Marjolaine Georges1, Dan Adler1, Olivier Contal1, Fabrice Espa2, Stephen Perrig2, Jean-Louis Pépin3, Jean-Paul Janssens4. 1. Division of Pulmonary Diseases, Department of Medical Specialties. 2. Sleep Laboratory, Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland. 3. Sleep Laboratory, Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland. Sleep Laboratory, University Hospital, and Université Grenoble Alpes, Grenoble, France. 4. Division of Pulmonary Diseases, Department of Medical Specialties jean-paul.janssens@hcuge.ch.
Abstract
BACKGROUND: Ventilators designed for home care provide clinicians with built-in software that records items such as compliance, leaks, average tidal volume, total ventilation, and indices of residual apnea and hypopnea. Recent studies have showed, however, an important variability between devices regarding reliability of data provided. In this study, we aimed to compare apnea-hypopnea indices (AHI) provided by home ventilators (AHINIV) versus data scored manually during polysomnography (AHIPSG) in subjects on noninvasive ventilation (NIV) for obesity-hypoventilation syndrome. METHODS: Stable subjects with obesity-hypoventilation syndrome on NIV, all using the same device, underwent 3 consecutive polysomnographic sleep studies with different backup breathing frequencies (spontaneous mode, low and high backup breathing frequencies). During each recording, AHINIV was compared with AHIPSG. RESULTS: Ten subjects (30 polysomnogram tracings) were analyzed. For each backup breathing frequency (spontaneous mode, low and high backup breathing frequencies), AHI values were 62 ± 7/h, 26 ± 7/h, and 17 ± 5/h (mean ± SD), respectively. Correlation between AHINIV and AHIPSG was highly significant (r(2) = 0.89, P < .001). As determined by Bland-Altman analysis, mean bias was 6.5 events/h, and limits of agreement were +26.0 and -12.9 events/h. Bias increased significantly with higher AHI values. Using a threshold AHI value of 10/h to define appropriate control of respiratory events, the ventilator software had a sensitivity of 90.9%, a specificity and positive predictive value of 100%, and a negative predictive value of 71%. CONCLUSIONS: In stable subjects with obesity-hypoventilation syndrome, the home ventilator software tested was appropriate for determining if control of respiratory events was satisfactory on NIV or if further testing or adjustment of ventilator settings was required. (ClinicalTrials.gov registration NCT01130090.).
BACKGROUND: Ventilators designed for home care provide clinicians with built-in software that records items such as compliance, leaks, average tidal volume, total ventilation, and indices of residual apnea and hypopnea. Recent studies have showed, however, an important variability between devices regarding reliability of data provided. In this study, we aimed to compare apnea-hypopnea indices (AHI) provided by home ventilators (AHINIV) versus data scored manually during polysomnography (AHIPSG) in subjects on noninvasive ventilation (NIV) for obesity-hypoventilation syndrome. METHODS: Stable subjects with obesity-hypoventilation syndrome on NIV, all using the same device, underwent 3 consecutive polysomnographic sleep studies with different backup breathing frequencies (spontaneous mode, low and high backup breathing frequencies). During each recording, AHINIV was compared with AHIPSG. RESULTS: Ten subjects (30 polysomnogram tracings) were analyzed. For each backup breathing frequency (spontaneous mode, low and high backup breathing frequencies), AHI values were 62 ± 7/h, 26 ± 7/h, and 17 ± 5/h (mean ± SD), respectively. Correlation between AHINIV and AHIPSG was highly significant (r(2) = 0.89, P < .001). As determined by Bland-Altman analysis, mean bias was 6.5 events/h, and limits of agreement were +26.0 and -12.9 events/h. Bias increased significantly with higher AHI values. Using a threshold AHI value of 10/h to define appropriate control of respiratory events, the ventilator software had a sensitivity of 90.9%, a specificity and positive predictive value of 100%, and a negative predictive value of 71%. CONCLUSIONS: In stable subjects with obesity-hypoventilation syndrome, the home ventilator software tested was appropriate for determining if control of respiratory events was satisfactory on NIV or if further testing or adjustment of ventilator settings was required. (ClinicalTrials.gov registration NCT01130090.).