Decreased facial expression variability in patients with serious cardiopulmonary disease in the emergency care setting

Abstract

Background and objective The hypothesis of the present work derives from clinical experience that suggests that patients who are more ill have less facial expression variability in response to emotional cues. Methods Prospective study of diagnostic accuracy from a convenience sample of adult patients with dyspnoea and chest pain in an emergency department. Patients viewed three stimulus slides on a laptop computer that were intended to evoke a change in facial affect. The computer simultaneously video recorded patients' facial expressions. Videos were examined by two independent blinded observers who analysed patients' facial expressions using the Facial Action Coding System (FACS). Patients were followed for predefined serious cardiopulmonary diagnosis (Disease+) within 14 days (acute coronary syndrome, pulmonary embolism, pneumonia, aortic or oesophageal disasters or new cancer). The main analysis compared total FACS scores, and action units of smile, surprise and frown between Disease+ and Disease−. Results Of 50 patients, 8 (16%) were Disease+. The two observers had 92% exact agreement on the FACS score from the first stimulus slide. During stimulus slide 1, the median of all FACS values from Disease+ patients was 3.4 (1st-3rd quartiles 1-6), significantly less than the median of 7 (3-14) from D−patients ( p=0.019, Mann-Whitney U). Expression of surprise had the largest difference between Disease+ and Disease−(area under the receiver operating characteristic curve 0.75, 95% CI 0.52 to 0.87). Conclusions With a single visual stimulus, patients with serious cardiopulmonary diseases lacked facial expression variability and surprise affect. Our preliminary findings suggest that stimulus-evoked facial expressions from emergency department patients with cardiopulmonary symptoms might be a useful component of gestalt pretest probability assessment.

Role of physician perception of patient smile on pretest probability assessment for acute pulmonary embolism

“…With respect to the Noldus output, this device is not designed to differentiate authentic versus feigned emotions. This may explain why our prior study, which used manual facial affect coding (FACS) to analyse patients' facial expressions, did not find patients with cardiopulmonary emergencies to express more happy expressions than patients without an emergent diagnosis 8. However, only two patients in that study had PE+.…”

“…In daily practice, many clinicians use the phrase ‘He/she looks sick’ or ‘He/she does not look sick’ to describe their patients, yet this fundamental mode of deciding and then communicating the presence of acute illness has almost no published evidence to support its use 8. Based primarily upon experience, we believe patient affect may be one factor from the physical examination that contributes to physicians’ assessment of sickness.…”

“…In a former study, we analysed facial affect of ED patients being tested for PE, using the gold standard manual coding system to derive emotional states (the facial action coding system (FACS)7), and found that patients who had any serious cardiopulmonary diagnosis had less variability in affect and more negative emotions expressed, including absence of smile 8. Others have interpreted ‘sick’ facial expressions as conveying negative emotions, including disgust, anger and contempt 9.…”

Impact of Patient Affect on Physician Estimate of Probability of Serious Illness and Test Ordering

“…Understanding how facial affect may impact the way clinicians formulate their probability estimates and make test-ordering decisions is important because sicker patients have been found to manifest different affect than those who were less sick. 25 In particular, facial expressions of disgust and anger appear to represent more serious illness. 25 Moreover, in an earlier study, we found that, among patients undergoing CTPA scanning, physician recall of patients smiling was surprisingly more common among patients with PE than among patients without PE.…”

Making the black box more useful

“…Gestalt allows clinicians to incorporate features that clinicians always use to make decisions, but may never be part of a rule. Examples might include the patient's demeanour and affect, results of recent diagnostic studies, presence of an alternative diagnosis, or patient-specific conditions such as his or her social situation, concomitant psychiatric conditions, alcohol intoxication or drug dependencies 2 3. And, its flexibility leads to gestalt's fallibility; it remains a black-box process that can change with each patient and escapes transparency that allows root cause analysis in case of failure.…”