The use of event-related potentials in the investigation of cognitive performance in people with Multiple Sclerosis: Systematic review.

A biomarker of cognition in Multiple Sclerosis (MS) that is independent from the response of people with MS (PwMS) to test questions would provide a more holistic assessment of cognitive decline. One suggested method involves event-related potentials (ERPs). This systematic review tried to answer five questions about the use of ERPs in distinguishing PwMS from controls: which stimulus modality, which experimental paradigm, which electrodes, and which ERP components are most discriminatory, and whether amplitude or latency is a better measure. Our results show larger pooled effect sizes for visual stimuli than auditory stimuli, and larger pooled effect sizes for latency measurements than amplitude measurements. We observed great heterogeneity in methods and suggest that future research would benefit from more uniformity in methods and that results should be reported for the individual subtypes of PwMS. With more standardised methods, ERPs have the potential to be developed into a clinical tool in MS.

Evaluating Effects of Resting-State Electroencephalography Data Pre-Processing on a Machine Learning Task for Parkinson's Disease.

Resting-state electroencephalography pre-processing methods in machine learning studies into Parkinson's disease classification vary widely. Here three separate data sets were pre-processed to four different stages to investigate the effects on evaluation metrics, using power features from six regions-of-interest, Random Forest Classifiers for feature selection, and Support Vector Machines for classification. This showed muscle artefact inflated evaluation metrics, and alpha and theta band features produced the best results when fully pre-processing data.

Explaining predictors of discharge destination assessed along the patients' acute stroke journey.

INTRODUCTION: Accurate prediction of outcome destination at an early stage would help manage patients presenting with stroke. This study assessed the predictive ability of three machine learning (ML) algorithms to predict outcomes at four different stages as well as compared the predictive power of stroke scores. METHODS: Patients presenting with acute stroke to the Canberra Hospital between 2015 and 2019 were selected retrospectively. 16 potential predictors and one target variable (discharge destination) were obtained from the notes. k-Nearest Neighbour (kNN) and two ensemble-based classification algorithms (Adaptive Boosting and Bootstrap Aggregation) were employed to predict outcomes. Predictive accuracy was assessed at each of the four stages using both overall and per-class accuracy. The contribution of each variable to the prediction outcome was evaluated by the ensemble-based algorithm and using the Relief feature selection algorithm. Various combinations of stroke scores were tested using the aforementioned models. RESULTS: Of the three ML models, Adaptive Boosting demonstrated the highest accuracy (90%) at Stage 4 in predicting death while the highest overall accuracy (81.7%) was achieved by kNN (k=2/City-block distance). Feature importance analysis has shown that the most important features are the 24-hour Scandinavian Stroke Scale (SSS) and 24-hour National Institutes of Health Stroke Scale (NIHSS) scores, dyslipidaemia, hypertension and premorbid mRS score. For the initial and 24-hour scores, there was a higher correlation (0.93) between SSS scores than for NIHSS scores (0.81). Reducing the overall four scores to InitSSS/24hrNIHSS increased accuracy to 95% in predicting death (Adaptive Boosting) and overall accuracy to 85.4% (kNN). Accuracies at Stage 2 (pre-treatment, 11 predictors) were not far behind those at Stage 4. CONCLUSION: Our findings suggest that even in the early stages of management, a clinically useful prediction regarding discharge destination can be made. Adaptive Boosting might be the best ML model, especially when it comes to predicting death. The predictors' importance analysis also showed that dyslipidemia and hypertension contributed to the discharge outcome even more than expected. Further, surprisingly using mixed score systems might also lead to higher prediction accuracies.

Evaluating methods of oversampling and averaging resting-state electroencephalography data in classifying Parkinson's disease.

