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.

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.

Diabetic neuropathy (DPN) is one of the most severe and yet most poorly understood complications of diabetes mellitus. imaging of dorsal root ganglia (DRG), a key structure for the understanding of DPN, has been restricted to animal studies. These have shown a correlation of decreased DRG volume with neuropathic symptom severity. Our objective was to investigate correlations of DRG morphology and signal characteristics at 3 Tesla (3T) magnetic resonance neurography (MRN) with clinical and serological data in diabetic patients with and without DPN. In this cross-sectional study, participants underwent 3T MRN of both L5 DRG using an isotropic 3D T2-weighted, fat-suppressed sequence with subsequent segmentation of DRG volume and analysis of normalized signal properties. Overall, 55 diabetes patients (66 ± 9 years; 32 men; 30 with DPN) took part in this study. DRG volume was smaller in patients with severe DPN when compared to patients with mild or moderate DPN (134.7 ± 21.86 vs 170.1 ± 49.22; = 0.040). In DPN patients, DRG volume was negatively correlated with the neuropathy disability score ( = -0.43; 95%CI = -0.66 to -0.14; = 0.02), a measure of neuropathy severity. DRG volume showed negative correlations with triglycerides ( = -0.40; 95%CI = -0.57 to -0.19; = 0.006), and LDL cholesterol ( = -0.33; 95%CI = -0.51 to -0.11; = 0.04). There was a strong positive correlation of normalized MR signal intensity (SI) with the neuropathy symptom score in the subgroup of patients with painful DPN ( = 0.80; 95%CI = 0.46 to 0.93; = 0.005). DRG SI was positively correlated with HbA1c levels ( = 0.30; 95%CI = 0.09 to 0.50; = 0.03) and the triglyceride/HDL ratio ( = 0.40; 95%CI = 0.19 to 0.57; = 0.007). In this first study, we found DRG morphological degeneration and signal increase in correlation with neuropathy severity. This elucidates the potential importance of MR-based DRG assessments in studying structural and functional changes in DPN.

Post-herpetic neuralgia (PHN) is a common herpes zoster (HZ) complication, where pain persists 90 days after the initial HZ diagnosis. Evaluating PHN risk is essential for determining the burden on patients and health-care systems, but research shows variable estimates. The extent to which these differences are related to the assessment method has not been examined. The purpose of this study is to compare the proportion of PHN among HZ patients measured by medical chart review and self-report surveys.