ABSTRACT
UNLABELLED: The critical closing pressure (PCRIT), a quantitative assessment of upper airway collapsibility, is derived from pressure flow relationship during sleep. The analytic generation of the pressure flow relationships are non-standardized due to various regression models (linear, spline, median), breath characteristics (flow limited, non-flow limited) or known covariates (sleep stage, body position). We propose a GUI based PCRIT Analysis Software (PAS) to streamline PCRIT analysis and validate its reliability and accuracy compared to current analysis procedures. METHODS: Seventeen subjects underwent a physiology sleep study in which the PCRIT was determined during NREM sleep. Data analysis was performed independently using three paradigms: (1) PAS (Igor Pro; median regression), (2) non-graphic statistics application (SAS; spline regression), and (3) manual spreadsheet calculations (Excel; linear regression). The reliability and accuracy of the PAS was examined through the agreement between each approach using Bland-Altman plots of the mean difference and within-individual variation using intra-class correlation (ICC). RESULTS: There was no difference in the group mean values for PCRIT using the PAS (-1.7 ± 0.7 cm H2O) compared to spline regression (-1.6 ± 0.7 cm H2O; p=0.69) or linear regression (-2.1 ± 0.7 cm H2O; p=0.92). The Bland-Altman analysis did not demonstrate a systematic bias between the PAS and either approach. There was a mean difference of 0.39 ± 0.2 cm H2O between the PAS and linear regression approaches, with upper and lower limits of agreement of 1.81 and -1.02 cm H2O, respectively. The PAS and spline analyses demonstrated an even smaller mean difference of -0.10 ± 0.1cm H2O, with upper and lower limits of 0.90 and -1.08 cm H2O, respectively. CONCLUSION: PAS preserves the reliability and accuracy of the original PCRIT analysis methods while vastly improving their efficiency through graphic user interface and automation of analytic processes. Providing a standardized platform for physiologic data processing offers the ability to implement quality assurance and control procedures for multicenter studies as well as cost saving by improving the efficiency of complex repetitive tasks.