Archetypal analysis of visual fields in optic neuritis reveals functional biomarkers associated with outcome and treatment response.

BACKGROUND AND OBJECTIVES: Archetypal analysis (AA), a form of unsupervised machine learning, can identify quantifiable visual field (VF) patterns seen in optic neuritis (ON), known as archetypes (ATs). We hypothesized that AT weight changes over time would reflect the course of recovery and the effects of therapy in ON. We explored whether baseline AT weights would be associated with VF status at the clinical trial outcome and if ATs would indicate residual VF defects in eyes with mean deviation (MD) ≥ -2.00 at six months. METHODS: We used a published 16-AT model derived from 3892 Optic Neuritis Treatment Trial VFs (456 eyes) for all analyses. We measured AT weight changes over the six-month study period and used asymptotic regression to analyze the rate of change. We compared AT weights at six months between treatment groups. We evaluated associations between baseline AT weight thresholds and VF outcome or treatment effect. We calculated residual AT weights in eyes with MD ≥ -2.00 dB at six months. RESULTS: Over six months, AT1 (a normal VF pattern) demonstrated the greatest median weight change, increasing from 0.00% (IQR 0.00-0.00%) at baseline to 60.0% (IQR 38.3-70.8%) at six months (p < 0.001). At outcome, the intravenous methylprednisolone (IVMP) group had the highest median AT1 weight (IVMP: 63.3%, IQR 51.3-72.8%; placebo: 56.2%, IQR 35.1-71.6%; prednisone 58.3%, IQR 35.1-71.6%; p = 0.019). Eyes with AT1 weight ≥ 19% at baseline had superior median MD values (-0.91 vs. -2.07 dB, p < 0.001) and AT1 weights (70.8% vs. 57.8% p < 0.001) at six months. Only eyes with AT1 weight < 19% at baseline showed a treatment benefit for IVMP, with a higher six-month median AT1 weight compared to placebo (p = 0.015) and prednisone (p = 0.016), and a higher median MD compared to placebo (p = 0.027). At six months, 182 (80.2%) VFs with MD ≥ -2.00 had at least one abnormal AT. DISCUSSION: Changes in quantifiable, archetypal patterns of VF loss reflect recovery in ON. Machine learning analysis of the VFs in optic neuritis reveals associations with response to therapy and VF outcome, and uncovers residual deficits, not readily seen with standard evaluations.

Unsupervised Machine Learning Shows Change in Visual Field Loss in the Idiopathic Intracranial Hypertension Treatment Trial.

PURPOSE: We previously reported that archetypal analysis (AA), a type of unsupervised machine learning, identified and quantified patterns of visual field (VF) loss in idiopathic intracranial hypertension (IIH), referred to as archetypes (ATs). We assessed whether AT weight changes over time are consistent with changes in conventional global indices, whether visual outcome or treatment effects are associated with select AT, and whether AA reveals residual VF defects in eyes deemed normal after treatment. DESIGN: Analysis of data collected from a randomized controlled trial. PARTICIPANTS: Two thousand eight hundred sixty-two VFs obtained from 165 participants during the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT). METHODS: We applied a 14-AT model derived from IIHTT VFs. We examined changes in individual AT weights over time for all study eyes and evaluated differences between treatment groups. We created an AT change score to assess overall VF change from baseline. We tested threshold baseline AT weights for association with VF outcome and treatment effect at 6 months. We determined the abnormal ATs with meaningful weight at outcome for VFs with a mean deviation (MD) of -2.00 dB or more. MAIN OUTCOME MEASURES: Individual AT weighting coefficients and MD. RESULTS: Archetype 1 (a normal VF pattern) showed the greatest weight change for all study eyes, increasing from 11.9% (interquartile range [IQR], 0.44%-24.1%) at baseline to 31.2% (IQR, 16.0%-45.5%) at outcome (P < 0.001). Archetype 1 weight change (r = 0.795; P < 0.001) and a global score of AT change (r = 0.988; P < 0.001) correlated strongly with MD change. Study eyes with baseline AT2 (a mild diffuse VF loss pattern) weight of 44% or more (≥ 1 standard deviation more than the mean) showed higher AT2 weights at outcome than those with AT2 weight of < 44% at baseline (P < 0.001). Only the latter group showed a significant acetazolamide treatment effect. Archetypal analysis revealed residual VF loss patterns, most frequently representing mild diffuse loss and an enlarged blind spot in 64 of 66 study eyes with MD of -2.00 dB or more at outcome. CONCLUSIONS: Archetypal analysis provides a quantitative approach to monitoring VF changes in IIH. Baseline AT features may be associated with treatment response and VF outcome. Archetypal analysis uncovers residual VF defects not otherwise revealed by MD.

