2024
Incorporation of the Central Vein Sign into the McDonald Criteria
Amin M, Nakamura K, Daboul L, O'Donnell C, Cao Q, Rodrigues P, Derbyshire J, Azevedo C, Bar-Or A, Caverzasi E, Calabresi P, Cree B, Freeman L, Henry R, Longbrake E, Oh J, Papinutto N, Pelletier D, Prčkovska V, Raza P, Ramos M, Samudralwar R, Schindler M, Sotirchos E, Sicotte N, Solomon A, Shinohara R, Reich D, Sati P, Ontaneda D. Incorporation of the Central Vein Sign into the McDonald Criteria. Multiple Sclerosis And Related Disorders 2024, 106182. DOI: 10.1016/j.msard.2024.106182.Peer-Reviewed Original ResearchCentral vein signDIS criteriaDiagnostic performanceMultiple sclerosisDeep white matter lesionsDiagnosis of multiple sclerosisMulti-center studyInternational multi-center studyWhite matter lesionsNorth American ImagingMcDonald CriteriaProspective studyDiagnostic accuracyMRI disseminationDemyelinating diseaseBackground DiagnosisMS diagnosisDiagnostic biomarkersCompare sensitivityLesionsBrain locationsMethods DataBrain imagingBrainSignsMulticenter validation of automated detection of paramagnetic rim lesions on brain MRI in multiple sclerosis
Chen L, Ren Z, Clark K, Lou C, Liu F, Cao Q, Manning A, Martin M, Luskin E, O'Donnell C, Azevedo C, Calabresi P, Freeman L, Henry R, Longbrake E, Oh J, Papinutto N, Bilello M, Song J, Kaisey M, Sicotte N, Reich D, Solomon A, Ontaneda D, Sati P, Absinta M, Schindler M, Shinohara R, Cooperative T. Multicenter validation of automated detection of paramagnetic rim lesions on brain MRI in multiple sclerosis. Journal Of Neuroimaging 2024, 34: 750-757. PMID: 39410780, DOI: 10.1111/jon.13242.Peer-Reviewed Original ResearchParamagnetic rim lesionsArea under the curveRim lesionsMultiple sclerosisPrognosis of MSBiomarkers of chronic inflammationWhite matter lesionsMulticenter settingMulticenter studyMulticenter validationChronic inflammationBrain MRIClinical trialsIdentified lesionsMulticenterMS diagnosisLesionsParamagnetic rimAutomated segmentation methodMRIMRI biomarkersMulticenter datasetDiagnosisSclerosisTeam of trained ratersDifferential diagnosis of suspected multiple sclerosis: global health considerations
Correale J, Solomon A, Cohen J, Banwell B, Gracia F, Gyang T, de Bedoya F, Harnegie M, Hemmer B, Jacob A, Kim H, Marrie R, Mateen F, Newsome S, Pandit L, Prayoonwiwat N, Sahraian M, Sato D, Saylor D, Shi F, Siva A, Tan K, Viswanathan S, Wattjes M, Weinshenker B, Yamout B, Fujihara K. Differential diagnosis of suspected multiple sclerosis: global health considerations. The Lancet Neurology 2024, 23: 1035-1049. PMID: 39304243, DOI: 10.1016/s1474-4422(24)00256-4.Peer-Reviewed Original ResearchConceptsWestern EuropeLatin AmericaEastern EuropeMiddle EastSoutheast AsiaNorth AmericaLatinEuropeHealth considerationsDifferential diagnosis of multiple sclerosisAfricaAmericaPacific regionAsiaDiagnosis of multiple sclerosisDifferential diagnosisCollaborative effortsComprehensive approachMultiple sclerosisEastMedical careDifferential diagnosis of suspected multiple sclerosis: considerations in people from minority ethnic and racial backgrounds in North America, northern Europe, and Australasia
