Age estimation from sleep studies using deep learning predicts life expectancy

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Age estimation from sleep studies using deep learning predicts life expectancy. / Brink-Kjaer, Andreas; Leary, Eileen B.; Sun, Haoqi; Westover, M. Brandon; Stone, Katie L.; Peppard, Paul E.; Lane, Nancy E.; Cawthon, Peggy M.; Redline, Susan; Jennum, Poul; Sorensen, Helge B.D.; Mignot, Emmanuel.

In: npj Digital Medicine, Vol. 5, No. 1, 103, 2022.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Brink-Kjaer, A, Leary, EB, Sun, H, Westover, MB, Stone, KL, Peppard, PE, Lane, NE, Cawthon, PM, Redline, S, Jennum, P, Sorensen, HBD & Mignot, E 2022, 'Age estimation from sleep studies using deep learning predicts life expectancy', npj Digital Medicine, vol. 5, no. 1, 103. https://doi.org/10.1038/s41746-022-00630-9

APA

Brink-Kjaer, A., Leary, E. B., Sun, H., Westover, M. B., Stone, K. L., Peppard, P. E., Lane, N. E., Cawthon, P. M., Redline, S., Jennum, P., Sorensen, H. B. D., & Mignot, E. (2022). Age estimation from sleep studies using deep learning predicts life expectancy. npj Digital Medicine, 5(1), [103]. https://doi.org/10.1038/s41746-022-00630-9

Vancouver

Brink-Kjaer A, Leary EB, Sun H, Westover MB, Stone KL, Peppard PE et al. Age estimation from sleep studies using deep learning predicts life expectancy. npj Digital Medicine. 2022;5(1). 103. https://doi.org/10.1038/s41746-022-00630-9

Author

Brink-Kjaer, Andreas ; Leary, Eileen B. ; Sun, Haoqi ; Westover, M. Brandon ; Stone, Katie L. ; Peppard, Paul E. ; Lane, Nancy E. ; Cawthon, Peggy M. ; Redline, Susan ; Jennum, Poul ; Sorensen, Helge B.D. ; Mignot, Emmanuel. / Age estimation from sleep studies using deep learning predicts life expectancy. In: npj Digital Medicine. 2022 ; Vol. 5, No. 1.

Bibtex

@article{57b156f7c8da4ebd91102beedcf33e52,

title = "Age estimation from sleep studies using deep learning predicts life expectancy",

abstract = "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.",

author = "Andreas Brink-Kjaer and Leary, {Eileen B.} and Haoqi Sun and Westover, {M. Brandon} and Stone, {Katie L.} and Peppard, {Paul E.} and Lane, {Nancy E.} and Cawthon, {Peggy M.} and Susan Redline and Poul Jennum and Sorensen, {Helge B.D.} and Emmanuel Mignot",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

doi = "10.1038/s41746-022-00630-9",

language = "English",

volume = "5",

journal = "npj Digital Medicine",

issn = "2398-6352",

publisher = "Nature Publishing Group",

number = "1",

}

RIS

TY - JOUR

T1 - Age estimation from sleep studies using deep learning predicts life expectancy

AU - Brink-Kjaer, Andreas

AU - Leary, Eileen B.

AU - Sun, Haoqi

AU - Westover, M. Brandon

AU - Stone, Katie L.

AU - Peppard, Paul E.

AU - Lane, Nancy E.

AU - Cawthon, Peggy M.

AU - Redline, Susan

AU - Jennum, Poul

AU - Sorensen, Helge B.D.

AU - Mignot, Emmanuel

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

U2 - 10.1038/s41746-022-00630-9

DO - 10.1038/s41746-022-00630-9

M3 - Journal article

C2 - 35869169

AN - SCOPUS:85134627620

VL - 5

JO - npj Digital Medicine

JF - npj Digital Medicine

SN - 2398-6352

IS - 1

M1 - 103

ER -

ID: 323983223

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