How well can a large number of polysomnography sleep measures predict subjective sleep quality in insomnia patients?
Svetnik V, Snyder ES, Tao P, Roth T, Lines C, and Herring WJ. How well can a large number of polysomnography sleep measures predict subjective sleep quality in insomnia patients? Sleep Med 2020; 67:137-146.
OBJECTIVE: The determinants of sleep quality (sQUAL) are poorly understood. We evaluated how well a large number of objective polysomnography (PSG) parameters can predict sQUAL in insomnia patients participating in trials of sleep medications or placebo.
METHODS: PSG recordings over multiple nights from two clinical drug development programs involving 1158 insomnia patients treated with suvorexant or placebo and 903 insomnia patients treated with gaboxadol or placebo were used post-hoc to analyze univariate and multivariate associations between sQUAL and 98 PSG sleep parameters plus patient's age and gender. Analyses were performed separately for each of the two clinical trial databases. For univariate associations, within-subject correlations were estimated using mixed effect modeling of bi-variate longitudinal data with one variable being a given PSG variable and the other being sQUAL. To evaluate how accurately sQUAL could be predicted by all PSG variables jointly plus patient's age and gender, the Random Forest multivariate technique was used. Random Forest was also used to evaluate the accuracy of sQUAL prediction by subjective sleep measures plus age and gender, and to quantitatively describe the relative importance of each variable for predicting sQUAL.
RESULTS: In the univariate analyses, total sleep time (TST) had the largest correlation with sQUAL compared with all other PSG sleep parameters, and the magnitude of the correlation between each PSG sleep architecture parameter and sQUAL generally increased with the strength of their associations with TST. In the multivariate analyses, the overall accuracy of sQUAL prediction, even with the large number of PSG parameters plus patient's age and gender, was moderate (area under the Receiver Operating Characteristic curve (AROC): 71.2-71.8%). Ranking of PSG parameters by their contribution to sQUAL indicated that TST was the most important predictor of sQUAL among all PSG variables. Subjective TST and subjective number of awakenings jointly with patient's age classified sQUAL with higher accuracy (AROC: 78.7-81.7%) than PSG variables plus age and gender. The pattern of findings was consistent across the two clinical trial databases.
CONCLUSION: In insomnia patients participating in trials of sleep medications or placebo, PSG variables had a moderate but consistent pattern of association with sQUAL across two separate clinical trial databases. Of the PSG variables evaluated, TST was the best predictor of sQUAL.