Collecting resting-state electroencephalography (RSEEG) data is time-consuming and data sets are therefore often small. Because many machine learning (ML) algorithms work better with ample data, researchers looking to use RSEEG and ML to develop diagnostic models have used oversampling methods that may seem to contradict averaging methods used in conventional electroencephalography (EEG) research to improve the signal-to-noise ratio. Using eyes open (EO) and eyes closed (EC) recordings from 3 different research groups, we investigated the effect of different averaging and oversampling methods on classification metrics when classifying people with Parkinson's disease (PD) and controls. Both EC and EO recordings were used due to differences found between these methods. Our results indicated that grouping 58 electrodes into regions-of-interest (ROI) based on anatomical location is preferable to using single electrodes. Furthermore, although recording EO data led to slightly better classification, the number of data points for each participant was reduced and recordings for three participants entirely lost during pre-processing due to a higher level of artefacts than in the EC data.Clinical relevance- RSEEG is a potential biomarker for the diagnosis and prognostication of PD, but for RSEEG to have clinical relevance, it is necessary to establish which averaging and oversampling of data most reliably segregates the classes for people with PD and controls. We found that using of ROIs and EC data performed the best, as EO data was often contaminated with artefacts.

The Impact of Depression, Anxiety and Personality Disorders on the Outcome of Patients with Functional Limb Weakness - Individual Patient Data Meta-Analysis.

OBJECTIVE: Psychiatric comorbidities such as depression, anxiety, and personality disorders are common in patients with functional limb weakness/paresis (FND-par). The impact of these conditions on the prognosis of FND-par has not been systematically reviewed. The aim of this study was to identify a potential prognostic effect of comorbid depression, anxiety, and/or personality disorder on prognosis in patients with FND-par. METHODS: A systematic review was performed to identify studies that reported measures of baseline depression, anxiety, and/or personality disorder, and physical disability. An individual patient data meta-analysis was subsequently performed. RESULTS: Eight studies comprising 348 individuals were included (7 prospective cohorts; 1 case-control study). There was heterogeneity in sample size, follow-up duration, and treatment modality. Depression and anxiety were present in 51.4% and 53.0% of FND-par patients, respectively. In individuals whose FND-par improved, there was no significant difference between those with versus without depression (52.6% vs 47.4%, p = 0.69) or those with versus without anxiety (50.3% vs 49.7%, p = 0.38). Meta-analysis showed no clear impact of baseline depression or anxiety per se [pooled OR for depression 0.85 (95%CI 0.50-1.45; p = 0.40) and anxiety 0.84 (95%CI 0.51-1.38; p = 0.91)]; and of depression or anxiety severity [pooled OR for depression 1.23 (95%CI 0.63-2.39; p = 0.91) and anxiety 1.40 (95%CI 0.70-2.78; p = 0.58)] on FND-par outcome. Insufficient data were available to assess the impact of personality disorders. CONCLUSION: We found no evidence that depression or anxiety influenced outcome in FND-par. Large-scale, prospective studies in FND-par, and other FND subtypes, are needed to fully contextualize the impact of concurrent mental health concerns on outcomes.

Standardized Systematic Description of Provision of Care for Multiple Sclerosis at a Local Level: A Demonstration Study.

BACKGROUND: This study evaluates and describes the pattern of services provided for people living with multiple sclerosis (MS) in a local area as a starting point for a more global assessment. METHODS: A health care ecosystem approach has been followed using an internationally standardized service classification instrument-the Description and Evaluation of Services and DirectoriEs for Long Term Care (DESDE-LTC)-to identify and describe all services providing care to people with MS in the Australian Capital Territory, Australia. Available services were classified according to the target population into those specifically dedicated to people living with MS and those providing general neurologic services, both public and private, and across both social and health sectors. RESULTS: A limited range of services was available. There were no local facilities providing or coordinating multidisciplinary integrated care specific to people with MS. Subspecialty services specific to MS were limited in number (6 of the 28 services), and use of specialist services provided in neighboring states was frequently reported. Overall, very few services were provided outside the core health sector (4%). CONCLUSIONS: The provision of care to people living with MS in the Australian Capital Territory is fragmented and relies heavily on generic neurology services in the public and private sectors. More widespread use of the DESDE-LTC as a standardized method of service classification in MS will facilitate comparison with other local areas, allow monitoring of changes over time, and permit comparison with services provided for other health conditions (eg, dementia, mental disorders).

