PDF(Pubmed)
Abstract：
After stroke rehabilitation, patients need to reintegrate back into their daily life, workplace and society. Reintegration involves complex processes depending on age, sex, stroke severity, cognitive, physical, as well as socioeconomic factors that impact long-term outcomes post-stroke. Moreover, post-stroke quality of life can be impacted by social risks of inadequate family, social, economic, housing and other supports needed by the patients. Social risks and barriers to successful reintegration are poorly understood yet critical for informing clinical or social interventions. Therefore, the aim of this work is to predict social risk at rehabilitation discharge using sociodemographic and clinical variables at rehabilitation admission and identify factors that contribute to this risk. A Gradient Boosting modelling methodology based on decision trees was applied to a Catalan 217-patient cohort of mostly young (mean age 52.7), male (66.4%), ischemic stroke survivors. The modelling task was to predict an individual\'s social risk upon discharge from rehabilitation based on 16 different demographic, diagnostic and social risk variables (family support, social support, economic status, cohabitation and home accessibility at admission). To correct for imbalance in patient sample numbers with high and low-risk levels (prediction target), five different datasets were prepared by varying the data subsampling methodology. For each of the five datasets a prediction model was trained and the analysis involves a comparison across these models. The training and validation results indicated that the models corrected for prediction target imbalance have similarly good performance (AUC 0.831-0.843) and validation (AUC 0.881 - 0.909). Furthermore, predictor variable importance ranked social support and economic status as the most important variables with the greatest contribution to social risk prediction, however, sex and age had a lesser, but still important, contribution. Due to the complex and multifactorial nature of social risk, factors in combination, including social support and economic status, drive social risk for individuals.
摘要：
中风康复后，患者需要重新融入日常生活，工作场所和社会。重新整合涉及复杂的过程，取决于年龄，性别,中风严重程度,认知，物理，以及影响卒中后长期结局的社会经济因素.此外，家庭不健全的社会风险会影响脑卒中后生活质量,社会,经济,患者所需的住房和其他支持。人们对成功重返社会的社会风险和障碍知之甚少，但对于告知临床或社会干预措施至关重要。因此,这项工作的目的是使用康复入院时的社会人口统计学和临床变量来预测康复出院时的社会风险，并确定导致这种风险的因素。基于决策树的梯度提升建模方法应用于加泰罗尼亚217名患者的队列，这些患者大多是年轻人（平均年龄52.7岁）。男性(66.4%)，缺血性卒中幸存者.建模任务是根据16种不同的人口统计学来预测个人康复出院后的社会风险，诊断和社会风险变量(家庭支持，社会支持,经济地位,入院时的同居和家庭可访问性)。为了纠正高风险和低风险水平（预测目标）的患者样本数量不平衡，通过改变数据二次抽样方法，准备了五个不同的数据集。对于五个数据集中的每一个,训练预测模型,并且分析涉及跨这些模型的比较。训练和验证结果表明，针对预测目标失衡校正的模型具有类似的良好性能（AUC0.831-0.843）和验证（AUC0.881-0.909）。此外，预测变量重要性将社会支持和经济地位列为对社会风险预测贡献最大的最重要变量，然而,性别和年龄较小，但仍然很重要,contribution.由于社会风险的复杂性和多因素性，综合因素，包括社会支持和经济地位，为个人带来社会风险。
