The journal of pain : official journal of the American Pain Society
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Large variability in the individual response to even the most-efficacious pain treatments is observed clinically, which has led to calls for a more personalized, tailored approach to treating patients with pain (ie, "precision pain medicine"). Precision pain medicine, currently an aspirational goal, would consist of empirically based algorithms that determine the optimal treatments, or treatment combinations, for specific patients (ie, targeting the right treatment, in the right dose, to the right patient, at the right time). Answering this question of "what works for whom" will certainly improve the clinical care of patients with pain. ⋯ It further presents a set of evidence-based recommendations for accelerating the application of precision pain methods in chronic pain research. PERSPECTIVE: Given the considerable variability in treatment outcomes for chronic pain, progress in precision pain treatment is critical for the field. An array of phenotypes and mechanisms contribute to chronic pain; this review summarizes current knowledge regarding which treatments are most effective for patients with specific biopsychosocial characteristics.
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The 0 to 10 numeric rating scale of pain intensity is a standard outcome in randomized controlled trials (RCTs) of pain treatments. For individuals taking analgesics, there may be a disparity between "observed" pain intensity (pain intensity with concurrent analgesic use) and pain intensity without concurrent analgesic use (what the numeric rating scale would be had analgesics not been taken). Using a contemporary causal inference framework, we compare analytic methods that can potentially account for concurrent analgesic use, first in statistical simulations, and second in analyses of real (non-simulated) data from an RCT of lumbar epidural steroid injections. ⋯ We propose alternative methods that should be considered in the analysis of pain RCTs. PERSPECTIVE: This article presents the conceptual framework behind a new quantitative pain and analgesia composite outcome, the QPAC1.5, and the results of statistical simulations and analyses of trial data supporting improvements in power and bias using the QPAC1.5. Methods of this type should be considered in the analysis of pain RCTs.
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Clinical pain is often linked to poor body mechanics, with individuals sometimes presenting multiple painful disorders. Such disorders may be influenced by behaviors that affect the general resiliency and health of the musculoskeletal system. We aimed to develop a self-reported scale using the Malmö Diet and Cancer Study questions on work-related body mechanical exposures. ⋯ This measure provides reliable assessment of body mechanics strain in adults and can be useful when evaluating different contributions to musculoskeletal problems affecting pain-treatment success in future clinical research. PERSPECTIVE: This article presents the development and psychometric properties of a new measure, "Work-related Body Mechanics and Strain Scale (WR-BMSS)." The scale has 13-items or alternatively an 8-item short form. This measure could potentially help clinicians who seek to assess how musculoskeletal problems may contribute to patient pain and disability.
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Chronic pain (CP) is a major public health issue. While new onset CP is known to occur frequently after some pediatric surgeries, its incidence after the most common pediatric surgeries is unknown. This retrospective cohort study used insurance claims data from 2002 to 2017 for patients 0 to 21 years of age. ⋯ Given the long-term consequences of CP, resources should be allocated toward identification of high-risk pediatric patients and strategies to prevent CP after surgery. PERSPECTIVE: This study identifies the incidences of and risk factors for chronic pain after common surgeries in patients 0 to 21 years of age. Our findings suggest that resources should be allocated toward the identification of high-risk pediatric patients and strategies to prevent CP after surgery.
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Prior research has demonstrated disparities in general medical care for patients with mental health conditions, but little is known about disparities in pain care. The objective of this retrospective cohort study was to determine whether mental health conditions are associated with indicators of pain care quality (PCQ) as documented by primary care clinicians in the Veterans Health Administration (VHA). We used natural language processing to analyze electronic health record data from a national sample of Veterans with moderate to severe musculoskeletal pain during primary care visits in the Fiscal Year 2017. ⋯ Overall, results suggest that in this patient population, presence of a mental health condition is not associated with lower quality pain care. PERSPECTIVE: This study used a natural language processing approach to analyze medical records to determine whether mental health conditions are associated with indicators of pain care quality as documented by primary care clinicians. Findings suggest that presence of a diagnosed mental health condition is not associated with lower quality pain care.