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人口统计学和临床特征如何促进中风后吞咽困难的恢复?

How demographic and clinical characteristics contribute to the recovery of post-stroke dysphagia?

作者信息

Xi Xiao, Li Heping, Wang Liugen, Yin Xiran, Zeng Jing, Song Yunyun, Zhai Yali, Zeng Xi, Zhao Xingna

机构信息

Department of Rehabilitation Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

出版信息

Medicine (Baltimore). 2021 Jan 29;100(4):e24477. doi: 10.1097/MD.0000000000024477.

Abstract

According to the analysis to find out how demographic and clinical characteristics influent the dysphagia outcome after stroke, furthermore, giving some insights to clinical treatment.One hundred eighty post-stroke dysphagia (PSD) patients were enrolled in this retrospective study at the stroke rehabilitation department. The outcome measurements are beside water swallow test at discharge and length of stay at hospital. Twenty-five demographic and clinical variables were collected for this study. Logistic regression and multilinear regression were utilized to estimate models to identify the risk and protect predictors of PSD outcome.Mouth-opening degree, drooling severity scale (DSS) level, mini-mental state exam (MMSE) level, Barthel index and Berg balance scale were significant different between recovered and unrecovered group. Type of stroke, MMSE degree, DSS and hemoglobin level shown significant predictive value for PSD outcome in logistic regression. In addition, obstructive sleep apnea (OSA) and DSS degree were important risk factors for PSD outcome. Gender, body mass index, drinking, hypertension, recurrent stroke, water swallow test level on admission, Berg balance scale, DSS and days between onset to admission shown significant predictive value for length of stay of PSD patients.PSD outcome was influenced by type of stroke, MMSE degree, DSS and hemoglobin level significantly and obstructive sleep apnea act as an important risk role for PSD recovery.

摘要

通过分析找出人口统计学和临床特征如何影响中风后吞咽困难的结果,并为临床治疗提供一些见解。本回顾性研究纳入了180例中风康复科的中风后吞咽困难(PSD)患者。结局指标包括出院时的饮水试验和住院时间。本研究收集了25个人口统计学和临床变量。采用逻辑回归和多元线性回归估计模型,以识别PSD结局的风险和保护预测因素。恢复组和未恢复组之间的张口度、流涎严重程度量表(DSS)水平、简易精神状态检查(MMSE)水平、Barthel指数和Berg平衡量表有显著差异。在逻辑回归中,中风类型、MMSE程度、DSS和血红蛋白水平对PSD结局显示出显著的预测价值。此外,阻塞性睡眠呼吸暂停(OSA)和DSS程度是PSD结局的重要危险因素。性别、体重指数、饮酒、高血压、复发性中风、入院时的饮水试验水平、Berg平衡量表、DSS以及发病至入院之间的天数对PSD患者的住院时间显示出显著的预测价值。PSD结局受中风类型、MMSE程度、DSS和血红蛋白水平的显著影响,阻塞性睡眠呼吸暂停对PSD恢复起着重要的风险作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd8d/7850691/abd817c9cfca/medi-100-e24477-g001.jpg

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