Pain
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Youth with chronic pain and their parents face uncertainty regarding their diagnosis, treatment, and prognosis. Given the uncertain nature of chronic pain and high comorbidity of anxiety among youth, intolerance of uncertainty (IU) may be critical to the experience of pediatric chronic pain. This study longitudinally examined major tenets of the Interpersonal Fear Avoidance Model of Pain and included parent and youth IU as key factors in the model. ⋯ Youth IU had a significant indirect effect on 3-month pain interference through youth pain catastrophizing and fear of pain. The results suggest that parent and youth IU contribute to increases in youth pain interference over time through increased pain catastrophizing, parent protectiveness, and youth fear of pain. Thus, parent and youth IU play important roles as risk factors in the maintenance of pediatric chronic pain over time and may be important targets for intervention.
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Meta Analysis
Psychological and psychosocial predictors of chronic post-surgical pain: a systematic review and meta-analysis.
Knowledge about psychological and psychosocial predictors of chronic postsurgical pain is important to identify patients at risk for poor outcomes. The objective of this systematic review with meta-analysis was to assess the effect of such predictors. A comprehensive search of the available literature on this topic was performed using the electronic databases PubMed, Scopus, Embase, and PsycInfo. ⋯ The narrative synthesis showed that evidence about the effect of psychological predictors is heterogeneous, with few expected predictors, such as optimism, state anxiety and psychological distress, consistently associated with chronic postsurgical pain. By contrast, the meta-analyses showed that state anxiety, trait anxiety, mental health, depression, catastrophizing and, to a lesser extent, kinesiophobia and self-efficacy have a weak but significant association with chronic postsurgical pain. In conclusion, this study showed that psychological predictors have a significant association with chronic postsurgical pain and that state anxiety is the most explicative one.
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Randomized Controlled Trial
Machine learning suggests sleep as a core factor in chronic pain.
Patients with chronic pain have complex pain profiles and associated problems. Subgroup analysis can help identify key problems. We used a data-based approach to define pain phenotypes and their most relevant associated problems in 320 patients undergoing tertiary pain management. ⋯ In addition, among 59 demographic, pain etiology, comorbidity, lifestyle, psychological, and treatment-related variables, sleep problems appeared 638 and 439 times among the most important characteristics in 1000 cross-validation runs where patients were assigned to the 2 extreme pain phenotype clusters. Also important were the parameters "fear of pain," "self-rated poor health," and "systolic blood pressure." Decision trees trained with this information assigned patients to the extreme pain phenotype with an accuracy of 67%. Machine learning suggested sleep problems as key factors in the most difficult pain presentations, therefore deserving priority in the treatment of chronic pain.