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..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:28 |
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| Enthalten in: |
Sleep and breathing - 28(2023), 1 vom: 01. Sept., Seite 281-289 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Henríquez-Beltrán, Mario [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| BKL: | |
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| Themen: |
Mortality |
| Anmerkungen: |
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| doi: |
10.1007/s11325-023-02909-x |
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| Förderinstitution / Projekttitel: |
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SPR055225349 |
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| 520 | |a 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. | ||
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