Characteristics and Outcomes of 360 Consecutive COVID-19 Patients Discharged From the Emergency Department With Supplemental Oxygen.

STUDY OBJECTIVE: To describe characteristics and outcomes of coronavirus disease (COVID-19) patients with new supplemental oxygen requirements discharged from a large public urban emergency department (ED) with supplemental oxygen. METHODS: This observational case series describes the characteristics and outcomes of 360 consecutive COVID-19 patients with new supplemental oxygen requirements discharged from a large urban public ED between April 2020 and March 2021 with supplemental oxygen. Primary outcomes included 30-day survival and 30-day survival without unscheduled inpatient admission. Demographic and clinical data were collected through a structured chart review. RESULTS: Among 360 patients with COVID-19 discharged from the ED with supplemental oxygen, 30-day survival was 97.5% (95% confidence interval (CI) 95.3 to 98.9%; n=351), and 30-day survival without unscheduled admission was 81.1% (95% CI 76.7 to 85.0%; n=292). A sensitivity analysis incorporating worst-case-scenario for 12 patients without complete follow-up 30 days after index visit yields 30-day survival of 95.5% (95% CI 92.5 to 97.2%; n=343), and 30-day survival without unscheduled admission of 78.9% (95% CI 74.3 to 83.0%; n=284). Among study patients, 32.2% (n=116) had a nadir ED oxygen saturation of <90%, among these 30-day survival was 97.4% (95% CI 92.6 to 99.4%; n=113), and 30-day survival without unscheduled admission was 76.7% (95% CI 68.8 to 84.1%; n=89). CONCLUSION: COVID-19 patients with new supplemental oxygen requirements discharged from the ED had survival comparable to COVID-19 ED patients with mild exertional hypoxia treated with supplemental oxygen in other settings, and this held true when the analysis was restricted to patients with nadir ED index visit oxygen saturations <90%. Discharge of select COVID-19 patients with supplemental oxygen from the ED may provide a viable alternative to hospitalization, particularly when inpatient capacity is limited.

Generalisability and performance of an OCT-based deep learning classifier for community-based and hospital-based detection of gonioscopic angle closure.

PURPOSE: To assess the generalisability and performance of a deep learning classifier for automated detection of gonioscopic angle closure in anterior segment optical coherence tomography (AS-OCT) images. METHODS: A convolutional neural network (CNN) model developed using data from the Chinese American Eye Study (CHES) was used to detect gonioscopic angle closure in AS-OCT images with reference gonioscopy grades provided by trained ophthalmologists. Independent test data were derived from the population-based CHES, a community-based clinic in Singapore, and a hospital-based clinic at the University of Southern California (USC). Classifier performance was evaluated with receiver operating characteristic curve and area under the receiver operating characteristic curve (AUC) metrics. Interexaminer agreement between the classifier and two human examiners at USC was calculated using Cohen's kappa coefficients. RESULTS: The classifier was tested using 640 images (311 open and 329 closed) from 127 Chinese Americans, 10 165 images (9595 open and 570 closed) from 1318 predominantly Chinese Singaporeans and 300 images (234 open and 66 closed) from 40 multiethnic USC patients. The classifier achieved similar performance in the CHES (AUC=0.917), Singapore (AUC=0.894) and USC (AUC=0.922) cohorts. Standardising the distribution of gonioscopy grades across cohorts produced similar AUC metrics (range 0.890-0.932). The agreement between the CNN classifier and two human examiners (Ò =0.700 and 0.704) approximated interexaminer agreement (Ò =0.693) in the USC cohort. CONCLUSION: An OCT-based deep learning classifier demonstrated consistent performance detecting gonioscopic angle closure across three independent patient populations. This automated method could aid ophthalmologists in the assessment of angle status in diverse patient populations.

PATIENT WITH UNILATERAL CHOROIDAL AND SEROUS RETINAL DETACHMENT WITH A HISTORY OF TREATED PROSTATE CANCER AND UNTREATED SARCOIDOSIS.

