Building and validating trend-based multiple sclerosis case definitions: a population-based cohort study for Manitoba, Canada.

OBJECTIVE: This study aims to (1) build and validate model-based case definitions for multiple sclerosis (MS) that use trends (ie, trend-based case definitions) and (2) to apply dynamic classification to identify the average number of data years needed for classification (ie, average trend needed). DESIGN: Retrospective cohort study design. PARTICIPANTS: 608 MS cases and 59 620 MS non-cases. SETTING: Data from 1 April 2004 to 31 March 2022 were obtained from the Manitoba Population Research Data Repository. MS case status was ascertained from homecare records and linked to health data. Trend-based case definitions were constructed using multivariate generalised linear mixed models applied to annual numbers of general and specialist physician visits, hospitalisations and MS healthcare contacts or medication dispensations. Dynamic classification, which ascertains cases and non-cases annually, was used to estimate mean classification time. Classification accuracy performance measures, including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), proportion correctly classified (PCC) and F1-scores, were compared for trend-based case definitions and a deterministic case definition of 3+MS healthcare contacts or medication dispensations. RESULTS: When applied to the full study period, classification accuracy performance measure estimates for all case definitions exceeded 0.90, except sensitivity and PPV for the trend-based dynamic case definition (0.88, 0.64, respectively). PCC was high for all case definitions (0.94-0.99); F1-scores were lower for the trend-based case definitions compared with the deterministic case definition (0.74-0.93 vs 0.96). Dynamic classification identified 5 years as the average trend needed. When applied to the average trend windows, accuracy estimates for trend-based case definitions were lower than the estimates from the full study period (sensitivity: 0.77-0.89; specificity: 0.90-0.97; PPV: 0.54-0.81; NPV: 0.97-0.99; F1-score: 0.64-0.84). Accuracy estimates for the deterministic case definition remained high, except sensitivity (0.42-0.80). F1-score was variable (0.59-0.89). CONCLUSIONS: Trend-based and deterministic case definitions classifications were similar to a population-based clinician assessment reference standard for multiple measures of classification accuracy. However, accuracy estimates for both trend-based and deterministic case definitions varied as the years of data used for classification were reduced. Dynamic classification appears to be a viable option for identifying the average trend needed for trend-based case definitions.

Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis.

BACKGROUND: Robust predictive models of clinical impairment and worsening in multiple sclerosis (MS) are needed to identify patients at risk and optimize treatment strategies. OBJECTIVE: To evaluate whether machine learning (ML) methods can classify clinical impairment and predict worsening in people with MS (pwMS) and, if so, which combination of clinical and magnetic resonance imaging (MRI) features and ML algorithm is optimal. METHODS: We used baseline clinical and structural MRI data from two MS cohorts (Berlin: n = 125, Amsterdam: n = 330) to evaluate the capability of five ML models in classifying clinical impairment at baseline and predicting future clinical worsening over a follow-up of 2 and 5 years. Clinical worsening was defined by increases in the Expanded Disability Status Scale (EDSS), Timed 25-Foot Walk Test (T25FW), 9-Hole Peg Test (9HPT), or Symbol Digit Modalities Test (SDMT). Different combinations of clinical and volumetric MRI measures were systematically assessed in predicting clinical outcomes. ML models were evaluated using Monte Carlo cross-validation, area under the curve (AUC), and permutation testing to assess significance. RESULTS: The ML models significantly determined clinical impairment at baseline for the Amsterdam cohort, but did not reach significance for predicting clinical worsening over a follow-up of 2 and 5 years. High disability (EDSS ≥ 4) was best determined by a support vector machine (SVM) classifier using clinical and global MRI volumes (AUC = 0.83 ± 0.07, p = 0.015). Impaired cognition (SDMT Z-score ≤ -1.5) was best determined by a SVM using regional MRI volumes (thalamus, ventricles, lesions, and hippocampus), reaching an AUC of 0.73 ± 0.04 (p = 0.008). CONCLUSION: ML models could aid in classifying pwMS with clinical impairment and identify relevant biomarkers, but prediction of clinical worsening is an unmet need.

Exploring subtypes of multiple sclerosis through unsupervised machine learning of automated fiber quantification.