Archetypal Analysis Reveals Quantifiable Patterns of Visual Field Loss in Optic Neuritis.

Purpose: Identifying and monitoring visual field (VF) defects due to optic neuritis (ON) relies on qualitative clinician interpretation. Archetypal analysis (AA), a form of unsupervised machine learning, is used to quantify VF defects in glaucoma. We hypothesized that AA can identify quantifiable, ON-specific patterns (as archetypes [ATs]) of VF loss that resemble known ON VF defects. Methods: We applied AA to a dataset of 3892 VFs prospectively collected from 456 eyes in the Optic Neuritis Treatment Trial (ONTT), and decomposed each VF into component ATs (total weight = 100%). AA of 568 VFs from 61 control eyes was used to define a minimum meaningful (≤7%) AT weight and weight change. We correlated baseline ON AT weights with global VF indices, visual acuity, and contrast sensitivity. For eyes with a dominant AT (weight ≥50%), we compared the ONTT VF classification with the AT pattern. Results: AA generated a set of 16 ATs containing patterns seen in the ONTT. These were distinct from control ATs. Baseline study eye VFs were decomposed into 2.9 ± 1.5 ATs. AT2, a global dysfunction pattern, had the highest mean weight at baseline (36%; 95% confidence interval, 33%-40%), and showed the strongest correlation with MD (r = -0.91; P < 0.001), visual acuity (r = 0.70; P < 0.001), and contrast sensitivity (r = -0.77; P < 0.001). Of 191 baseline VFs with a dominant AT, 81% matched the descriptive classifications. Conclusions: AA identifies and quantifies archetypal, ON-specific patterns of VF loss. Translational Relevance: AA is a quantitative, objective method for demonstrating and monitoring change in regional VF deficits in ON.

Unsupervised Machine Learning Identifies Quantifiable Patterns of Visual Field Loss in Idiopathic Intracranial Hypertension.

Purpose: Archetypal analysis, a form of unsupervised machine learning, identifies archetypal patterns within a visual field (VF) dataset such that any VF is described as a weighted sum of its archetypes (ATs) and has been used to quantify VF defects in glaucoma. We applied archetypal analysis to VFs affected by nonglaucomatous optic neuropathy caused by idiopathic intracranial hypertension (IIH). Methods: We created an AT model from 2862 VFs prospectively collected from 330 eyes in the IIH Treatment Trial (IIHTT). We compared baseline IIH AT patterns with their descriptive VF classifications from the IIHTT. Results: The optimum IIH AT model yielded 14 ATs resembling VF patterns reported in the IIHTT. Baseline VFs contained four or fewer meaningful ATs in 147 (89%) of study eyes. AT2 (mild general VF depression pattern) demonstrated the greatest number of study eyes with meaningful AT weight at baseline (n = 114), followed by AT1 (n = 91). Other ATs captured patterns of blind spot enlargement, hemianopia, arcuate, nasal defects, and more nonspecific patterns of general VF depression. Of all ATs, AT1 (normal pattern) had the strongest correlation with mean deviation (r = 0.69, P < 0.001). For 65 of the 93 VFs with a dominant AT, this AT matched the expert classification. Conclusions: Archetypal analysis identifies quantifiable, archetypal VF defects that resemble those commonly seen in IIH. Translational Relevance: Archetypal analysis provides a quantitative, objective method of measuring and monitoring disease-specific regional VF defects in IIH.

Impact of Technology-Based Interventions on Patient-Reported Outcomes in Asthma: A Systematic Review.

BACKGROUND: Technology-based interventions (TBIs) can improve asthma management by facilitating patient education, symptom monitoring, environmental trigger control, comorbid condition management, and medication adherence. Collecting patient-reported outcomes (PROs) can identify effective interventions and ensure patient-centered care, but it is unclear which TBIs have been formally evaluated using PROs. OBJECTIVES: We aim to: (1) identify the TBIs that have been evaluated in clinical trials using PROs; (2) identify the most commonly used PROs in these trials; and (3) determine the impact of TBIs on PROs in the management of chronic asthma. METHODS: We searched the PubMed and Clinicaltrials.gov databases for studies published in English between January 2000 and February 2020 using the following search criteria: "asthma," "IT-based interventions," "information technology," "technology," "dyspnea," "patient reported outcomes," "PROs," "telehealth," "telemedicine," and "mobile devices." Two independent reviewers screened the studies and determined study inclusion. Studies were examined for the types of interventions used, the types of PROs collected, and outcomes. RESULTS: The final analysis included 14 clinical trials with either 1, 2, or 3 arms. Five different types of TBIs were identified, most commonly involving multimedia education. Four different categories of PROs were identified, most commonly involving treatment self-efficacy. Positive outcomes in at least 1 PRO domain were reported in 12 of 14 studies. Pooled meta-analysis was not possible due to the heterogeneity of PRO instruments across studies. CONCLUSION: TBIs improve PROs overall in patients with asthma. Future trials investigating TBIs should include standardized PROs as endpoints to better clarify this relationship.