BACKGROUND/PURPOSE: To report a case of unilateral choroidal detachment and serous retinal detachment in a patient with a history of untreated sarcoidosis. METHODS: Case report. The patient is a 67-year-old African American man with a history of nontreated sarcoidosis and prostate cancer. His prostate cancer was treated several years earlier with external beam radiation therapy. The patient presented with blurred visual acuity of 20/30 and floaters in the right eye. He was discovered to have several hypopigmented choroidal lesions, 360-degree choroidal detachment, and shallow serous retinal detachment in the right eye. RESULTS: The patient was treated with subtenons kenalog and oral prednisone with subsequent improvement of vision and resolution of choroidal and retinal detachment. CONCLUSION: Ocular sarcoidosis can involve any part of the eye and its adnexal tissues and may cause uveitis, episcleritis, scleritis, eyelid abnormalities, conjunctival granuloma, optic neuropathy, lacrimal gland enlargement, and orbital inflammation. Most patients with ophthalmic sarcoidosis have evidence of systemic involvement at the time of the initial examination and have bilateral ocular presentation. We present here the unique case of a 67-year-old man with unilateral 360-degree choroidal detachment and serous retinal detachment as an ocular presentation of sarcoidosis.

The Effects of Breastfeeding on Retinoblastoma Development: Results from an International Multicenter Retinoblastoma Survey.

The protective effects of breastfeeding on various childhood malignancies have been established but an association has not yet been determined for retinoblastoma (RB). We aimed to further investigate the role of breastfeeding in the severity of nonhereditary RB development, assessing relationship to (1) age at diagnosis, (2) ocular prognosis, measured by International Intraocular RB Classification (IIRC) or Intraocular Classification of RB (ICRB) group and success of eye salvage, and (3) extraocular involvement. Analyses were performed on a global dataset subgroup of 344 RB patients whose legal guardian(s) consented to answer a neonatal questionnaire. Patients with undetermined or mixed feeding history, family history of RB, or sporadic bilateral RB were excluded. There was no statistically significant difference between breastfed and formula-fed groups in (1) age at diagnosis (p = 0.20), (2) ocular prognosis measures of IIRC/ICRB group (p = 0.62) and success of eye salvage (p = 0.16), or (3) extraocular involvement shown by International Retinoblastoma Staging System (IRSS) at presentation (p = 0.74), lymph node involvement (p = 0.20), and distant metastases (p = 0.37). This study suggests that breastfeeding neither impacts the sporadic development nor is associated with a decrease in the severity of nonhereditary RB as measured by age at diagnosis, stage of disease, ocular prognosis, and extraocular spread. A further exploration into the impact of diet on children who develop RB is warranted.

Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study.

PURPOSE: To compare the performance of a novel convolutional neural network (CNN) classifier and human graders in detecting angle closure in EyeCam (Clarity Medical Systems, Pleasanton, California, USA) goniophotographs. DESIGN: Retrospective cross-sectional study. METHODS: Subjects from the Chinese American Eye Study underwent EyeCam goniophotography in 4 angle quadrants. A CNN classifier based on the ResNet-50 architecture was trained to detect angle closure, defined as inability to visualize the pigmented trabecular meshwork, using reference labels by a single experienced glaucoma specialist. The performance of the CNN classifier was assessed using an independent test dataset and reference labels by the single glaucoma specialist or a panel of 3 glaucoma specialists. This performance was compared to that of 9 human graders with a range of clinical experience. Outcome measures included area under the receiver operating characteristic curve (AUC) metrics and Cohen kappa coefficients in the binary classification of open or closed angle. RESULTS: The CNN classifier was developed using 29,706 open and 2,929 closed angle images. The independent test dataset was composed of 600 open and 400 closed angle images. The CNN classifier achieved excellent performance based on single-grader (AUC = 0.969) and consensus (AUC = 0.952) labels. The agreement between the CNN classifier and consensus labels (κ = 0.746) surpassed that of all non-reference human graders (κ = 0.578-0.702). Human grader agreement with consensus labels improved with clinical experience (P = 0.03). CONCLUSION: A CNN classifier can effectively detect angle closure in goniophotographs with performance comparable to that of an experienced glaucoma specialist. This provides an automated method to support remote detection of patients at risk for primary angle closure glaucoma.