TY - JOUR
T1 - Residual events during use of CPAP
T2 - Prevalence, predictors, and detection accuracy
AU - Reiter, Joel
AU - Zleik, Bashar
AU - Bazalakova, Mihaela
AU - Mehta, Pankaj
AU - Thomas, Robert Joseph
N1 - Publisher Copyright:
© 2016, American Academy of Sleep Medicine. All rights reserved.
PY - 2016
Y1 - 2016
N2 - Study Objectives: To assess the frequency, severity, and determinants of residual respiratory events during continuous positive airway therapy (CPAP) for obstructive sleep apnea (OSA) as determined by device output. Methods: Subjects were consecutive OSA patients at an American Academy of Sleep Medicine accredited multidisciplinary sleep center. Inclusion criteria included CPAP use for a minimum of 3 months, and a minimum nightly use of 4 hours. Compliance metrics and waveform data from 217 subjects were analyzed retrospectively. Events were scored manually when there was a clear reduction of amplitude (≥ 30%) or flow-limitation with 2-3 larger recovery breaths. Automatically detected versus manually scored events were subjected to statistical analyses included Bland-Altman plots, correlation coefficients, and logistic regression exploring predictors of residual events. Results: The mean patient age was 54.7 ± 14.2 years; 63% were males. All patients had a primary diagnosis of obstructive sleep apnea, 26% defined as complex sleep apnea. Residual flow measurement based apnea-hypopnea index (AHIFLOW) > 5, 10, and 15/h was seen in 32.3%, 9.7%, and 1.8% vs. 60.8%, 23%, and 7.8% of subjects based on automated vs. manual scoring of waveform data. Automatically detected versus manually scored average AHIFLOW was 4.4 ± 3.8 vs. 7.3 ± 5.1 per hour. In a logistic regression analysis, the only predictors for a manual AHIFLOW > 5/h were the absolute central apnea index (CAI), (odds ratio [OR]: 1.5, p: 0.01, CI: 1.1-2.0), or using a CAI threshold of 5/h of sleep (OR: 5.0, p: < 0.001, CI: 2.2-13.8). For AHIFLOW > 10/h, the OR was 1.14, p: 0.03 (CI: 1.1-1.3) per every CAI unit of 1/hour. Conclusions: Residual respiratory events are common during CPAP treatment, may be missed by automated device detection and predicted by a high central apnea index on the baseline diagnostic study. Direct visualization of flow data is generally available and improves detection.
AB - Study Objectives: To assess the frequency, severity, and determinants of residual respiratory events during continuous positive airway therapy (CPAP) for obstructive sleep apnea (OSA) as determined by device output. Methods: Subjects were consecutive OSA patients at an American Academy of Sleep Medicine accredited multidisciplinary sleep center. Inclusion criteria included CPAP use for a minimum of 3 months, and a minimum nightly use of 4 hours. Compliance metrics and waveform data from 217 subjects were analyzed retrospectively. Events were scored manually when there was a clear reduction of amplitude (≥ 30%) or flow-limitation with 2-3 larger recovery breaths. Automatically detected versus manually scored events were subjected to statistical analyses included Bland-Altman plots, correlation coefficients, and logistic regression exploring predictors of residual events. Results: The mean patient age was 54.7 ± 14.2 years; 63% were males. All patients had a primary diagnosis of obstructive sleep apnea, 26% defined as complex sleep apnea. Residual flow measurement based apnea-hypopnea index (AHIFLOW) > 5, 10, and 15/h was seen in 32.3%, 9.7%, and 1.8% vs. 60.8%, 23%, and 7.8% of subjects based on automated vs. manual scoring of waveform data. Automatically detected versus manually scored average AHIFLOW was 4.4 ± 3.8 vs. 7.3 ± 5.1 per hour. In a logistic regression analysis, the only predictors for a manual AHIFLOW > 5/h were the absolute central apnea index (CAI), (odds ratio [OR]: 1.5, p: 0.01, CI: 1.1-2.0), or using a CAI threshold of 5/h of sleep (OR: 5.0, p: < 0.001, CI: 2.2-13.8). For AHIFLOW > 10/h, the OR was 1.14, p: 0.03 (CI: 1.1-1.3) per every CAI unit of 1/hour. Conclusions: Residual respiratory events are common during CPAP treatment, may be missed by automated device detection and predicted by a high central apnea index on the baseline diagnostic study. Direct visualization of flow data is generally available and improves detection.
KW - Auto-CPAP
KW - Residual apnea
KW - Sleep
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U2 - 10.5664/jcsm.6056
DO - 10.5664/jcsm.6056
M3 - Article
C2 - 27166303
AN - SCOPUS:84985995808
SN - 1550-9389
VL - 12
SP - 1153
EP - 1158
JO - Journal of Clinical Sleep Medicine
JF - Journal of Clinical Sleep Medicine
IS - 8
ER -