Objective To understand the quality of life of patients with acute mild to moderate ischemic stroke one year after the stroke, analyze the factors affecting their quality of life, and provide a scientific basis for improving their health-related quality of life. Methods This study included patients diagnosed with acute mild to moderate ischemic stroke between March 2019 and March 2021 in four hospitals in Nanchang. Sociodemographic information and relevant clinical data were collected during hospitalization. The EQ-5D-5L questionnaire was administered to assess health-related quality of life one year after discharge. The Mann-Whitney U test (for two groups) and Kruskal-Wallis one-way ANOVA (for multiple groups) were used to analyze differences in utility scores among various factors. A Tobit regression model was built to investigate the factors influencing quality of life one-year post-stroke. Results A total of 1 181 patients participated in the study, including 791 males (66.98%) and 390 females (33.02%), with an average age of 63.7±10.9 years. Health-related quality of life data collected one year after the stroke revealed that 22.69% of patients experienced pain/discomfort, 17.27% suffered anxiety/depression, 15.66% had mobility issues, 10.33% had difficulties with daily activities, and 8.64% had trouble with self-care. Tobit regression results showed that age (β=−0.263, 95%CI −0.327 to −0.198), gender (β=−0.134, 95%CI −0.189 to −0.080), previous hypertension (β=−0.068, 95%CI −0.120 to −0.016), previous dyslipidemia (β=−0.068, 95%CI −0.126 to −0.011), admission NIHSS score (β=−0.158, 95%CI −0.198 to −0.118), and discharge mRS score (β=−0.193, 95%CI −0.250 to −0.136) were negatively associated with health utility values. Current employment status (β=0.141, 95%CI 0.102 to 0.181) and admission GCS score (β=0.209, 95%CI 0.142 to 0.276) were positively correlated with health utility values. Conclusion One year after an acute mild to moderate ischemic stroke, patients commonly face pain/discomfort and anxiety/depression. Factors affecting overall quality of life include age, gender, current employment status, previous hypertension, previous dyslipidemia, admission NIHSS score, admission GCS score, and discharge mRS score. Clinically, developing scientifically sound and reasonable rehabilitation plans post-discharge is crucial for improving long-term quality of life.
This study introduced the construction of individualized risk assessment model based on Bayesian networks, comparing with traditional regression-based logistic models using practical examples. It evaluates the model's performance and demonstrates its implementation in the R software, serving as a valuable reference for researchers seeking to understand and utilize Bayesian network models.