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Age estimation from sleep studies using deep learning predicts life expectancy

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DOI

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  • Andreas Brink-Kjaer
  • Eileen B Leary
  • Haoqi Sun
  • M Brandon Westover
  • Katie L Stone
  • Paul E Peppard
  • Nancy E Lane
  • Peggy M Cawthon
  • Susan Redline
  • Poul Jennum
  • Helge B D Sorensen
  • Emmanuel Mignot
Vis graf over relationer

Sleep disturbances increase with age and are predictors of mortality. Here, we present deep neural networks that estimate age and mortality risk through polysomnograms (PSGs). Aging was modeled using 2500 PSGs and tested in 10,699 PSGs from men and women in seven different cohorts aged between 20 and 90. Ages were estimated with a mean absolute error of 5.8 ± 1.6 years, while basic sleep scoring measures had an error of 14.9 ± 6.29 years. After controlling for demographics, sleep, and health covariates, each 10-year increment in age estimate error (AEE) was associated with increased all-cause mortality rate of 29% (95% confidence interval: 20-39%). An increase from -10 to +10 years in AEE translates to an estimated decreased life expectancy of 8.7 years (95% confidence interval: 6.1-11.4 years). Greater AEE was mostly reflected in increased sleep fragmentation, suggesting this is an important biomarker of future health independent of sleep apnea.

OriginalsprogEngelsk
Artikelnummer103
TidsskriftNPJ digital medicine
Vol/bind5
Udgave nummer1
Sider (fra-til)103
ISSN2398-6352
DOI
StatusUdgivet - 22 jul. 2022

Bibliografisk note

© 2022. The Author(s).

ID: 79705490