The scope and severity of white-nose syndrome on hibernating bats in North America.

Assessing the scope and severity of threats is necessary for evaluating impacts on populations to inform conservation planning. Quantitative threat assessment often requires monitoring programs that provide reliable data over relevant spatial and temporal scales, yet such programs can be difficult to justify until there is an apparent stressor. Leveraging efforts of wildlife management agencies to record winter counts of hibernating bats, we collated data for 5 species from over 200 sites across 27 U.S. states and 2 Canadian provinces from 1995 to 2018 to determine the impact of white-nose syndrome (WNS), a deadly disease of hibernating bats. We estimated declines of winter counts of bat colonies at sites where the invasive fungus that causes WNS (Pseudogymnoascus destructans) had been detected to assess the threat impact of WNS. Three species undergoing species status assessment by the U.S. Fish and Wildlife Service (Myotis septentrionalis, Myotis lucifugus, and Perimyotis subflavus) declined by more than 90%, which warrants classifying the severity of the WNS threat as extreme based on criteria used by NatureServe. The scope of the WNS threat as defined by NatureServe criteria was large (36% of Myotis lucifugus range) to pervasive (79% of Myotis septentrionalis range) for these species. Declines for 2 other species (Myotis sodalis and Eptesicus fuscus) were less severe but still qualified as moderate to serious based on NatureServe criteria. Data-sharing across jurisdictions provided a comprehensive evaluation of scope and severity of the threat of WNS and indicated regional differences that can inform response efforts at international, national, and state or provincial jurisdictions. We assessed the threat impact of an emerging infectious disease by uniting monitoring efforts across jurisdictional boundaries and demonstrated the importance of coordinated monitoring programs, such as the North American Bat Monitoring Program (NABat), for data-driven conservation assessments and planning.

Alcance y Severidad del Síndrome de Nariz Blanca en los Murciélagos Hibernando en América del Norte Resumen La evaluación del alcance y la severidad de las amenazas es necesaria para los análisis de impacto sobre las poblaciones que se usan para orientar a la planeación de la conservación. La evaluación cuantitativa de amenazas con frecuencia requiere de programas de monitoreo que proporcionen datos confiables en escalas espaciales y temporales, aunque dichos programas pueden ser difíciles de justificar hasta que exista un estresante aparente. Gracias a una movilización de esfuerzos de las agencias de manejo de fauna para registrar los conteos invernales de murciélagos hibernadores, recopilamos datos para cinco especies en más de 200 sitios a lo largos de 27 estados de EUA y dos provincias canadienses entre 1995 y 2018 para determinar el impacto del síndrome de nariz blanca (SNB), una enfermedad mortal de los murciélagos hibernadores. Estimamos declinaciones en los conteos invernales de las colonias de murciélagos en sitios en donde el hongo invasivo que ocasiona el SNB (Pseudogymnoascus destructans) había sido detectado para evaluar el impacto de amenaza del SNB. Tres especies que se encuentran bajo valoración por parte del Servicio de Pesca y Vida Silvestre de los EUA (Myotis septentrionalis, Myotis lucifugus y Perimyotis subflavus) tuvieron una declinación de más del 90%, lo que justifica la clasificación de la severidad de la amenaza del SNB como extrema con base en el criterio usado por NatureServe. El alcance de la amenaza del SNB definido por el criterio de NatureServe fue desde amplio (36% de la distribución de Myotis lucifugus) hasta dominante (79% de la distribución de Myotis septentrionalis) para estas especies. Las declinaciones de otras dos especies (Myotis sodalis y Eptesicus fuscus) fueron menos severas, pero de igual manera quedaron clasificadas desde moderada hasta seria con base en los criterios de NatureServe. El intercambio de datos entre las jurisdicciones proporcionó una evaluación completa del alcance y la severidad de la amenaza del SNB e indicó las diferencias regionales que pueden guiar a los esfuerzos de respuesta realizados en las jurisdicciones internacionales, nacionales, estatales o provinciales. Evaluamos el impacto de amenaza de una enfermedad infecciosa emergente mediante la combinación de los esfuerzos de monitoreo que sobrepasan fronteras jurisdiccionales y demostramos la importancia que tienen para la planeación y la evaluación basadas en datos de la conservación los programas de monitoreo coordinados, como el Programa de Monitoreo de los Murciélagos Norteamericanos (NABat).

