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

Summary 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