慢性心力衰竭患者社会衰弱现状及其影响因素可解释性分析研究

Interpretable Analysis of Influencing Factors and the Current State of Social Frailty in Patients with Chronic Heart Failure

背景心力衰竭(简称心衰)合并衰弱的综合管理对衰弱评估提出了多维需求,然而作为心衰患者不良健康结局的增量预测因子,衰弱的社会维度尚未得到充分关注。目的分析慢性心衰患者社会衰弱现状及其影响因素。方法采用便利抽样法,选取2022年9月—2023年7月在南京医科大学第一附属医院心血管内科住院治疗的慢性心衰患者作为调查对象。采用一般资料调查表、社会衰弱量表(HALFT量表,评估社会衰弱)、中文简化版孤独感量表(评估孤独感)、简化版双向社会支持量表(评估社会支持)、个人掌控感量表(PMS,评估个人掌控感)、患者健康问卷(PHQ-9,评估抑郁情况)进行调查。采用单因素分析、支持向量机-特征递归消除进行特征筛选,构建支持向量机分类模型,引入SHAP值进行影响因素分析。结果本研究筛选患者228例,其中8例拒绝填写,共计发放问卷220份,回收有效问卷213份,有效问卷回收率为96.81%。慢性心衰患者中社会衰弱前期及社会衰弱人数占比分别为46.0%(98/213)、17.8%(38/213)。不同社会衰弱情况的慢性心衰患者文化程度、居住地、工作状态、疾病经济负担、个人月收入、病程、运动习惯、就医满意度、交通出行、孤独感量表得分、简化版双向社会支持量表得分、PMS得分、PHQ-9得分比较,差异有统计学意义(P<0.05)。支持向量机-特征递归消除模型性能最佳时,采用最优特征子集(以3∶7拆分样本作为测试集和训练集)构建分类模型后进行SHAP值可解释性分析,模型预测准确率在训练集和测试集中分别为0.73和0.63,此时特征重要性排序及影响方向从高到低为无运动习惯(+)、个人掌控感(-)、疾病经济负担重(+)、双向社会支持(-)、抑郁(+)、孤独感(+)、无业(+)。结论慢性心衰患者中社会衰弱占60%以上,医护人员应对患者的社会功能予以重视,关注患者缺失的社会资源属性及其上游因素,通过强化外部支持系统、培养患者内在信念、克服负性情绪体验,统筹制订管理方案,整合医疗资源实施干预延缓或逆转患者社会衰弱进程,改善其预后及生活质量。

Background The comprehensive management of heart failure in conjunction with frailty necessitates a multidimensional approach to frailty assessment.However,the social frailty,despite being an incremental predictor of negative health outcomes in heart failure patients,has not been adequately addressed.Objective To understand the current status of social frailty in patients with chronic heart failure and analyze its influencing factors.Methods From September 2022 to July 2023,convenience sampling was used to select patients with chronic heart failure from the First Affiliated Hospital with Nanjing Medical University as the research objects,the general information questionnaire,the HALFT Scale,the Loneliness Scale,the Brief 2-Way Social Support Scale,Personal Mastery Scale,and the Patient Health Questionnaire were used to investigate.Univariate analysis and support vector machine-feature recursive elimination were used to filter the feature,SVM classification model was constructed,and SHAP value was introduced to analyze the influencing factors.Results A total of 228 patients were screened in this study,of which 8 patients refused to fill in.A total of 220 questionnaires were distributed and 213 valid questionnaires were returned,with an effective recovery rate of 96.81%.The proportion of pre-social frailty and social frailty in patients with chronic heart failure was 46.0%(98/213)and 17.8%(38/213),respectively.Statistically significant differences were observed among chronic heart failure patients with different degrees of social frailty in terms of education level,place of residence,working status,economic burden of disease,personal monthly income,course of disease,exercise habits,medical satisfaction,traffic,the UCLA Loneliness Scale score,the Brief 2-Way Social Support Scale score,the PMS score,and the PHQ-9 score.When the SVM-RFE model play the best performance,the optimal feature subset was used to construct the SVM classification prediction model and perform SHAP interpretability analysis.The accuracy of the model was 0.73 in the training set and 0.63 in the test set,respectively.At this time,the ranking of feature importance from high to low was no exercise habit(+),personal mastery(-),heavy economic burden of disease(+),2-way social support(-),depression(+),loneliness(+),unemployment(+).Conclusion Patients with chronic heart failure experiencing severe social frailty.Healthcare providers should prioritize identifying and addressing the resource deficits of patients and the underlying factors contributing to social frailty.Targeted interventions should be implemented to mitigate social frailty in patients with heart failure by enhancing external support systems,fostering positive beliefs,addressing negative emotional experiences,developing comprehensive management plans,coordinating medical resources,and implementing strategies to delay or reverse social frailty progression.These interventions aim to enhance the prognosis and quality of life for patients with heart failure.