中老年脑卒中患者康复期生活质量的影响因素及列线图预测
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章艳(1987—),女,江苏建湖人,硕士研究生在读,研究方向为脑卒中早期康复;

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国家自然科学基金“选择性多聚腺苷酸化关联的遗传变异对肺腺癌发病风险的影响及机制研究”(82273715);江苏省研究生科研与实践创新计划项目“个性化干预治疗肥胖型2型糖尿病的路径和疗效评价”(SJCX22_1640)


Analysis of the influencing factors of quality of life during the rehabilitation period of middle⁃aged and elderly stroke patients and construction of prediction model
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    摘要:

    对建湖县人民医院康复期脑卒中患者进行问卷调查和分析。结果显示,家庭月收入 ≥3 000元、有系统康复训练、社会支持得分越高、有兴趣爱好、亲属陪护、被探望次数≥2次、发病后救治时间<3小时可提高3.65~9.95倍的中老年脑卒中患者康复期生活质量。与完全残疾相比,重度、中度和轻度残疾可提高患者生活质量。而有伴随疾病、焦虑或抑郁症状、发病后救治时间≥3小时可降低患者生活质量。研究构建的列线图模型预测总分值高达280分。当这些影响因素预测的分值达到164分及以上,患者可获得高生活质量的概率为90%及以上,列线图模型内部验证等结果显示其预测能力良好。因此,建议医院辅以预测模型早期识别和干预可控的影响因素,制定个性化的康复方案,以提高患者的生活质量。

    Abstract:

    Our study surveyed stroke patients during rehabilitation in Jianhu County People’s Hospital. The results showed that patients whose family monthly income is ≥3 000 yuan,having systematic rehabilitation training,higher social support score,have hobbies,family care,family visited times ≥2,and treatment time < 3 h after the onset of stroke have increased by 3.65-9.95 times of high quality of life in the rehabilitation period of middle-aged and elderly stroke patients compared with total disability,severe,moderate,and mild disability increased patients’quality of life. While the proportion of patients with other concomitant diseases,psychological symptoms of anxiety or depression,and treatment time ≥3 h after the onset decreased the quality of life. The nomogram model constructed in our study predicted a total score of up to 280 points. When the predicted scores of these factors reached 164 and above,the incidence of patients with high quality of life reached 90% and above,and the verification results of the nomogram model suggested good accuracy and precision. Therefore, we suggest that hospitals should identify and intervene early on controllable influencing factors supplemented by predictive models and develop personalized rehabilitation programs to improve patients’quality of life.

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章艳,闫国栋,蔡婉丽,褚敏捷,肖静.中老年脑卒中患者康复期生活质量的影响因素及列线图预测[J].南京医科大学学报(社会科学版),2023,(4):317~323

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  • 收稿日期:2023-07-17
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  • 在线发布日期: 2023-09-04
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