A new approach to integrating patient-generated data with expert knowledge for personalized goal setting: A pilot study.

INTRODUCTION: Self-monitoring technologies produce patient-generated data that could be leveraged to personalize nutritional goal setting to improve population health; however, most computational approaches are limited when applied to individual-level personalization with sparse and irregular self-monitoring data. We applied informatics methods from expert suggestion systems to a challenging clinical problem: generating personalized nutrition goals from patient-generated diet and blood glucose data. MATERIALS AND METHODS: We applied qualitative process coding and decision tree modeling to understand how registered dietitians translate patient-generated data into recommendations for dietary self-management of diabetes (i.e., knowledge model). We encoded this process in a set of functions that take diet and blood glucose data as an input and output diet recommendations (i.e., inference engine). Dietitians assessed face validity. Using four patient datasets, we compared our inference engine's output to clinical narratives and gold standards developed by expert clinicians. RESULTS: To dietitians, the knowledge model represented how recommendations from patient data are made. Inference engine recommendations were 63 % consistent with the gold standard (range = 42 %-75 %) and 74 % consistent with narrative clinical observations (range = 63 %-83 %). DISCUSSION: Qualitative modeling and automating how dietitians reason over patient data resulted in a knowledge model representing clinical knowledge. However, our knowledge model was less consistent with gold standard than narrative clinical recommendations, raising questions about how best to evaluate approaches that integrate patient-generated data with expert knowledge. CONCLUSION: New informatics approaches that integrate data-driven methods with expert decision making for personalized goal setting, such as the knowledge base and inference engine presented here, demonstrate the potential to extend the reach of patient-generated data by synthesizing it with clinical knowledge. However, important questions remain about the strengths and weaknesses of computer algorithms developed to discern signal from patient-generated data compared to human experts.

Detecting Social and Behavioral Determinants of Health with Structured and Free-Text Clinical Data.

BACKGROUND: Social and behavioral determinants of health (SBDH) are environmental and behavioral factors that often impede disease management and result in sexually transmitted infections. Despite their importance, SBDH are inconsistently documented in electronic health records (EHRs) and typically collected only in an unstructured format. Evidence suggests that structured data elements present in EHRs can contribute further to identify SBDH in the patient record. OBJECTIVE: Explore the automated inference of both the presence of SBDH documentation and individual SBDH risk factors in patient records. Compare the relative ability of clinical notes and structured EHR data, such as laboratory measurements and diagnoses, to support inference. METHODS: We attempt to infer the presence of SBDH documentation in patient records, as well as patient status of 11 SBDH, including alcohol abuse, homelessness, and sexual orientation. We compare classification performance when considering clinical notes only, structured data only, and notes and structured data together. We perform an error analysis across several SBDH risk factors. RESULTS: Classification models inferring the presence of SBDH documentation achieved good performance (F1 score: 92.7-78.7; F1 considered as the primary evaluation metric). Performance was variable for models inferring patient SBDH risk status; results ranged from F1 = 82.7 for LGBT (lesbian, gay, bisexual, and transgender) status to F1 = 28.5 for intravenous drug use. Error analysis demonstrated that lexical diversity and documentation of historical SBDH status challenge inference of patient SBDH status. Three of five classifiers inferring topic-specific SBDH documentation and 10 of 11 patient SBDH status classifiers achieved highest performance when trained using both clinical notes and structured data. CONCLUSION: Our findings suggest that combining clinical free-text notes and structured data provide the best approach in classifying patient SBDH status. Inferring patient SBDH status is most challenging among SBDH with low prevalence and high lexical diversity.

