Heavy drinkers are at risk for a spectrum of histologic alcohol-related liver injury: steatosis, alcoholic steatohepatitis (ASH), alcohol-related fibrosis, and cirrhosis. Alcoholic hepatitis (AH), the clinical entity associated with severe ASH, has high short-term mortality. The standard-of-care therapy, prednisolone, has limited efficacy and many side effects; no other treatment has consistently shown survival benefit. The National Institute on Alcohol Abuse and Alcoholism (NIAAA)-funded Alcoholic Hepatitis Consortia carry out translational research on pathophysiologic mechanisms, genetic and environmental risk factors, phase II clinical trials, and development of biomarkers. The consortia members were convened by the National Institutes of Health to address diagnostic criteria and practical issues related to clinical AH research, and to develop a set of common data elements to harmonize ongoing and future trials. This was accomplished through 3 face-to-face meetings of the investigators and representatives of the National Institutes of Health, and subsequent electronic communications over the course of 6 months. Evidence for the recommendations was based on published trials and observational data from several of the consortia members. A draft manuscript was iteratively reviewed by members of the consortia. The goal was to reach agreements on recommendations and definitions that could facilitate trial design, and simultaneously be tested by research groups pooling their data. The recommendations made here are specifically directed to achieve better uniformity in clinical trials, rather than serving as clinical practice guidelines.

Aims

We compared feasibility, quality and outcomes of visual field (VF) testing in children with neuro-ophthalmic disease between the discontinued 'gold-standard' Goldmann and Octopus perimeters.

Methods

Children with neuro-ophthalmic disease, attending Great Ormond Street Hospital, London, were assessed using standardised protocols by one examiner in a single sitting, using Goldmann and Octopus kinetic perimetry. Outputs were classified to compare severity of loss and defect type. Test quality was assessed using both qualitative and quantitative methods.

Results

Thirty children (40% female) aged 5-15 years participated. Goldmann perimetry was completed in full by 90.0% vs 72.4% for Octopus. Inability to plot the blind spot was the most common reason for not completing testing. Over 75% completed a test in ≤20 min. Duration was similar between perimeters (paired t-test, mean difference: 0.48min (-1.2, 2.2), p=0.559). The lowest quality tests were for Octopus perimetry in children <8 years, without significant differences between perimeters in older children (McNemar's test, χ²=1.0, p=0.317). There was broad agreement between Goldmann and Octopus outputs (good quality, n=21, Bland-Altman, mean difference for isopters I4e (-514.3 deg² (-817.4, -211.2), p=0.814), I2e (-575.5 deg² (-900.1, -250.9), p=0.450) and blind spot (20.8 deg² (5.7, 35.8), p=0.451)). However, VF severity grades and defect type matched in only 57% and 69% of tests, respectively. Octopus perimetry underestimated severe VF defects.

Conclusions

Informative perimetry is feasible in children ≥8 years with neuro-ophthalmic conditions, with either Goldmann or Octopus perimeters. However, meaningful differences exist between the two approaches with implications for consistency in longitudinal assessments.

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.