Is the time below 90% of SpO2 during sleep (T90%) a metric of good health? A longitudinal analysis of two cohorts
© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:28 |
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| Enthalten in: |
Sleep & breathing = Schlaf & Atmung - 28(2024), 1 vom: 23. März, Seite 281-289 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Henríquez-Beltrán, Mario [VerfasserIn] |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 21.03.2024 Date Revised 21.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s11325-023-02909-x |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
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| 245 | 1 | 0 | |a Is the time below 90% of SpO2 during sleep (T90%) a metric of good health? A longitudinal analysis of two cohorts |
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| 520 | |a © 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG. | ||
| 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 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) | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Mortality | |
| 650 | 4 | |a Nocturnal hypoxemia | |
| 650 | 4 | |a Obstructive sleep apnea | |
| 650 | 4 | |a Sleep apnea | |
| 650 | 7 | |a Oxygen |2 NLM | |
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| 700 | 1 | |a Dreyse, Jorge |e verfasserin |4 aut | |
| 700 | 1 | |a Jorquera, Jorge |e verfasserin |4 aut | |
| 700 | 1 | |a Weissglas, Bunio |e verfasserin |4 aut | |
| 700 | 1 | |a Del Rio, Javiera |e verfasserin |4 aut | |
| 700 | 1 | |a Cendoya, Montserrat |e verfasserin |4 aut | |
| 700 | 1 | |a Jorquera-Diaz, Jorge |e verfasserin |4 aut | |
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| 700 | 1 | |a Labarca, Gonzalo |e verfasserin |4 aut | |
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