Traditionally, physicians have struggled to separate the physical and psychosocial components of pain, leading to nonspecific treatment strategies. Opioid painkillers, while effective in managing pain, come with a host of drawbacks, including side effects, diminishing efficacy over time, and the risk of addiction and overdose.

Making treatment more individualized

In recent years, researchers at ETH Zurich and Balgrist University Hospital have developed a groundbreaking approach to differentiate and quantify the physical and psychosocial aspects of pain. This new method, outlined in the journal Med, aims to provide more personalized and targeted treatment options for patients.

“Our new approach will allow physicians to assess pain more accurately and offer tailored treatment plans based on the underlying causes,” says Stanisa Raspopovic, former Professor of Neuroengineering at ETH Zurich.

Utilizing a large dataset

The researchers conducted a comprehensive analysis involving 118 volunteers with chronic pain and healthy controls. Participants were evaluated for their pain perception, as well as psychosocial factors such as depression, anxiety, and coping mechanisms. Physical reactions to painful stimuli were measured using brain activity analysis and skin conductivity testing.

Machine learning for precision medicine

Machine learning algorithms were employed to analyze the vast amount of data and create two distinct indices: one for the physical component of pain and another for the psychosocial component. These indices provide a clear understanding of the underlying factors contributing to each individual’s pain experience.

“Our method empowers physicians to accurately characterize a patient’s pain profile and determine the most effective treatment approach,” explains Gozzi.

The research team is currently collaborating with medical institutions in Switzerland and Italy to further validate the effectiveness of their new method in long-term studies.