Introduction Modern CPAP devices have an inbuilt algorithm to detect reductions and cessations in patient-generated airflow, generating an estimated apnoea-hypopnoea index (AHI) for each therapy session. This data is often used by clinical teams to guide the management of patient’s CPAP therapy. Whilst previous comparison studies have shown good agreement with scored AHI from respiratory polygraphy and polysomnography, CPAP-generated AHI is poorly validated in paediatric populations. To address this, we compared same-night CPAP-AHI with physiologist-scored respiratory polygraphy AHI in children.
Methods 31 patients (20 male/11 female, mean [SD] age 11.4 years [4.3], mean [SD] weight 65.9 kg [35.4]) on CPAP therapy for sleep-disordered breathing underwent a respiratory polygraphy study (pressure flow, thoracic & abdominal effort, ECG, SpO2) on their CPAP device for therapy optimisation as part of their routine clinical care. Each study was scored by an experienced sleep physiologist to generate an AHI (poly-AHI). Each CPAP device (ResMed Ltd) was interrogated the following morning via remote monitoring (Airview, ResMed Ltd) to obtain the CPAP-AHI from the same night. Poly-AHI and CPAP-AHI were compared using Bland-Altman analysis and Wilcoxon signed-rank test (Graphpad Prism 9.0, GraphPad Software).
Results There was no significant difference between Poly-AHI (median 1.2/hr (IQR 0.5–5.3)) and CPAP-AHI (median 1.2/hr (IQR 0.4–3.3)), W = 24, P = 0.792.
Bland-Altman analysis showed good agreement between Poly-AHI and CPAP-AHI (bias 0.26, SD 4.43, 95% LoA -8.42–9.95) (figure 1).
6/31 patients had a Poly-AHI to CPAP-AHI difference of >5.
Discussion The AHI generated by the internal algorithm of PAP devices can be used to provide some indication of how well PAP therapy is controlling sleep disordered breathing in paediatric populations. Notable disparities between poly-AHI to CPAP-AHI [figure 2] in several patients indicate caution should still be exercised when using CPAP-AHI to guide clinical care.
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