Evaluation of a noninvasive algorithm for differentiation of obstructive and central hypopneas.

STUDY

OBJECTIVES

The clear discrimination of central and obstructive hypopneas is highly relevant to avoid misinterpretation and inappropriate treatment of complicated breathing patterns. Esophageal manometry is the accepted standard for the differentiation of the phenotypes of sleep apnea. However, it is limited in its use due to poor acceptance by patients and therefore rarely performed in routine clinical practice. Flattening of the inspiratory airflow curve, paradoxical breathing, arousal position, sleep stages, and breathing pattern at the end of the hypopnea can each give hints for the classification of hypopnea. The aim of this study was to evaluate a standardized algorithm combining these polysomnographic parameters for the discrimination of hypopneas in everyday practice.

METHODS

Polysomnography (PSG) and esophageal manometry were performed in 41 patients suspected of having sleep apnea (33 male, 52.3 ± 15.9 yr, body mass index 28.6 ± 4.5 kg/m(2)). Hypopneas were independently discriminated by blinded investigators based on esophageal pressure and the PSG-based algorithm. Only those hypopneas that could be differentiated with both methods were evaluated.

RESULTS

There were 1,175 of 1,837 hypopneas (64%) that could be defined by esophageal pressure, 1,812 (98.6%) by the PSG-based algorithm. Using esophageal pressure as a reference, the new algorithm correctly defined 76.9% of central and 60.5% of obstructive hypopneas. The overall accuracy was 68%. The isolated analysis of single PSG parameters revealed a lower accuracy compared with the combined algorithm.

CONCLUSIONS

The PSG-based algorithm allows for discrimination of most hypopneas. It is advantageous in comparison with esophageal pressure because it is noninvasive and less impaired by artefacts. Therefore, it is a potentially helpful tool for sleep specialists.

CITATION: Randerath WJ; Treml M; Priegnitz C; Stieglitz S; Hagmeyer L; Morgenstern C. Evaluation of a noninvasive algorithm for differentiation of obstructive and central hypopneas. SLEEP 2013;36(3):363-368.