Is the time below 90% of SpO2 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 SpO2 (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 SpO2, we conducted linear regression models. Incremental changes in R2 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. R2 explains 62% of the variability in T90%. The main contributors were baseline-mean change in SpO2, baseline SpO2, 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.

Association between Acute Respiratory Distress Syndrome Due to COVID-19 and Long-Term Sleep and Circadian Sleep-Wake Disorders.

Current studies agree on the impact of sleep and circadian rest-activity rhythm alterations in acute respiratory distress syndrome (ARDS) survivors. However, research on the duration of this impact is scarce. In this study, we evaluate the impact of ARDS on the sleep and circadian rest-activity rhythm of COVID-19 survivors twelve months after hospital discharge. This is a prospective study including COVID-19 survivors with and without ARDS during hospitalization. Data was collected four and twelve months after hospital discharge. The interventions included one-week wrist actigraphy and a home sleep apnea test (HSAT), and evaluations were conducted according to the Pittsburgh sleep quality index (PSQI), Epworth sleepiness scale (ESS), and insomnia severity index (ISI). Fifty-two patients were evaluated (ARDS = 31 and non-ARDS = 21); they had a median age of 49.0 [39.0;57.2] years and 53.8% were male. After twelve months, 91.3% presented poor sleep quality, 58.7% presented insomnia, 50% presented daytime somnolence, and 37% presented comorbid insomnia and obstructive sleep apnea (COMISA). No significant improvement was observed in relation to sleep or the circadian rest-activity rhythm between four and twelve months. A tendency of poor sleep quality, insomnia, daytime somnolence, and COMISA was observed. Finally, there was no significant impact on the circadian rest-activity rhythm between four and twelve months or between the groups.

The U-Shaped Association between Sleep Duration, All-Cause Mortality and Cardiovascular Risk in a Hispanic/Latino Clinically Based Cohort.

Sleep is essential for life, and inappropriate sleep duration patterns may lead to chronic consequences regarding human health. Several studies have confirmed the presence of a U-shaped association between sleep duration and mortality. Moreover, many consequences related to cardiometabolic aspects have been suggested in patients with abnormal sleep durations. In this study, we analyzed the associations between sleep duration, total sleep time (TST), the risk of all-cause mortality, and 10-year cardiovascular risk in a cohort of patients at a sleep medicine center in Santiago, Chile. We conducted a prospective cohort study of patients (SantOSA). A short TST was defined as ≤6 h, a normal TST as 6 to 9 h, and a long TST as ≥9 h. Adjusted hazard ratios (aHRs) for all-cause mortality were calculated. A cross-sectional analysis between TST and 10-year cardiovascular risk (calculated using the Framingham 2008 formula) was determined using logistic regression models. A total of 1385 subjects were included in the results (78% male; median age: 53, interquartile range (IQR): 42-64 years; median BMI: 29.5, IQR: 16.7-33.1). A total of 333 subjects (24%) reported short TSTs, 938 (67.7%) reported normal TSTs, and 114 (8.3%) reported long TSTs. In the fully adjusted model, the association remained significant for short (aHR: 2.51 (1.48-4.25); p-value = 0.01) and long TSTs (aHR: 3.97 (1.53-10.29); p-value = 0.04). Finally, a U-shaped association was found between short and long TSTs, with an increase in cardiovascular risk at 10 years. Compared with normal TSTs, short (≤6 h) and long (≥9 h) TSTs were significantly associated with all-cause mortality and increased 10-year cardiovascular risk.

Clinical Application of the Multicomponent Grading System for Sleep Apnea Classification and Incident Cardiovascular Mortality.

Objective To evaluate the clinical utility of the Baveno classification in predicting incident cardiovascular mortality after five years of follow-up in a clinic-based cohort of patients with obstructive sleep apnea (OSA). Materials and Methods We evaluated the reproducibility of the Baveno classification using data from the Santiago Obstructive Sleep Apnea (SantOSA) study. The groups were labeled Baveno A (minor symptoms and comorbidities), B (severe symptoms and minor comorbidities), C (minor symptoms and severe comorbidities), and D (severe symptoms and comorbidities). Within-group comparisons were performed using analysis of variance (ANOVA) and post hoc tests. The associations between groups and incident cardiovascular mortality were determined through the Mantel-Cox and Cox proportional hazard ratios (HRs) adjusted by covariables. Results A total of 1,300 OSA patients were included (Baveno A: 27.7%; B: 28%; C: 16.8%; and D: 27.5%). The follow-up was of 5.4 years. Compared to Baveno A, the fully-adjusted risk of cardiovascular mortality with Baveno B presented an HR of 1.38 (95% confidence interval [95%CI]: 0.14-13.5; p = 0.78); with Baveno C, it was of 1.71 (95%CI: 0.18-16.2; p = 0.63); and, with Baveno D, of 1.04 (95%CI: 0.12-9.2; p = 0.98). We found no interactions involving Baveno group, sex and OSA severity. Discussion Among OSA patients, the Baveno classification can describe different subgroups. However, its utility in identifying incident cardiovascular mortality is unclear. Long-term follow-up studies and the inclusion of demographic variables in the classification could improve its ability to detect a high-risk phenotype associated with cardiovascular mortality. Conclusion The Baveno classification serves as a valuable method for categorizing varying groups of patients afflicted with OSA. Nevertheless, its precision in identifying occurrence of cardiovascular mortality is still unclear.