PURPOSE: This study aimed to subtype multiple sclerosis (MS) patients using unsupervised machine learning on white matter (WM) fiber tracts and investigate the implications for cognitive function and disability outcomes. MATERIALS AND METHODS: We utilized the automated fiber quantification (AFQ) method to extract 18 WM fiber tracts from the imaging data of 103 MS patients in total. Unsupervised machine learning techniques were applied to conduct cluster analysis and identify distinct subtypes. Clinical and diffusion tensor imaging (DTI) metrics were compared among the subtypes, and survival analysis was conducted to examine disability progression and cognitive impairment. RESULTS: The clustering analysis revealed three distinct subtypes with variations in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). Significant differences were observed in clinical and DTI metrics among the subtypes. Subtype 3 showed the fastest disability progression and cognitive decline, while Subtype 2 exhibited a slower rate, and Subtype 1 fell in between. CONCLUSIONS: Subtyping MS based on WM fiber tracts using unsupervised machine learning identified distinct subtypes with significant cognitive and disability differences. WM abnormalities may serve as biomarkers for predicting disease outcomes, enabling personalized treatment strategies and prognostic predictions for MS patients.

Rituximab in Multiple Sclerosis: Are We Ready for Regulatory Approval?

Despite the availability of a lot of effective disease-modifying drugs, multiple sclerosis (MS) (in particular the progressive forms) still represents an important unmet medical need, because of issues in terms of effectiveness, duration of response, safety, and patient compliance. An increasing body of evidence from randomized clinical trials and real-world data suggest that rituximab is a highly effective alternative in both relapsing and progressive MS, with a low discontinuation rate, related to a good benefit/risk profile, and a good compliance. To date, the use of rituximab in patients with multiple sclerosis is not in accordance with the authorized product information (off-label use). However, the use of this medicine is widespread in several countries, and in some cases, it is the most commonly used disease-modifying drug for MS subtypes. This use could be officially recognized by national regulatory authorities, according to specific procedures, to ensure equal access for patients to a safe and effective option.

Gene expression profiles of YAP1, TAZ, CRB3, and VDR in familial and sporadic multiple sclerosis among an Iranian population.

Alterations in the regulatory mechanisms that control the process of myelination in the nervous system, may lead to the impaired myelination in the Multiple sclerosis. The Hippo pathway is an important mediator of myelination in the nervous system and might contribute to the pathophysiology of MS. This study examined via qPCR the RNA expression of YAP1, TAZ, and CRB3 as the key effectors of the Hippo pathway and also, VDR in the peripheral blood of 35 sporadic, 37 familial MS patients; and also 34 healthy first-degree relatives of the familial MS patients (HFR) and 40 healthy individuals without a family history of the disease (control). The results showed the increased expression of VDR in the sporadic group, as compared to other groups. There was also an increased expression of TAZ in the familial and HFR groups, as compared to the control group. The familial and sporadic patients displayed a significantly lower level of expression of YAP1 in comparison to the HFR group. The increased expression level in the sporadic patients and control group, as compared to the HFR group, was seen in CRB3. We also assessed different clinical parameters and MRI characteristics of the patients. Overall, these findings suggest that Hippo pathway effectors and also VDR gene may play a potential role in the pathophysiology of the sporadic and familial forms of MS. Confirmation of different gene expression patterns in sporadic and familial MS groups may have obvious implications for the personalization of therapies in the disease.

Classification Criteria for Multiple Sclerosis-Associated Intermediate Uveitis.

PURPOSE: The purpose of this study was to determine classification criteria for multiple sclerosis-associated intermediate uveitis. DESIGN: Machine learning of cases with multiple sclerosis-associated intermediate uveitis and 4 other intermediate uveitides. METHODS: Cases of intermediate uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used in the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated in the validation set. RESULTS: A total of 589 cases of intermediate uveitides, including 112 cases of multiple sclerosis-associated intermediate uveitis, were evaluated by machine learning. The overall accuracy for intermediate uveitides was 99.8% in the training set and 99.3% in the validation set (95% confidence interval: 96.1-99.9). Key criteria for multiple sclerosis-associated intermediate uveitis included unilateral or bilateral intermediate uveitis and multiple sclerosis diagnosed by the McDonald criteria. Key exclusions included syphilis and sarcoidosis. The misclassification rates for multiple sclerosis-associated intermediate uveitis were 0 % in the training set and 0% in the validation set. CONCLUSIONS: The criteria for multiple sclerosis-associated intermediate uveitis had a low misclassification rate and appeared to perform sufficiently well enough for use in clinical and translational research.

