An epidemiological study of neuropathic pain symptoms in Canadian adults

The reported prevalence of neuropathic pain ranges from 6.9% to 10%; however the only Canadian study reported 17.9%. The objective of this study was to describe the epidemiology of neuropathic pain in Canada. A cross-sectional survey was conducted in a random sample of Canadian adults. The response rate was 21.1% (1504/7134). Likely or possible neuropathic pain was defined using a neuropathic pain-related diagnosis and a positive outcome on the Self-Report Leeds Assessment of Neuropathic Symptoms and Signs pain scale (S-LANSS) or the Douleur Neuropathique 4 (DN4) Questions. The prevalence of likely neuropathic pain was 1.9% (S-LANSS) and 3.4% (DN4) and that of possible neuropathic pain was 5.8% (S-LANSS) and 8.1% (DN4). Neuropathic pain was highest in economically disadvantaged males. There is a significant burden of neuropathic pain in Canada. The low response rate and a slightly older and less educated sample than the Canadian population may have led to an overestimate of neuropathic pain. Population prevalence varies by screening tool used, indicating more work is needed to develop reliable measures. Population level screening targeted towards high risk groups should improve the sensitivity and specificity of screening, while clinical examination of those with positive screening results will further refine the estimate of prevalence

Computer-vision based method for quantifying rising from chair in Parkinson's disease patients

BACKGROUND: The ability to arise from a sitting to a standing position is often impaired in Parkinson's disease (PD). This impairment is associated with an increased risk of falling, and higher risk of dementia. We propose a novel approach to estimate Movement Disorder Society Unified PD Rating Scale (MDS-UPDRS) ratings for “item 3.9” (arising from chair) using a computer vision-based method, whereby we use clinically informed reasoning to engineer a small number of informative features from high dimensional markerless pose estimation data. METHODS: We analysed 447 videos collected via the KELVIN-PD™ platform, recorded in clinical settings at multiple sites, using commercially available mobile smart devices. Each video showed an examination for item 3.9 of the MDS-UPDRS and had an associated severity rating from a trained clinician on the 5-point scale (0, 1, 2, 3 or 4). The deep learning library OpenPose was used to extract pose estimation key points from each frame of the videos, resulting in time-series signals for each key point. From these signals, features were extracted which capture relevant characteristics of the movement; velocity variation, smoothness, whether the patient used their hands to push themselves up, how stooped the patient was while sitting and how upright the patient was when fully standing. These features were used to train an ordinal classification system (with one class for each of the possible ratings on the UPDRS), based on a series of random forest classifiers. RESULTS: The UPDRS ratings estimated by this system, using leave-one-out cross validation, corresponded exactly to the ratings made by clinicians in 79% of videos, and were within one of those made by clinicians in 100% of cases. The system was able to distinguish normal from Parkinsonian movement with a sensitivity of 62.8% and a specificity of 90.3%. Analysis of misclassified examples highlighted the potential of the system to detect potentially mislabelled data. CONCLUSION: We show that our computer-vision based method can accurately quantify PD patients’ ability to perform the arising from chair action. As far as we are aware this is the first study estimating scores for item 3.9 of the MDS-UPDRS from singular monocular video. This approach can help prevent human error by identifying unusual clinician ratings, and provides promise for such a system being used routinely for clinical assessments, either locally or remotely, with potential for use as stratification and outcome measures in clinical trials

Pregabalin, celecoxib, and their combination for treatment of chronic low-back pain

Background - The efficacy and safety of the association of celecoxib [a selective cyclooxygenase-2 (COX-2) inhibitor] and pregabalin (commonly used to control neuropathic pain), compared with monotherapy of each, were evaluated for the treatment of chronic low-back pain, a condition known to be due to neuropathic as well as nociceptive pain mechanisms. Materials and methods - In this prospective randomized trial, 36 patients received three consecutive 4-week treatment regimes, randomly assigned: celecoxib plus placebo, pregabalin plus placebo, and celecoxib plus pregabalin. All patients were assessed by using a visual analogue scale (VAS, 0\u2013100 mm) and the Leeds Assessment of Neuropathic Symptoms and Signs (LANSS) pain scale by an investigator blinded to the administered pharmacological treatment. Results - Celecoxib and pregabalin were effective in reducing low-back pain when patients were pooled according to LANSS score. The association of celecoxib and pregabalin was more effective than either monotherapy in a mixed population of patients with chronic low-back pain and when data were pooled according to LANSS score. Adverse effects of drug association and monotherapies were similar, with reduced drug consumption in the combined therapy. Conclusions - Combination of celecoxib and pregabalin is more effective than monotherapy for chronic low-back pain, with similar adverse effects

A Clinically Interpretable Computer-Vision Based Method for Quantifying Gait in Parkinson's Disease.

