The relationship of daytime hypoxemia and nocturnal hypoxia in obstructive sleep apnea syndrome

Francesco Fanfulla, Mario Grassi, Anna Eugenia Taurino, Nadia D'Artavilla Lupo, Rossella Trentin, Francesco Fanfulla, Mario Grassi, Anna Eugenia Taurino, Nadia D'Artavilla Lupo, Rossella Trentin

Abstract

Question of the study: Prevalence and determinants of daytime hypoxemia in patients with obstructive sleep apnea (OSA) syndrome are not well established. The aims of this study, conducted in a large series of OSA patients, were to estimate the prevalence of daytime hypoxemia, to assess the reciprocal effects between daytime PaO2 and nocturnal SpO2, and to investigate the direct and indirect role of sleep apnea severity in determining feedback gas exchange abnormalities.

Materials and methods: In 456 patients a daytime hypoxemia-nocturnal hypoxia feedback structural equations model was designed. PaO2 adjusted for age (% of predicted), percent sleep time spent with SpO2 <90% (TST90), oxygen desaturation index and the apnea-hypopnea index, were determined as the measures of daytime hypoxemia, nocturnal hypoxia, and sleep apnea severity, respectively, after adjusting for the severity of obesity and lung volumes.

Results: The TST90-PaO2 feed-back structural equations modeling showed that daytime PaO2 was inversely related (P<0.001) to nocturnal hypoxia (-4.0% of PaO2 per 1 SD of TST90). The severity of OSA (-1.0%) was an indirect determinant of daytime PaO2 via the TST90 pathway. In contrast, daytime PaO2 did not influence (P>0.05) the extent of nocturnal hypoxia.

Conclusions: In OSA patients, the extent of nocturnal hypoxia seems to be both a direct determinant and a mediator of the indirect effect of sleep apnea on the development of daytime hypoxemia.

Figures

Figure 1
Figure 1
Path diagram of the dependencies of the TST90−PaO2 feed back structural equations designed (*). The observed variables are enclosed in boxes, and the unobserved variables (error terms) are circled. An arrow from one variable to another indicates that the first variable has a direct influence on the second, two straight single-headed arrows connecting two variables indicate a reciprocal direct influence, and a curved two-headed arrow signifies a correlation between two variables. The path coefficients displayed on the arrows are the regression coefficients for the observed variables, and the residual variances/covariances for the unobserved errors. (*) VC%: vital capacity in % of predicted; BMI: body mass index (Kg/m2); TST90: percent sleep time spent with SaO2 <90%; PaO2%: partial oxygen arterial pressure in % of predicted; ODI: oxygen desaturation index; AHI: apnea-hypopnea index. The SEM is written as: TST90 = α1 BMI + α2 VC% + α3 PaO2% + α4 ODI + α5 AHI + e1 PaO2% = β1 BMI + β2 VC% + β3 TST90 + e2 var (e1)= σ1; var (e2)= σ2; cov (e1; e2) = σ12

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