Classification of multiple sclerosis based on patterns of CNS regional atrophy covariance.

There is evidence that multiple sclerosis (MS) pathology leads to distinct patterns of volume loss over time (VLOT) in different central nervous system (CNS) structures. We aimed to use such patterns to identify patient subgroups. MS patients of all classical disease phenotypes underwent annual clinical, blood, and MRI examinations over 6 years. Spinal, striatal, pallidal, thalamic, cortical, white matter, and T2-weighted lesion volumes as well as serum neurofilament light chain (sNfL) were quantified. CNS VLOT patterns were identified using principal component analysis and patients were classified using hierarchical cluster analysis. 225 MS patients were classified into four distinct Groups A, B, C, and D including 14, 59, 141, and 11 patients, respectively). These groups did not differ in baseline demographics, disease duration, disease phenotype distribution, and lesion-load expansion. Interestingly, Group A showed pronounced spinothalamic VLOT, Group B marked pallidal VLOT, Group C small between-structure VLOT differences, and Group D myelocortical volume increase and pronounced white matter VLOT. Neurologic deficits were more severe and progressed faster in Group A that also had higher mean sNfL levels than all other groups. Group B experienced more frequent relapses than Group C. In conclusion, there are distinct patterns of VLOT across the CNS in MS patients, which do not overlap with clinical MS subtypes and are independent of disease duration and lesion-load but are partially associated to sNfL levels, relapse rates, and clinical worsening. Our findings support the need for a more biologic classification of MS subtypes including volumetric and body-fluid markers.

Inferring Multiple Sclerosis Stages from the Blood Transcriptome via Machine Learning.

Peripheral blood mononuclear cells (PBMCs) bear specific dysregulations in genes and pathways at distinct stages of multiple sclerosis (MS) that may help with classifying MS and non-MS subjects, specifying the early stage of disease, or discriminating among MS courses. Here we describe an unbiased machine learning workflow to build MS stage-specific classifiers based on PBMC transcriptomics profiles from more than 300 individuals, including healthy subjects and patients with clinically isolated syndromes, relapsing-remitting MS, primary or secondary progressive MS, or other neurological disorders. The pipeline, designed to optimize and compare the performance of distinct machine learning algorithms in the training cohort, generates predictive models not influenced by demographic features, such as age and gender, and displays high accuracy in the independent validation cohort. Proper application of machine learning to transcriptional profiles of circulating blood cells may allow identification of disease state and stage in MS.

A Descriptive Cross-Sectional Study of Manipulative Dexterity on Different Subtypes of Multiple Sclerosis.

BACKGROUND: Manipulative dexterity impairments affect 76% of individuals with multiple sclerosis (MS). Patients with MS can experience reduced skill when performing both basic activities of daily living and instrumental activities of daily living. Many studies consider that physical rehabilitation leads to a decrease in the level of disability, especially at the mild and moderate stages of the disease. However, most studies fail to distinguish between the different MS subtypes. PURPOSE: Our aims were (1) to describe the manipulative skills of people according to the different subtypes of MS, (2) to analyze the correlation between dexterity and self-perception variables, and (3) to identify possible predictors of functionality. Study Design. A descriptive cross-sectional study. METHODS: 30 individuals with MS. The measurement tools used were the ABILHAND, the Purdue Pegboard Test (PPT), the Nine-Hole Peg Test, and the Box and Block Test. RESULTS: All subtypes of MS obtained lower scores for manipulative dexterity compared to normal skill levels, with individuals with primary progressive MS displaying the lowest values. However, the analysis of differences between the different subtypes did not reveal statistically significant intragroup differences. In addition, differences were found regarding the effect size of practically all the variables analyzed for both manipulative dexterity (PPT, NHPT, and BBT) and the self-perception of ADLs (ABILHAND), for which high values (d r = 0.72) and very high values (d r = 1.46) were obtained. CONCLUSIONS: Although no significant differences were found between the different types of MS, the assessment of manual dexterity and perceived efficacy of daily activities must be considered as prognostic factors in the progression of the disease. These findings may help support further research on targeted interventions to improve dexterity deficiencies, as well as promote an improved quality of therapeutic interventions.

Is Initial Misdiagnosis Associated with Reaching Disability Milestones in Patients with Multiple Sclerosis?

