Faculty Bibliography

RGS8-associated paraneoplastic neurologic syndrome (PNS) is a recently-described disorder associated with lymphomas and typically presenting with severe, rapidly-progressing cerebellar dysfunction. We describe a patient who presented with mild signs of cerebellar dysfunction, including ocular motor abnormalities and impaired tandem gait. CSF showed elevated protein and a neural-restricted antibody pattern. Mesenteric lymphadenopathy on abdominal CT was biopsied and diagnosed as follicular B-cell lymphoma. After four years, the previously-detected antibody pattern was identified as RGS8 antibodies. This case describes the first RGS8-PNS patient presenting with a subtle and ocular motor predominant cerebellar syndrome with low-grade lymphoma.

BACKGROUND:Visual symptoms are common after concussion. Rapid automatized naming (RAN) tasks are simple performance measures that demonstrate worse time scores in the setting of acute or more remote injury. METHODS:We evaluated the capacity for the Mobile Universal Lexicon Evaluation System (MULES) and Staggered Uneven Number (SUN) testing to be feasibly administered during preseason testing in a cohort of youth ice hockey athletes using a novel computerized app, the Mobile Integrated Cognitive Kit (MICK). Participants from a youth hockey league underwent preseason testing. RESULTS:Among 60 participants, the median age was 13 years (range 6-17). The median best time for the MULES was 49.8 seconds (range = 34.2-141.0) and the median best time for the SUN was 70.1 (range = 36.6-200.0). As is characteristic of timed performance measures, there were learning effects between the first and second trials for both the MULES (median improvement = 10.6 seconds, range = -32.3 to 92.0, P < 0.001, Wilcoxon signed-rank test) and SUN (median improvement = 2.4 seconds, range= -8.0 to 15.1, P = 0.001, Wilcoxon signed-rank test). Age was a predictor of best baseline times, with longer (worse) times for younger participants for MULES (P < 0.001, rs = -0.67) and SUN (P < 0.001, rs = -0.54 Spearman rank correlation). Degrees of learning effect did not vary with age (P > 0.05, rs = -0.2). CONCLUSIONS:Vision-based RAN tasks, such as the MULES and SUN, can be feasibly administered using the MICK app during preseason baseline testing in youth sports teams. The results suggest that more frequent baseline tests are necessary for preadolescent athletes because of the relation of RAN task performance to age.

BACKGROUND:In many parts of the world including India, the prevalence of autoimmune inflammatory diseases such as neuromyelitis optica spectrum disorders (NMOSD), myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and multiple sclerosis (MS) is rising. A diagnosis is often delayed due to insufficient diagnostic tools. Machine learning (ML) models have accurately differentiated eyes of patients with MS from those of healthy controls (HCs) using optical coherence tomography (OCT)-based retinal images. Examining OCT characteristics may allow for early differentiation of these conditions. The objective of this study was to determine feasibility of ML analyses to distinguish between patients with different autoimmune inflammatory diseases, other ocular diseases, and HCs based on OCT measurements of the peripapillary retinal nerve fiber layer (pRNFL), ganglion cell-inner plexiform layer (GCIPL), and inner nuclear layers (INLs). METHODS:Eyes of people with MS (n = 99 patients), NMOSD (n = 40), MOGAD (n = 74), other ocular diseases (OTHER, n = 16), and HCs (n = 54) from the Mangalore Demyelinating Disease Registry were included. Support vector machine (SVM) classification models incorporating age, pRNFL, GCIPL, and INL were performed. Data were split into training (70%) and testing (30%) data and accounted for within-patient correlations. Cross-validation was used in training to choose the best parameters for the SVM model. Accuracy and area under receiver operating characteristic curves (AUROCs) were used to assess model performance. RESULTS:The SVM models distinguished between eyes of patients with each condition (i.e., MOGAD vs NMOSD, NMOSD vs HC, MS vs OTHER, etc) with strong discriminatory power demonstrated from the AUROCs for these comparisons ranging from 0.81 to 1.00. These models also performed with moderate to high accuracy, ranging from 0.66 to 0.81, with the exception of the MS vs NMOSD comparison, which had an accuracy of 0.53. CONCLUSIONS:ML models are useful for distinguishing between autoimmune inflammatory diseases and for distinguishing these from HCs and other ocular diseases based on OCT measures. This study lays the groundwork for future deep learning studies that use analyses of raw OCT images for identifying eyes of patients with such disorders and other etiologies of optic neuropathy.

