INTRODUCTION: Polysomnography (PSG) is considered the gold standard in the diagnosis of sleep disordered breathing (SDB). Because of costs and labor-intensity it is, however, performed last in graded diagnostic protocols that often involve respiratory pressure measurements via nasal canula as an alternative sensitive method for SDB detection. MicroMESAM, a newly developed screening device based on this method, allows automated analysis of apnoeas, hypopnoeas and snoring. AIM AND METHODS: To validate the device, we first compared signal quality of MicroMESAM flow-time curves with those generated by a pneumotachograph. Then, in 50 patients suspected of having obstructive sleep apnoea, we compared MicroMESAM-generated automated analysis with manually scored results of simultaneously collected PSG data. RESULTS: MicroMESAM-generated flow-time curves correspond with pneumotachograph-generated curves in 95% of respiratory events, resulting in less 4 +/- 2% difference in respective area under the curves. MicroMESAM and PSG generated numbers of apnoeas (r = 0.99) and hypopnoea (r = 0.81), as well as AHI (r = 0.98) correlated highly, displaying mean differences in AHI of 3.8, and in 1.96 sigma interval of + 11.1 to - 3.5/h. Sensitivities and specificities for SDB were 97.3%, respective 46% at SDB-defining AHI of 5, and 100%, respective 87.5%, at SDB-defining AHI of 10. SUMMARY: MicroMESAM-generated flow-time curves correspond well with pneumotachograph generated curves, producing automated AHIs that are highly sensitive in detecting SDB. MicroMESAM, therefore, is suitable as a screening device for SDB.
INTRODUCTION: Polysomnography (PSG) is considered the gold standard in the diagnosis of sleep disordered breathing (SDB). Because of costs and labor-intensity it is, however, performed last in graded diagnostic protocols that often involve respiratory pressure measurements via nasal canula as an alternative sensitive method for SDB detection. MicroMESAM, a newly developed screening device based on this method, allows automated analysis of apnoeas, hypopnoeas and snoring. AIM AND METHODS: To validate the device, we first compared signal quality of MicroMESAM flow-time curves with those generated by a pneumotachograph. Then, in 50 patients suspected of having obstructive sleep apnoea, we compared MicroMESAM-generated automated analysis with manually scored results of simultaneously collected PSG data. RESULTS: MicroMESAM-generated flow-time curves correspond with pneumotachograph-generated curves in 95% of respiratory events, resulting in less 4 +/- 2% difference in respective area under the curves. MicroMESAM and PSG generated numbers of apnoeas (r = 0.99) and hypopnoea (r = 0.81), as well as AHI (r = 0.98) correlated highly, displaying mean differences in AHI of 3.8, and in 1.96 sigma interval of + 11.1 to - 3.5/h. Sensitivities and specificities for SDB were 97.3%, respective 46% at SDB-defining AHI of 5, and 100%, respective 87.5%, at SDB-defining AHI of 10. SUMMARY: MicroMESAM-generated flow-time curves correspond well with pneumotachograph generated curves, producing automated AHIs that are highly sensitive in detecting SDB. MicroMESAM, therefore, is suitable as a screening device for SDB.
Authors: Lynda D Lisabeth; Brisa N Sánchez; Ronald D Chervin; Lewis B Morgenstern; Darin B Zahuranec; Susan D Tower; Devin L Brown Journal: Sleep Med Date: 2016-02-12 Impact factor: 3.492
Authors: Kim L Ward; Nigel McArdle; Alan James; Alexandra P Bremner; Laila Simpson; Matthew N Cooper; Lyle J Palmer; Annette C Fedson; Sutapa Mukherjee; David R Hillman Journal: J Clin Sleep Med Date: 2015-04-15 Impact factor: 4.062
Authors: Wendy M Troxel; Daniel J Buysse; Karen A Matthews; Kevin E Kip; Patrick J Strollo; Martica Hall; Oliver Drumheller; Steven E Reis Journal: Sleep Date: 2010-12 Impact factor: 5.849
Authors: Devin L Brown; Ashkan Mowla; Mollie McDermott; Lewis B Morgenstern; Garnett Hegeman; Melinda A Smith; Nelda M Garcia; Ronald D Chervin; Lynda D Lisabeth Journal: J Stroke Cerebrovasc Dis Date: 2014-12-10 Impact factor: 2.136
Authors: Hui Chen; Alan A Lowe; Yuxing Bai; Peter Hamilton; John A Fleetham; Fernanda R Almeida Journal: Sleep Breath Date: 2008-12-04 Impact factor: 2.816