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Quantitative sensory testing for assessment of somatosensory function in children and adolescents: a scoping review.

Lauren C HeathcoteNicole E MacKenzieChristine T ChambersSiobhan CoffmanLaura CornelissenBrittany CormierKristen S HigginsJackie PhinneyMarkus BlankenburgSuellen Walker
Published in: Pain reports (2024)
Quantitative sensory testing (QST) refers to a group of noninvasive psychophysical tests that examine responses to a range of calibrated mechanical and thermal stimuli. Quantitative sensory testing has been used extensively in adult pain research and has more recently been applied to pediatric pain research. The aims of this scoping review were to map the current state of the field, to identify gaps in the literature, and to inform directions for future research. Comprehensive searches were run in 5 databases. Titles, abstracts, and full texts were screened by 2 reviewers. Data related to the study aims were extracted and analyzed descriptively. A total of 16,894 unique studies were identified, of which 505 were screened for eligibility. After a full-text review, 301 studies were retained for analysis. Date of publication ranged from 1966 to 2023. However, the majority of studies (61%) were published within the last decade. Studies included participants across the developmental trajectory (ie, early childhood to adolescence) and most often included a combination of school-age children and adolescents (49%). Approximately 23% of studies were conducted in healthy samples. Most studies (71%) used only one QST modality. Only 14% of studies reported using a standardized QST protocol. Quantitative sensory testing in pediatric populations is an emerging and rapidly growing area of pain research. Future work is needed using comprehensive, standardized QST protocols to harness the full potential that this procedure can offer to our understanding of pediatric pain.
Keyphrases
  • chronic pain
  • case control
  • pain management
  • neuropathic pain
  • high resolution
  • randomized controlled trial
  • depressive symptoms
  • risk assessment
  • multidrug resistant
  • current status
  • deep learning