Data Analytics Boosts Chronic Pain Management, Personalized Medicine

August 09, 2021 - University of Pittsburg researchers used a data analytics algorithm to algorithmically cluster individuals with chronic pain by pain distribution, paving the way for better more personalized medicine. By using a body map to pinpoint pain distribution, researchers assigned patients to distinct subgroups associated with differences in pain intensity, pain quality, and pain impact.

In clinical practice, the distribution of chronic pain in the body typically coincides 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,” the press release stated.

Through the study, researchers examined data on 21,658 patients at the University of Pittsburg’s seven pain management clinics between 2016 and 2019. Every patient completed a pain body map, in which they selected areas of pain are selected on two side-by-side drawings of the front and back of the body. The map identifies 74 possible regions of pain.

Additionally, other information on patients’ pain, health, and outcomes were collected from electronic health records (EHRs). Data from all study participants revealed nine9 distinct groupings of pain distribution.

Demographic and medical characteristics, pain intensity, pain impact, and neuropathic pain quality differed 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,” the researchers explained.

In a group of 7,138 patients who completed the three-month follow-up questionnaires, subgroups predicted the likelihood of improvement in pain and physical function. Individuals in the “Abdominal Pain” group were most improved, with 49 percent self-reporting significant improvements.

Those in the “Neck, Should and Lower Back Pain” group saw the least improvement, with 37 percent reporting improvements. The research team explained maintained that algorithmic clustering by pain distribution could be critical for improving personalized medicine for pain management for those with chronic pain conditions.

“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,” the researchers concluded.