Amezcua L, Rotstein D, Shirani A, Ciccarelli O, Ontaneda D, Magyari M, Rivera V, Kimbrough D, Dobson R, Taylor B, Williams M, Marrie R, Banwell B, Hemmer B, Newsome S, Cohen J, Solomon A, Royal W. Differential diagnosis of suspected multiple sclerosis: considerations in people from minority ethnic and racial backgrounds in North America, northern Europe, and Australasia. The Lancet Neurology 2024, 23: 1050-1062. PMID: 39304244, DOI: 10.1016/s1474-4422(24)00288-6.Peer-Reviewed Original ResearchConceptsSocial determinants of healthDeterminants of healthRacial backgroundSocial determinantsDiagnosis of multiple sclerosisClinical characteristics of peopleCharacteristics of peopleWhite peopleMultiple sclerosisDifferential diagnosisPrevalence of multiple sclerosisSocial challengesMinorityChanging demographicsDifferential diagnosis of multiple sclerosisWhite populationNorthern EuropePeopleEuropeDifferential Diagnosis of Suspected Multiple Sclerosis in Pediatric and Late-Onset Populations
Hua L, Solomon A, Tenembaum S, Scalfari A, Rovira À, Rostasy K, Newsome S, Marrie R, Magyari M, Kantarci O, Hemmer B, Hemingway C, Harnegie M, Graves J, Cohen J, Bove R, Banwell B, Corboy J, Waubant E. Differential Diagnosis of Suspected Multiple Sclerosis in Pediatric and Late-Onset Populations. JAMA Neurology 2024, 81: 1210-1222. PMID: 39283621, DOI: 10.1001/jamaneurol.2024.3062.Peer-Reviewed Original ResearchDifferential diagnosisMultiple sclerosisAdult-onset MSClinical presentation of MSPresentation of MSNon-MS diagnosisClinical presentationAdult MSSuspected MSNeuronal injuryLate-onsetConsensus guidanceDiagnostic approachApproximately 5%Age groupsDiagnosisRed flagsMS expertsAgeEarly adulthoodBiological differencesSclerosisOlder adultsYearsAdultsMisdiagnosis and underdiagnosis of multiple sclerosis: A systematic review and meta-analysis
Zürrer W, Cannon A, Ilchenko D, Gaitán M, Granberg T, Piehl F, Solomon A, Ineichen B. Misdiagnosis and underdiagnosis of multiple sclerosis: A systematic review and meta-analysis. Multiple Sclerosis Journal 2024, 30: 1409-1422. PMID: 39246018, DOI: 10.1177/13524585241274527.Peer-Reviewed Original ResearchMeta-analysisSystematic reviewPooled proportionDiagnostic errorsPotential impact of sexMultiple sclerosisMS careMS misdiagnosisHealthcare systemQualitative synthesisImpact patientsMeta-analysesMS diagnosisHighest prevalenceImpact of sexDiagnostic delayUnderdiagnosisFrequency of misdiagnosisDiagnostic challengeMisdiagnosisCareHealthcarePatientsDiagnosisPotential impactMisdiagnosis of Multiple Sclerosis: Past, Present, and Future
Rjeily N, Solomon A. Misdiagnosis of Multiple Sclerosis: Past, Present, and Future. Current Neurology And Neuroscience Reports 2024, 24: 547-557. PMID: 39243340, DOI: 10.1007/s11910-024-01371-w.Peer-Reviewed Original ResearchMS diagnostic criteriaMultiple sclerosisDiagnostic criteriaCentral vein signParamagnetic rim lesionsAssociated with misdiagnosisRecent FindingsRecent studiesMS misdiagnosisMRI findingsEvaluating patientsSuspected MSDiagnostic accuracyRim lesionsFindingsRecent studiesImaging biomarkersMisdiagnosisDiagnostic biomarkersCommon disordersPatientsBiomarkersPotential strategyMultiple sclerosis presenting with paroxysmal symptoms: Patients at the limitations of current diagnostic criteria
