Phenotypic profile clustering pragmatically identifies diagnostically and mechanistically informative subgroups of chronic pain patients.
Journal
Pain
ISSN: 1872-6623
Titre abrégé: Pain
Pays: United States
ID NLM: 7508686
Informations de publication
Date de publication:
01 05 2021
01 05 2021
Historique:
received:
01
05
2020
accepted:
13
11
2020
pubmed:
2
12
2020
medline:
20
5
2021
entrez:
1
12
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 33259458
pii: 00006396-202105000-00024
doi: 10.1097/j.pain.0000000000002153
pmc: PMC8049946
mid: NIHMS1648523
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1528-1538Subventions
Organisme : NINDS NIH HHS
ID : P01 NS045685
Pays : United States
Organisme : NIDCR NIH HHS
ID : U01 DE017018
Pays : United States
Informations de copyright
Copyright © 2020 International Association for the Study of Pain.
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