Evidence map›Paper›PMID 34516888›Full record

ArticleScience advances2021

Classifying chronic pain using multidimensional pain-agnostic symptom assessments and clustering analysis.

Gadi Gilam, Eric M Cramer, Kenneth A Webber, Maisa S Ziadni, Ming-Chih Kao, Sean C Mackey

Abstract read
In one paragraph

Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Gadi GilamDivision of Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.ORCID 0000-0003-3925-5273
Eric M CramerDivision of Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.ORCID 0000-0003-2085-3679
Kenneth A WebberDivision of Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.ORCID 0000-0002-0916-9174
Maisa S ZiadniDivision of Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.ORCID 0000-0001-8371-0646
Ming-Chih KaoDivision of Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Sean C MackeyDivision of Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.ORCID 0000-0001-7744-5348

Funding

Stanford CAM Center for Chronic Back PainP01AT006651 · NCCIH · STANFORD UNIVERSITY · PI MACKEY, SEAN C · 2011 to 2018
$8.2M
Prognostic biomarkers for high-impact chronic pain: Development and validationR61NS118651 · NINDS · STANFORD UNIVERSITY · PI MACKEY, SEAN C · 2020 to 2020
$7.3M
Characterization of central pain mechanisms using simultaneous spinal cord-brain functional imagingR01NS109450 · NINDS · STANFORD UNIVERSITY · PI GLOVER, GARY H, MACKEY, SEAN C · 2018 to 2022
$2.9M
Neuroimaging and Mentoring in Translational Pain ResearchK24DA029262 · NIDA · STANFORD UNIVERSITY · PI MACKEY, SEAN C · 2010 to 2019
$1.9M
Neuroimaging-Based Brain and Spinal Cord Biomarkers for Cervical RadiculopathyK23NS104211 · NINDS · STANFORD UNIVERSITY · PI WEBER, KENNETH ARNOLD · 2018 to 2022
$875k
Single Session Pain Catastrophizing Class: Efficacy & Mechanisms for Reducing Opioid Use Among Chronic Pain PatientsK23DA047473 · NIDA · STANFORD UNIVERSITY · PI ZIADNI, MAISA · 2019 to 2023
$796k
6 · The paper itself

Abstract

Chronic pain conditions present in various forms, yet all feature symptomatic impairments in physical, mental, and social domains. Rather than assessing symptoms as manifestations of illness, we used them to develop a chronic pain classification system. A cohort of real-world treatment-seeking patients completed a multidimensional patient-reported registry as part of a routine initial evaluation in a multidisciplinary academic pain clinic. We applied hierarchical clustering on a training subset of 11,448 patients using nine pain-agnostic symptoms. We then validated a three-cluster solution reflecting a graded scale of severity across all symptoms and eight independent pain-specific measures in additional subsets of 3817 and 1273 patients. Negative affect–related factors were key determinants of cluster assignment. The smallest subset included follow-up assessments that were predicted by baseline cluster assignment. Findings provide a cost-effective classification system that promises to improve clinical care and alleviate suffering by providing putative markers for personalized diagnosis and prognosis.

Identifiers

PMID34516888
PMCPMC8442889

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.