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Published on: May 22, 2019
Characteristics of early-onset depression in first-episode stroke patients: A latent profile analysis
Xiao-Xu Han1, Yu-Ping Zhang1, Jing-Fen Jin1
1Department of Nursing, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China.
Background:
Early-onset post-stroke depression (PSD)-symptoms arising within two weeks of stroke-is a frequent but under-characterised complication that worsens functional recovery.
Purpose:
To identify latent symptom profiles of early-onset PSD in first-ever stroke survivors and to determine demographic, clinical and social-support factors associated with membership in more severe profiles.
Methods:
In this cross-sectional study, 276 adults hospitalised for first-episode stroke (January-December 2021) completed the Self-Rating Depression Scale 14 ± 2 days post-stroke. Item-level responses were analysed with latent profile analysis. Social support (Social Support Rating Scale) and patient characteristics were compared across classes; predictors of profile membership were examined with multinomial logistic regression.
Results:
A threeclass model showed optimal fit (entropy = 0.94): (1) mild depression-weak somatization (48.6 %); (2) moderate depression-moderate somatization (35.6 %); (3) severe depression-strong somatization (15.9 %). Compared with class 1, odds of belonging to classes 2-3 were higher in unmarried patients (OR = 3.7, 95 % CI 1.6-8.5), those lethargic at admission (OR = 22.2, 95 % CI 7.0-70.3), and those with affected-limb muscle strength ≤ grade 2 (OR = 7.4, 95 % CI 2.6-21.0; all p < 0.01). Perceived social-support scores declined step-wise across classes (p < 0.001).
Conclusions:
Early-onset PSD is heterogeneous, clustering into three distinct symptom profiles that align with decrements in social support. Absence of a spouse, reduced alertness and profound limb weakness markedly increase the likelihood of moderate-to-severe profiles. Routine mood screening combined with muscle-strength and social-support assessments can help nurses triage high-risk patients for targeted psycho-social interventions.
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