Gait is a core motor function and is impaired in numerous neurological diseases, including Parkinson's disease (PD). Treatment changes in PD are frequently driven by gait assessments in the clinic, commonly rated as part of the Movement Disorder Society (MDS) Unified PD Rating Scale (UPDRS) assessment (item 3.10). We proposed and evaluated a novel approach for estimating severity of gait impairment in Parkinson's disease using a computer vision-based methodology. The system we developed can be used to obtain an estimate for a rating to catch potential errors, or to gain an initial rating in the absence of a trained clinician-for example, during remote home assessments. Videos (n=729) were collected as part of routine MDS-UPDRS gait assessments of Parkinson's patients, and a deep learning library was used to extract body key-point coordinates for each frame. Data were recorded at five clinical sites using commercially available mobile phones or tablets, and had an associated severity rating from a trained clinician. Six features were calculated from time-series signals of the extracted key-points. These features characterized key aspects of the movement including speed (step frequency, estimated using a novel Gamma-Poisson Bayesian model), arm swing, postural control and smoothness (or roughness) of movement. An ordinal random forest classification model (with one class for each of the possible ratings) was trained and evaluated using 10-fold cross validation. Step frequency point estimates from the Bayesian model were highly correlated with manually labelled step frequencies of 606 video clips showing patients walking towards or away from the camera (Pearson's r=0.80, p<0.001). Our classifier achieved a balanced accuracy of 50% (chance = 25%). Estimated UPDRS ratings were within one of the clinicians' ratings in 95% of cases. There was a significant correlation between clinician labels and model estimates (Spearman's ρ=0.52, p<0.001). We show how the interpretability of the feature values could be used by clinicians to support their decision-making and provide insight into the model's objective UPDRS rating estimation. The severity of gait impairment in Parkinson's disease can be estimated using a single patient video, recorded using a consumer mobile device and within standard clinical settings; i.e., videos were recorded in various hospital hallways and offices rather than gait laboratories. This approach can support clinicians during routine assessments by providing an objective rating (or second opinion), and has the potential to be used for remote home assessments, which would allow for more frequent monitoring

Proceedings of the 11th Annual Deep Brain Stimulation Think Tank: pushing the forefront of neuromodulation with functional network mapping, biomarkers for adaptive DBS, bioethical dilemmas, AI-guided neuromodulation, and translational advancements

The Deep Brain Stimulation (DBS) Think Tank XI was held on August 9–11, 2023 in Gainesville, Florida with the theme of “Pushing the Forefront of Neuromodulation”. The keynote speaker was Dr. Nico Dosenbach from Washington University in St. Louis, Missouri. He presented his research recently published in Nature inn a collaboration with Dr. Evan Gordon to identify and characterize the somato-cognitive action network (SCAN), which has redefined the motor homunculus and has led to new hypotheses about the integrative networks underpinning therapeutic DBS. The DBS Think Tank was founded in 2012 and provides an open platform where clinicians, engineers, and researchers (from industry and academia) can freely discuss current and emerging DBS technologies, as well as logistical and ethical issues facing the field. The group estimated that globally more than 263,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. This year's meeting was focused on advances in the following areas: cutting-edge translational neuromodulation, cutting-edge physiology, advances in neuromodulation from Europe and Asia, neuroethical dilemmas, artificial intelligence and computational modeling, time scales in DBS for mood disorders, and advances in future neuromodulation devices

Computer vision quantification of whole-body Parkinsonian bradykinesia using a large multi-site population.

Parkinson's disease (PD) is a common neurological disorder, with bradykinesia being one of its cardinal features. Objective quantification of bradykinesia using computer vision has the potential to standardise decision-making, for patient treatment and clinical trials, while facilitating remote assessment. We utilised a dataset of part-3 MDS-UPDRS motor assessments, collected at four independent clinical and one research sites on two continents, to build computer-vision-based models capable of inferring the correct severity rating robustly and consistently across all identifiable subgroups of patients. These results contrast with previous work limited by small sample sizes and small numbers of sites. Our bradykinesia estimation corresponded well with clinician ratings (interclass correlation 0.74). This agreement was consistent across four clinical sites. This result demonstrates how such technology can be successfully deployed into existing clinical workflows, with consumer-grade smartphone or tablet devices, adding minimal equipment cost and time