Background and objectives: multiple sclerosis (MS) is a chronic demyelinating disorder of the CNS with a variable course and disability progression. The latter may be prevented with disease-modifying therapy (DMT). Initial misdiagnosis may postpone the use of DMT. There are no studies to explore whether initial misdiagnosis is indeed associated with a higher rate of reaching disability in MS patients. We aimed to investigate the association between initial misdiagnosis and reaching disability milestones in relapsing-remitting MS (RR-MS) patients. Materials and methods: Data from 128 RR-MS patients were retrospectively reviewed. EDSS 4 and EDSS 6 were chosen as disability milestones as those associated with a significant decrease in ambulation. Survival analysis was used, and Kaplan-Meier curves were generated to investigate how initial misdiagnosis affects reaching the defined milestones. Results: 53 patients (41.4%, 31 females, 22 males) were initially misdiagnosed. Initially misdiagnosed patients had a lesser risk of reaching EDSS 4 up to 11 years and EDSS 6 up to 22 years from the onset than non-misdiagnosed patients (p = 0.22 and p = 0.25 correspondingly). Median time to reaching EDSS 4 and 6 was eight years (95% CI 0.0-17.6) and 10 years (95% CI 4.25-20.75) in misdiagnosed and three years (95% CI 0.0-20.0 years) and five years (95% CI 0.0-13.73 years) in non-misdiagnosed patients correspondingly. Conclusions: Initially misdiagnosed RR-MS patients tended to reach disability milestones later than non-misdiagnosed ones, which might reflect an intrinsically milder disease. Individuals presenting with mild or non-specific symptoms suspicious of MS, must be deliberately managed.

Antibody signatures in patients with histopathologically defined multiple sclerosis patterns.

Early active multiple sclerosis (MS) lesions can be classified histologically into three main immunopathological patterns of demyelination (patterns I-III), which suggest pathogenic heterogeneity and may predict therapy response. Patterns I and II show signs of immune-mediated demyelination, but only pattern II is associated with antibody/complement deposition. In pattern III lesions, which include Baló's concentric sclerosis, primary oligodendrocyte damage was proposed. Serum antibody reactivities could reflect disease pathogenesis and thus distinguish histopathologically defined MS patterns. We established a customized microarray with more than 700 peptides that represent human and viral antigens potentially relevant for inflammatory demyelinating CNS diseases, and tested sera from 66 patients (pattern I n = 12; II n = 29; III n = 25, including 8 with Baló's), healthy controls, patients with Sjögren's syndrome and stroke patients. Cell-based assays were performed for aquaporin 1 (AQP1) and AQP4 antibody detection. No single peptide showed differential binding among study cohorts. Because antibodies can react with different peptides from one protein, we also analyzed groups of peptides. Patients with pattern II showed significantly higher reactivities to Nogo-A peptides as compared to patterns I (p = 0.02) and III (p = 0.02). Pattern III patients showed higher reactivities to AQP1 (compared to pattern I p = 0.002, pattern II p = 0.001) and varicella zoster virus (VZV, compared to pattern II p = 0.05). In patients with Baló's, AQP1 reactivity was also significantly higher compared to patients without Baló's (p = 0.04), and the former revealed distinct antibody signatures. Histologically, Baló's patients showed loss of AQP1 and AQP4 in demyelinating lesions, but no antibodies binding conformational AQP1 or AQP4 were detected. In summary, higher reactivities to Nogo-A peptides in pattern II patients could be relevant for enhanced axonal repair and remyelination. Higher reactivities to AQP1 peptides in pattern III patients and its subgroup of Baló's patients possibly reflect astrocytic damage. Finally, latent VZV infection may cause peripheral immune activation.

Multiple Sclerosis Severity Score: Concept and applications.

Severity score represents disease duration-adjusted mean rank of disability in multiple sclerosis (MS) patients from the reference population. This measure allows one to compare the relative rates of disease progression among patients, patient subgroups, and across epochs, which opens up new question of what accounts for the observed differences in severity, and can be used to assess correlation between disease severity and clinical, radiologic, immunologic, genetic, and environmental variables of interest. Severity score can also prove useful for developing prognostic tools in MS. This article discusses the diverse applications of severity score concept in MS research, and (re)introduces Herbert's proposal of severity-based MS classification in the context of variability of MS severity.

Metabolomics in multiple sclerosis disease course and progression.

