Threading the Needle: A Case of Periorbital Gold Threading.

While a rare periorbital finding, the aesthetic practice of gold threading is increasingly identified in Western care setting and may be misidentified as the practice of inserting charm needles (susuk). The authors present a unique case of gold threading discovered incidentally during workup of chronic sinusitis and report a rarely seen delayed local site reaction. The practice of gold threading and mimickers including the practice of inserting charm needles (susuk) are reviewed with emphasis on clinical and radiographic differentiation by oculoplastic surgeons.

Breaking the mold: a case of recalcitrant eyelid subconjunctival infection by Exophilia Phaeomuriformis.

While a rare ophthalmic pathogen, infections from Exophilia spp. are increasingly identified and have been associated with catastrophic vision loss. In this case report we present a previously undescribed manifestation of the melanin-producing fungus Exophilia Phaeomuriformis to the lower eyelid, establish an effective treatment, and review related cases. Previous cases of ophthalmic E. Phaeomuriformis were confined to the cornea and included iatrogenic tissue trauma. This case shares neither associations however includes a remote SJS history that likely led to changes in conjunctival tissue integrity. Previous cases of Exophilia spp. infecting the eyelid both included surgical source control and adjuvant antibiotic. In this case, topical therapy was deferred due to SJS-related ocular cicatricial disease. Fortunately, a full resolution was achieved with surgical resection and oral antifungal treatment.

Comparing Telephone Survey Responses to Best-Corrected Visual Acuity to Estimate the Accuracy of Identifying Vision Loss: Validation Study.

BACKGROUND: Self-reported questions on blindness and vision problems are collected in many national surveys. Recently released surveillance estimates on the prevalence of vision loss used self-reported data to predict variation in the prevalence of objectively measured acuity loss among population groups for whom examination data are not available. However, the validity of self-reported measures to predict prevalence and disparities in visual acuity has not been established. OBJECTIVE: This study aimed to estimate the diagnostic accuracy of self-reported vision loss measures compared to best-corrected visual acuity (BCVA), inform the design and selection of questions for future data collection, and identify the concordance between self-reported vision and measured acuity at the population level to support ongoing surveillance efforts. METHODS: We calculated accuracy and correlation between self-reported visual function versus BCVA at the individual and population level among patients from the University of Washington ophthalmology or optometry clinics with a prior eye examination, randomly oversampled for visual acuity loss or diagnosed eye diseases. Self-reported visual function was collected via telephone survey. BCVA was determined based on retrospective chart review. Diagnostic accuracy of questions at the person level was measured based on the area under the receiver operator curve (AUC), whereas population-level accuracy was determined based on correlation. RESULTS: The survey question, "Are you blind or do you have serious difficulty seeing, even when wearing glasses?" had the highest accuracy for identifying patients with blindness (BCVA ≤20/200; AUC=0.797). The highest accuracy for detecting any vision loss (BCVA <20/40) was achieved by responses of "fair," "poor," or "very poor" to the question, "At the present time, would you say your eyesight, with glasses or contact lenses if you wear them, is excellent, good, fair, poor, or very poor" (AUC=0.716). At the population level, the relative relationship between prevalence based on survey questions and BCVA remained stable for most demographic groups, with the only exceptions being groups with small sample sizes, and these differences were generally not significant. CONCLUSIONS: Although survey questions are not considered to be sufficiently accurate to be used as a diagnostic test at the individual level, we did find relatively high levels of accuracy for some questions. At the population level, we found that the relative prevalence of the 2 most accurate survey questions were highly correlated with the prevalence of measured visual acuity loss among nearly all demographic groups. The results of this study suggest that self-reported vision questions fielded in national surveys are likely to yield an accurate and stable signal of vision loss across different population groups, although the actual measure of prevalence from these questions is not directly analogous to that of BCVA.

Validity of Administrative Claims and Electronic Health Registry Data From a Single Practice for Eye Health Surveillance.

