Title: Quantitative Assessment of First Night Effect in a Polysomnographic Insomnia Study through a Multinomial Mixed-Effect Markov-Chain Model
E. Mezzalana (1), R. Bizzotto (2), S. Zamuner (3)
(1) Department of Computer Engineering and Systems Science, University of Pavia, Pavia, Italy (2) Institute of Biomedical Engineering, National Research Council, Padova, Italy; (3) Clinical Pharmacology Modelling and Simulation , GlaxoSmithKline, Stockley Park, UK.
Objectives: Sleep structure recorded in a laboratory is distorted, particularly during the first night . This phenomenon, called First Night Effect (FNE), is likely due to change in sleeping environment or discomfort caused by electrodes. The FNE may significantly influence the reliability of a single-night study, therefore drug efficacy is usually evaluated on the average of two nights. A multinomial mixed-effect Markov-chain model has been recently validated for describing sleep structure in primary insomnia [2,3]. The aim of this work was to apply this model to assess the impact of FNE on sleep structure.
Methods: Data were obtained from the first two nights of a polysomnographic study in insomnia patients treated with placebo. Each individual sequence of sleep stages (awake (AW), stage 1 (ST1), stage 2 (ST2), slow-wave sleep (SWS), and REM sleep) was treated as a Markov-chain and transition probabilities were modeled as piecewise-linear multinomial logistic functions of time [2,3] in NONMEM VI. Transition probabilities among the different stages were separately estimated from night 1 and 2.Results: Most of the probabilities were well characterized in terms of parameters precision (SE<25%). Model predictions showed a small difference between night 1 and 2 in the typical transition probabilities from AW and ST1. During the whole first night the probability of staying awake was slightly higher (Δ=4%) compared to the second night while the transition probability from AW to ST1 was reduced (Δ=4%). Moreover in the first two hours of night 1, the transition probabilities from ST1 to AW and from ST1 to REM sleep were 8% higher and lower respectively, resulting in a lower probability of staying in ST1 (Δ=8%).
Conclusions: FNE is characterized by an increased probability of staying awake as a result of a lower probability to go from AW to ST1 and a higher probability of moving from ST1 to AW in the first part of the night. Moreover, transitions from ST1 to REM appeared to be less likely at night 1. These results are consistent with the observed sleep parameters for night 1: reduced total sleep and REM sleep time, increased intermittent wake time and longer latency to REM sleep. Overall FNE appeared more marked in the first part of the night and not sufficiently large to invalidate the one-night PSG assessment. In conclusion, the multinomial mixed-effect Markov-chain model is a valuable quantitative tool to interpret sleep structure differences induced by FNE.
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