M Gugger1, J Mathis, C Bassetti. 1. Department of Medicine, University of Berne, Inselspital, Switzerland.
Abstract
BACKGROUND: In patients with sleep apnoea early diagnostic evaluation and treatment may be delayed due to limited access to full polysomnography (PSG). For "typical" patients, simplified strategies are needed. A study was performed to evaluate the accuracy of a new continuous positive airway pressure (CPAP) device with in-built diagnostic abilities (Autoset) in detecting apnoeas. METHODS: Twenty seven patients underwent full overnight polysomnography. Data with the Autoset were acquired simultaneously. Standard nasal prongs were used. Apnoeas were detected by special analysis of the flow signal. As the Autoset derives all its data from one signal, careful examination of the raw data is important to assess the quality of the flow signal. RESULTS: There was a correlation between the apnoea index (AI) assessed by the Autoset (AI-Autoset) and by polysomnography AI-PSG (r = 0.85) and between the AI-Autoset and the apnoea/hypopnoea index (AHI) during polysomnography (r = 0.87). The Autoset identified patients with an AHI-PSG of > 20 (a level of respiratory disturbance that would warrant consideration for treatment in most centres for sleep disorders) with a sensitivity of 82% and a specificity of 90%. CONCLUSIONS: The good correlation between the apnoea index measured by the Autoset and by polysomnography, and the high sensitivity in detecting patients with an AHI of > 20, may make the Autoset a valuable tool for the management of typical patients with sleep apnoea. However, very low values for nasal ventilation on the printout raises the suspicion of poor signal quality and misleading results.
BACKGROUND: In patients with sleep apnoea early diagnostic evaluation and treatment may be delayed due to limited access to full polysomnography (PSG). For "typical" patients, simplified strategies are needed. A study was performed to evaluate the accuracy of a new continuous positive airway pressure (CPAP) device with in-built diagnostic abilities (Autoset) in detecting apnoeas. METHODS: Twenty seven patients underwent full overnight polysomnography. Data with the Autoset were acquired simultaneously. Standard nasal prongs were used. Apnoeas were detected by special analysis of the flow signal. As the Autoset derives all its data from one signal, careful examination of the raw data is important to assess the quality of the flow signal. RESULTS: There was a correlation between the apnoea index (AI) assessed by the Autoset (AI-Autoset) and by polysomnography AI-PSG (r = 0.85) and between the AI-Autoset and the apnoea/hypopnoea index (AHI) during polysomnography (r = 0.87). The Autoset identified patients with an AHI-PSG of > 20 (a level of respiratory disturbance that would warrant consideration for treatment in most centres for sleep disorders) with a sensitivity of 82% and a specificity of 90%. CONCLUSIONS: The good correlation between the apnoea index measured by the Autoset and by polysomnography, and the high sensitivity in detecting patients with an AHI of > 20, may make the Autoset a valuable tool for the management of typical patients with sleep apnoea. However, very low values for nasal ventilation on the printout raises the suspicion of poor signal quality and misleading results.
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