Madrigal-Garcia, MI; Rodrigues, M; Shenfield, A; Singer, M; Moreno-Cuesta, J; (2018) What Faces Reveal: A Novel Method to Identify Patients at Risk of Deterioration Using Facial Expressions. Critical Care Medicine , 46 (7) pp. 1057-1062. 10.1097/CCM.0000000000003128.

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Abstract

OBJECTIVES: To identify facial expressions occurring in patients at risk of deterioration in hospital wards. DESIGN: Prospective observational feasibility study. SETTING: General ward patients in a London Community Hospital, United Kingdom. PATIENTS: Thirty-four patients at risk of clinical deterioration. INTERVENTIONS: A 5-minute video (25 frames/s; 7,500 images) was recorded, encrypted, and subsequently analyzed for action units by a trained facial action coding system psychologist blinded to outcome. MEASUREMENTS AND MAIN RESULTS: Action units of the upper face, head position, eyes position, lips and jaw position, and lower face were analyzed in conjunction with clinical measures collected within the National Early Warning Score. The most frequently detected action units were action unit 43 (73%) for upper face, action unit 51 (11.7%) for head position, action unit 62 (5.8%) for eyes position, action unit 25 (44.1%) for lips and jaw, and action unit 15 (67.6%) for lower face. The presence of certain combined face displays was increased in patients requiring admission to intensive care, namely, action units 43 + 15 + 25 (face display 1, p < 0.013), action units 43 + 15 + 51/52 (face display 2, p < 0.003), and action units 43 + 15 + 51 + 25 (face display 3, p < 0.002). Having face display 1, face display 2, and face display 3 increased the risk of being admitted to intensive care eight-fold, 18-fold, and as a sure event, respectively. A logistic regression model with face display 1, face display 2, face display 3, and National Early Warning Score as independent covariates described admission to intensive care with an average concordance statistic (C-index) of 0.71 (p = 0.009). CONCLUSIONS: Patterned facial expressions can be identified in deteriorating general ward patients. This tool may potentially augment risk prediction of current scoring systems.

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