An investigation of the information technology needs associated with delivering chronic disease care to large clinical populations.

BACKGROUND: The growing number of individuals with complex medical and social needs has motivated the adoption of care management (CM) - programs wherein multidisciplinary teams coordinate and monitor the clinical and non-clinical aspects of care for patients with chronic disease. Despite claims that health information technology (IT) is essential to CM, there has been limited research focused on the IT needs of clinicians providing care management to large groups of patients with chronic disease. OBJECTIVE: To assess clinicians' needs pertaining to CM and to identify inefficiencies and bottlenecks associated with the delivery of CM to large groups of patients with chronic disease. METHODS: A qualitative study of two HIV care programs. Methods included observations of multidisciplinary care team meetings and semi-structured interviews with physicians, care managers, and social workers. Thematic analysis was conducted to analyze the data. RESULTS: CM was perceived by staff as requiring the development of novel strategies including patient prioritization and patient monitoring, which was supported by patient registries but also required the creation of additional homegrown tools. Common challenges included: limited ability to identify pertinent patient information, specifically in regards to social and behavioral determinants of health, limited assistance in matching patients to appropriate interventions, and limited support for communication within multidisciplinary care teams. CONCLUSION: Clinicians delivering care management to chronic disease patients are not adequately supported by electronic health records and patient registries. Tools that better enable population monitoring, facilitate communication between providers, and help address psychosocial barriers to treatment could enable more effective care.

Longitudinal analysis of social and behavioral determinants of health in the EHR: exploring the impact of patient trajectories and documentation practices.

Social and behavioral determinants of health (SBDH) are environmental and behavioral factors that impede disease self-management and can exacerbate clinical conditions. While recent research in the informatics community has focused on building systems that can automatically infer SBDH from the patient record, it is unclear how such determinants change overtime. This study analyzes the longitudinal characteristics of 4 common SBDH as expressed in the patient record and compares the rates of change among distinct SBDH. In addition, manual review of patient notes was undertaken to establish whether changes in patient SBDH status reflected legitimate changes in patient status or rather potential data quality issues. Our findings suggest that a patient's SBDH status is liable to change over time and that some changes reflect poor social history taking by clinicians.

Development and Validation of an Electronic Medical Record-Based Algorithm to Identify Patient Milestones in the Hepatitis C Virus Care Cascade.

Disease-specific care cascades are important public health and organizational tools to characterize gaps in care and target resources, but they are labor-intensive to maintain. Using data available from the electronic medical record, we developed an algorithm with high accuracy for correctly representing an individual's status in the hepatitis C virus care cascade.

A visual analytics approach for pattern-recognition in patient-generated data.

Objective: To develop and test a visual analytics tool to help clinicians identify systematic and clinically meaningful patterns in patient-generated data (PGD) while decreasing perceived information overload. Methods: Participatory design was used to develop Glucolyzer, an interactive tool featuring hierarchical clustering and a heatmap visualization to help registered dietitians (RDs) identify associative patterns between blood glucose levels and per-meal macronutrient composition for individuals with type 2 diabetes (T2DM). Ten RDs participated in a within-subjects experiment to compare Glucolyzer to a static logbook format. For each representation, participants had 25 minutes to examine 1 month of diabetes self-monitoring data captured by an individual with T2DM and identify clinically meaningful patterns. We compared the quality and accuracy of the observations generated using each representation. Results: Participants generated 50% more observations when using Glucolyzer (98) than when using the logbook format (64) without any loss in accuracy (69% accuracy vs 62%, respectively, p = .17). Participants identified more observations that included ingredients other than carbohydrates using Glucolyzer (36% vs 16%, p = .027). Fewer RDs reported feelings of information overload using Glucolyzer compared to the logbook format. Study participants displayed variable acceptance of hierarchical clustering. Conclusions: Visual analytics have the potential to mitigate provider concerns about the volume of self-monitoring data. Glucolyzer helped dietitians identify meaningful patterns in self-monitoring data without incurring perceived information overload. Future studies should assess whether similar tools can support clinicians in personalizing behavioral interventions that improve patient outcomes.

