Logistic Regression Analysis-Based Study of Risk Factors and Assessment Model of Non-Acute-Phase Post-Stroke Depression

Abstract

Background: This work aimed to investigate the risk factors for post-stroke depression (PSD) during the non-acute phase of stroke, and to construct and validate a risk assessment model for PSD using logistic regression analysis, thereby providing a basis for early clinical screening and intervention.

Methods: A total of 900 stroke patients admitted to our hospital from April 2021 to April 2023 were enrolled in a prospective cohort study. After 6 months of followup, patients were divided into a PSD group (n = 485) and a non-PSD group (n = 415) based on the 17-item Hamilton Depression Rating Scale (HAMD-17) scores and diagnostic criteria. Data on demographics, disease characteristics, lifestyle factors, biochemical indicators, and scale scores were collected. Independent influencing factors were identified using univariate and multivariate logistic regression analyses. A risk prediction model was then constructed and validated using the receiver operating characteristic (ROC) curve.

Results: Multivariate logistic regression analysis showed thatsex, parietal lobe lesions, alcohol consumption, smoking, hypertension, family support, National Institutes of Health Stroke Scale (NIHSS) score, Mini-Mental State Examination (MMSE) score, Social Support Rating Scale (SSRS) score, and Barthel Index (BI) were independent influencing factors for depression in non-acute stroke patients (all p < 0.05). The optimal cut-off value of the model was 18.12 points, and the area under the ROC curve (AUC) was 0.91 (p = 0.001), indicating good predictive performance.

Conclusions: This study successfully constructed a risk assessment model with excellent predictive performance for PSD during the non-acute phase of stroke. The model can be used to assess PSD symptoms and screen high-risk populations, providing a reference for early clinical intervention.