BACKGROUND:The randomized, phase 2 RENEW trial (NCT01721161) evaluated efficacy/safety of opicinumab (anti-LINGO-1) versus placebo in patients with first-episode unilateral acute optic neuritis (AON). Although no significant differences in the latency recovery of visual evoked potential (VEP) were observed between opicinumab and placebo groups in the intention to treat (ITT) population, the prespecified per-protocol (PP) population showed better recovery with opicinumab than with placebo. RENEWED (NCT02657915) was a one-visit, follow-up study 2 years after the last RENEW study visit (Week 32) designed to assess the long-term electrophysiological and clinical outcomes for participants previously enrolled and having received study treatment in RENEW. METHODS:In the original study (RENEW), participants (aged 18-55 years) with a first unilateral AON episode were enrolled ≤28 days from first symptom onset and after treatment with methylprednisolone 1 g/day intravenously for 3-5 days; these participants were randomized to receive opicinumab 100 mg/kg or placebo intravenously once every 4 weeks from baseline to Week 20, assessed up to Week 32. Participants who received ≥1 dose of opicinumab 100 mg/kg or placebo in RENEW were eligible for the RENEWED follow-up study. Participants enrolled in RENEWED at 2 years (with an additional up to 12-month window) after the last RENEW study visit (Week 32) in both ITT and PP populations. The primary endpoint was change in full-field VEP (FF-VEP) latency of the affected eye at RENEWED study visit versus baseline of the fellow eye in RENEW, comparing between participants who received opicinumab and placebo in RENEW. Clinical progression and severity of multiple sclerosis (MS) were assessed. A substudy evaluated latency recovery using multifocal VEP (mfVEP) as an exploratory endpoint. RESULTS:Of 82 RENEW participants, 52 (63.4 %; opicinumab n = 28, placebo n = 24) enrolled in and completed RENEWED. The adjusted mean (95 % CI) difference in FF-VEP latency delay between opicinumab and placebo groups was -6.0 (-14.6, 2.6) msec (p = 0.165) for the PP population and -4.5 (-12.6, 3.7) msec (p = 0.274) for the ITT population at the RENEWED study visit. Nominally significant improvement on mfVEP latency in the opicinumab group versus placebo was observed in participants of the mfVEP substudy (p = 0.009). In participants from the PP population without clinically definite MS (CDMS) at RENEW baseline,12 (55 %) in the opicinumab group and 12 (67 %) in the placebo group developed CDMS from enrollment in the RENEW study up to RENEWED Day 1; the estimated proportion of participants with CDMS at 2 years after the last study visit assessment in RENEW was lower when treated with opicinumab (0.50) than when treated with placebo (0.61) (hazard ratio p-value = 0.23). No benefit on visual acuity or other neurological functions was observed in the opicinumab group vs placebo in RENEWED. CONCLUSION/CONCLUSIONS:The numerically increased VEP latency recovery with opicinumab treatment in RENEWED was consistent with those observed in the parent study RENEW. However, the VEP latency and clinical data in RENEWED should be interpreted with caution, given the nature of the follow-up study, the small sample size and the limitation in study design.

BACKGROUND AND OBJECTIVES/UNASSIGNED:We determined inter-modality (in-person vs telemedicine examination) and inter-rater agreement for telemedicine assessments (2 different examiners) using the Telemedicine Buffalo Concussion Physical Examination (Tele-BCPE), a standardized concussion examination designed for remote use. METHODS/UNASSIGNED:Patients referred for an initial evaluation for concussion were invited to participate. Participants had a brief initial assessment by the treating neurologist. After a patient granted informed consent to participate in the study, the treating neurologist obtained a concussion-related history before leaving the examination room. Using the Tele-BCPE, 2 virtual examinations in no specific sequence were then performed from nearby rooms by the treating neurologist and another neurologist. After the 2 telemedicine examinations, the treating physician returned to the examination room to perform the in-person examination. Intraclass correlation coefficients (ICC) determined inter-modality validity (in-person vs remote examination by the same examiner) and inter-rater reliability (between remote examinations done by 2 examiners) of overall scores of the Tele-BCPE within the comparison datasets. Cohen's kappa, κ, measured levels of agreement of dichotomous ratings (abnormality present vs absent) on individual components of the Tele-BCPE to determine inter-modality and inter-rater agreement. RESULTS/UNASSIGNED:< 0.001]) were reliable (ICC >0.70). There was at least substantial inter-modality agreement (κ ≥ 0.61) for 25 of 29 examination elements. For inter-rater agreement (2 telemedicine examinations), there was at least substantial agreement for 8 of 29 examination elements. DISCUSSION/UNASSIGNED:Our study demonstrates that the Tele-BCPE yielded consistent clinical results, whether conducted in-person or virtually by the same examiner, or when performed virtually by 2 different examiners. The Tele-BCPE is a valid indicator of neurologic examination findings as determined by an in-person concussion assessment. The Tele-BCPE may also be performed with excellent levels of reliability by neurologists with different training and backgrounds in the virtual setting. These findings suggest that a combination of in-person and telemedicine modalities, or involvement of 2 telemedicine examiners for the same patient, can provide consistent concussion assessments across the continuum of care.