Assessing the relative vulnerability of sensitive karst habitats containing rare, threatened, and endangered species in the Chesapeake and Ohio Canal National Historical Park.

We were asked to provide the Chesapeake and Ohio Canal National Historical Park with information and guidance to help park managers protect and manage karst-related rare, threatened, and endangered species on park land. To do this we developed a vulnerability risk matrix based on a variety of data collected. The purpose of the matrix was to provide the park with an interactive means of evaluating the relative vulnerability of the different sites. The data collected included (1) an inventory of karst resources in the park, (2) collection of water chemistry data, and (3) an RTE assessment. Useful outcomes included a standardized scoring system for the RTE species in the park for each site, an assessment of relative risk (vulnerability of site to negative events) and impact (a measure of the damage to RTE species if a negative event would occur), and a vulnerability matrix that identifies the sites needing management or future assessment. This matrix can be easily modified and used to assess other scenarios or to accommodate the addition of new data. Other parks and sites could reproduce this type of matrix in order to manage their resources.

Using Clinical Notes and Natural Language Processing for Automated HIV Risk Assessment.

OBJECTIVE: Universal HIV screening programs are costly, labor intensive, and often fail to identify high-risk individuals. Automated risk assessment methods that leverage longitudinal electronic health records (EHRs) could catalyze targeted screening programs. Although social and behavioral determinants of health are typically captured in narrative documentation, previous analyses have considered only structured EHR fields. We examined whether natural language processing (NLP) would improve predictive models of HIV diagnosis. METHODS: One hundred eighty-one HIV+ individuals received care at New York Presbyterian Hospital before a confirmatory HIV diagnosis and 543 HIV negative controls were selected using propensity score matching and included in the study cohort. EHR data including demographics, laboratory tests, diagnosis codes, and unstructured notes before HIV diagnosis were extracted for modeling. Three predictive algorithms were developed using machine-learning algorithms: (1) a baseline model with only structured EHR data, (2) baseline plus NLP topics, and (3) baseline plus NLP clinical keywords. RESULTS: Predictive models demonstrated a range of performance with F measures of 0.59 for the baseline model, 0.63 for the baseline + NLP topic model, and 0.74 for the baseline + NLP keyword model. The baseline + NLP keyword model yielded the highest precision by including keywords including "msm," "unprotected," "hiv," and "methamphetamine," and structured EHR data indicative of additional HIV risk factors. CONCLUSIONS: NLP improved the predictive performance of automated HIV risk assessment by extracting terms in clinical text indicative of high-risk behavior. Future studies should explore more advanced techniques for extracting social and behavioral determinants from clinical text.

Towards the Inference of Social and Behavioral Determinants of Sexual Health: Development of a Gold-Standard Corpus with Semi-Supervised Learning.

Social and behavioral determinants of health (SBDH) are environmental and behavioral factors that are increasingly recognized for their impact on health outcomes. We describe ongoing research to extract SBDH related to sexual health from clinical documentation. Our work addresses several challenges. First, there is no standard set of SBDHs for sexual health; we describe our curation of 38 such SBDHs. Second, it is unknown how SBDH related to sexual health are expressed in clinical notes; we detail the characteristics of an annotated corpus. Third, SBDH documentations are rare; we describe the use of semi-supervised learning to accelerate the annotation process by identifying notes likely to document SBDH. Fourth, we describe preliminary results to infer an array of SBDH from clinical documentation using supervised learning.

Understanding Determinants of Racial and Ethnic Disparities in Viral Load Suppression.