The Utility of 18FDG PET/CT Versus Bone Scan for Identification of Bone Metastases in a Pediatric Sarcoma Population and a Review of the Literature.

BACKGROUND: Outcome of patients with osteosarcoma (OS) and Ewing sarcoma (EWS) is dependent on presence of metastases. Imaging guidelines for OS and EWS include radiographs, computed tomography (CT), and magnetic resonance imaging for primary tumor evaluation and CT chest and bone scintigraphy (BS) for metastatic detection. 18Fluorodeoxyglucose (18FDG) positron emission tomography (PET)/CT has become more common for disease evaluation, yet there is no consensus for its use in this population. OBJECTIVE: We aimed to compare identification of osseous metastases using BS versus 18FDG PET/CT in our patient population. We hypothesized that 18FDG PET/CT is more likely to detect osseous metastases both at diagnosis and relapse. MATERIALS AND METHODS: We performed retrospective chart reviews of pediatric sarcoma patients treated at our institution from 2008 to 2019. Paired BS and 18FDG PET/CT scans were reviewed. Review of the literature was also performed. RESULTS: Thirty-three patients had paired BS and 18FDG PET/CT during diagnosis or treatment. Fifteen patients had distant osseous metastases. In the OS cohort, 8/16 patients had osseous metastases; 100% of these patients were detected on 18FDG PET/CT and 75% on BS. Thirty-one bony lesions were seen on imaging in OS patients; 100% of these were identified on 18FDG PET/CT but only 29% on BS. In the EWS cohort, 6/15 patients had osseous metastases; 100% of these patients were detected on 18FDG PET/CT and 50% on BS. Eighteen bony lesions were seen on imaging in EWS patients; 94% of these were identified on 18FDG PET/CT, but only 28% on BS. CONCLUSION: For patients in our institution with OS or EWS, osseous metastases were more likely detected using 18FDG PET/CT.

Building Your Brand: Analysis of Successful Oculoplastic Surgeons on Social Media.

PURPOSE: To determine the impact of social media content from oculoplastic surgeon-held accounts on public engagement. METHODS: This study analyzed successful Instagram accounts (defined by >500 followers as of May 2019) held by American Society of Ophthalmic Plastic Reconstruction Surgery members. Each account's 20 most and 20 least successful posts (based on number of likes) were characterized by the post's subject, location, media type, and tag usage. Posts were then analyzed using a web-based application that calculates the engagement level of Instagram users with the selected post. RESULTS: A total of 4,812 posts were analyzed from 37 Instagram accounts. Of these American Society of Ophthalmic Plastic Reconstruction Surgery members, the majority were men (67.6%, n = 25) in private practice solely (70.3%, n = 26). Numerous post characteristics, like content and media type, impacted success in a statistically significant manner; for example, public engagement rose when featuring the posting doctor, especially when smiling and wearing a white coat. Meanwhile, posts featuring an office procedure had a negative effect. Formatting was also found to contribute to success. For example, photographs were preferred over videos, while multiple images were best presented sequentially as a carousel (with the viewer swiping through the series) rather than in a large collage. Use of tags (hashtags, accounts, and locations) were associated with increased engagement. CONCLUSIONS: Social media is an important communication and marketing tool, especially in esthetic fields like oculoplastics. Guidelines for success are presented in this study; content, location, subject, media type, and tags statistically significantly impact public engagement.The influence of social media is rapidly growing and can be strategically harnessed by oculoplastic surgeons to educate both patients and healthcare providers, collaborate with colleagues, and for referrals and marketing.Supplemental Digital Content is available in the text.

The influence of social media is rapidly growing and can be strategically harnessed by oculoplastic surgeons to educate both patients and healthcare providers, collaborate with colleagues, and for referrals and marketing. Supplemental Digital Content is available in the text.