0634 Volume-Assured Pressure Support is Effective Treatment for Obstructive Sleep Apnea Patients Who Failed CPAP Titration

Introduction

Obstructive sleep apnea (OSA) is a common disease, often treated using continuous positive airway pressure (CPAP). In many cases, patients fail an attended CPAP titration study, often due to inadequate control of AHI, and treatment-emergent central apneas as CPAP is increased. Here, we report our experience using volume-assured pressure support (VAPS) for these patients.

Methods

We retrospectively reviewed records of 45 adults who had OSA diagnosed on polysomnography (PSG) in whom CPAP titration had failed. In these patients, VAPS-AE (adjustable expiratory pressure) titrations were performed. Patients with central sleep apnea on baseline PSG were excluded.

Results

Reasons for CPAP titration failure included: treatment emergent central apneas (25), failure of maximum CPAP pressure to treat OSA (18), and persistent hypoxia (2). Average age was 57.9±13.1, BMI was 40.2±8.7, 26 males, Epworth sleepiness score was 10.7±7.9. The following significant changes from baseline PSG to VAPS titration were observed: AHI: 65.3±29.3 to 22.3 ±16.1 (p<.001) events/hour. Time < 88% saturation: 63.7 (median) to 6.9 (median) min (p<.001). The number of patients with AHI<15 was 0 on PSG and 16 (36%) on VAPS-AE, while the number of patients with AHI<30 was 7 (16%) on PSG and 32 (71%) on VAPS-AE. Improvement in AHI was not related to gender, age, or narcotic use, but was correlated with BMI: ΔAHI = 12.2 - (1.4 * BMI); p=.05. VAPS resulted in improved sleep architecture: slow wave sleep increased (medians: 1.4% to 19.6% total sleep time (TST)) (p<.001), REM sleep increased (medians 6.4% to 13.6% TST) (p<.01).

Conclusion

For OSA patients for whom CPAP titration failed, titration with VAPS-AE was an effective treatment for many patients.

Support

N/A

Multivariable empirical modeling of ALS systems using polynomials

Multivariable polynomial regression (MPR) was used to model plant motion time-series and nutrient recovery data for Advanced Life Support (ALS). MPR has capabilities similar to neural network models in terms of ability to fit multiple-input single-output nonlinear data. It has advantages over neural networks including: reduced overfitting, produces models that are more tractable for optimization, sensitivity analysis, and prediction of confidence intervals. MPR was used to produce nonlinear polynomial time-series models predicting plant projected canopy area versus time and temperature. Temperature was found to not have a statistically significant effect. Models were developed to relate rate and extent of nutrient recovery to treatment parameters, including temperature and use of heat pretreatment or nutrient supplementation. These applications demonstrate MPR's capability to fill "gaps" in an integrated model of ALS. Fundamental models should be used whenever available. However, some components may require empirical modeling. Furthermore, even fundamental models often have empirical constituents. MPR models are proposed to satisfy these needs.