Trajectory classification and influencing factors of sleep disturbances in patients with pulmonary infections based on latent class growth mixture model
XU Wen-xuan
LIU Na-na
Abstract：Objective To explore the developmental trajectories of sleep disturbances in patients with pulmonary infections and to identify factors contributing to intergroup differences,thereby revealing the dynamic patterns of sleep dis-turbances in this population.Methods A total of 250 patients with pulmonary infections who received treatment in our hospital between January and December 2023 were enrolled.The Pittsburgh Sleep Quality Index(PSQI)was used to eval-uate sleep disturbances at baseline(T1),day 3 of treatment(T2),day 7 of treatment(T3),and 1 month after treatment(T4).Latent growth mixture modeling(LGMM)was applied to determine the optimal number of latent classes and classi-fy trajectories.Multivariate logistic regression was used to analyze independent influencing factors associated with trajectory class differences.Results Of the 250 patients,238 valid cases were included in the analysis.LGMM identified three distinct trajectories of sleep disturbance:low-level type(n=50,21.01％),remission type(n=132,55.46％),and high-level type(n=56,23.53％).Both univariate and multinomial logistic regression analyses revealed that age,in-fection severity,anxiety,and depression were independent predictors of trajectory class differences(P＜0.05).Conclu-sion Sleep disturbance trajectories in patients with pulmonary infections exhibit significant heterogeneity.These findings suggest that healthcare providers should consider individualized nursing interventions targeting specific influencing factors to alleviate sleep disturbances in this patient population.
Keywords：latent class growth mixture modelpulmonary infectionsleep disturbancetrajectoryfactors
Publication Date：2025-10-15
Online Publishing Date：2025-11-28(First online date of this platform, not the publication date of the document)
Pages：6( 1580-1585 )