Modelling accessibility of adult neurology care in Australia, 2020-2034.

Introduction: In 2015/2016, annual national expenditure on neurological conditions exceeded $A3 billion. However, a comprehensive study of the Australian neurological workforce and supply/demand dynamics has not previously been undertaken. Methods: Current neurological workforce was defined using neurologist survey and other sources. Workforce supply modelling used ordinary differential equations to simulate neurologist influx and attrition. Demand for neurology care was estimated by reference to literature regarding incidence and prevalence of selected conditions. Differences in supply versus demand for neurological workforce were calculated. Potential interventions to increase workforce were simulated and effects on supply versus demand estimated. Results: Modelling of the workforce from 2020 to 2034 predicted an increase in neurologist number from 620 to 89. We estimated a 2034 capacity of 638 024 Initial and 1 269 112 Review encounters annually, and deficits against demand estimated as 197 137 and 881 755, respectively. These deficits were proportionately greater in regional Australia, which has 31% of Australia's population (Australian Bureau of Statistics) but is served by only 4.1% of its neurologists as determined by our 2020 survey of Australia and New Zealand Association of Neurologists members. Nationally, simulated additions to the neurology workforce had some effect on the review encounter supply deficit (37.4%), but in Regional Australia, this impact was only 17.2%. Interpretation: Modelling of the neurologist workforce in Australia for 2020-2034 demonstrates a significant shortfall of supply relative to current and projected demand. Interventions to increase neurologist workforce may attenuate this shortfall but will not eliminate it. Thus, additional interventions are needed, including improved efficiency and additional use of support staff.

Has machine learning over-promised in healthcare?: A critical analysis and a proposal for improved evaluation, with evidence from Parkinson's disease.

Adoption of artificial intelligence (AI) by the medical community has long been anticipated, endorsed by a stream of machine learning literature showcasing AI systems that yield extraordinary performance. However, many of these systems are likely over-promising and will under-deliver in practice. One key reason is the community's failure to acknowledge and address the presence of inflationary effects in the data. These simultaneously inflate evaluation performance and prevent a model from learning the underlying task, thus severely misrepresenting how that model would perform in the real world. This paper investigated the impact of these inflationary effects on healthcare tasks, as well as how these effects can be addressed. Specifically, we defined three inflationary effects that occur in medical data sets and allow models to easily reach small training losses and prevent skillful learning. We investigated two data sets of sustained vowel phonation from participants with and without Parkinson's disease, and revealed that published models which have achieved high classification performances on these were artificially enhanced due to the inflationary effects. Our experiments showed that removing each inflationary effect corresponded with a decrease in classification accuracy, and that removing all inflationary effects reduced the evaluated performance by up to 30%. Additionally, the performance on a more realistic test set increased, suggesting that the removal of these inflationary effects enabled the model to better learn the underlying task and generalize. Source code is available at https://github.com/Wenbo-G/pd-phonation-analysis under the MIT license.

Clinician perspectives on rapid transition to telehealth during COVID-19 in Australia - a qualitative study.

