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.