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Table 3 Associations of nocturnal hypoxemia parameters with aortic diameters in overall study patients

From: Influence of nocturnal hypoxemia on follow-up course after type B acute aortic syndrome

Aortic diameters

Ventilatory parameters

Unadjusted

Adjusted*

β (SD)

R2

p value

β (SD)

R2†

p value

False lumen TD aorta

ODIa

0.209 (1.236)

0.001

0.87

− 0.479 (1.394)

0.039

0.73

AHIb

1.058 (1.002)

0.011

0.29

0.807 (1.079)

0.046

0.45

Lowest nocturnal oxygen saturation

− 0.233 (0.165)

0.022

0.16

− 0.176 (0.188)

0.047

0.35

Mean nocturnal oxygen saturation

− 0.521 (0.481)

0.012

0.28

− 0.378 (0.549)

0.045

0.49

Percentage of nocturnal time under a saturation of 90%a

1.069 (0.781)

0.02

0.17

0.928 (0.921)

0.049

0.32

TD diameter of the aorta

ODIa

0.601 (1.001)

0.004

0.55

0.941 (1.077)

0.129

0.39

AHIb

1.558 (0.800)

0.037

0.054

1.751 (0.820)

0.159

0.035

Lowest nocturnal oxygen saturation

− 0.033 (0.135)

0.001

0.81

− 0.001 (0.146)

0.123

0.99

Mean nocturnal oxygen saturation

− 0.224 (0.391)

0.003

0.57

− 0.392 (0.424)

0.133

0.36

Percentage of nocturnal time under a saturation of 90%a

0.593 (0.635)

0.009

0.35

0.682 (0.711)

0.135

0.34

Ascending aorta diameter

ODIa

1.102 (0.591)

0.039

0.066

0.559 (0.581)

0.294

0.34

AHIb

1.002 (0.491)

0.042

0.044

0.472 (0.449)

0.332

0.3

Lowest nocturnal oxygen saturation

− 0.052 (0.078)

0.005

0.51

0.042 (0.076)

0.295

0.58

Mean nocturnal oxygen saturation

− 0.443 (0.230)

0.038

0.057

− 0.133 (0.228)

0.313

0.56

Percentage of nocturnal time under a saturation of 90%a

1.217 (0.361)

0.11

0.001

0.570 (0.377)

0.328

0.13

  1. The results were highlighted (bold) when the p value was inferior or equal to 0.05
  2. TD, thoracic descending; ODI, oxygen desaturation index; AHI, apnea–hypopnea index
  3. aAfter logarithm + 1 transformation
  4. bAfter logarithm transformation
  5. *Adjusted for age, BMI, sex, diabetes and systolic blood pressure
  6. Partial R squared values calculated in multiple linear regression models