Clinical characteristics and patterns of healthcare utilization in patients with painful neuropathic disorders in UK general practice: a retrospective cohort study

<p>Abstract</p> <p>Background</p> <p>Clinical characteristics and patterns of healthcare utilization in patients with painful neuropathic disorders (PNDs) who are under the care of general practitioners (GPs) in the UK are not well understood.</p> <p>Methods</p> <p>Using a large electronic UK database, we identified all adults (age ≥ 18 years) with any GP encounters between 1 January 2006 - 31 December 2006 at which a diagnosis of PND was noted ("PND patients"). An age-and gender-matched comparison group also was constituted consisting of randomly selected patients with one or more GP encounters-but no mention of PNDs-during this period. Characteristics and patterns of healthcare utilization of patients in the two groups were then examined over the one-year study period.</p> <p>Results</p> <p>The study sample consisted of 31,688 patients with mention of PNDs and an equal number of matched comparators; mean age was 56 years, and 62% were women. The prevalence of various comorbidities was higher among patients in the PND group, including digestive disorders (31% vs. 17% for comparison group), circulatory disorders (29% vs. 22%), and depression (4% vs. 3%) (all <it>p </it>< 0.01). Receipt of prescriptions for pain-related pharmacotherapy also was higher among PND patients, including nonsteroidal anti-inflammatory drugs (56% of PND patients had one or more such prescriptions vs. only 22% in the comparison group), opioids (49% vs. 12%), tricyclic antidepressants (20% vs. 1%), and antiepileptics (12% vs. 1%) (all <it>p </it>< 0.01). PND patients also averaged significantly more GP visits (22.8 vs. 14.2) and referrals to specialists (2.8 vs. 1.4) over one year (both comparisons <it>p </it>< 0.01).</p> <p>Conclusions</p> <p>Patients with PNDs under the care of GPs in the UK have relatively high levels of use of healthcare services and pain-related pharmacotherapy.</p

Tolerability of NGX-4010, a capsaicin 8% dermal patch, following pretreatment with lidocaine 2.5%/prilocaine 2.5% cream in patients with post-herpetic neuralgia

<p>Abstract</p> <p>Background</p> <p>Post-herpetic neuralgia (PHN) is a common type of neuropathic pain that can severely affect quality of life. NGX-4010, a capsaicin 8% dermal patch, is a localized treatment that can provide patients with significant pain relief for up to 3 months following a single 60-minute application. The NGX-4010 application can be associated with application-site pain and in previous clinical trials pretreatment with a topical 4% lidocaine anesthetic was used to enhance tolerability. The aim of the current investigation was to evaluate tolerability of NGX-4010 after pretreatment with lidocaine 2.5%/prilocaine 2.5% anesthetic cream.</p> <p>Methods</p> <p>Twenty-four patients with PHN were pretreated with lidocaine 2.5%/prilocaine 2.5% cream for 60 minutes before receiving a single 60-minute application of NGX-4010. Tolerability was assessed by measuring patch application duration, the proportion of patients completing over 90% of the intended treatment duration, application site-related pain using the Numeric Pain Rating Scale (NPRS), and analgesic medication use to relieve such pain. Safety was assessed by monitoring adverse events (AEs) and dermal irritation using dermal assessment scores.</p> <p>Results</p> <p>The mean treatment duration of NGX-4010 was 60.2 minutes and all patients completed over 90% of the intended patch application duration. Pain during application was transient. A maximum mean change in NPRS score of +3.0 was observed at 55 minutes post-patch application; pain scores gradually declined to near pre-anesthetic levels (+0.71) within 85 minutes of patch removal. Half of the patients received analgesic medication on the day of treatment; by Day 7, no patients required medication. The most common AEs were application site-related pain, erythema, edema, and pruritus. All patients experienced mild dermal irritation 5 minutes after patch removal, which subsequently decreased; at Day 7, no irritation was evident. The maximum recorded dermal assessment score was 2.</p> <p>Conclusion</p> <p>NGX-4010 was well tolerated following pretreatment with lidocaine 2.5%/prilocaine 2.5% cream in patients with PHN. The tolerability of the patch application appeared comparable with that seen in other studies that used 4% lidocaine cream as the pretreatment anesthetic. This study is registered at <url>http://www.clinicaltrials.gov</url> as number <a href="http://www.clinicaltrials.gov/ct2/show/NCT00916942">NCT00916942</a>.</p