Objective The coronavirus disease 2019 (COVID-19) pandemic precipitated a major shift in the use of telehealth in Australia. The changes highlighted gaps in our knowledge regarding the efficacy of, and clinician attitudes to, the use of telehealth. The current study expands and deepens the available evidence as a result of being collected in unique circumstances that removed one of the major barriers (lack of Medicare rebates) and also one major enablers (willingness) of telehealth uptake. Methods Using a semi-structured interview, we invited clinicians (N = 39) to share their perspectives, attitudes and experiences of using telehealth. Topics covered included perceptions of the strengths and challenges of telehealth, and how experience of using telehealth during the COVID-19 pandemic had influenced clinicians' views and intentions regarding their future practice. Participants included clinicians from five disciplines across public and private practice: paediatrics, neurology, immunology, rural general practice, and orthopaedics. Results We found three key dimensions for consideration when assessing the suitability of telehealth for ongoing practice: the attributes of the patient population, the attributes of the clinical context and environment, and the risks and benefits of a telehealth approach. These findings map to the existing literature and allow us to infer that the experiences of clinicians who previously would have chosen telehealth did not differ significantly from those of our 'pandemic-conscripted' clinicians. Conclusions Our findings map clearly to the existing literature and allow us to infer that the experiences of the clinicians who have chosen telehealth (and are already represented in the literature) did not differ significantly from those trying out telehealth under the unique circumstances of the removal of the Medicare Benefits Scheme barrier and external pressure that over-rides the 'willingness' enabling factor in uptake decisions.

Rapid, non-contact multifocal visual assessment in multiple sclerosis.

OBJECTIVE: Previous work on temporally sparse multifocal methods suggests that the results are correlated with disability and progression in people with multiple sclerosis (PwMS). Here, we assess the diagnostic power of three cortically mediated sparse multifocal pupillographic objective perimetry (mfPOP) methods that quantified response-delay and light-sensitivity at up to 44 regions of both visual fields concurrently. METHODS: One high-spatial-resolution mfPOP method, P129, and two rapid medium-resolution methods, W12 and W20, were tested on 44 PwMS and controls. W12 and W20 took 82 s to test both visual fields concurrently, providing response delay and sensitivity at each field location, while P129 took 7 min. Diagnostic power was assessed using areas under the receiver operating characteristic (AUROC) curves and effect-size (Hedges' g). Linear models examined significance. Concurrent testing of both eyes permitted assessment of between-eye asymmetries. RESULTS: Per-region response delays and asymmetries achieved AUROCs of 86.6% ± 4.72% (mean ± SE) in relapsing-remitting MS, and 96.5% ± 2.30% in progressive MS. Performance increased with increasing disability scores, with even moderate EDSS 2 to 4.5 PwMS producing AUROCs of 82.1 to 89.8%, Hedge's g values up to 2.06, and p = 4.0e - 13. All tests performed well regardless of any history of optic neuritis. W12 and W20 performed as well or better than P129. CONCLUSION: Overall, the 82-s tests (W12 and W20) performed better than P129. The results suggest that mfPOP assesses a correlate of disease severity rather than a history of inflammation, and that it may be useful in the clinical management of PwMS.

Cardiovascular Rehabilitation for transient ischaemic Attack and Mild Stroke: the CRAMS effectiveness-implementation hybrid study protocol.

BACKGROUND: Internationally, stroke and cardiac rehabilitation clinicians agree that current cardiac rehabilitation models are a suitable secondary prevention program for people following a transient ischaemic attack (TIA) or mild stroke. There is strong evidence for exercise-based cardiac rehabilitation in people with heart disease, however, the evidence for cardiac rehabilitation post-TIA or stroke is limited. Here we will explore the effectiveness and implementation of an integrated (TIA, mild stroke, heart disease) traditional exercise-based cardiovascular rehabilitation (CVR) program for people with TIA or mild stroke over 6-months. METHODS: This type 1 effectiveness-implementation hybrid study will use a 2-arm single-centre assessor-blind randomised controlled trial design, recruiting 140 participants. Adults who have had a TIA or mild stroke in the last 12-months will be recruited by health professionals from hospital and primary healthcare services. Participants will be assessed and randomly allocated (1:1) to the 6-week CVR program or the usual care 6-month wait-list control group. Distance completed in the 6-min walk test will be the primary effectiveness outcome, with outcomes collected at baseline, 6-weeks (complete CVR) and 6-months in both groups. Other effectiveness outcome measures include unplanned cardiovascular disease-related emergency department and hospital admissions, daily minutes of accelerometer moderate-to-vigorous physical activity, body mass index, waist circumference, blood pressure, quality of life, anxiety and depression. Implementation outcomes will be assessed using the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework, including a cost-effectiveness analysis. Semi-structured interviews will be conducted with participants and CVR program health professionals, investigating the acceptability, value, and impact of the CVR program. Qualitative analyses will be guided by the Consolidated Framework for Implementation Research. DISCUSSION: Few studies have assessed the effectiveness of cardiac rehabilitation for people with TIA and mild stroke, and no studies appear to have investigated the cost-effectiveness or implementation determinants of such programs. If successful, the CVR program will improve health outcomes and quality of life of people who have had a TIA or mild stroke, guiding future research, policy, and clinical practice, reducing the risk of repeat heart attacks and strokes for this population. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12621001586808 , Registered 19 November 2021.

