The present prospective longitudinal study on chronic postoperative pain was conducted to assess the predictive power of attentional and emotional variables specifically assumed to augment pain, such as pain hypervigilance, pain-related anxiety, pain catastrophizing and attentional biases to pain. Their relevance was determined in comparison with other psychological and physiological predictors (depression, anxiety, somatization, cortisol reactivity, pain sensitivity). In 84 young male patients the predictor variables were assessed one day before surgery (correction of chest malformation). Postoperative outcome (subjective pain intensity and pain-related disability) was assessed three (N=84) and six months (N=78) after surgery. Patients were classified into good and poor outcome groups. Patients with high pain intensity three (25%) or six months (14%) after surgery, differed significantly from those low in pain with regard to their preoperative performance in the dot-probe task (high attentional bias towards positive words). A sizeable portion (54%) of patients still felt disabled due to pain after three months and a few patients after six months (13%). These patients were those with high preoperative ratings in the Pain Vigilance and Awareness Questionnaire. The few subjectively disabled patients after six months could be identified in addition by low pressure pain and high cold pain thresholds before surgery. An attentional bias towards positive stimuli prior to surgery may indicate a maladaptive coping style, which avoids necessary confrontation with pain and predisposes patients to chronic postoperative pain. Lasting subjectively felt pain-related disability occurs predominantly in patients with high levels of pain hypervigilance before surgery.

Objectives Pain hypervigilance—a strong attentional bias toward pain—is thought to accompany chronic pain and modulate pain management. Its usefulness as predisposing factor for the development and maintenance of pain has been discussed. The aim of our study was to demonstrate the predictive power of hypervigilance for the development of acute postoperative pain. Methods Fifty-four young male patients were assessed 1 day before surgery (correction of chest malformation) on a range of psychologic predictors. These predictors included the assessment of hypervigilance (questionnaires as the Pain Catastrophizing Scale, Pain Anxiety Symptom Scale, the Pain Vigilance and Awareness Questionnaire, and the dot-probe task) and affective state, experimental pain sensitivity, and cortisol reactivity. Acute postoperative pain was assessed by ratings of pain intensity 1 week postsurgery and through the amount of analgesics [patient-controlled epidural analgesia (PCEA)] requested during the first days after surgery. Results Pain intensity was significantly explained (17% explained variance) by hypervigilance, whereas PCEA performance was not (10%). Adding all other predictors led to a significant increase of explained variance (35%) for pain ratings and a nonsignificant increase (19%) for PCEA. A more parsimonious solution with only heat pain threshold added led to a significant increase in explained variance (30%) for pain intensity. Hypervigilance was only moderately correlated with the other predictors. Discussion Hypervigilance proved to be a powerful predictor of subjective acute postoperative pain, but was less useful with regard to the amount of requested analgesics. The overlap with other psychologic predictors (affective state, experimental pain sensitivity, and cortisol reactivity) is sufficiently small to consider hypervigilance a promising supplement in psychologic predictor research.

Pain sensation is essential for survival, since it draws attention to physical threat to the body. Pain assessment is usually done through self-reports. However, self-assessment of pain is not available in the case of noncommunicative patients, and therefore, observer reports should be relied upon. Observer reports of pain could be prone to errors due to subjective biases of observers. Moreover, continuous monitoring by humans is impractical. Therefore, automatic pain detection technology could be deployed to assist human caregivers and complement their service, thereby improving the quality of pain management, especially for noncommunicative patients. Facial expressions are a reliable indicator of pain, and are used in all observer-based pain assessment tools. Following the advancements in automatic facial expression analysis, computer vision researchers have tried to use this technology for developing approaches for automatically detecting pain from facial expressions. This paper surveys the literature published in this field over the past decade, categorizes it, and identifies future research directions. The survey covers the pain datasets used in the reviewed literature, the learning tasks targeted by the approaches, the features extracted from images and image sequences to represent pain-related information, and finally, the machine learning methods used.

Pain is common in people with dementia, yet identification is challenging. A number of pain assessment tools exist, utilizing observation of pain-related behaviours, vocalizations and facial expressions. Whilst they have been developed robustly, these often lack sufficient evidence of psychometric properties, like reliability, face and construct validity, responsiveness and usability, and are not internationally implemented. The EU-COST initiative “Pain in impaired cognition, especially dementia” aims to combine the expertise of clinicians and researchers to address this important issue by building on previous research in the area, identifying existing pain assessment tools for dementia, and developing consensus for items for a new universal meta-tool for use in research and clinical settings. This paper reports on the initial phase of this collaboration task. All existing observational pain behaviour tools were identified and elements categorised using a three-step reduction process. Selection and refinement of items for the draft Pain Assessment in Impaired Cognition (PAIC) meta-tool was achieved through scrutiny of the evidence, consensus of expert opinion, frequency of use and alignment with the American Geriatric Society guidelines. The main aim of this process was to identify key items with potential empirical, rather than theoretical value to take forward for testing. 12 eligible assessment tools were identified, and pain items categorised according to behaviour, facial expression and vocalisation according to the AGS guidelines (Domains 1 – 3). This has been refined to create the PAIC meta-tool for validation and further refinement. A decision was made to create a supporting comprehensive toolkit to support the core assessment tool to provide additional resources for the assessment of overlapping symptoms in dementia, including AGS domains four to six, identification of specific types of pain and assessment of duration and location of pain. This multidisciplinary, cross-cultural initiative has created a draft meta-tool for capturing pain behaviour to be used across languages and culture, based on the most promising items used in existing tools. The draft PAIC meta-tool will now be taken forward for evaluation according to COSMIN guidelines and the EU-COST protocol in order to exclude invalid items, refine included items and optimise the meta-tool.