Predicting long-term pain by combining brain imaging, genetics and health questionnaire data with Swedish national registries using a prospective superstruct design.

Abstract

Background: Long-term pain is a common health problem that results in disability for patients of all ages, leading to an enormous economic burden. Over 20% of the population suffer from long-term pain. Unfortunately, there are no clinical tests that predicts who will develop long-term pain. The overall aim is to predict future pain incidence based on brain function, pain behavior, health status, and genetic variability.

Method: PrePain utilizes a superstruct design, which involves recruiting participants from ongoing research projects. Eligible individuals for participation in PrePain were over 18 years old and free from long-term pain. During the baseline visit, participants provide pain and health-related questionnaires, undergo structural and functional MRI scans, and provide a saliva sample for DNA extraction. Individual baseline measures are then routinely followed-up via national registries.

Result: We present quality-assessed data from over 300 participants. The average age was 34 years, and most participants were women (75%). Participants rated their pain sensitivity above average and reported low avoidance. Catastrophizing thoughts during painful episodes were rated as moderate. Assessments of (f)MRI data indicated generally good image quality. In this first follow-up, we found that 45 participants had a pain-related diagnoses.

Conclusion: Results indicate that a superstruct design is feasible for collecting a large number of high-quality data. The incidence of long-term pain indicates that a sufficient number of participants have been recruited to complete the prediction analyses. PrePain is a unique prospective pain database with a fair prognosis to determine risk factors of long-term pain.