• A doctor will often ask a person with chronic pain to create a “pain map” to show where in their body they experience pain.
• The doctor will then combine the map with other symptoms to diagnose and treat the person.
• Researchers have now found that the distribution of pain is significantly associated with its intensity, its effect on mood and sleep, and how it affects the ability to perform physical tasks.
• The distribution of pain in the participants’ bodies was also sufficient to predict whether pain and physical function would improve 3 months later.

Chronic pain is one of the most common reasons why people seek medical care, and it is linked to decreased quality of life, opioid dependence, and poor mental health.

According to the Centers for Disease Control and Prevention (CDC), in 2019, about 1 in 5 adults in the United States reported that they had experienced chronic pain in the past 3 months.

Over the same period, more than 7% of adults said that they had pain that frequently limited their life or work activities.

However, the experience of pain is complex and subjective, which makes it difficult to quantify and communicate to healthcare professionals.

Doctors will often ask people with chronic pain to mark a pain map, also known as a body map or pain body map, to indicate where in their body they feel pain.

They may then combine this information with other signs and symptoms to confirm a diagnosis and create a treatment plan for each individual.

However, a new study suggests that the distinctive patterns of pain distribution that pain maps reveal could serve as a quick and easy screening tool in their own right.

Researchers at the University of Pittsburgh, PA, found that these patterns successfully predicted the likelihood that people’s pain and physical function would improve 3 months later.

The study appears in the journal PLOS ONE.

Pain map

The scientists analyzed data from 21,658 people who attended pain management clinics at the university between 2016 and 2019.

Among this cohort, 83% of participants were white and 60% were female.

The researchers gleaned information about the participants’ overall health and pain from electronic medical records.

As part of routine care, all of the participants also completed a pain map in the waiting room prior to their appointment.

The pain map comprised drawings of the front and back of the body, showing a total of 74 possible regions of pain.

The instructions to participants were: “Select the areas where you are experiencing pain.”

The researchers used a type of algorithm called hierarchical clustering to identify shared characteristics among the thousands of unique patterns of pain distribution in these pain maps.

This algorithm revealed nine distinct clusters, to which the researchers assigned labels such as “neck and shoulder,” “neck, shoulder, and low back pain,” and “widespread heavy pain.”

When the scientists looked at the medical records of the individuals in each of the nine clusters, they found statistically significant differences between the clusters on scales such as pain intensity, physical function, mood, and sleep quality.

For instance, pain intensity among participants in the “neck and shoulder” cluster was significantly less than pain intensity among those in the “neck, shoulder, and low back pain” cluster.

Meanwhile, those in the “widespread heavy pain” cluster experienced the highest pain intensity, alongside low levels of physical function and high levels of anxiety, depression, and sleep disturbance.

At 3 months after their initial assessment, 7,138 of the participants completed follow-up pain assessments.

Subsequent analysis revealed that each cluster of pain distribution independently predicted the following outcomes:

• improvement in pain
• improvement in physical function
• people’s overall impression of change since their first visit to the pain clinic

The authors conclude:

“Overall, this study demonstrates that patterns of pain distribution provide information beyond a simple tally of the number of pain body regions. Given its associations with pain intensity, quality, and patient-reported outcomes, hierarchical cluster assignment based on the body map may help identify patients at risk of poor outcomes.”

Dr. Benedict Alter, Ph.D., who led the research team, told Medical News Today that one of the study goals was to develop the algorithm into a tool for diagnosing chronic pain.

“I foresee our method as contributing to the generation of a larger ‘biosignature’ that would include patients’ responses to standardized surveys and other clinical testing that may include sensory tests, genetics, brain imaging, etc.,” he said.

“Although it’s possible that a single facet of this biosignature, like our method, really does jump out as predicting an individual’s response to treatment, given the complexity of chronic pain, I think the multifaceted ‘biosignature’ concept is more likely,” he added.

A new category of pain

The scientists believe that their findings support the notion of nociplastic pain.

Researchers have proposed this as a third category of pain. The other two categories are nociceptive pain, which results from inflammation and tissue damage, and neuropathic pain, which is due to nerve damage.

Nociplastic pain involves changes in the processing and modulation of pain signals in the central nervous system.

It is more widespread than the amount of tissue or nerve damage would suggest, and it may include symptoms such as fatigue, sleep, memory, and mood problems.

A recent study of people with fibromyalgia found a correlation between this type of pain and changes in connectivity between parts of the brain that play a role in emotion.

Fibromyalgia not only causes widespread pain but is also associated with sleep disturbance, depression, and anxiety.

“[C]hronic pain is more than ‘where does it hurt,’ with important emotional aspects including suffering, depression, anger, etc.,” Dr. Alter told MNT.

“Our interesting finding is that the bodily distribution of pain correlates with many of these emotional aspects, and this finding dovetails with changes in brain connectivity,” he said.

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