Is the time below 90% of SpO 2 during sleep (T90%) a metric of good health? A longitudinal analysis of two cohorts.

Background: Novel wireless-based technologies can easily record pulse oximetry at home. One of the main parameters that are recorded in sleep studies is the time under 90% of SpO ₂ (T90%) and the oxygen desaturation index 3% (ODI-3%). We assessed the association of T90% and/or ODI-3% in two different scenarios (a community-based study and a clinical setting) with all-cause mortality (primary outcome).
Methods: We included all individuals from the Sleep Heart Health Study (SHHS, community-based cohort) and Santiago Obstructive Sleep Apnea (SantOSA, clinical cohort) with complete data at baseline and follow-up. Two measures of hypoxemia (T90% and ODI-3%) were our primary exposures. The adjusted hazard ratios (HRs) per standard deviation (pSD) between T90% and incident all-cause mortality (primary outcome) were determined by adjusted Cox regression models. In the secondary analysis, to assess whether T90% varies across clinical factors, anthropometrics, abdominal obesity, metabolic rate, and SpO ₂ , we conducted linear regression models. Incremental changes in R ² were conducted to test the hypothesis.
Results: A total of 4323 (56% male, median 64 years old, follow-up: 12 years, 23% events) and 1345 (77% male, median 55 years old, follow-up: 6 years, 11.6% events) patients were included in SHHS and SantOSA, respectively. Every 1 SD increase in T90% was associated with an adjusted HR of 1.18 [95% CI: 1.10-1.26] (p value < 0.001) in SHHS and HR 1.34 [95% CI: 1.04-1.71] (p value = 0.021) for all-cause mortality in SantOSA. Conversely, ODI-3% was not associated with worse outcomes. R ² explains 62% of the variability in T90%. The main contributors were baseline-mean change in SpO ₂ , baseline SpO ₂ , respiratory events, and age.
Conclusion: The findings suggest that T90% may be an important marker of wellness in clinical and community-based scenarios. Although this nonspecific metric varies across the populations, ventilatory changes during sleep rather than other physiological or comorbidity variables explain their variability.
(© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)