Cook Jesse D, Barata Filipe, Plante David T, Woodward Steve, Zeitzer Jamie M, Lok Renske
Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin, USA.
University of Wisconsin-Madison, School of Medicine and Public Health, Madison, Wisconsin, USA.
J Sleep Res. 2025 Apr 21:e70073. doi: 10.1111/jsr.70073.
This study aimed to advance the understanding of factors that predict mean sleep latency (MSL) on the multiple sleep latency test (MSLT) by applying machine learning methodology on a high-dimensional dataset from a large community sample. A cross-sectional analytic dataset of first visit clinical-protocol MSLTs (without shift workers) was developed from the Wisconsin Sleep Cohort Study, a community-based longitudinal study of middle-aged to older adults in Wisconsin, USA. Fifty predictors captured demographics, medical and psychiatric health, sleep (diary; polysomnography [PSG]) and circadian characteristics. The random forest (RF) algorithm identified the 10 most important predictors, which underwent subsequent regression analyses. Primary analyses focused on MSLT MSL, whereas secondary analyses centred on nap-specific sleep latencies. Post hoc analyses further explored the relationship between circadian preference and MSLT MSL. The primary sample (n = 301) of middle-aged adults (mean age = 57.5 ± 7.71 years) was predominantly non-Hispanic White (97%) and nearly equal across sexes (percentage female = 51.8%). RF model showed low explanatory value for MSLT MSL (R = 12%) with PSG sleep onset latency, circadian preference, daily caffeine use and Epworth Sleepiness Scale emerging as the most important predictors of MSLT MSL in the dataset. Top predictors varied across nap-specific sleep latency. Morning preference displayed significantly longer MSLT MSL, relative to neither and evening preferences. The low explanatory value observed in our high-dimensional RF models seemingly reflects the complexity and variability of the MSLT. Additionally, our results underscore the importance and challenge of accounting for circadian characteristics when utilising the MSLT.
本研究旨在通过对来自大型社区样本的高维数据集应用机器学习方法，增进对多次睡眠潜伏期试验（MSLT）中预测平均睡眠潜伏期（MSL）的因素的理解。首次就诊临床方案MSLT（不包括轮班工作者）的横断面分析数据集是从美国威斯康星州的威斯康星睡眠队列研究中开发出来的，该研究是一项针对威斯康星州中年至老年成年人的基于社区的纵向研究。50个预测变量涵盖了人口统计学、医疗和精神健康、睡眠（日记；多导睡眠图[PSG]）以及昼夜节律特征。随机森林（RF）算法确定了10个最重要的预测变量，随后对其进行回归分析。主要分析集中在MSLT的MSL，而次要分析则围绕特定小睡的睡眠潜伏期。事后分析进一步探讨了昼夜偏好与MSLT的MSL之间的关系。中年成年人的主要样本（n = 301）（平均年龄 = 57.5 ± 7.71岁）主要为非西班牙裔白人（97%），且性别分布几乎相等（女性比例 = 51.8%）。RF模型对MSLT的MSL解释价值较低（R = 12%），PSG睡眠开始潜伏期、昼夜偏好、每日咖啡因摄入量和爱泼华嗜睡量表是数据集中MSLT的MSL最重要的预测变量。顶级预测变量因特定小睡的睡眠潜伏期而异。与无偏好和傍晚偏好相比，早晨偏好的MSLT的MSL明显更长。在我们的高维RF模型中观察到的低解释价值似乎反映了MSLT的复杂性和变异性。此外，我们的结果强调了在使用MSLT时考虑昼夜节律特征 的重要性和挑战性。