Racial and ethnic disparities in viral load suppression (VLS) have been well documented among people living with HIV (PLWH). The authors hypothesized that a contemporary analytic technique could reveal factors underlying these disparities and provide more explanatory power than broad stereotypes. Classification and regression tree analysis was used to detect factors associated with VLS among 11 419 adult PLWH receiving treatment from 186 New York State HIV clinics in 2013. A total of 8885 (77.8%) patients were virally suppressed. The algorithm identified 8 mutually exclusive subgroups characterized by age, housing stability, drug use, and insurance status but neither race nor ethnicity. Our findings suggest that racial and ethnic disparities in VLS exist but likely reflect underlying social and behavioral determinants of health.

Web-based Real-Time Case Finding for the Population Health Management of Patients With Diabetes Mellitus: A Prospective Validation of the Natural Language Processing-Based Algorithm With Statewide Electronic Medical Records.

BACKGROUND: Diabetes case finding based on structured medical records does not fully identify diabetic patients whose medical histories related to diabetes are available in the form of free text. Manual chart reviews have been used but involve high labor costs and long latency. OBJECTIVE: This study developed and tested a Web-based diabetes case finding algorithm using both structured and unstructured electronic medical records (EMRs). METHODS: This study was based on the health information exchange (HIE) EMR database that covers almost all health facilities in the state of Maine, United States. Using narrative clinical notes, a Web-based natural language processing (NLP) case finding algorithm was retrospectively (July 1, 2012, to June 30, 2013) developed with a random subset of HIE-associated facilities, which was then blind tested with the remaining facilities. The NLP-based algorithm was subsequently integrated into the HIE database and validated prospectively (July 1, 2013, to June 30, 2014). RESULTS: Of the 935,891 patients in the prospective cohort, 64,168 diabetes cases were identified using diagnosis codes alone. Our NLP-based case finding algorithm prospectively found an additional 5756 uncodified cases (5756/64,168, 8.97% increase) with a positive predictive value of .90. Of the 21,720 diabetic patients identified by both methods, 6616 patients (6616/21,720, 30.46%) were identified by the NLP-based algorithm before a diabetes diagnosis was noted in the structured EMR (mean time difference = 48 days). CONCLUSIONS: The online NLP algorithm was effective in identifying uncodified diabetes cases in real time, leading to a significant improvement in diabetes case finding. The successful integration of the NLP-based case finding algorithm into the Maine HIE database indicates a strong potential for application of this novel method to achieve a more complete ascertainment of diagnoses of diabetes mellitus.

Readmissions in HIV-Infected Inpatients: A Large Cohort Analysis.

OBJECTIVE: Hospital readmissions impose considerable physical and psychological hardships on patients and represent a high, but possibly preventable, cost for insurers and hospitals alike. The objective of this study was to identify patient characteristics associated with 30-day readmission among persons living with HIV/AIDS (PLWH) using a statewide administrative database and to characterize the movement of patients between facilities. DESIGN: Retrospective cohort analysis of HIV-infected individuals in New York State using a comprehensive, all-payer database. SETTING: All hospitals in New York State. PARTICIPANTS: HIV-infected adults admitted to a medical service in 2012. PLWH identified using International Classification of Disease (ICD)-9 diagnosis codes 042 and V08. RESULTS: Of 23,544 index hospitalizations, 21.8% (5121) resulted in readmission. Multivariable predictors of readmission included insurance status, housing instability, psychoses, multiple comorbid chronic conditions, substance use, and past inpatient and emergency department visits. Over 30% of readmissions occurred at a different facility than that of the initial hospitalization. CONCLUSION: A number of patient characteristics were independently associated with hospital readmission within 30 days. Behavioral health disorders and comorbid conditions may be the strongest predictors of readmission in PLWH. Readmissions, especially those in urban areas, often result in fragmented care which may compromise the quality of care and result in harmful discontinuity of medical treatment.