Objective perimetry and progression of multiple sclerosis.

Introduction: We re-examined the per-region response amplitudes and delays obtained from multifocal pupillographic objective perimetry (mfPOP) after 10 years in 44 persons living with multiple sclerosis (PwMS), both to examine which parts of the visual field had progressed in terms of response properties and to examine if the baseline data could predict the overall progression of disease. Methods: Expanded Disability Status Scale (EDSS) scores were assessed in 2009 and 2019. Both eyes of each participant were concurrently tested at 44 locations/eye on both occasions. Several measures of clinical progression were examined, using logistic regression to determine the odds of progression. Results: At the second examination the 44 PwMS (31 females) were aged 61.0 ± 12.2 y. Mean EDSS had not changed significantly (3.69 ± 1.23 in 2009, 3.81 ± 2.00 in 2019). mfPOP delay increased progressively from inferior to superior regions of the visual fields while amplitudes demonstrated a temporal to nasal gradient. The mean of the 3 most delayed visual field regions was correlated with progression of MS by 2019 (p = 0.023). Logistic regression indicated a significant association between delay and odds of progression (p = 0.045): an individual with 3 regions at least 1 SD (40 ms) slower than the mean in 2009 had 2.05× (±SE: 1.43× to 2.95×) the odds of progression by 2019. A 1 SD shorter delay was associated with 2.05× lower odds of progression. Amplitude changes were not predictive of progression. Significance: mfPOP may provide a rapid, convenient method of monitoring and predicting MS progression.

The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review.

BACKGROUND: Multiple sclerosis (MS) is a neurological condition whose symptoms, severity, and progression over time vary enormously among individuals. Ideally, each person living with MS should be provided with an accurate prognosis at the time of diagnosis, precision in initial and subsequent treatment decisions, and improved timeliness in detecting the need to reassess treatment regimens. To manage these three components, discovering an accurate, objective measure of overall disease severity is essential. Machine learning (ML) algorithms can contribute to finding such a clinically useful biomarker of MS through their ability to search and analyze datasets about potential biomarkers at scale. Our aim was to conduct a systematic review to determine how, and in what way, ML has been applied to the study of MS biomarkers on data from sources other than magnetic resonance imaging. METHODS: Systematic searches through eight databases were conducted for literature published in 2014-2020 on MS and specified ML algorithms. RESULTS: Of the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms. CONCLUSIONS: ML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. However, applying ML research to develop decision aids to help clinicians optimize treatment strategies and analyze treatment responses in individual patients calls for creating appropriate data resources and shared experimental protocols. They should target proceeding from segregated classification of signals or natural language to both holistic analyses across data modalities and clinically-meaningful differentiation of disease.

Response characteristics of objective perimetry in persons living with epilepsy.

