Do Temporal Changes in Facial Expressions Help Identify Patients at Risk of Deterioration in Hospital Wards? A Post Hoc Analysis of the Visual Early Warning Score Study.
Do Temporal Changes in Facial Expressions Help Identify Patients at Risk of Deterioration in Hospital Wards? A Post Hoc Analysis of the Visual Early Warning Score Study.
复制标题
面部表情的时间变化是否有助于识别医院病房中面临病情恶化风险的患者？
DOI：
10.1097/cce.0000000000000115
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发表时间：
2020
影响因子：
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通讯作者：
Madrigal-Garcia MI
中科院分区：
文献类型：
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作者：
Madrigal-Garcia MI
目的：探讨面部表情的时间序列分析和Shannon信息熵对综合医院病房患者急性临床恶化的预测作用。设计：前瞻性观察可行性研究（视觉早期预警评分研究）的事后分析。环境：社区医院普通病房病人。患者：34例患者有临床恶化风险。干预措施：一名训练有素的心理学家使用面部动作编码系统对每名进入视觉早期预警评分研究数据库的患者的3分钟视频（153,000帧）进行分析，以测量面部表情作为动作单位。测量和主要结果：在34个3分钟的研究期间，分析了三千六百八十八个行动单位。考虑的动作单位时间变量包括开始、顶点、偏移和总持续时间。采用广义线性回归模型和时间序列分析。根据面部表情的频率和特征库计算Shannon信息熵（Hn）和多样性（Dn）。随后入住重症监护室的患者与未入住ICU的患者相比，出现频率降低（95% CI移动平均值：9.5-10.9 vs 26.1-28.9）， Shannon信息熵（0.30±0.06 vs 0.26±0.05;p= 0.019）和多样性指数（1.36±0.08 vs 1.30±0.07;p= 0.020）更高，动作单位反应时间延长（23.5 vs 9.4 s）。在时间序列分析中，每个窗口确定的行动单元数预测进入重症监护的曲线下面积为0.88。国家预警评分单独、Hn单独、国家预警评分+ Hn、国家预警评分+ Hn + Dn的曲线下面积分别为0.53、0.75、0.76、0.81。结论：入住重症监护室的患者单位时间内面部表情的数量减少，面部表情的多样性增加。
Objectives:To determine whether time-series analysis and Shannon information entropy of facial expressions predict acute clinical deterioration in patients on general hospital wards.Design:Post hoc analysis of a prospective observational feasibility study (Visual Early Warning Score study).Setting:General ward patients in a community hospital.Patients:Thirty-four patients at risk of clinical deterioration.Interventions:A 3-minute video (153,000 frames) for each of the patients enrolled into the Visual Early Warning Score study database was analyzed by a trained psychologist for facial expressions measured as action units using the Facial Action Coding System.Measurements and Main Results:Three-thousand six-hundred eighty-eight action unit were analyzed over the 34 3-minute study periods. The action unit time variables considered were onset, apex, offset, and total time duration. A generalized linear regression model and time-series analyses were performed. Shannon information entropy (Hn) and diversity (Dn) were calculated from the frequency and repertoire of facial expressions. Patients subsequently admitted to critical care displayed a reduced frequency rate (95% CI moving average of the mean: 9.5–10.9 vs 26.1–28.9 in those not admitted), a higher Shannon information entropy (0.30±0.06 vs 0.26±0.05; p= 0.019) and diversity index (1.36±0.08 vs 1.30±0.07; p= 0.020) and a prolonged action unit reaction time (23.5 vs 9.4 s) compared with patients not admitted to ICU. The number of action unit identified per window within the time-series analysis predicted admission to critical care with an area under the curve of 0.88. The area under the curve for National Early Warning Score alone, Hn alone, National Early Warning Score plus Hn, and National Early Warning Score plus Hn plus Dn were 0.53, 0.75, 0.76, and 0.81, respectively.Conclusions:Patients who will be admitted to intensive care have a decrease in the number of facial expressions per unit of time and an increase in their diversity.