Phenotypic profile clustering pragmatically identifies diagnostically and mechanistically informative subgroups of chronic pain patients.

Autor:	Gaynor SM; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States., Bortsov A; Department of Anesthesiology, Center for Translational Pain Medicine, Duke University, Durham, NC, United States., Bair E; Center for Pain Research and Innovation, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States., Fillingim RB; Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, United States.; Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL, United States., Greenspan JD; Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, MD, United States.; Brotman Facial Pain Clinic, University of Maryland School of Dentistry, Baltimore, MD, United States., Ohrbach R; Department of Oral Diagnostic Sciences, University at Buffalo, Buffalo, NY, United States., Diatchenko L; Department of Anesthesia, Alan Edwards Centre for Research on Pain, School of Medicine, School of Dentistry, McGill University, Montréal, QC, Canada., Nackley A; Department of Anesthesiology, Center for Translational Pain Medicine, Duke University, Durham, NC, United States.; Department of Pharmacology and Cancer Biology, Duke University, Durham, NC, United States., Tchivileva IE; Division of Oral and Craniofacial Health Sciences, Center for Pain Research and Innovation, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, United States., Whitehead W; Division of Gastroenterology and Hepatology, Center for Functional GI and Motility Disorders, School of Medicine, University of North Carolina, Chapel Hill, NC, United States., Alonso AA; Department of Anesthesiology, Center for Translational Pain Medicine, Duke University, Durham, NC, United States.; Department of Anesthesiology, Duke Innovative Pain Therapies, Duke University, Durham, NC, United States., Buchheit TE; Department of Anesthesiology, Center for Translational Pain Medicine, Duke University, Durham, NC, United States.; Anesthesiology Service, Durham Veterans Affairs Health Care System, Durham, NC, United States., Boortz-Marx RL; Department of Anesthesiology, Pain Medicine Division, Duke University, Durham, NC, United States., Liedtke W; Department of Anesthesiology, Duke Innovative Pain Therapies, Duke University, Durham, NC, United States.; Department of Neurology, Duke University School of Medicine, Durham, NC, United States.; Department of Neurobiology, Duke University School of Medicine, Durham, NC, United States., Park JJ; Department of Anesthesiology, Center for Translational Pain Medicine, Duke University, Durham, NC, United States., Maixner W; Department of Anesthesiology, Center for Translational Pain Medicine, Duke University, Durham, NC, United States., Smith SB; Department of Anesthesiology, Center for Translational Pain Medicine, Duke University, Durham, NC, United States.
Jazyk:	angličtina
Zdroj:	Pain [Pain] 2021 May 01; Vol. 162 (5), pp. 1528-1538.
DOI:	10.1097/j.pain.0000000000002153
Abstrakt:	Abstract: Traditional classification and prognostic approaches for chronic pain conditions focus primarily on anatomically based clinical characteristics not based on underlying biopsychosocial factors contributing to perception of clinical pain and future pain trajectories. Using a supervised clustering approach in a cohort of temporomandibular disorder cases and controls from the Orofacial Pain: Prospective Evaluation and Risk Assessment study, we recently developed and validated a rapid algorithm (ROPA) to pragmatically classify chronic pain patients into 3 groups that differed in clinical pain report, biopsychosocial profiles, functional limitations, and comorbid conditions. The present aim was to examine the generalizability of this clustering procedure in 2 additional cohorts: a cohort of patients with chronic overlapping pain conditions (Complex Persistent Pain Conditions study) and a real-world clinical population of patients seeking treatment at duke innovative pain therapies. In each cohort, we applied a ROPA for cluster prediction, which requires only 4 input variables: pressure pain threshold and anxiety, depression, and somatization scales. In both complex persistent pain condition and duke innovative pain therapies, we distinguished 3 clusters, including one with more severe clinical characteristics and psychological distress. We observed strong concordance with observed cluster solutions, indicating the ROPA method allows for reliable subtyping of clinical populations with minimal patient burden. The ROPA clustering algorithm represents a rapid and valid stratification tool independent of anatomic diagnosis. ROPA holds promise in classifying patients based on pathophysiological mechanisms rather than structural or anatomical diagnoses. As such, this method of classifying patients will facilitate personalized pain medicine for patients with chronic pain. (Copyright © 2020 International Association for the Study of Pain.)
Databáze:	MEDLINE
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