PURPOSE: To investigate safety and visual-field changes in people with epilepsy undergoing multifocal Pupillographic Objective Perimetry (mfPOP). METHODS: 15 people with epilepsy and 15 controls underwent mfPOP in the context of routine clinical EEG testing. Safety measures comprised the proportion of participants developing an aura or seizure, a photoparoxysmal response, or increased epileptiform activity on their EEG during mfPOP. Pupil responses were obtained concurrently from 44 regions/field of each eye. Changes in standardised amplitude of constriction and time-to-peak were compared between people with generalised and focal epilepsy, and controls. RESULTS: No participant developed an epileptic aura or clinical seizure during (or after) testing. One participant demonstrated EEG evidence of a focal subclinical seizure which began before mfPOP testing and continued unchanged during testing. Regional field sensitivities were increased in people with generalised epilepsy (+3.80 ± 1.43 dB compared to controls) but were reduced in individuals taking antiepileptic medication (-4.04 ± 1.74 dB). An extra delay of 24.9 ± 10.2 ms was seen in the time-to-peak of the responses in people with focal epilepsy. Based on receiver-operating characteristic analyses, discrimination of people with epilepsy from controls was greatest when using the 4 to 10 most abnormal visual field regions of each eye (%AUC 77.3 ± 9.70). SIGNIFICANCE: In the absence of any safety signal, mfPOP appears harmless in people with epilepsy. The observed abnormalities in per-region sensitives and delays suggest that mfPOP may provide significant new insights into the study of epilepsy.

The current understanding of precision medicine and personalised medicine in selected research disciplines: study protocol of a systematic concept analysis.

INTRODUCTION: The terms 'precision medicine' and 'personalised medicine' have become key terms in health-related research and in science-related public communication. However, the application of these two concepts and their interpretation in various disciplines are heterogeneous, which also affects research translation and public awareness. This leads to confusion regarding the use and distinction of the two concepts. Our aim is to provide a snapshot of the current understanding of these concepts. METHODS AND ANALYSIS: Our study will use Rodgers' evolutionary concept analysis to systematically examine the current understanding of the concepts 'precision medicine' and 'personalised medicine' in clinical medicine, biomedicine (incorporating genomics and bioinformatics), health services research, physics, chemistry, engineering, machine learning and artificial intelligence, and to identify their respective attributes (clusters of characteristics) and surrogate and related terms. A systematic search of the literature will be conducted for 2016-2022 using databases relevant to each of these disciplines: ACM Digital Library, CINAHL, Cochrane Library, F1000Research, IEEE Xplore, PubMed/Medline, Science Direct, Scopus and Web of Science. These are among the most representative databases for the included disciplines. We will examine similarities and differences in definitions of 'precision medicine' and 'personalised medicine' in the respective disciplines and across (sub)disciplines, including attributes of each term. This will enable us to determine how these two concepts are distinguished. ETHICS AND DISSEMINATION: Following ethical and research standards, we will comprehensively report the methodology for a systematic analysis following Rodgers' concept analysis method. Our systematic concept analysis will contribute to the clarification of the two concepts and distinction in their application in given settings and circumstances. Such a broad concept analysis will contribute to non-systematic syntheses of the concepts, or occasional systematic reviews on one of the concepts that have been published in specific disciplines, in order to facilitate interdisciplinary communication, translational medical research and implementation science.

The Use of Event-Related Potentials and Machine Learning to Improve Diagnostic Testing and Prediction of Disease Progression in Parkinson's Disease.

Current tests of disease status in Parkinson's disease suffer from high variability, limiting their ability to determine disease severity and prognosis. Event-related potentials, in conjunction with machine learning, may provide a more objective assessment. In this study, we will use event-related potentials to develop machine learning models, aiming to provide an objective way to assess disease status and predict disease progression in Parkinson's disease.

Personalizing Medicine and Technologies to Address the Experiences and Needs of People with Multiple Sclerosis.