Heward K, Roy-Hewitson C, Solomon A. Multiple sclerosis presenting with paroxysmal symptoms: Patients at the limitations of current diagnostic criteria. Multiple Sclerosis Journal 2024, 30: 1566-1570. PMID: 38751226, DOI: 10.1177/13524585241253513.Peer-Reviewed Original ResearchMS diagnostic criteriaCentral nervous systemMultiple sclerosisDiagnostic criteriaClinically isolated syndromeRelapsing remitting MSParoxysmal neurological symptomsLhermitte's phenomenonOptic neuritisClinical attacksTrigeminal neuralgiaCase seriesNeurological symptomsTonic spasmsPatient presentationParoxysmal symptomsPatientsClinical guidanceNervous systemSyndromeOptimal careSymptomsAnecdotal reportsSclerosisNeuralgiaMultiple sclerosis can be diagnosed solely with dissemination in space: No
Hemond C, Solomon A. Multiple sclerosis can be diagnosed solely with dissemination in space: No. Multiple Sclerosis Journal 2024, 30: 639-641. PMID: 38616532, DOI: 10.1177/13524585241245297.Peer-Reviewed Original ResearchChoroid plexus volume differentiates MS from its mimics
Levit E, Ren Z, Gonzenbach V, Azevedo C, Calabresi P, Cree B, Freeman L, Longbrake E, Oh J, Schindler M, Sicotte N, Reich D, Ontaneda D, Sati P, Cao Q, Shinohara R, Solomon A. Choroid plexus volume differentiates MS from its mimics. Multiple Sclerosis Journal 2024, 30: 1072-1076. PMID: 38481081, PMCID: PMC11288781, DOI: 10.1177/13524585241238094.Peer-Reviewed Original ResearchAssessing Free-Living Postural Sway in Persons With Multiple Sclerosis
Meyer B, Cohen J, DePetrillo P, Ceruolo M, Jangraw D, Cheney N, Solomon A, McGinnis R. Assessing Free-Living Postural Sway in Persons With Multiple Sclerosis. IEEE Transactions On Neural Systems And Rehabilitation Engineering 2024, 32: 967-973. PMID: 38373134, PMCID: PMC10966905, DOI: 10.1109/tnsre.2024.3366903.Peer-Reviewed Original ResearchConceptsMeasures of postural swayPostural swayFall riskPostural instabilityAssessment of postural instabilityRemote patient monitoring technologyPostural sway assessmentMeasures of balanceMeasures of swayPredicting fall riskPatient-reported measuresPatient monitoring technologyForce platformBalance impairmentDisease statusMobility impairmentsWearable accelerometersLab-based measuresAssociated with disease statusSwayMultiple sclerosisAnalyzed dataRiskArea under curveDaily lifeMultiple Sclerosis Diagnostic Delay and Misdiagnosis
Kaisey M, Solomon A. Multiple Sclerosis Diagnostic Delay and Misdiagnosis. Neurologic Clinics 2024, 42: 1-13. PMID: 37980109, DOI: 10.1016/j.ncl.2023.07.001.Peer-Reviewed Original ResearchConceptsMultiple sclerosis misdiagnosisPatients' clinical outcomesMS diagnostic criteriaMS symptomsClinical outcomesDiagnostic delayDiagnostic challengeDiagnostic criteriaMS misdiagnosisDiagnostic biomarkersAccurate diagnosisIncorrect diagnosisPatientsDiagnosisMisdiagnosisEducational effortsSymptoms
2023
A multicenter pilot study evaluating simplified central vein assessment for the diagnosis of multiple sclerosis
Daboul L, O’Donnell C, Amin M, Rodrigues P, Derbyshire J, Azevedo C, Bar-Or A, Caverzasi E, Calabresi P, Cree B, Freeman L, Henry R, Longbrake E, Oh J, Papinutto N, Pelletier D, Prchkovska V, Raza P, Ramos M, Samudralwar R, Schindler M, Sotirchos E, Sicotte N, Solomon A, Shinohara R, Reich D, Sati P, Ontaneda D. A multicenter pilot study evaluating simplified central vein assessment for the diagnosis of multiple sclerosis. Multiple Sclerosis Journal 2023, 30: 25-34. PMID: 38088067, PMCID: PMC11037932, DOI: 10.1177/13524585231214360.Peer-Reviewed Original ResearchConceptsCentral vein signMultiple sclerosisPositive lesionsInter-rater agreementDiagnosis of MSMagnetic resonance imaging (MRI) biomarkersDiagnostic performancePossible multiple sclerosisInter-rater reliability assessmentGood diagnostic performanceMcDonald criteriaMulticenter studyVein assessmentMean ageVein signImaging biomarkersLesionsMRI sequencesCharacteristic curveSclerosisPatientsDiagnosisAssessmentParticipantsOptimal methodDigital Phenotypes of Instability and Fatigue Derived From Daily Standing Transitions in Persons With Multiple Sclerosis
