Zeng Juntong, Lin Shen, Li Zhigang, Sun Runchen, Yu Xuexin, Lian Xiaocong, Zhao Yan, Ji Xiangyang, Zheng Zhe
National 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.
State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Fuwai Hospital, No. 167 North Lishi Road, Xicheng District, Beijing 100037, People's Republic of China.
Eur Heart J Digit Health. 2024 May 20;5(4):469-480. doi: 10.1093/ehjdh/ztae031. eCollection 2024 Jul.
Cardiovascular disease (CVD) may not be detected in time with conventional clinical approaches. Abnormal gait patterns have been associated with pathological conditions and can be monitored continuously by gait video. We aim to test the association between non-contact, video-based gait information and general CVD status.
Individuals undergoing confirmatory CVD evaluation were included in a prospective, cross-sectional study. Gait videos were recorded with a Kinect camera. Gait features were extracted from gait videos to correlate with the composite and individual components of CVD, including coronary artery disease, peripheral artery disease, heart failure, and cerebrovascular events. The incremental value of incorporating gait information with traditional CVD clinical variables was also evaluated. Three hundred fifty-two participants were included in the final analysis [mean (standard deviation) age, 59.4 (9.8) years; 25.3% were female]. Compared with the baseline clinical variable model [area under the receiver operating curve (AUC) 0.717, (0.690-0.743)], the gait feature model demonstrated statistically better performance [AUC 0.753, (0.726-0.780)] in predicting the composite CVD, with further incremental value when incorporated with the clinical variables [AUC 0.764, (0.741-0.786)]. Notably, gait features exhibited varied association with different CVD component conditions, especially for peripheral artery disease [AUC 0.752, (0.728-0.775)] and heart failure [0.733, (0.707-0.758)]. Additional analyses also revealed association of gait information with CVD risk factors and the established CVD risk score.
We demonstrated the association and predictive value of non-contact, video-based gait information for general CVD status. Further studies for gait video-based daily living CVD monitoring are promising.
传统临床方法可能无法及时检测出心血管疾病（CVD）。异常步态模式与病理状况相关，且可通过步态视频进行连续监测。我们旨在测试基于视频的非接触式步态信息与一般CVD状态之间的关联。
接受CVD确诊评估的个体纳入一项前瞻性横断面研究。使用Kinect摄像头记录步态视频。从步态视频中提取步态特征，以与CVD的综合及个体组成部分相关联，包括冠状动脉疾病、外周动脉疾病、心力衰竭和脑血管事件。还评估了将步态信息与传统CVD临床变量相结合的增量价值。最终分析纳入了352名参与者[平均（标准差）年龄，59.4（9.8）岁；25.3%为女性]。与基线临床变量模型[受试者操作特征曲线下面积（AUC）0.717，（0.690 - 0.743）]相比，步态特征模型在预测综合CVD方面表现出统计学上更好的性能[AUC 0.753，（0.726 - 0.780）]，与临床变量结合时具有进一步的增量价值[AUC 0.764，（0.741 - 0.786）]。值得注意的是，步态特征与不同的CVD组成状况表现出不同的关联，特别是在外周动脉疾病[AUC 0.752，（0.728 - 0.775）]和心力衰竭[0.733,（0.707 - 0.758）]方面。额外分析还揭示了步态信息与CVD危险因素及既定CVD风险评分之间的关联。
我们证明了基于视频的非接触式步态信息与一般CVD状态之间的关联及预测价值。基于步态视频的日常生活CVD监测的进一步研究前景广阔。
