Optimal level of continuous positive airway pressure: auto-adjusting titration versus titration with a predictive equation

Clinical and polysomnographic predictors of suboptimal auto-adjusting CPAP titration in adult OSA patients: a single-center study

PURPOSE

To examine potential clinical, demographic, anthropometric, and polysomnographic predictors of successful auto-adjusting continuous positive airway pressure (CPAP) titration for treatment of obstructive sleep apnea (OSA).

METHODS

This cross-sectional study was conducted in adults diagnosed with moderate-to-severe OSA (baseline apnea-hypopnea index [AHI] ≥ 15.0/h), who underwent auto-adjusting CPAP titration (S9 or S10 AutoSet ResMed^®) in a sleep laboratory setting while wearing a nasal or pillow mask. Participants were then grouped into two groups: optimal CPAP titration (residual AHI < 5.0/h) or suboptimal CPAP titration (residual AHI ≥ 5.0/h). Multivariate logistic regression analysis was used to assess possible independent predictive factors for suboptimal CPAP titration.

RESULTS

A total of 1222 adults consisting of 874 subjects with optimal CPAP titration (71.5%) and 348 subjects with suboptimal CPAP titration (28.5%) were evaluated. Multivariate analysis resulted in a model with an adequate calibration (Hosmer-Lemeshow chi-square-test: 7.088; p = 0.527), with male sex, higher values of baseline AHI, therapeutic pressure (95th percentile), and mask leak (95th percentile) emerging as significant and independent predictors for suboptimal CPAP titration: adjusted odds ratio (OR): 1.456 (95% confidence interval [CI] 1.076-1.971; p = 0.015), OR: 1.009 (95% CI 1.002-1.016; p = 0.013), OR: 1.281 (95% CI 1.206-1.361; p < 0.001), and 1.035 (1.026-1.043; p < 0.001), respectively.

CONCLUSIONS

In a large cohort of adults undergoing auto-adjusting CPAP titration due to moderate-to-severe OSA, male sex, increased values of baseline AHI, pressure requirements, and mask leak were significant predictors for less than optimal CPAP titration.

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.

Titration with automatic continuous positive airway pressure in obstructive sleep apnea

BACKGROUND AND OBJECTIVE

Autotitrating positive airway pressure (APAP) is an accepted titration method to determine the optimal positive airway pressure (PAP), for the treatment of obstructive sleep apnea (OSA). The required duration of APAP monitoring to determine a fixed continuous positive airway pressure level still remains to be established. We aimed to evaluate the variation in PAP level, delivered by APAP devices, at different periods of treatment, to determine the APAP treatment duration required to reach an effective and stable PAP level.

METHODS

A cross-sectional study of 62 patients newly diagnosed with OSA were evaluated after 3 months of APAP therapy. APAP data corresponding to the first day (D1), first week (W1), seventh week (W7) and twelfth week (W12) under APAP therapy was collected. For the analysis of the pressure behaviour, the difference of P95th pressure level between W12 and W7 (P W12-W7), W12 and W1 (P W12-W1) and W12 and D1 (P W12-D1) was calculated.

RESULTS

There was a high correlation in P95th pressure level between D1 and W12 (r=0.771; p>0.0001), W1 and W12 (r=0.817; p>0.0001), and W7 and W12 (r=0.926; p>0.0001). This correlation progressively increased with APAP use. A significance difference was found in concordance between P W12-W7 and P W12-D1 (p=0.046) within the pressure range ±2cmH₂O. However there was no significant difference in concordance between P W12-W7 and P W12-W1.

CONCLUSIONS

One week of APAP therapy seems sufficient to determine an effective and stable PAP level, within the pressure range ±2cmH₂O.

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

Objective. To systematically review the international literature for mathematical equations used to predict effective pressures for positive airway pressure (PAP) devices. Methods. Google Scholar, PubMed, Scopus, Embase, Web of Science, CINAHL, and The Cochrane Library were searched through June 27, 2015. The PRISMA statement was followed. There was no language limitation. Results. 709 articles were screened, fifty were downloaded, and twenty-six studies presented equations that met the inclusion and exclusion criteria. In total, there were 4,436 patients in the development phases and 3,489 patients in the validation phases. Studies performed multiple linear regressions analyses as part of the equation(s) development and included the following variables: physical characteristics, polysomnography data, behavioral characteristics, and miscellaneous characteristics, which were all predictive to a variable extent. Of the published variables, body mass index (BMI) and mean oxygen saturation are the most heavily weighted, while BMI (eighteen studies), apnea-hypopnea index (seventeen studies), and neck circumference (eleven studies) were the variables most frequently used in the mathematical equations. Ten studies were from Asian countries and sixteen were from non-Asian countries. Conclusion. This systematic review identified twenty-six unique studies reporting mathematical equations which are summarized. Overall, BMI and mean oxygen saturation are the most heavily weighted.