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Wallace ML, Coleman TS, Mentch LK, et al. Physiological sleep measures predict time to 15-year mortality in community adults: Application of a novel machine learning framework. J Sleep Res. 2021;30(6):e13386doi: 10.1111/jsr.13386.
Wallace, M. L., Coleman, T. S., Mentch, L. K., Buysse, D. J., Graves, J. L., Hagen, E. W., Hall, M. H., Stone, K. L., Redline, S., & Peppard, P. E. (2021). Physiological sleep measures predict time to 15-year mortality in community adults: Application of a novel machine learning framework. Journal of sleep research, 30(6), e13386. https://doi.org/10.1111/jsr.13386
Wallace, Meredith L, et al. "Physiological sleep measures predict time to 15-year mortality in community adults: Application of a novel machine learning framework." Journal of sleep research vol. 30,6 (2021): e13386. doi: https://doi.org/10.1111/jsr.13386
Wallace ML, Coleman TS, Mentch LK, Buysse DJ, Graves JL, Hagen EW, Hall MH, Stone KL, Redline S, Peppard PE. Physiological sleep measures predict time to 15-year mortality in community adults: Application of a novel machine learning framework. J Sleep Res. 2021 Dec;30(6):e13386. doi: 10.1111/jsr.13386. Epub 2021 May 15. PMID: 33991144; PMCID: PMC8591145.
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J Sleep Res. 2021 Dec;30(6):e13386. doi: 10.1111/jsr.13386. Epub 2021 May 15.

Physiological sleep measures predict time to 15-year mortality in community adults: Application of a novel machine learning framework.

Journal of sleep research

Meredith L Wallace, Timothy S Coleman, Lucas K Mentch, Daniel J Buysse, Jessica L Graves, Erika W Hagen, Martica H Hall, Katie L Stone, Susan Redline, Paul E Peppard

Affiliations

  1. Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
  2. Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA.
  3. University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
  4. Department of Population Health Sciences, University of Wisconsin, Madison, WI, USA.
  5. California Pacific Medical Center Research Institute, San Francisco, CA, USA.
  6. Departments of Medicine, Brigham and Women's Hospital, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA.

PMID: 33991144 PMCID: PMC8591145 DOI: 10.1111/jsr.13386

Abstract

Clarifying whether physiological sleep measures predict mortality could inform risk screening; however, such investigations should account for complex and potentially non-linear relationships among health risk factors. We aimed to establish the predictive utility of polysomnography (PSG)-assessed sleep measures for mortality using a novel permutation random forest (PRF) machine learning framework. Data collected from the years 1995 to present are from the Sleep Heart Health Study (SHHS; n = 5,734) and the Wisconsin Sleep Cohort Study (WSCS; n = 1,015), and include initial assessments of sleep and health, and up to 15 years of follow-up for all-cause mortality. We applied PRF models to quantify the predictive abilities of 24 measures grouped into five domains: PSG-assessed sleep (four measures), self-reported sleep (three), health (eight), health behaviours (four), and sociodemographic factors (five). A 10-fold repeated internal validation (WSCS and SHHS combined) and external validation (training in SHHS; testing in WSCS) were used to compute unbiased variable importance metrics and associated p values. We observed that health, sociodemographic factors, and PSG-assessed sleep domains predicted mortality using both external validation and repeated internal validation. The PSG-assessed sleep efficiency and the percentage of sleep time with oxygen saturation <90% were among the most predictive individual measures. Multivariable Cox regression also revealed the PSG-assessed sleep domain to be predictive, with very low sleep efficiency and high hypoxaemia conferring the highest risk. These findings, coupled with the emergence of new low-burden technologies for objectively assessing sleep and overnight oxygen saturation, suggest that consideration of physiological sleep measures may improve risk screening.

© 2021 European Sleep Research Society.

Keywords: hypoxaemia; rapid eye movement; risk screening; sleep efficiency

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