What Faces Reveal: A Novel Method to Identify Patients at Risk of Deterioration Using Facial Expressions.

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.

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.

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.