Establishing a Gold Standard for Noninvasive Identification of Painful Lumbar Discs: Prospective Comparison of Magnetic Resonance Spectroscopy vs Low-Pressure Provocation Discography

PURPOSE

Verifying lumbar disc pain can present a clinical challenge. Low-pressure provocative discography (PD) has served as the gold standard, although it is invasive and often a challenge to interpret. We reported that magnetic resonance spectroscopy (MRS) biomarkers accurately predict PD results in lumbar discs and improved outcomes for patients with surgery at positive MRS levels versus nonsurgery. To further substantiate MRS for diagnosing painful discs, we report a prospective comparison of 2 MRS-derived measures: NOCISCORE (pain) and SI-SCORE (degeneration severity).

METHODS

Lumbar MRS and software-based postprocessing (NOCISCAN-LS, Aclarion Inc.) was performed in 44 discs in 14 patients (prospective cohort [PC]). PC data were compared to prior data used to establish the NOCISCORE (training cohort [TC]). The NOCISCORE was converted to an ordinal value (high/intermediate/low; NOCI+/mild/-) and compared against painful (P) versus nonpainful (NP) control diagnosis (PD) for 19 discs where PD was performed in the PC (12 NP; 7 P). Sensitivity, specificity, and positive and negative predictive values were calculated. The SI-SCORE was compared against MRI Pfirrmann Grades for 465 discs in 126 patients (PC plus TC).

RESULTS

For the PC, MRS (NOCI+/-) compared to PD (P/NP) with an accuracy of 87% and sensitivity and specificity of 100%. The positive and negative predictive values of MRS were 100%. NOCISCOREs were significantly higher for PD+ versus PD- discs for PC and TC (P < 0.05), and the NOCISCORE distributions for PD+/- group were not statistically different between the PC and TC (P > 0.05). SI-SCORES differed between Pfirrmann Grades 1 and 2 (less degenerated) versus Grades 3 and 4 (more degenerated; P < 0.05), with a progressively decreasing trend with Pfirrmann Grades 1-5.

CONCLUSION

These current data provide prospective confirmation of the predictive value of disc MRS for distinguishing painful discs and for assessing the disc structural integrity.

CLINICAL RELEVANCE

NOCISCAN is an adoptable, noninvasive, and objectively quantitative test to improve management of low back pain patients.

LEVEL OF EVIDENCE: 2

Multisensory sensitivity differentiates between multiple chronic pain conditions and pain-free individuals

ABSTRACT

Multisensory sensitivity (MSS) to nonpainful stimuli has been identified as a risk factor for the presence of coexisting chronic pain conditions. However, it remains unclear whether MSS can differentiate pain phenotypes involving different levels of central sensitivity. Both pain-free and those with chronic pain, particularly fibromyalgia (FM), migraine, or low back pain (LBP) were recruited, with pain comorbidities assessed. MSS was highest in FM, followed by migraine, then LBP, and lowest in pain-free individuals (adjusted between condition Cohen d = 0.32-1.2, P ≤ 0.0007). However, when secondly grouping patients by the total number of pain comorbidities reported, those with a single pain condition (but not FM) did not have significantly elevated MSS vs pain-free individuals (adj d= 0.17, P = 0.18). Elevated MSS scores produced increased odds of having 2 or more pain comorbidities; OR [95% CI] =2.0 [1.15, 3.42], without, and 5.6 [2.74, 11.28], with FM ( P ≤ 0.0001). Furthermore, those with low MSS levels were 55% to 87% less likely to have ≥ 2 pain comorbidities with or without FM (OR 0.45 [0.22, 0.88]-0.13 [0.05, 0.39]; P ≤ 0.0001). Our findings support that MSS can differentiate between pain phenotypes with different degrees of expected central mechanism involvement and also serve as a risk and resilience marker for total coexisting chronic pain conditions. This supports the use of MSS as a marker of heightened central nervous system processing and thus may serve as a clinically feasible assessment to better profile pain phenotypes with the goal of improving personalized treatment.

Pain management using photobiomodulation: Mechanisms, location, and repeatability quantified by pain threshold and neural biomarkers in mice

Photobiomodulation (PBM) is a simple, efficient and cost-effective treatment for both acute and chronic pain. We previously showed that PBM applied to the mouse head inhibited nociception in the foot. Nevertheless, the optimum parameters, location for irradiation, duration of the effect and the mechanisms of action remain unclear. In the present study, the pain threshold in the right hind paw of mice was studied, after PBM (810 nm CW laser, spot size 1 or 6 cm² , 1.2-36 J/cm² ) applied to various anatomical locations. The pain threshold, measured with von Frey filaments, was increased more than 3-fold by PBM to the lower back (dorsal root ganglion, DRG), as well as to other neural structures along the pathway such as the head, neck and ipsilateral (right) paw. On the other hand, application of PBM to the contralateral (left) paw, abdomen and tail had no effect. The optimal effect occurred 2 to 3 hours post-PBM and disappeared by 24 hours. Seven daily irradiations showed no development of tolerance. Type 1 metabotropic glutamate receptors decreased, and prostatic acid phosphatase and tubulin-positive varicosities were increased as shown by immunofluorescence of DRG samples. These findings elucidate the mechanisms of PBM for pain and provide insights for clinical practice.

Biomarkers for Chronic Neuropathic Pain and their Potential Application in Spinal Cord Stimulation: A Review

This review was focused on understanding which substances inside the human body increase and decrease with increasing neuropathic pain. We reviewed various studies, and saw correlations between neuropathic pain and components of the immune system (this system defends the body against diseases and infections). Our findings will especially be useful for understanding ways to reduce or eliminate the discomfort, chronic neuropathic pain brings with it. Spinal cord stimulation (SCS) procedure is one of the few fairly efficient remedial treatments for pain. A follow-up study will apply our findings from this review to SCS, in order to understand the mechanism, and further optimize efficaciousness.

Comparison of machine classification algorithms for fibromyalgia: neuroimages versus self-report

Recent studies have posited that machine learning (ML) techniques accurately classify individuals with and without pain solely based on neuroimaging data. These studies claim that self-report is unreliable, making "objective" neuroimaging classification methods imperative. However, the relative performance of ML on neuroimaging and self-report data have not been compared. This study used commonly reported ML algorithms to measure differences between "objective" neuroimaging data and "subjective" self-report (ie, mood and pain intensity) in their ability to discriminate between individuals with and without chronic pain. Structural magnetic resonance imaging data from 26 individuals (14 individuals with fibromyalgia and 12 healthy controls) were processed to derive volumes from 56 brain regions per person. Self-report data included visual analog scale ratings for pain intensity and mood (ie, anger, anxiety, depression, frustration, and fear). Separate models representing brain volumes, mood ratings, and pain intensity ratings were estimated across several ML algorithms. Classification accuracy of brain volumes ranged from 53 to 76%, whereas mood and pain intensity ratings ranged from 79 to 96% and 83 to 96%, respectively. Overall, models derived from self-report data outperformed neuroimaging models by an average of 22%. Although neuroimaging clearly provides useful insights for understanding neural mechanisms underlying pain processing, self-report is reliable and accurate and continues to be clinically vital.

PERSPECTIVE

The present study compares neuroimaging, self-reported mood, and self-reported pain intensity data in their ability to classify individuals with and without fibromyalgia using ML algorithms. Overall, models derived from self-reported mood and pain intensity data outperformed structural neuroimaging models.