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步行视频信息与一般心血管疾病之间的关联:一项前性横截面研究
Juntong Zeng1,2,3, Shen Lin1,2,3,4,5, Zhigang Li6,7
1National Clinical Research Center of Cardiovascular Diseases, National Center for Cardiovascular Diseases, Fuwai Hospital, No. 167 North Lishi Road, Xicheng District, Beijing 100037, People's Republic of China.
European heart journal. Digital health
|July 31, 2024
概括
步行模式的视频分析可以帮助比传统方法更早地发现心血管疾病 (CVD). 这项研究表明,步态信息可以改善心血管疾病的预测,特别是对外周动脉疾病和心力衰竭.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 数字健康数字健康
背景情况:
- 传统的心血管疾病 (CVD) 检测方法在早期诊断方面可能存在局限性.
- 异常的步行模式与各种病理状况有关,可以通过步行视频分析持续监测.
研究的目的:
- 调查非接触式,基于视频的步态信息与一般心血管疾病状态之间的关联.
- 在与传统的临床CVD变量相结合时,评估步态数据的增量预测值.
主要方法:
- 一项前性,横截面研究包括352名接受心血管疾病评估的参与者.
- 使用Kinect摄像头捕捉了步行视频,并提取了步行特征.
- 步行特征与复合和单个心血管疾病成分相关,包括冠状动脉疾病,外周动脉疾病,心力衰竭和脑血管事件.
主要成果:
- 步态特征模型显示,与基线临床模型 (AUC 0.717) 相比,复合心血管疾病 (AUC 0.753) 的预测得到了改善.
- 将步态信息与临床变量相结合,进一步增强了心血管疾病预测 (AUC 0.764).
- 步行特征与外围动脉疾病 (AUC 0.752) 和心力衰竭 (AUC 0.733) 以及心血管疾病风险因素有显著关联.
结论:
- 无接触,基于视频的步态分析是评估和预测一般心血管疾病状况的宝贵工具.
- 步行视频分析显示,在日常生活中持续的家庭心血管疾病监测是有前途的.
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