VanDyk T, Meyer B, DePetrillo P, Donahue N, O’Leary A, Fox S, Cheney N, Ceruolo M, Solomon A, McGinnis R. Digital Phenotypes of Instability and Fatigue Derived From Daily Standing Transitions in Persons With Multiple Sclerosis. IEEE Transactions On Neural Systems And Rehabilitation Engineering 2023, 31: 2279-2286. PMID: 37115839, PMCID: PMC10408384, DOI: 10.1109/tnsre.2023.3271601.Peer-Reviewed Original ResearchConceptsPrediction of fall riskStanding transitionsDigital phenotypingFall riskNon-fallersSymptom monitoringPhysician assessmentMultiple sclerosisBiweekly assessmentsClinical metricsCharacterize symptomsHome monitoringSymptomsFatigueMotor instabilityAccelerometryFallersAssessmentPwMSPersonsApplications of wearablePhysiciansIsolation periodActivity classifierInterventionChest-Based Wearables and Individualized Distributions for Assessing Postural Sway in Persons With Multiple Sclerosis
Meyer B, Cohen J, Donahue N, Fox S, O’Leary A, Brown A, Leahy C, VanDyk T, DePetrillo P, Ceruolo M, Cheney N, Solomon A, McGinnis R. Chest-Based Wearables and Individualized Distributions for Assessing Postural Sway in Persons With Multiple Sclerosis. IEEE Transactions On Neural Systems And Rehabilitation Engineering 2023, 31: 2132-2139. PMID: 37067975, PMCID: PMC10408383, DOI: 10.1109/tnsre.2023.3267807.Peer-Reviewed Original ResearchConceptsPatient-reported measuresPostural sway featuresPostural swaySway featuresForce platformBalance assessmentMeasures of postural swayPostural sway measuresAssess postural swayStanding tasksSway measuresFall statusBalance impairmentLower backMobility impairmentsConcurrent validityChest accelerometerEyes-openMultiple sclerosisSensor patchSwayStatus groupsEyes-closedAccelerometerTraditional computers
2022
Open-source dataset reveals relationship between walking bout duration and fall risk classification performance in persons with multiple sclerosis
Meyer B, Tulipani L, Gurchiek R, Allen D, Solomon A, Cheney N, McGinnis R. Open-source dataset reveals relationship between walking bout duration and fall risk classification performance in persons with multiple sclerosis. PLOS Digital Health 2022, 1: e0000120. PMID: 36812538, PMCID: PMC9931255, DOI: 10.1371/journal.pdig.0000120.Peer-Reviewed Original ResearchWalking boutsFall riskGait parametersOpen-source datasetsNon-fallersFeature-based modelWalking bout durationClassification performanceWearable sensorsFall risk classificationDaily activity performancePatient-reported surveysBiannual clinical visitsFall risk estimationBout durationDeep learning modelsFall historyShort boutsClinic visitsInvestigate fall riskWalking dataRisk estimatesAssociated with morbiditySensor dataDeep learningHow Much Data Is Enough? A Reliable Methodology to Examine Long-Term Wearable Data Acquisition in Gait and Postural Sway
