Department of Nursing, Renmin Hospital of Wuhan University, Wuhan, China.
Department of Coronary Heart Disease, Renmin Hospital of Wuhan University, Wuhan, China.
Front Public Health. 2024 Jan 12;11:1271294. doi: 10.3389/fpubh.2023.1271294. eCollection 2023.
This study aims to develop and validate a prediction model for evaluating the social participation in the community middle-aged and older adult stroke survivors.
The predictive model is based on data from the China Health and Retirement Longitudinal Study (CHARLS), which focused on individuals aged 45 years or older. The study utilized subjects from the CHARLS 2015 and 2018 wave, eighteen factors including socio-demographic variables, behavioral and health status, mental health parameters, were analyzed in this study. To ensure the reliability of the model, the study cohort was randomly split into a training set (70%) and a validation set (30%). The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was used to identify the most effective predictors of the model through a 10-fold cross-validation. The logistic regression model was employed to investigate the factors associated with social participation in stroke patients. A nomogram was constructed to develop a prediction model. Calibration curves were used to assess the accuracy of the nomogram model. The model's performance was evaluated using the area under the curve (AUC) and decision curve analysis (DCA).
A total of 1,239 subjects with stroke from the CHARLS database collected in 2013 and 2015 wave were eligible in the final analysis. Out of these, 539 (43.5%) subjects had social participation. The model considered nineteen factors, the LASSO regression selected eleven factors, including age, gender, residence type, education level, pension, insurance, financial dependence, physical function (PF), self-reported healthy,cognition and satisfaction in the prediction model. These factors were used to construct the nomogram model, which showed a certain extent good concordance and accuracy. The AUC values of training and internal validation sets were 0.669 (95%CI 0.631-0.707) and 0.635 (95% CI 0.573-0.698), respectively. Hosmer-Lemeshow test values were = 0.588 and = 0.563. Calibration curves showed agreement between the nomogram model and actual observations. ROC and DCA indicated that the nomogram had predictive performance.
The nomogram constructed in this study can be used to evaluate the probability of social participation in middle-aged individuals and identify those who may have low social participation after experiencing a stroke.
本研究旨在开发和验证一种评估社区中年和老年卒中幸存者社会参与的预测模型。
预测模型基于中国健康与养老追踪调查（CHARLS）的数据，该调查重点关注 45 岁或以上的个体。本研究利用 CHARLS 2015 年和 2018 年的数据，分析了 18 个因素，包括社会人口统计学变量、行为和健康状况、心理健康参数。为确保模型的可靠性，研究队列被随机分为训练集（70%）和验证集（30%）。通过 10 折交叉验证，使用最小绝对收缩和选择算子（LASSO）回归分析确定模型的最有效预测因子。采用 logistic 回归模型调查与卒中患者社会参与相关的因素。建立列线图以开发预测模型。校准曲线用于评估列线图模型的准确性。使用曲线下面积（AUC）和决策曲线分析（DCA）评估模型的性能。
最终分析纳入了 CHARLS 数据库中 2013 年和 2015 年波次收集的 1239 名卒中患者，其中 539 名（43.5%）患者有社会参与。该模型考虑了 19 个因素，LASSO 回归选择了 11 个因素，包括年龄、性别、居住类型、教育水平、养老金、保险、财务依赖、身体功能（PF）、自我报告的健康状况、认知和满意度。这些因素用于构建列线图模型，该模型显示出一定程度的良好一致性和准确性。训练集和内部验证集的 AUC 值分别为 0.669（95%CI 0.631-0.707）和 0.635（95%CI 0.573-0.698）。Hosmer-Lemeshow 检验值分别为 = 0.588 和 = 0.563。校准曲线显示列线图模型与实际观察结果之间存在一致性。ROC 和 DCA 表明列线图具有预测性能。
本研究构建的列线图可用于评估中年个体社会参与的概率，并识别经历卒中后可能社会参与度较低的个体。