There is enormous variation in the manifestations of disease experienced by people with multiple sclerosis (PwMS). While this variation makes personalized medicine an attractive goal, there are many challenges to be overcome before this opportunity can be realized. Personalized medicine often focuses on targeted therapies and detailed monitoring, but we also need to recognize that there will be variation in acceptance of these approaches by different PwMS. In other words, deep personalization of medicine will encompass targeted therapy, precision monitoring, tailored to variation in personal attitudes to these transformations in health care. In order to meet the promise of personalized medicine for MS, understanding the experiences of PwMS is necessary both to aid in the uptake of personalized medicine, and to ensure that personalized approaches to monitoring disease and treatment provide a net benefit to PwMS rather than placing additional burdens and stressors on them. Here, we describe recent research that identified five experiential themes for PwMS, and then interpret these themes according to the foundations of personalized medicine to provide a road map for implementation of personalized medicine solutions for PwMS.

'They're getting a taste of our world': A qualitative study of people with multiple sclerosis' experiences of accessing health care during the COVID-19 pandemic in the Australian Capital Territory.

BACKGROUND: People with multiple sclerosis (MS), who are often immunocompromised, require complex care and engage with a variety of health-care providers to manage their health. OBJECTIVE: To elucidate people with MS' experiences of accessing health care during the COVID-19 pandemic in Australia. DESIGN: A qualitative study involving semi-structured interviews and thematic analysis. SETTINGS AND PARTICIPANTS: Eight adults with a clinical diagnosis of MS participated in telephone or video call interviews between June and July 2020. RESULTS: Participants were aware that having MS made them more vulnerable to contracting COVID-19. In some cases, usual care was postponed or not sought. Some circumstances warranted the risk of a face-to-face consultation. Benefits of telehealth consultations included improved access, convenience and being contact-free. In comparison with video consultations, those via telephone were considered less personal and limited capacity to read body language, and for physical examination. Most participants hoped to incorporate telehealth into their future health-care routines. DISCUSSION AND CONCLUSION: Personal risk assessment and trust in health-care professionals are determinants of the mode through which people with MS accessed health care during the COVID-19 pandemic. Telehealth has been a valuable tool to mitigate COVID-19 transmission through enabling contact-free consultations. People with MS may find specific value in video consultations, which enable visualization of physical function. There is a need for training and support for all clinicians to conduct remote consultations. PATIENT OR PUBLIC CONTRIBUTION: This study was conducted by a team comprised of four people with MS, a neurologist and four health services researchers.

Assessing migraine patients with multifocal pupillographic objective perimetry.

BACKGROUND: To establish the effects of stimulating intrinsically-photosensitive retinal ganglion cells (ipRGCs) on migraine severity, and to determine if migraine produces objectively-measured visual field defects. METHODS: A randomized, open labelled, crossover study tested migraineurs and normal controls using multifocal pupillographic objective perimetry (mfPOP) with 44 test-regions/eye. A slow blue protocol (BP) stimulated ipRGCs, and a fast yellow protocol (YP) stimulated luminance channels. Migraine diaries assessed migraine severity. Per-region responses were analyzed according to response amplitude and time-to-peak. RESULTS: Thirty-eight migraineurs (42.0 ± 16.5 years, 23 females) and 24 normal controls (39.2 ± 15.2 years, 14 females) were tested. The proportion of subjects developing a migraine did not differ after either protocol, either during the 1st day (odds ratio 1.0; 95% confidence interval 0.2-4.4, p = 0.48) or during the first 3 days after testing (odds ratio 0.8; 95% confidence interval 0.3-2.1, p = 0.68). Migraine days/week did not increase following testing with either protocol in comparison to the baseline week (1.4 ± 1.6 pre-testing (mean ± SD), 1.3 ± 1.4 post-BP, and 1.3 ± 1.2 post-YP; p = 0.96), neither did other measures of severity. Migraine occurring up to 2 weeks before testing significantly lowered amplitudes, - 0.64 ± 0.14 dB (mean ± SE), while triptan use increased amplitudes by 0.45 ± 0.10 dB, both at p < 0.001. CONCLUSIONS: Stimulating ipRGCs did not affect migraine occurrence or severity. Pupillary response characteristics were influenced by the occurrence of a recent migraine attack and a history of triptan use.