Human Studies

Spinal pain is the leading worldwide cause of patient-years lived with chronic pain and disability. While opioids are well documented as an effective short-term pain-relieving medication, more than a few weeks of treatment may result in a diminishing clinical effect as well as the development of addictive behavior. Even though opioid addiction in pain patients is a major problem commonly experienced in the clinic, no reference material exists on the scope of long-term problems in novel opioid users and the link to clinical outcomes.

The relationship between elevated inflammatory cytokine levels and peak pain intensity following acute musculoskeletal injury has not been fully elucidated in high risk subgroups. Identifying the role that these cytokines have on pain responses may help with developing tailored therapeutic approaches.

The aim of this study was 1) to statistically identify distinct trajectories of pain following surgery in infants less than six months of age, and 2) to compare these trajectories to descriptions of chronic pain in infants in the neonatal intensive care unit (NICU).

Opioid-reduction policies have been enacted by US states to address the opioid epidemic. Evidence of an association between policy implementation and decreased rates of pediatric opioid poisoning provides further justification for expanded implementation of these policies.

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. Unsupervised machine learning analysis of parameters "pain intensity", "number of pain areas", "pain duration", "activity pain interference" and "affective pain interference", implemented as emergent self-organizing maps, identified three patient phenotype clusters. Supervised analyses, implemented as different types of decision rules, identified "affective pain interference" and the "number of pain areas" as most relevant for cluster assignment. These appeared 698 and 637 times, respectively, in 1000 cross-validation runs among the most relevant characteristics in an item categorization approach in a computed ABC analysis. Cluster assignment was achieved with a median balanced accuracy of 79.9%, a sensitivity of 74.1%, and a specificity of 87.7%. 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 two 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.

Randomised trials on antibiotic treatment for patients with chronic low back pain and vertebral endplate changes visible on MRI (Modic changes) have shown mixed results. A possible explanation might be a real treatment effect in subgroups of the study populations. The purpose of the present study was to explore potential clinical effect modifiers of 3-months oral amoxicillin treatment in patients with chronic low back pain and type I or II Modic changes at the level of a previous lumbar disc herniation.