INTRODUCTION AND PROBLEM STATEMENT/UNASSIGNED:A chief resident's role incorporates administrative, academic, and interpersonal responsibilities essential to managing a successful residency program. However, rising chief residents receive little formal exposure to leadership training. OBJECTIVES/UNASSIGNED:To (1) define leadership styles; (2) understand the effect of cultural competence on leadership styles; (3) learn effective methods to advocate as the chief resident; (4) provide effective peer feedback; (5) provide effective supervisor feedback; (6) learn effective conflict management; (7) ensure psychological safety. METHODS AND CURRICULUM DESCRIPTION/UNASSIGNED:We developed a 1-day curriculum combining didactics and simulation activities for our program's rising chief residents. Implementation of our curricular design included a morning session focusing on small groups and didactic-based lectures on specific topics pertinent to leadership, along with a debriefing of a psychometric evaluation tool administered before the curriculum day. The simulation activity consisted of 3 group objective structured clinical examination (G-OSCE) scenarios: (1) providing a struggling junior trainee with feedback; (2) debriefing an adverse clinical outcome as the team leader; (3) navigating a challenging situation with a supervising physician. Standardized participants were surveyed for specific objectives. Learners completed precurricular and postcurricular surveys on their familiarity and preparedness for their chief year. RESULTS AND ASSESSMENT DATA/UNASSIGNED:= 0.421), learner-reported use of wellness resources was noted to be reduced after the curricular intervention and remains a result of further interest for exploration. DISCUSSION AND LESSONS LEARNED/UNASSIGNED:A 1-day leadership development curriculum combining didactics and simulation is an effective means of preparing rising chief residents to succeed in their transition to this leadership role.

BACKGROUND:Neuro-ophthalmology frequently requires a complex and multi-faceted clinical assessment supported by sophisticated imaging techniques in order to assess disease status. The current approach to diagnosis requires substantial expertise and time. The emergence of AI has brought forth innovative solutions to streamline and enhance this diagnostic process, which is especially valuable given the shortage of neuro-ophthalmologists. Machine learning algorithms, in particular, have demonstrated significant potential in interpreting imaging data, identifying subtle patterns, and aiding clinicians in making more accurate and timely diagnosis while also supplementing nonspecialist evaluations of neuro-ophthalmic disease. EVIDENCE ACQUISITION/METHODS:Electronic searches of published literature were conducted using PubMed and Google Scholar. A comprehensive search of the following terms was conducted within the Journal of Neuro-Ophthalmology: AI, artificial intelligence, machine learning, deep learning, natural language processing, computer vision, large language models, and generative AI. RESULTS:This review aims to provide a comprehensive overview of the evolving landscape of AI applications in neuro-ophthalmology. It will delve into the diverse applications of AI, optical coherence tomography (OCT), and fundus photography to the development of predictive models for disease progression. Additionally, the review will explore the integration of generative AI into neuro-ophthalmic education and clinical practice. CONCLUSIONS:We review the current state of AI in neuro-ophthalmology and its potentially transformative impact. The inclusion of AI in neuro-ophthalmic practice and research not only holds promise for improving diagnostic accuracy but also opens avenues for novel therapeutic interventions. We emphasize its potential to improve access to scarce subspecialty resources while examining the current challenges associated with the integration of AI into clinical practice and research.