The HIV Workforce in New York State: Does Patient Volume Correlate with Quality?

BACKGROUND: Knowledge of care practices among clinicians who annually treat <20 human immunodeficiency virus (HIV)-positive patients with antiretroviral therapy (ART) is insufficient, despite their number, which is likely to increase given shifting healthcare policies. We analyze the practices, distribution and quality of care provided by low-volume prescribers (LVPs) based on available data sources in New York State. METHODS: We communicated with 1278 (66%) of the LVPs identified through a statewide claims database to determine the circumstances under which they prescribed ART in federal fiscal year 2009. We reviewed patient records from 84 LVPs who prescribed ART routinely and compared their performance with that of experienced clinicians practicing in established HIV programs. RESULTS: Of the surveyed LVPs, 368 (29%) provided routine ambulatory care for 2323 persons living with HIV/AIDS, and 910 LVPs cited other reasons for prescribing ART. Although the majority of LVPs (73%) practiced in New York City, patients living upstate were more likely to be cared for by a LVP (odds ratio, 1.7; 95% confidence interval, 1.4-1.9). Scores for basic HIV performance measures, including viral suppression, were significantly higher in established HIV programs than for providers who wrote prescriptions for <20 persons living with HIV/AIDS (P < .01). We estimate that 33% of New York State clinicians who provide ambulatory HIV care are LVPs. CONCLUSIONS: Our findings suggest that the quality of care associated with providers who prescribe ART for <20 patients is lower than that provided by more experienced providers. Access to experienced providers as defined by patient volume is an important determinant of delivering high-quality care and should guide HIV workforce policy decisions.

Management of sexually transmitted infections in New York State health care organizations: who is thinking about the quality of STI care?

BACKGROUND: Rising rates of sexually transmitted infections (STIs) warrant a renewed focus on the management of STIs in health care organizations. The extent to which hospitals and community health centers (CHCs) have established processes and allocated staff for the management of STIs within their organizations remains poorly understood. METHODS: A New York State Department of Health survey was distributed electronically through a closed state communication network to targeted administrators at New York State hospitals and CHCs. The survey asked if STI management in their facilities included the following: the ability to measure and report rates of STIs, a process to assess the quality of STI care and treatment outcomes, and a centralized person/unit to coordinate its work throughout the facility. Multivariate analysis was performed to identify whether organizational characteristics were associated with survey findings. RESULTS: Ninety-five percent (243/256) of hospitals and CHCs responded to the survey. Fifty percent of respondents had a person or unit to report rates of STIs; 30% reported an organization-wide process for monitoring the quality of STI care, which, according to the multivariate analysis, was associated with CHCs; only 23% reported having a centralized person or unit for coordinating STI management. CONCLUSIONS: Most facilities report STI cases to comply with public health surveillance requirements but do not measure infection rates, assess the quality of STI care, or coordinate its work throughout the facility. The development of this organizational capacity would likely decrease STI rates, improve treatment outcomes, and address local public health goals.

Ganglion cell loss in relation to visual disability in multiple sclerosis.