Multiple sclerosis (MS) is associated with changes in the metabolome. Numerous studies employing varying metabolomics platforms have examined a range of biological material ranging from brain tissue to urine and demonstrated consistently alterations in multiple metabolic pathways in MS. We review not only the studies that describe the ability of metabolomics to differentiate MS patients from healthy controls and other neurological disease but also discuss the potential of metabolomics-based methods to build predictive models that are able to stage disease, monitor progression, and select the most appropriate therapy. The increasing number of impressive claims for the capacity of metabolomics to distinguish between different types of demyelinating disease suggests that the provision of such tests may be close at hand. Besides the ability to provide potential diagnostic and prognostic biomarkers, metabolomics also provides us with unique insights into the pathophysiology of the disease and helps identify metabolic pathways that may be potential therapeutic targets. Future studies will integrate metabolomics data with other omics techniques to provide further insight into the source of these metabolic abnormalities and help with identification of the most promising targets for therapeutic intervention.

Impact of multiple sclerosis phenotypes on burden of disease in Finland.

Aims: The aim of this study was to quantify how multiple sclerosis (MS) phenotypes differ from each other in respect of costs and quality-of-life.Materials and methods: The study is based on survey data from Finnish patients with MS (n = 553). The information contained disease type, disease severity according to self-reported Expanded Disease Severity Scale (EDSS), healthcare resource use, and medication use. In addition, information related to employment and early retirement was collected. EQ-5D-VAS and Multiple Sclerosis Impact Scale-29 (MSIS-29) instruments were used to collect quality-of-life data, and Fatigue Severity Scale (FSS) instrument for evaluating fatigue. Patients were stratified based on their disease type (relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), primary progressive MS (PPMS)) and disease severity. The data were primarily analyzed using summary statistics.Results: SPMS had the highest annual total cost (71,177€) followed by PPMS (51,082€) and RRMS (36,492€). Early retirement covered the greatest share of costs in RRMS (39%) and SPMS (43%). In PPMS, early retirement and professional care were the two most equally important cost drivers, contributing together 56% of the total costs. Direct healthcare costs were responsible for 33%, 19%, and 18% of total costs in RRMS, SPMS, and PPMS. The mean EDSS in RRMS, SPMS, and PPMS were 2.5, 5.5, and 5.9, respectively. Differences in the quality-of-life were observed with both disease specific (MSIS-29) and generic (EQ-5D-VAS) instruments. The mean utility value from EQ-5D among patients with RRMS, SPMS, and PPMS was 0.76, 0.52, and 0.49, respectively. In addition, patients with SPMS and PPMS were more likely to report fatigue than patients with RRMS.Conclusions: MS phenotype has an impact on costs and quality-of-life of the patients. Early retirement seems to be one of the most important contributors to MS-related costs.

Diffusion tensor imaging tractography reveals altered fornix in all diagnostic subtypes of multiple sclerosis.

INTRODUCTION: Diffusion tensor imaging (DTI) has shown abnormalities of the fornix and other limbic white matter tracts in multiple sclerosis (MS), mainly focusing on relapsing-remitting MS. METHODS: The goal here was to evaluate the fornix, cingulum, and uncinate fasciculus with DTI tractography at 1.7 mm isotropic resolution in three MS subgroups (11 relapsing-remitting (RRMS), nine secondary progressive (SPMS), eight primary progressive (PPMS)) versus 11 controls, and assess correlations with cognitive and clinical scores. RESULTS: The MS group overall showed extensive diffusion abnormalities of the fornix with less volume, lower fractional anisotropy (FA), and higher mean and radial diffusivities, which were similarly affected in all three MS subgroups. The uncinate fasciculus had lower FA only in the secondary progressive subgroup, and the cingulum had no DTI differences in any MS subgroup. The FA and/or volumes of these tracts correlated negatively with larger total lesion volume. The only DTI-cognitive correlation was lower right cingulum FA and greater depression over the entire MS cohort. CONCLUSIONS: Diffusion tractography identified abnormalities in the fornix that appears to be affected early and consistently across all three primary MS phenotypes of RRMS, SPMS, and PPMS regardless of Expanded Disability Status Scale, time since diagnosis, or cognitive scores.

Associations Between Self-Reported Symptoms and Gait Parameters Using In-Home Sensors in Persons With Multiple Sclerosis.

