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在中风患者中开发和验证睡眠障碍的诺米克图

Yinyin Fan¹, Xueni Yang¹, Meng Sun¹

¹School of Nursing and Rehabilitation, Nantong University, 19th Qixiu Road, Nantong 226001, China; Department of Nursing, Affiliated Hospital of Nantong University, 20th Xisi Road, Nantong 226001, China.

Clinical neurology and neurosurgery

|October 24, 2024

概括

这项研究开发了一种名录,用于预测中风患者的睡眠障碍,使用住所,输液时间,焦虑,抑郁,个性和疲劳等关键因素. 该工具有助于识别高风险个体,以便及时介入.

科学领域:

神经学神经学
睡眠医学睡眠医学
精神病学是一个精神病学.

背景情况:

睡眠障碍在中风患者中普遍存在,影响了康复和生活质量.
早期识别高风险个体对于有效管理至关重要.
预测工具可以帮助临床医生分层中风患者进行睡眠障碍查.

研究的目的:

开发和验证中风患者睡眠障碍的预测模型.
确定与这一群体睡眠障碍相关的关键因素.
为临床风险评估创建一个用户友好的工具.

主要方法:

使用450名中风患者的数据构建了一个名图模型.
拉索回归和随机森林方法确定了重要的预测因素.
用校准曲线,ROC曲线,内部验证和决策曲线分析 (DCA) 来评估模型性能.

主要成果:

关键预测因素包括居住地,平均每日输液时间,医院焦虑和抑郁量表 (HADS),D型人格量表-14 (DS14) 和疲劳严重程度量表 (FSS).
该模型在验证集中显示出强烈的歧视,AUC为0.903.
校准曲线显示出良好的一致性,DCA表明了跨广泛值的临床效用.

结论:

一个经过验证的诺莫格拉姆模型有效地预测了中风患者的睡眠障碍.
该模型帮助医疗保健专业人员识别和查高风险个体.
这种工具可以促进及时干预,以改善患者的治疗结果.

关键词:

机器学习是机器学习.这个名字叫做Nomogram.睡眠障碍睡眠障碍睡眠障碍一次性中风,中风.

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