'Where does it hurt?' predicts chronic pain outcomes, study shows

Date:
August 4, 2021
Source:
PLOS
Summary:
Pain distribution as reported on a body map, on its own, can be
used to assign patients to distinct subgroups that are associated
with differences in pain intensity, pain quality, pain impact and
clinically- relevant three-month outcomes, according to a new study.



FULL STORY ==========================================================================
Pain distribution as reported on a body map, on its own, can be used to
assign patients to distinct subgroups that are associated with differences
in pain intensity, pain quality, pain impact and clinically-relevant three-month outcomes, according to a new study published this week in
the open-access journal PLOS ONE by Benedict Alter of University of
Pittsburgh, US, and colleagues.


==========================================================================
In clinical practice, the bodily distribution of chronic pain is often
used in conjunction with other signs and symptoms to diagnose and treat patients.

Recent work on fibromyalgia has revealed that clinical pain syndromes
thought to be distinct entities may share clinically-relevant features, especially regarding the impact of pain distribution on outcomes. However patterns of pain distribution have not been previously examined in a
systematic way as predictors of pain characteristics or outcomes.

In the new study, researchers analyzed data on 21,658 patients seen
at the seven pain management clinics of the University of Pittsburgh
between 2016 and 2019. All patients completed a pain body map, in which
areas of pain are selected on two side-by-side drawings of the front and
back of the body, with 74 possible regions of pain. Other information
on patients' pain, health, and outcomes was available in the electronic
medical record. Patients were 83% white, 60% female, 22% insured by
Medicaid and 10% had at least one comorbidity.

Data from all patients revealed 9 distinct groupings of pain distribution.

Demographic and medical characteristics, pain intensity, pain impact,
and neuropathic pain quality all varied significantly across cluster
subgroups. For instance, the pain intensity of the "Neck and Shoulder"
group was less than that of "Lower Back Pain below knee" and "Neck,
Shoulder and Lower Back Pain," while the group with the highest pain
intensity consisted of patients with widespread heavy pain, also
associated with low physical function, high anxiety and depression and
high sleep disturbance. In a subset of 7,138 patients who completed
3-month follow-up questionnaires, subgroups predicted the likelihood
of improvement in pain and physical function; those in the "Abdominal
Pain" group were the most improved, with 49% self-reporting clinically significant improvements, while those in the "Neck, Shoulder and Lower
Back Pain" group were the least improved, with only 37% reporting
improvements. The authors conclude that algorithmic clustering by
pain distribution may, in the future, be an important facet of the personalization of pain management.

The authors add: "Using an algorithmic approach, we found that how a
patient reports the bodily distribution of their chronic pain affects
nearly all aspects of the pain experience, including what happens three
months later. This emphasizes that chronic pain is a disease process and suggests that this facet of the chronic pain phenotype will be important
for future developments in diagnosis and personalized pain management." ========================================================================== Story Source: Materials provided by PLOS. Note: Content may be edited
for style and length.


========================================================================== Journal Reference:
1. Benedict J. Alter, Nathan P. Anderson, Andrea G. Gillman, Qing
Yin, Jong-
Hyeon Jeong, Ajay D. Wasan. Hierarchical clustering by
patient-reported pain distribution alone identifies distinct
chronic pain subgroups differing by pain intensity, quality,
and clinical outcomes. PLOS ONE, 2021; 16 (8): e0254862 DOI:
10.1371/journal.pone.0254862 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/08/210804141155.htm

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