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Quantification of facial cues for acute illness: a systematic scoping review
Iris C Cramer1,2, Eline G M Cox3, Jip W T M de Kok4,5
1Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Importance:
The patient's face provides healthcare professionals with important information about the patient's general appearance and clinical condition.
Objective:
The primary aim of this review is to identify patients' facial cues that healthcare providers can use at the bedside to monitor the clinical condition of acutely ill patients.
Evidence Review:
Studies about facial cues for acute illness were systematically searched in PubMed, Embase, Cochrane, and Cumulative Index to Nursing & Allied Health (CINAHL) databases. Studies on vital signs, pain, psychiatric illnesses, animal studies, qualitative studies, case reports, and systematic reviews were excluded. Acute illness was defined as any life-threatening condition or condition that required immediate intervention to prevent serious morbidity, permanent disability, or mortality. An overview of all identified facial cues was created.
Findings:
In total, 35 different facial cues were identified in 13 studies. A total of 21 were related to facial appearance, with the most frequently reported cues being closed eyes (2 studies), pale lips (2 studies), parted lips (3 studies), droopy mouth (3 studies), and paler skin tone (2 studies). In addition, 14 facial expression features were identified, characterized primarily by more sad, less happy, and less surprised. Most cues have only been described in a single study without external validation, limiting the generalizability of definitions of these cues and their clinical applicability.
Conclusions And Relevance:
This systematic scoping review identified 35 facial cues associated with acute illness in patients in the hospital, highlighting the potential of facial observation to enhance clinical assessments. However, the lack of standardization limits applicability in healthcare. Future research should refine the setting of acute illness, develop diverse datasets, and validate the predictive value of facial cues across various populations.
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