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

Publication ,  Journal Article
Gaynor, SM; Bortsov, A; Bair, E; Fillingim, RB; Greenspan, JD; Ohrbach, R; Diatchenko, L; Nackley, A; Tchivileva, IE; Whitehead, W; Alonso, AA ...
Published in: Pain
May 1, 2021

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.

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Published In

Pain

DOI

EISSN

1872-6623

Publication Date

May 1, 2021

Volume

162

Issue

5

Start / End Page

1528 / 1538

Location

United States

Related Subject Headings

  • Prospective Studies
  • Humans
  • Facial Pain
  • Cluster Analysis
  • Chronic Pain
  • Anxiety Disorders
  • Anesthesiology
  • 52 Psychology
  • 42 Health sciences
  • 32 Biomedical and clinical sciences
 

Citation

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Gaynor, S. M., Bortsov, A., Bair, E., Fillingim, R. B., Greenspan, J. D., Ohrbach, R., … Smith, S. B. (2021). Phenotypic profile clustering pragmatically identifies diagnostically and mechanistically informative subgroups of chronic pain patients. Pain, 162(5), 1528–1538. https://doi.org/10.1097/j.pain.0000000000002153
Gaynor, Sheila M., Andrey Bortsov, Eric Bair, Roger B. Fillingim, Joel D. Greenspan, Richard Ohrbach, Luda Diatchenko, et al. “Phenotypic profile clustering pragmatically identifies diagnostically and mechanistically informative subgroups of chronic pain patients.Pain 162, no. 5 (May 1, 2021): 1528–38. https://doi.org/10.1097/j.pain.0000000000002153.
Gaynor SM, Bortsov A, Bair E, Fillingim RB, Greenspan JD, Ohrbach R, et al. Phenotypic profile clustering pragmatically identifies diagnostically and mechanistically informative subgroups of chronic pain patients. Pain. 2021 May 1;162(5):1528–38.
Gaynor, Sheila M., et al. “Phenotypic profile clustering pragmatically identifies diagnostically and mechanistically informative subgroups of chronic pain patients.Pain, vol. 162, no. 5, May 2021, pp. 1528–38. Pubmed, doi:10.1097/j.pain.0000000000002153.
Gaynor SM, Bortsov A, Bair E, Fillingim RB, Greenspan JD, Ohrbach R, Diatchenko L, Nackley A, Tchivileva IE, Whitehead W, Alonso AA, Buchheit TE, Boortz-Marx RL, Liedtke W, Park JJ, Maixner W, Smith SB. Phenotypic profile clustering pragmatically identifies diagnostically and mechanistically informative subgroups of chronic pain patients. Pain. 2021 May 1;162(5):1528–1538.

Published In

Pain

DOI

EISSN

1872-6623

Publication Date

May 1, 2021

Volume

162

Issue

5

Start / End Page

1528 / 1538

Location

United States

Related Subject Headings

  • Prospective Studies
  • Humans
  • Facial Pain
  • Cluster Analysis
  • Chronic Pain
  • Anxiety Disorders
  • Anesthesiology
  • 52 Psychology
  • 42 Health sciences
  • 32 Biomedical and clinical sciences