context of the obesity epidemic, and is associated with a significant social, health and economic impact. The gold standard of treatment for moderate to severe OSA is continuous positive airway pressure (CPAP). However compliance rates can be low. Methodology to improve patient tolerance to CPAP alongside with alternative, non-surgical and surgical, management strategies are discussed. All patients that fail CPAP therapy would benefit from formal upper airway evaluation by the otolaryngologist to identify any obvious causes and consider site-specific surgical therapies. Patient selection is integral to ensuring successful outcomes. A multidisciplinary team is needed to manage these patients.

Five-Minute Awake Snoring Test for Determining CPAP Pressures (Five-Minute CPAP Test): A Pilot Study

Objective. To develop a quick, simple, bedside test for determining continuous positive airway pressures (CPAP) for obstructive sleep apnea (OSA) patients. Study Design. Prospective case series at a tertiary medical center. Methods. The Five-Minute Awake Snoring Test for Determining CPAP (Five-Minute CPAP Test) was developed and tested. Patients wear a soft-gel nasal triangle mask while holding a tongue depressor with the wide section (1.75 cm) between the teeth. Fixed pressure nasal CPAP is applied while the patient simulates snoring at 4 centimeters of water pressure. The pressure is incrementally titrated up and then down to determine the lowest pressure at which the patient cannot snore (Quiet Pressure). Results. Overall, thirty-eight patients participated. All could simulate snoring. Correlation coefficients were statistically significant between Quiet Pressures and body mass index (r s = 0.60 [strong positive relationship], p = 0.0088), apnea-hypopnea index (r s = 0.49 [moderate positive relationship], p = 0.039), lowest oxygen saturation (r s = -0.47 [moderate negative relationship], p = 0.048), and oxygen desaturation index (r s = 0.62 [strong positive relationship], p = 0.0057). Conclusion. This pilot study introduces a new concept, which is the final product of over one year of exploration, development, and testing. Five-Minute CPAP Test is a quick, inexpensive, and safe bedside test based on supine awake simulated snoring with nasal CPAP.

Inferior Turbinate Size and CPAP Titration Based Treatment Pressures: No Association Found among Patients Who Have Not Had Nasal Surgery

Objective. To evaluate the effect of turbinate sizes on the titrated continuous positive airway pressure (CPAP) therapeutic treatment pressures for patients with obstructive sleep apnea (OSA) who have not had nasal surgery. Study Design. Retrospective case series. Methods. A chart review was performed for 250 consecutive patients. Results. 45 patients met inclusion criteria. The mean ± standard deviation (M ± SD) for age was 54.6 ± 22.4 years and for body mass index was 28.5 ± 5.9 kg/m(2). The Spearman's rank correlation coefficient (r s ) between CPAP therapeutic treatment pressures and several variables were calculated and were weakly correlated (age r s = 0.29, nasal obstruction r s = -0.30), moderately correlated (body mass index r s = 0.42 and lowest oxygen saturation r s = -0.47), or strongly correlated (apnea-hypopnea index r s = 0.60 and oxygen desaturation index (r s = 0.62)). No statistical significance was found with one-way analysis of variance (ANOVA) between CPAP therapeutic treatment pressures and inferior turbinate size (right turbinates p value = 0.2012, left turbinate p value = 0.3064), nasal septal deviation (p value = 0.4979), or mask type (p value = 0.5136). Conclusion. In this study, CPAP titration based therapeutic treatment pressures were not found to be associated with inferior turbinate sizes; however, the CPAP therapeutic treatment pressures were strongly correlated with apnea-hypopnea index and oxygen desaturation index.

Nasal Septal Deviations: A Systematic Review of Classification Systems

Objective. To systematically review the international literature for internal nasal septal deviation classification systems and summarize them for clinical and research purposes. Data Sources. Four databases (including PubMed/MEDLINE) were systematically searched through December 16, 2015. Methods. Systematic review, adhering to PRISMA. Results. After removal of duplicates, this study screened 952 articles for relevance. A final comprehensive review of 50 articles identified that 15 of these articles met the eligibility criteria. The classification systems defined in these articles included C-shaped, S-shaped, reverse C-shaped, and reverse S-shaped descriptions of the septal deviation in both the cephalocaudal and anteroposterior dimensions. Additional studies reported use of computed tomography and categorized deviation based on predefined locations. Three studies graded the severity of septal deviations based on the amount of deflection. The systems defined in the literature also included an evaluation of nasal septal spurs and perforations. Conclusion. This systematic review ascertained that the majority of the currently published classification systems for internal nasal septal deviations can be summarized by C-shaped or reverse C-shaped, as well as S-shaped or reverse S-shaped deviations in the anteroposterior and cephalocaudal dimensions. For imaging studies, predefined points have been defined along the septum. Common terminology can facilitate future research.