Human Studies

Early detection of patients with chronic diseases at risk of developing persistent pain is clinically desirable for timely initiation of multimodal therapies. Quality follow-up registries may provide the necessary clinical data; however, their design is not focused on a specific research aim, which poses challenges on the data-analysis strategy. Here, machine-learning was used to identify early parameters that provide information about a future development of persistent pain in rheumatoid arthritis (RA). Data of 288 patients were queried from a registry based on the Swedish Epidemiological Investigation of RA (EIRA). Unsupervised machine-learning identified three distinct patient subgroups (low, median and high) persistent pain intensities. Next, supervised machine learning, implemented as random forests followed by computed ABC analysis-based item categorization, was used to select predictive parameters among 21 different demographic, patient rated and objective clinical factors. The selected parameters were used to train machine-learned algorithms to assign patients pain-related subgroups (1,000 random resamplings, 2/3 training, 1/3 test data). Algorithms trained with three-month data of patient global assessment and health assessment questionnaire provided pain group assignment at a balanced accuracy of 70 %. When restricting the predictors to objective clinical parameters of disease severity, swollen joint count and tender joint count acquired at three months provided a balanced accuracy of rheumatoid arthritis of 59 %. Results indicate that machine-learning is suited to extract knowledge from data queried from pain and disease related registries. Early functional parameters of RA are informative for the development and degree of persistent pain.

Pain is the primary symptom of chronic pancreatitis (CP), but methods for sensory testing and pain characterization have not previously been validated for clinical use. We present a clinically feasible method for the assessment and characterization of pain mechanisms in patients with CP based on quantitative sensory testing (QST).

The trigeminal nerve (TGN) is the largest cranial nerve and can be involved in multiple inflammatory, compressive, ischemic or other pathologies. Currently, imaging-based approaches to identify the TGN mostly rely on T2-weighted magnetic resonance imaging (MRI), which provides localization of the cisternal portion of the TGN where the contrast between nerve and cerebrospinal fluid (CSF) is high enough to allow differentiation. The course of the TGN within the brainstem as well as anterior to the cisternal portion, however, is more difficult to display on traditional imaging sequences. An advanced imaging technique, diffusion MRI (dMRI), enables tracking of the trajectory of TGN fibers and has the potential to visualize anatomical regions of the TGN not seen on T2-weighted imaging. This may allow a more comprehensive assessment of the nerve in the context of pathology. To date, most work in TGN tracking has used clinical dMRI acquisitions with a b-value of 1000 s/mm and conventional diffusion tensor MRI (DTI) tractography methods. Though higher b-value acquisitions and multi-tensor tractography methods are known to be beneficial for tracking brain white matter fiber tracts, there have been no studies conducted to evaluate the performance of these advanced approaches on nerve tracking of the TGN, in particular on tracking different anatomical regions of the TGN.