Problems of video-based pain detection in patients with dementia: a road map to an interdisciplinary solution.

BACKGROUND: Given the unreliable self-report in patients with dementia, pain assessment should also rely on the observation of pain behaviors, such as facial expressions. Ideal observers should be well trained and should observe the patient continuously in order to pick up any pain-indicative behavior; which are requisitions beyond realistic possibilities of pain care. Therefore, the need for video-based pain detection systems has been repeatedly voiced. Such systems would allow for constant monitoring of pain behaviors and thereby allow for a timely adjustment of pain management in these fragile patients, who are often undertreated for pain. METHODS: In this road map paper we describe an interdisciplinary approach to develop such a video-based pain detection system. The development starts with the selection of appropriate video material of people in pain as well as the development of technical methods to capture their faces. Furthermore, single facial motions are automatically extracted according to an international coding system. Computer algorithms are trained to detect the combination and timing of those motions, which are pain-indicative. RESULTS/CONCLUSION: We hope to encourage colleagues to join forces and to inform end-users about an imminent solution of a pressing pain-care problem. For the near future, implementation of such systems can be foreseen to monitor immobile patients in intensive and postoperative care situations.

Automatic Coding of Facial Expressions of Pain: Are We There Yet?

INTRODUCTION: The experience of pain is regularly accompanied by facial expressions. The gold standard for analyzing these facial expressions is the Facial Action Coding System (FACS), which provides so-called action units (AUs) as parametrical indicators of facial muscular activity. Particular combinations of AUs have appeared to be pain-indicative. The manual coding of AUs is, however, too time- and labor-intensive in clinical practice. New developments in automatic facial expression analysis have promised to enable automatic detection of AUs, which might be used for pain detection. OBJECTIVE: Our aim is to compare manual with automatic AU coding of facial expressions of pain. METHODS: FaceReader7 was used for automatic AU detection. We compared the performance of FaceReader7 using videos of 40 participants (20 younger with a mean age of 25.7 years and 20 older with a mean age of 52.1 years) undergoing experimentally induced heat pain to manually coded AUs as gold standard labeling. Percentages of correctly and falsely classified AUs were calculated, and we computed as indicators of congruency, "sensitivity/recall," "precision," and "overall agreement (F1)." RESULTS: The automatic coding of AUs only showed poor to moderate outcomes regarding sensitivity/recall, precision, and F1. The congruency was better for younger compared to older faces and was better for pain-indicative AUs compared to other AUs. CONCLUSION: At the moment, automatic analyses of genuine facial expressions of pain may qualify at best as semiautomatic systems, which require further validation by human observers before they can be used to validly assess facial expressions of pain.

Facial Features Underlying the Decoding of Pain Expressions.

Previous research has revealed that the face is a finely tuned medium for pain communication. Studies assessing the decoding of facial expressions of pain have revealed an interesting discrepancy, namely that, despite eyes narrowing being the most frequent facial expression accompanying pain, individuals mostly rely on brow lowering and nose wrinkling/upper lip raising to evaluate pain. The present study verifies if this discrepancy may reflect an interaction between the features coding pain expressions and the features used by observers and stored in their mental representations. Experiment 1 shows that more weight is allocated to the brow lowering and nose wrinkling/upper lip raising, supporting the idea that these features are allocated more importance when mental representations of pain expressions are stored in memory. These 2 features have been associated with negative valence and with the affective dimension of pain, whereas the eyes narrowing feature has been associated more closely with the sensory dimension of pain. However, experiment 2 shows that these 2 features remain more salient than eyes narrowing, even when attention is specifically directed toward the sensory dimension of pain. Together, these results suggest that the features most saliently coded in the mental representation of facial expressions of pain may reflect a bias toward allocating more weight to the affective information encoded in the face. PERSPECTIVE: This work reveals the relative importance of 3 facial features representing the core of pain expressions during pain decoding. The results show that 2 features are over-represented; this finding may potentially be linked with the estimation biases occurring when clinicians and lay persons evaluate pain based on facial appearance.

Symptoms and problem clusters in cancer and non-cancer patients in specialized palliative care-is there a difference?

CONTEXT: In clinical practice, some symptoms and problems frequently occur in combination, which may have consequences for symptom management. OBJECTIVES: Facing a growing number of non-cancer patients in palliative care, this study aimed to differentiate symptom clusters in the non-cancer population from those in cancer patients. METHODS: Inpatient data from the German Hospice and Palliative Care Evaluation between 2007 and 2011 were used for a cluster analysis of a 16-item symptom and problem checklist. An agglomerative hierarchical method was chosen. Coefficients from distance matrix ranging between 0 and 1 were calculated to indicate the interrelationship of clustered symptoms. RESULTS: The analysis identified five clusters in cancer patients: 1) nausea and vomiting (d = 0.000); 2) anxiety, tension, and feeling depressed (d = 0.125); 3) wound care and disorientation/confusion (d = 0.229); 4) organization of care and overburdening of family (d = 0.202); and 5) weakness, tiredness, need for assistance with activities of daily living, and loss of appetite (d = 0.207). Five comparable clusters were identified in non-cancer patients: 1) nausea and vomiting (d = 0.000); 2) anxiety, tension, and feeling depressed (d = 0.166); 3) organization of care and overburdening of family (d = 0.187); 4) weakness and need for assistance with activities of daily living (d = 0.139); and 5) tiredness and loss of appetite (d = 0.182). CONCLUSION: As symptom clusters do not significantly differ between cancer and non-cancer patients, specific frequent symptoms in non-cancer patients should be assessed. Identification of symptom clusters may help to target therapies and focus the use of medications to improve patients' quality of life.