BACKGROUND AND PURPOSE: Multiple sclerosis (MS) is a progressive neurological disorder, characterized by exacerbations and remissions, often resulting in disability affecting multiple neurological functions. The purpose of this article was (1) to describe the frequencies of self-reported symptoms in a natural environment and (2) to determine characteristics and associations between self-reported symptoms and home gait parameters (speed, stride time, and stride length) at baseline and at 3 months in patients with MS. METHODS: Participants completed the self-report MS-Related Symptom Scale to measure symptoms. A three-dimensional depth imaging system (Foresite Healthcare) was used to measure gait parameters in the home environment. RESULTS: These data show significant correlations between the following symptoms: knee locking or collapsing, difficulty sleeping, depression, and anxiety with decreased number of average walks per day; however, the symptoms including trouble-making toilet: day and difficulty in starting urine were positively correlated with average walks per day. The symptom numbness was significantly correlated with decreased speed and decreased stride length. DISCUSSION AND CONCLUSIONS: Our findings suggest that certain groups of symptoms were more frequently reported with certain gait parameters (stride time/speed) in persons with MS. Rehabilitation nurses can provide optimal care to prevent future decline in symptoms and gait.

Efficacy and safety of siponimod for multiple sclerosis: Protocol for a systematic review and meta-analysis.

BACKGROUND: Multiple sclerosis is the most common demyelinating disease of the central nervous system with serious social and economic burden. Siponimod is a sphingosine-1-phosphate receptor agonist, and clinical trials in the past decade have shown good prospects for the treatment of multiple sclerosis. But there is a lack of comprehensive understanding of the dose-effect relationship and safety in different subtypes of multiple sclerosis at present. METHODS: We will perform a systematic review and meta-analysis of clinical randomized controlled trials to evaluate the efficacy and safety of siponimod in multiple sclerosis. We will search PubMed, EMBASE, Cochrane Library, Clinical Trials, Cochrane Central Register of Controlled Trials (CENTRAL) using a comprehensive strategy. The reference lists of the articles we select for inclusion will be checked to identify additional studies for potential inclusion. Two reviewers will review all literature independently. Upon inclusion of articles, another 2 reviewers will extract available data using a standardized form and assess the potential bias. Review Manager will be used to conduct data synthesis. There is no requirement of ethical approval and informed consent. RESULT: This is the first systematic assessment of siponimod for the treatment of multiple sclerosis. We predict it will provide high-quality synthesis of existing evidence for the efficacy and safety of siponimod for multiple sclerosis and a relatively comprehensive reference for clinical practice and clinical trials about siponimod to be conducted. CONCLUSION: The results of the systematic review and meta-analysis will provide updated evidence for the use of siponimod for multiple sclerosis. REGISTRATION: The systematic review and meta-analysis is registered in the PROSPERO international prospective register of systematic review (PROSPERO#CRD42018112721).

McArdle Sign: A Specific Sign of Multiple Sclerosis.

OBJECTIVE: To measure McArdle sign (rapidly reversible weakness induced by neck flexion) both qualitatively and quantitatively and to evaluate its specificity and clinical utility for diagnosis of multiple sclerosis (MS). PATIENTS AND METHODS: In this prospective study, McArdle sign was evaluated by a technician blinded to diagnosis by measuring changes in finger extensor strength in successive trials of neck extension and flexion, first clinically and then with a torque measurement device. We studied 25 healthy controls and 81 patients with finger extensor weakness. Patients were not selected for having McArdle sign. Fifty-two patients had MS, 24 had other myelopathies, and 5 had peripheral nerve lesions accounting for their weakness. The study was conducted between February 1, 2016, and June 30, 2017. RESULTS: The median clinical McArdle sign and the 2 quantitative measures of neck flexion-induced strength reduction were greater in patients with MS than in the other groups (P<.001). Baseline strength did not confound the difference. The area under the receiver operating characteristic curve was 0.84 (95% CI, 0.75-0.93) comparing patients with MS vs healthy controls and 0.84 (95% CI, 0.75-0.93) comparing MS vs patients with other myelopathies. The 2 quantitative and 1 clinical measurement of McArdle sign by the technician who performed the quantitative testing were correlated (r=.57 and r=.58; P<.001), and in turn, the technician's and unblinded referring physician's clinical assessments were correlated (r=.58; P<.001). McArdle sign was evident in some patients who had minor disability and who were in early phases of MS. CONCLUSION: McArdle sign, when defined as greater than 10% neck flexion-induced reduction in strength, is entirely specific and 65% sensitive for a diagnosis of MS when compared with other conditions that mimic MS-associated myelopathy. It may facilitate diagnosis in certain clinical situations. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT03122873.