Reliability of respiratory event detection with continuous positive airway pressure in moderate to severe obstructive sleep apnea - comparison of polysomnography with a device-based analysis.

PURPOSE: Monitored polysomnography (PSG) is considered the gold standard technique to diagnose obstructive sleep apnea (OSA) and titrate continuous positive airway pressure (CPAP), the accepted primary treatment method. Currently, the American Academy of Sleep Medicine (AASM) considers automatic PAP therapy initiation at home comparable to laboratory titration and recommends telemonitoring-guided interventions. Advanced CPAP devices evaluate and report the residual apnea-hypopnea index (AHI). However, in order to control the effectiveness of the prescribed therapy outside of a PSG setting, the automatic event detection must provide reliable data. METHODS: A CPAP titration was performed in the sleep laboratory by PSG in patients with OSA. The residual event indices detected by the tested device (prismaLine, Loewenstein Medical Technology) were compared to the manually scored PSG indices. Results of the device (AHIFLOW) were compared according to the AASM scoring criteria 1A (AHI1A, hypopneas with a flow signal reduction of ≥ 30% with ≥ 3% oxygen reduction and/or an arousal) and 1B (AHI1B, hypopneas with a flow signal decrease by ≥ 30% with a ≥ 4% oxygen desaturation). RESULTS: In 50 patients with OSA, the mean PSG AHI1A was 10.5 ± 13.8/h and the PSG AHI1B was 7.4 ± 12.6/h compared to a mean device AHIFlow of 8.4 ± 10.0/h. The correlation coefficient regarding PSG AHI1A and AHIFlow was 0.968. The correlation regarding central hypopneas on the other hand was 0.153. There were few central events to be compared in this patient group. CONCLUSION: The device-based analysis showed a high correlation in the determination of residual obstructive AHI under therapy. The recorded residual respiratory event indices in combination with the data about leakage and adherence of the studied device provide reliable information for the implementation and follow-up of CPAP therapy in a typical group of patients with OSA. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov Identifier: NCT04407949, May 29, 2020, retrospectively registered.

Effect of a digital patient motivation and support tool on CPAP/APAP adherence and daytime sleepiness: a randomized controlled trial.

The purpose of this study was to assess the effect of a digital patient support (DPS) tool, complementary to standard care on continuous or automatic positive airway pressure (auto)CPAP adherence and daytime sleepiness after 12 weeks in patients diagnosed with severe obstructive sleep apnea (OSA). All patients with apnea-hypopnea index (AHI) ≥ 30 per hour were prospectively included and randomized to receive standard care (SC) or standard care with personalized DPS via a mobile app prototype version (SC + DPS). Patients in the SC + DPS arm received additionally automated feedback on their therapy, motivational messages and therapy recommendations. 100 patients completed the study (SC: 50, SC + DPS: 50). No differences were found in characteristics of SC vs. SC + DPS (mean ± SD) for age (53.9 ± 10.8 vs. 51.7 ± 12.3 years), initial diagnostic apnea-hypopnea index (51.1 ± 15.5 vs. 50.9 ± 17.7 events/h), BMI (33.8 ± 6.7 vs. 33.5 ± 4.5 kg/m), and Epworth Sleepiness Scale (ESS) baseline score (9.5 ± 4.2 vs. 9.1 ± 5.2). After 12 weeks, mean ESS score was significantly lower (SC: 7.6 ± 4.1 vs. SC + DPS: 5.5 ± 3.9; p = 0.006) in the SC + DPS group vs. standard care group. Therapy adherence was significantly higher (SC: 268.7 ± 122.1 vs. SC + DPS: 338.8 ± 106.8 min; p = 0.002) in the SC + DPS group compared to standard care group. No difference was found in the residual AHI between both groups. However, SC + DPS group showed a trend towards fewer phases with increased leakage compared to SC group. Intention-to-treat analysis (112 (56/56) patients) showed similar results. After 12 weeks, (auto)CPAP adherence and daytime sleepiness improved significantly in patients with severe OSA using the digital patient support tool. Clinical Trial Registration (retrospectively registered): Registry: NCT05440279; Title: Effects of Telemedical Support on Therapeutic Results of CPAP Patients; URL: https://clinicaltrials.gov/ct2/show/NCT05440279; Date of registration: June 30, 2022. Supplementary Information: The online version contains supplementary material available at 10.1007/s41105-023-00479-9.

