BACKGROUND: Patients under treatment with continuous positive airway pressure (CPAP) may have residual sleep apnea (RSA). OBJECTIVE: The main objective of our study was to evaluate a novel auto-CPAP for the diagnosis of RSA. METHODS: All patients referred to the sleep laboratory to undergo CPAP polysomnography were evaluated. Patients treated with oxygen or noninvasive ventilation and split-night polysomnography (PSG), PSG with artifacts, or total sleep time less than 180 min were excluded. The PSG was manually analyzed before generating the automatic report from auto-CPAP. PSG variables (respiratory disturbance index (RDI), obstructive apnea index, hypopnea index, and central apnea index) were compared with their counterparts from auto-CPAP through Bland-Altman plots and intraclass correlation coefficient. The diagnostic accuracy of autoscoring from auto-CPAP using different cutoff points of RDI (≥5 and 10) was evaluated by the receiver operating characteristics (ROCs) curve. RESULTS: The study included 114 patients (24 women; mean age and BMI, 59 years old and 33 kg/m(2); RDI and apnea/hypopnea index (AHI)-auto median, 5 and 2, respectively). The average difference between the AHI-auto and the RDI was -3.5 ± 3.9. The intraclass correlation coefficient (ICC) between the total number of central apneas, obstructive, and hypopneas between the PSG and the auto-CPAP were 0.69, 0.16, and 0.15, respectively. An AHI-auto >2 (RDI ≥ 5) or >4 (RDI ≥ 10) had an area under the ROC curve, sensitivity, specificity, positive likelihood ratio, and negative for diagnosis of residual sleep apnea of 0.84/0.89, 84/81%, 82/91%, 4.5/9.5, and 0.22/0.2, respectively. CONCLUSIONS: The automatic analysis from auto-CPAP (S9 Autoset) showed a good diagnostic accuracy to identify residual sleep apnea. The absolute agreement between PSG and auto-CPAP to classify the respiratory events correctly varied from very low (obstructive apneas, hypopneas) to moderate (central apneas).
BACKGROUND:Patients under treatment with continuous positive airway pressure (CPAP) may have residual sleep apnea (RSA). OBJECTIVE: The main objective of our study was to evaluate a novel auto-CPAP for the diagnosis of RSA. METHODS: All patients referred to the sleep laboratory to undergo CPAP polysomnography were evaluated. Patients treated with oxygen or noninvasive ventilation and split-night polysomnography (PSG), PSG with artifacts, or total sleep time less than 180 min were excluded. The PSG was manually analyzed before generating the automatic report from auto-CPAP. PSG variables (respiratory disturbance index (RDI), obstructive apnea index, hypopnea index, and central apnea index) were compared with their counterparts from auto-CPAP through Bland-Altman plots and intraclass correlation coefficient. The diagnostic accuracy of autoscoring from auto-CPAP using different cutoff points of RDI (≥5 and 10) was evaluated by the receiver operating characteristics (ROCs) curve. RESULTS: The study included 114 patients (24 women; mean age and BMI, 59 years old and 33 kg/m(2); RDI and apnea/hypopnea index (AHI)-auto median, 5 and 2, respectively). The average difference between the AHI-auto and the RDI was -3.5 ± 3.9. The intraclass correlation coefficient (ICC) between the total number of central apneas, obstructive, and hypopneas between the PSG and the auto-CPAP were 0.69, 0.16, and 0.15, respectively. An AHI-auto >2 (RDI ≥ 5) or >4 (RDI ≥ 10) had an area under the ROC curve, sensitivity, specificity, positive likelihood ratio, and negative for diagnosis of residual sleep apnea of 0.84/0.89, 84/81%, 82/91%, 4.5/9.5, and 0.22/0.2, respectively. CONCLUSIONS: The automatic analysis from auto-CPAP (S9 Autoset) showed a good diagnostic accuracy to identify residual sleep apnea. The absolute agreement between PSG and auto-CPAP to classify the respiratory events correctly varied from very low (obstructive apneas, hypopneas) to moderate (central apneas).
Authors: E Shahar; C W Whitney; S Redline; E T Lee; A B Newman; F J Nieto; G T O'Connor; L L Boland; J E Schwartz; J M Samet Journal: Am J Respir Crit Care Med Date: 2001-01 Impact factor: 21.405
Authors: Marcel A Baltzan; Ibrahim Kassissia; Osama Elkholi; Mark Palayew; Richard Dabrusin; Norman Wolkove Journal: Sleep Date: 2006-04 Impact factor: 5.849
Authors: Stephen D Pittman; Giora Pillar; Richard B Berry; Atul Malhotra; Mary M MacDonald; David P White Journal: Sleep Breath Date: 2006-09 Impact factor: 2.816