PURPOSE: We used high-resolution spectral-domain optical coherence tomography (SD-OCT) with retinal segmentation to determine how ganglion cell loss relates to history of acute optic neuritis (ON), retinal nerve fiber layer (RNFL) thinning, visual function, and vision-related quality of life (QOL) in multiple sclerosis (MS). DESIGN: Cross-sectional study. PARTICIPANTS: A convenience sample of patients with MS (n = 122; 239 eyes) and disease-free controls (n = 31; 61 eyes). Among MS eyes, 87 had a history of ON before enrollment. METHODS: The SD-OCT images were captured using Macular Cube (200×200 or 512×128) and ONH Cube 200×200 protocols. Retinal layer segmentation was performed using algorithms established for glaucoma studies. Thicknesses of the ganglion cell layer/inner plexiform layer (GCL+IPL), RNFL, outer plexiform/inner nuclear layers (OPL+INL), and outer nuclear/photoreceptor layers (ONL+PRL) were measured and compared in MS versus control eyes and MS ON versus non-ON eyes. The relation between changes in macular thickness and visual disability was also examined. MAIN OUTCOME MEASURES: The OCT measurements of GCL+IPL and RNFL thickness; high contrast visual acuity (VA); low-contrast letter acuity (LCLA) at 2.5% and 1.25% contrast; on the 25-item National Eye Institute Visual Function Questionnaire (NEI-VFQ-25) and 10-Item Neuro-Ophthalmic Supplement composite score. RESULTS: Macular RNFL and GCL+IPL were significantly decreased in MS versus control eyes (P<0.001 and P = 0.001) and in MS ON versus non-ON eyes (P<0.001 for both measures). Peripapillary RNFL, macular RNFL, GCL+IPL, and the combination of macular RNFL+GCL+IPL were significantly correlated with VA (P≤0.001), 2.5% LCLA (P<0.001), and 1.25% LCLA (P≤0.001). Among OCT measurements, reductions in GCL+IPL (P<0.001), macular RNFL (P = 0.006), and the combination (macular RNFL+GCL+IPL; P<0.001) were most strongly associated with lower (worse) NEI-VFQ-25 and 10-Item Supplement QOL scores; GCL+IPL thinning was significant even accounting for macular RNFL thickness (P = 0.03 for GCL+IPL, P = 0.39 for macular RNFL). CONCLUSIONS: We demonstrated that GCL+IPL thinning is most significantly correlated with both visual function and vision-specific QOL in MS, and may serve as a useful structural marker of disease. Our findings parallel those of magnetic resonance imaging studies that show gray matter disease is a marker of neurologic disability in MS. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Vision in multiple sclerosis: the story, structure-function correlations, and models for neuroprotection.

Visual dysfunction is one of the most common clinical manifestations of multiple sclerosis (MS). Just over a decade ago, MS clinical trials did not include visual outcomes, but experts recognized the need for more sensitive measures of visual function. Low-contrast letter acuity emerged as the leading candidate to measure visual disability in MS, and subsequent studies found low-contrast acuity testing to correlate well with brain MRI lesion burden, visual-evoked potentials, quality of life (QOL), and retinal nerve fiber layer (RNFL) loss, as measured by optical coherence tomography (OCT). OCT in MS has allowed for assessment of structure-function correlations that make the anterior visual pathway and acute optic neuritis (ON) ideal models for testing novel agents for neuroprotection and repair. New therapies that reduce axonal loss by neuroprotective or myelin repair mechanisms can now be assessed noninvasively by OCT and coupled with visual function data. Based on OCT studies in MS, RNFL thickness is reduced significantly among patients (92 µm) vs controls (105 µm) and is particularly reduced in MS eyes with a history of ON (85 µm). Worsening of visual function by a clinically significant ≥ 7 letters or approximately 1.5 lines for low-contrast acuity is associated with approximately 4.5 µm reductions in RNFL thickness in MS eyes. Longitudinal studies of OCT have also shown RNFL axonal loss over time that occurs even in the absence of acute ON and that correlates with clinically meaningful worsening of vision and QOL, even in patients with benign MS. The latest OCT investigations involve high-resolution spectral-domain (SD) OCT with segmentation and measurement of specific retinal layers using computerized algorithms. These methods allow quantitation of ganglion cell (neuronal) layer loss and axonal degeneration in MS in vivo. In this review, we examine the data from these studies and ongoing trials that highlight the entity of ON as a model to investigate neuroprotection and neurorepair. In doing so, we also present representative group data from studies that have examined visual function, OCT measures, and QOL scales in patients with MS and ON and disease-free controls. These data, and those from recent meta-analyses, may be used to provide reference values for the development of clinical trial protocols.