Bench model to simulate upper airway obstruction for analyzing automatic continuous positive airway pressure devices.

BACKGROUND: Automatic positive airway pressure (APAP) devices are increasingly being used in patients with obstructive sleep apnea. Some APAP devices present an unstable behavior when subjected to some events or artifacts. The aims were to develop a bench model capable of reproducing real flow, snoring, and obstructive patterns and to compare the response of APAP devices based on flow and snoring with other devices using, in addition, the forced oscillation technique (FOT). METHODS: The bench model subjected APAP devices to apneas with and without obstruction, obstructive hypopneas with and without snoring, periods of flow limitation, and artifacts such as leaks and mouth expiration. RESULTS: Almost all the devices increased the pressure when subjected to apneas with obstruction, but at different rates. The time required by each device to reach 10 cm H(2)O ranged from 2.5 to 13 min. In the presence of apneas without obstruction, all the devices based on flow and snoring increased the pressure at the same rate as during apneas with obstruction. However, the devices using FOT did not modify the pressure. Four devices did not modify the pressure in the presence of obstructive hypopneas, and all but one device increased the pressure in the presence of snoring. Mask leaks had little effect on the response of the devices, but four devices increased the pressure during mouth expiration artifacts. CONCLUSIONS: When, in addition to the flow and snoring signals, the measurement of the upper airway resistance is included, the accuracy of the event detection algorithms is improved.

An invasive and a noninvasive approach for the automatic differentiation of obstructive and central hypopneas.

The automatic differentiation of obstructive and central respiratory events is a major challenge in the diagnosis of sleep-disordered breathing. Esophageal pressure (Pes) measurement is the gold-standard method to identify these events. This study presents a new classifier that automatically differentiates obstructive and central hypopneas with the Pes signal and a new approach for an automatic noninvasive classifier with nasal airflow. An overall of 28 patients underwent night polysomnography with Pes recording, and a total of 769 hypopneas were manually scored by human experts to create a gold-standard annotation set. Features were automatically extracted from the Pes signal to train and test the classifiers (discriminant analysis, support vector machines, and adaboost). After a significantly (p < 0.01) higher incidence of inspiratory flow limitation episodes in obstructive hypopneas was objectively, invasively assessed compared to central hypopneas, the feasibility of an automatic noninvasive classifier with features extracted from the airflow signal was demonstrated. The automatic invasive classifier achieved a mean sensitivity, specificity, and accuracy of 0.90 after a 100-fold cross validation. The automatic noninvasive feasibility study obtained similar hypopnea differentiation results as a manual noninvasive classification algorithm. Hence, both systems seem promising for the automatic differentiation of obstructive and central hypopneas.

Assessment of changes in upper airway obstruction by automatic identification of inspiratory flow limitation during sleep.

New techniques for automatic invasive and noninvasive identification of inspiratory flow limitation (IFL) are presented. Data were collected from 11 patients with full nocturnal polysomnography and gold-standard esophageal pressure (Pes) measurement. A total of 38,782 breaths were extracted and automatically analyzed. An exponential model is proposed to reproduce the relationship between Pes and airflow of an inspiration and achieve an objective assessment of changes in upper airway obstruction. The characterization performance of the model is appraised with three evaluation parameters: mean-squared error when estimating resistance at peak pressure, coefficient of determination, and assessment of IFL episodes. The model's results are compared to the two best-performing models in the literature. The obtained gold-standard IFL annotations were then employed to train, test, and validate a new noninvasive automatic IFL classification system. Discriminant analysis, support vector machines, and Adaboost algorithms were employed to objectively classify breaths noninvasively with features extracted from the time and frequency domains of the breaths' flow patterns. The results indicated that the exponential model characterizes IFL and subtle relative changes in upper airway obstruction with the highest accuracy and objectivity. The new noninvasive automatic classification system also succeeded in identifying IFL episodes, achieving a sensitivity of 0.87 and a specificity of 0.85.