Not all pain is identical. Its cause can vary, leading to different treatment needs. A recent advancement by a research team enables doctors to differentiate more effectively between physical pain and psychosocial pain.
While intense pain typically stems from physical issues, emotional, psychological, and social elements can significantly shape our pain experience. “Pain generally consists of both physical and psychosocial aspects,” notes Noemi Gozzi, a doctoral candidate at ETH Zurich.
Physicians strive to consider these factors when devising treatment plans. However, distinguishing between the two has proven challenging. Doctors often depend on straightforward methods to gauge pain and its severity, which largely hinge on the patient’s own descriptions. This approach can result in generalized treatments. Opioid pain relievers remain common, but they carry severe drawbacks, including unwanted side effects, decreasing effectiveness, and the potential for addiction or overdose.
Creating personalized treatments
Recently, Stanisa Raspopovic’s team at ETH Zurich, including Gozzi, collaborated with researchers from Balgrist University Hospital in Zurich to formulate a strategy that distinctly identifies and measures the physical and psychosocial aspects of pain. Their findings were published in the latest issue of the journal Med. Raspopovic was previously a Professor of Neuroengineering at ETH Zurich.
“Our innovative approach aims to help doctors assess their patients’ pain more specifically, enabling them to provide better tailored treatments in the future,” Raspopovic explains. For pain mainly of a physical nature, treatments will likely target physical issues, possibly through medications or physical therapy. Conversely, when psychosocial factors heavily influence a patient’s pain experience, it may be beneficial to address pain perception through psychological or therapeutic support.
Extensive data analysis
The new method was developed by analyzing data from 118 participants, which included individuals with chronic pain and healthy volunteers. The researchers gathered detailed information on how participants experienced pain, along with psychosocial aspects like depression, anxiety, and fatigue, as well as instances where pain impeded their ability to work. Furthermore, they assessed how effectively participants could divert their attention away from pain and the extent to which pain caused them to dwell on it or feel helpless.
Using standardized measures for spontaneous pain, the researchers compared each participant’s pain perception. Test subjects received harmless, yet painful pulses of heat on their skin. To monitor their pain-related physical responses, the researchers measured brain activity through electroencephalography (EEG) and evaluated skin conductivity, which varies with sweat and is indicative of stress, pain, and emotional states. Their extensive dataset also encompassed diagnoses made by the Balgrist University Hospital team.
Machine learning for precise treatment
To manage the considerable data load, machine learning played a crucial role in enabling the researchers to differentiate clearly between the two pain components and to create a specific index for each. The index for the physical pain component denotes the degree to which physical factors contribute to pain, while the psychosocial index indicates the intensity of emotional and psychological influences on pain. Ultimately, the researchers confirmed these two factors utilizing the participants’ detailed measurement data.
This new method combines physiological measurements, self-reports, computational analysis, and the creation of two indices to assist physicians in pain management. “Our approach allows doctors to accurately describe the pain experienced by each individual, aiding them in determining the most appropriate treatment strategy,” Gozzi states.
The team at ETH Zurich and Balgrist University Hospital is advancing this project in collaboration with Clinique romande de réadaptation in Sion and a spinal cord injury unit in Pietra Ligure, Italy. They aim to explore the clinical significance of this new approach in a long-term study.
