PDF(Pubmed)
Abstract：
UNASSIGNED: To evaluate the health-related quality of life(HRQoL)status of elderly patients with hypertensive stroke, to understand the factors influencing it, and to provide a basis for the development of health intervention policies.
UNASSIGNED: This study used the EQ-5D-3L scale to assess the HRQoL among elderly patients who experienced a stroke related to high blood pressure. Various analytical methods were employed to examine the factors that influenced the patient\'s quality of life. Univariate analysis, Tobit regression, random forest, and XGBoost models were applied to analyze the HRQoL of the patients. Furthermore, to interpret the machine learning results, the SHAP method was utilized. This method involved assessing the importance of each feature, examining the effect of each feature on the prediction result of a single sample, and determining the impact of individual features on the overall prediction.
UNASSIGNED: The study found that the median health utility value for elderly patients with hypertensive stroke was 0.427, with an interquartile range of 0.186 to 0.745. The results of the Tobit regression model, Random Forest, and XGBoost model were compared. The results of the model evaluation show that the performance of the machine learning model and the Tobit regression model are not very different. The XGBoost model performs slightly better relative to the random forest model. The factors that strongly influenced the health utility value of patients included BMI, social activities, smoking, education level, alcohol consumption, urban/rural residence, annual income, physical activity level, and hours of sleep at night.
UNASSIGNED: Health-related quality of life in hypertensive stroke patients is influenced by a variety of factors. Health-related quality of life can be positively influenced by modifying these factors and making lifestyle adjustments. Maintaining a healthy weight, being socially active, quitting smoking, improving living conditions, increasing physical activity levels and getting enough sleep are recommended. Lifestyle changes need to be developed for each individual on a case-by-case basis and by medical advice.
UNASSIGNED: This study used the EQ-5D-3L scale to assess the HRQoL among elderly patients who experienced a stroke related to high blood pressure. Various analytical methods were employed to examine the factors that influenced the patient\'s quality of life. Univariate analysis, Tobit regression, random forest, and XGBoost models were applied to analyze the HRQoL of the patients. Furthermore, to interpret the machine learning results, the SHAP method was utilized. This method involved assessing the importance of each feature, examining the effect of each feature on the prediction result of a single sample, and determining the impact of individual features on the overall prediction.
UNASSIGNED: The study found that the median health utility value for elderly patients with hypertensive stroke was 0.427, with an interquartile range of 0.186 to 0.745. The results of the Tobit regression model, Random Forest, and XGBoost model were compared. The results of the model evaluation show that the performance of the machine learning model and the Tobit regression model are not very different. The XGBoost model performs slightly better relative to the random forest model. The factors that strongly influenced the health utility value of patients included BMI, social activities, smoking, education level, alcohol consumption, urban/rural residence, annual income, physical activity level, and hours of sleep at night.
UNASSIGNED: Health-related quality of life in hypertensive stroke patients is influenced by a variety of factors. Health-related quality of life can be positively influenced by modifying these factors and making lifestyle adjustments. Maintaining a healthy weight, being socially active, quitting smoking, improving living conditions, increasing physical activity levels and getting enough sleep are recommended. Lifestyle changes need to be developed for each individual on a case-by-case basis and by medical advice.
摘要：
■评估老年高血压卒中患者的健康相关生活质量（HRQoL）状况，为了了解影响它的因素，并为制定卫生干预政策提供依据。
■这项研究使用EQ-5D-3L量表评估了与高血压相关的中风的老年患者的HRQoL。采用各种分析方法来检查影响患者生活质量的因素。单变量分析,Tobit回归，随机森林,应用XGBoost模型分析患者的HRQoL。此外，来解释机器学习的结果，使用SHAP方法。这种方法涉及评估每个特征的重要性，检查每个特征对单个样本预测结果的影响，并确定个体特征对整体预测的影响。
■研究发现，老年高血压卒中患者的健康效用值中位数为0.427，四分位数间范围为0.186至0.745。Tobit回归模型的结果，随机森林,与XGBoost模型进行了比较。模型评估结果表明，机器学习模型和Tobit回归模型的性能差异不大。XGBoost模型的性能相对于随机森林模型略好。强烈影响患者健康效用价值的因素包括BMI，社会活动,吸烟,教育水平,酒精消费,城市/农村住宅，年收入,身体活动水平,晚上的睡眠时间。
■高血压卒中患者与健康相关的生活质量受多种因素的影响。通过修改这些因素并调整生活方式，可以对健康相关的生活质量产生积极影响。保持健康的体重，社会活跃，戒烟,改善生活条件,建议增加体力活动水平和获得足够的睡眠。需要根据具体情况和医疗建议为每个人制定生活方式的改变。
■这项研究使用EQ-5D-3L量表评估了与高血压相关的中风的老年患者的HRQoL。采用各种分析方法来检查影响患者生活质量的因素。单变量分析,Tobit回归，随机森林,应用XGBoost模型分析患者的HRQoL。此外，来解释机器学习的结果，使用SHAP方法。这种方法涉及评估每个特征的重要性，检查每个特征对单个样本预测结果的影响，并确定个体特征对整体预测的影响。
■研究发现，老年高血压卒中患者的健康效用值中位数为0.427，四分位数间范围为0.186至0.745。Tobit回归模型的结果，随机森林,与XGBoost模型进行了比较。模型评估结果表明，机器学习模型和Tobit回归模型的性能差异不大。XGBoost模型的性能相对于随机森林模型略好。强烈影响患者健康效用价值的因素包括BMI，社会活动,吸烟,教育水平,酒精消费,城市/农村住宅，年收入,身体活动水平,晚上的睡眠时间。
■高血压卒中患者与健康相关的生活质量受多种因素的影响。通过修改这些因素并调整生活方式，可以对健康相关的生活质量产生积极影响。保持健康的体重，社会活跃，戒烟,改善生活条件,建议增加体力活动水平和获得足够的睡眠。需要根据具体情况和医疗建议为每个人制定生活方式的改变。