Meyer B, Depetrillo P, Franco J, Donahue N, Fox S, O’Leary A, Loftness B, Gurchiek R, Buckley M, Solomon A, Ng S, Cheney N, Ceruolo M, McGinnis R. How Much Data Is Enough? A Reliable Methodology to Examine Long-Term Wearable Data Acquisition in Gait and Postural Sway. Sensors 2022, 22: 6982. PMID: 36146348, PMCID: PMC9503816, DOI: 10.3390/s22186982.Peer-Reviewed Original ResearchConceptsFree-living environmentBalance impairmentIntra-class correlationPostural sway measuresMeasures of gaitEvaluation of gaitWearable accelerometer dataWear durationSway measuresPostural swayCapturing gaitEstablished patientMovement variabilityImpairment measuresGaitPatient burdenAccelerometer dataClinical measuresDays of monitoringWearable sensor-based methodsSensor wearSwayRegression analysisComprehensive assessmentStudy costsThe Sit-to-Stand Transition as a Biomarker for Impairment: Comparison of Instrumented 30-Second Chair Stand Test and Daily Life Transitions in Multiple Sclerosis
Tulipani L, Meyer B, Fox S, Solomon A, Mcginnis R. The Sit-to-Stand Transition as a Biomarker for Impairment: Comparison of Instrumented 30-Second Chair Stand Test and Daily Life Transitions in Multiple Sclerosis. IEEE Transactions On Neural Systems And Rehabilitation Engineering 2022, 30: 1213-1222. PMID: 35468063, PMCID: PMC9204833, DOI: 10.1109/tnsre.2022.3169962.Peer-Reviewed Original ResearchConceptsSensory impairmentInvestigate impairmentsDaily lifeLevel of clinical disabilityPerformance metricsStructured tasksImpairmentPyramidal impairmentSub scoresMultiple sclerosisDaily life monitoringDichotomize participantsSit-stand transitionsClinical disabilityClassification performanceTaskWearable sensorsSupervised settingChest featuresFaller classificationDeficitsLife monitoringLow fall riskArea under the curveFunctional assessmentEvaluation of unsupervised 30-second chair stand test performance assessed by wearable sensors to predict fall status in multiple sclerosis
Tulipani L, Meyer B, Allen D, Solomon A, McGinnis R. Evaluation of unsupervised 30-second chair stand test performance assessed by wearable sensors to predict fall status in multiple sclerosis. Gait & Posture 2022, 94: 19-25. PMID: 35220031, PMCID: PMC9086135, DOI: 10.1016/j.gaitpost.2022.02.016.Peer-Reviewed Original ResearchConceptsWearable sensorsFall statusFall riskUnsupervised conditionsChair stand test performanceClassification AUCUnsupervised monitoringChair stand testAccelerometer-derived metricsPredicting fall riskStandard Functional AssessmentSupervised performanceBalance confidenceFunctional mobilityWearableNon-fallersStand testBalance deficitsRoutine clinical assessmentSupervision visitsSensorThree-month periodPerformanceFunctional assessmentMultiple sclerosis
2021
Wearables and Deep Learning Classify Fall Risk From Gait in Multiple Sclerosis
Meyer B, Tulipani L, Gurchiek R, Allen D, Adamowicz L, Larie D, Solomon A, Cheney N, McGinnis R. Wearables and Deep Learning Classify Fall Risk From Gait in Multiple Sclerosis. IEEE Journal Of Biomedical And Health Informatics 2021, 25: 1824-1831. PMID: 32946403, PMCID: PMC8221405, DOI: 10.1109/jbhi.2020.3025049.Peer-Reviewed Original ResearchConceptsWearable sensorsPreventive interventionsBidirectional long short-termInexpensive wearable sensorsDeep neural networksWearable sensor dataFall prevention interventionsSpatiotemporal gait parametersFall risk assessmentLong-Short-TermMachine learning modelsGait biomechanicsGait parametersSensor dataFall riskNeural networkHealthcare providersStatistical featuresLearning modelsPatient reportsAccelerometer dataIdentified measuresMultiple sclerosisWearableGood performance