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Published on: December 4, 2012
Perioperative Sleep Trajectories and Factors Associated With Sleep Trajectories Derived From Growth Mixture Modeling
Jiaqi Shi1, Ruhui Cai2, Jing Zheng3
1Jiaqi Shi, MM Clinical Research Nurse, Cardiac Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Background:
Cardiac surgery often disrupts patients' sleep patterns during the perioperative period, adversely affecting recovery. Understanding sleep trajectories and their influencing factors is crucial for developing personalized interventions to improve patient outcomes.
Objective:
To investigate perioperative sleep trajectories in cardiac surgery patients and identify the factors influencing these trajectories using a growth mixture model.
Methods:
We conducted this prospective observational study at a tertiary hospital in Zhejiang Province from March 2023 to September 2024. Data from 348 cardiac surgery patients were collected using a demographic questionnaire, the Chinese version of the Richard-Campbell Sleep Questionnaire, the Self-Rating Anxiety Scale, and the Mini-Mental State Examination. Perioperative sleep trajectories were analyzed using a growth mixture model, and multivariate logistic regression identified associated factors.
Results:
Four distinct perioperative sleep trajectory groups were identified: the progressively declining sleep (21.55%), the rapid sleep improvement (27.59%), the moderate sleep improvement (18.10%), and the persistent poor sleep (32.76%). Risk factors for poor sleep included female gender (odds ratio [OR]: 1.913; 95% confidence interval [CI]: 1.835-2.703, P < .05), age > 60 years (OR: 2.580; 95% CI: 2.339-2.935, P < .05), and a history of alcohol consumption (OR: 1.605; 95% CI: 1.488-1.796, P < .05). The appropriate use of sedative-hypnotic medications was potentially beneficial to sleep (OR: 0.227; 95% CI: 0.214-0.916, P < .05).
Conclusions:
Our findings highlight significant variability in perioperative sleep trajectories among cardiac surgery patients and underscore the importance of identifying high-risk individuals and implementing targeted interventions to optimize sleep and recovery outcomes.
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