Importance: Diagnostic information from administrative claims and electronic health record (EHR) data may serve as an important resource for surveillance of vision and eye health, but the accuracy and validity of these sources are unknown. Objective: To estimate the accuracy of diagnosis codes in administrative claims and EHRs compared to retrospective medical record review. Design, Setting, and Participants: This cross-sectional study compared the presence and prevalence of eye disorders based on diagnostic codes in EHR and claims records vs clinical medical record review at University of Washington-affiliated ophthalmology or optometry clinics from May 2018 to April 2020. Patients 16 years and older with an eye examination in the previous 2 years were included, oversampled for diagnosed major eye diseases and visual acuity loss. Exposures: Patients were assigned to vision and eye health condition categories based on diagnosis codes present in their billing claims history and EHR using the diagnostic case definitions of the US Centers for Disease Control and Prevention Vision and Eye Health Surveillance System (VEHSS) as well as clinical assessment based on retrospective medical record review. Main Outcome and Measures: Accuracy was measured as area under the receiver operating characteristic curve (AUC) of claims and EHR-based diagnostic coding vs retrospective review of clinical assessments and treatment plans. Results: Among 669 participants (mean [range] age, 66.1 [16-99] years; 357 [53.4%] female), identification of diseases in billing claims and EHR data using VEHSS case definitions was accurate for diabetic retinopathy (claims AUC, 0.94; 95% CI, 0.91-0.98; EHR AUC, 0.97; 95% CI, 0.95-0.99), glaucoma (claims AUC, 0.90; 95% CI, 0.88-0.93; EHR AUC, 0.93; 95% CI, 0.90-0.95), age-related macular degeneration (claims AUC, 0.87; 95% CI, 0.83-0.92; EHR AUC, 0.96; 95% CI, 0.94-0.98), and cataracts (claims AUC, 0.82; 95% CI, 0.79-0.86; EHR AUC, 0.91; 95% CI, 0.89-0.93). However, several condition categories showed low validity with AUCs below 0.7, including diagnosed disorders of refraction and accommodation (claims AUC, 0.54; 95% CI, 0.49-0.60; EHR AUC, 0.61; 95% CI, 0.56-0.67), diagnosed blindness and low vision (claims AUC, 0.56; 95% CI, 0.53-0.58; EHR AUC, 0.57; 95% CI, 0.54-0.59), and orbital and external diseases (claims AUC, 0.63; 95% CI, 0.57-0.69; EHR AUC, 0.65; 95% CI, 0.59-0.70). Conclusion and Relevance: In this cross-sectional study of current and recent ophthalmology patients with high rates of eye disorders and vision loss, identification of major vision-threatening eye disorders based on diagnosis codes in claims and EHR records was accurate. However, vision loss, refractive error, and other broadly defined or lower-risk disorder categories were less accurately identified by diagnosis codes in claims and EHR data.

Prevalence and Characteristics of Cytomegalovirus Ocular Disease in Children: A Multi-Center Study.

Purpose: The objective of this study was to identify the prevalence of CMV ocular disease in children and to identify associated risk factors for ocular involvement. Design: Retrospective multicenter, cross-sectional study. Methods: Setting: Hospitalized patients screened for CMV viremia by PCR between 2005 and 2018 at four pediatric referral centers. Participants: Seven-hundred and ninety-three children showed CMV viremia (>135 copies/mL by polymerase chain reaction; PCR). Main Outcomes and Measures: (1) Occurrence of ophthalmologic examination. (2) Presence of CMV ocular disease, defined as retinitis, vasculitis, hemorrhage, optic nerve atrophy, or anterior uveitis in the setting of CMV viremia without other identifiable causes. Results: A total of 296/793 (37%) underwent ophthalmologic examination following CMV viremia. A total of23/296 patients (8%) had ocular symptoms prompting evaluation while the rest had eye exams for baseline screening unrelated to CMV viremia. Of these, 13 cases (4% of those with an eye exam) with ocular disease were identified (three congenital CMV, five severe combined immunodeficiency disorder (SCID) status post-stem cell transplantation, three hematologic malignancy status post-stem cell transplantation for two of them, one Evans syndrome status post-stem cell transplantation, and one medulloblastoma status post-bone marrow transplantation). No patients with solid organ transplantation developed CMV ocular disease in our cohort. Conclusion: CMV ocular disease was a rare occurrence in this cohort without an identifiable pattern across sub-groups. Excluding the three congenital CMV cases, nine out of ten patients with CMV ocular disease were status post-stem cell transplantation. We provide integrated screening guidelines based on the best available evidence for this rare condition.

PeriorbitAI: Artificial Intelligence Automation of Eyelid and Periorbital Measurements.

PURPOSE: To develop a deep learning semantic segmentation network to automate the assessment of 8 periorbital measurements DESIGN: Development and validation of an artificial intelligence (AI) segmentation algorithm METHODS: A total of 418 photographs of periorbital areas were used to train a deep learning semantic segmentation model to segment iris, aperture, and brow areas. These data were used to develop a post-processing algorithm that measured margin reflex distance (MRD) 1 and 2, medial canthal height (MCH), lateral canthal height (LCH), medial brow height (MBH), lateral brow height (LBH), medial intercanthal distance (MID), and lateral intercanthal distance (LID). The algorithm validity was evaluated on a prospective hold-out test set against 3 graders. The main outcome measures were dice coefficient, mean absolute difference, intraclass correlation coefficient, and Bland-Altman analysis. A smartphone video was also segmented and evaluated as proof of concept. RESULTS: The AI algorithm performed in close agreement with all human graders, with a mean absolute difference of 0.5 mm for MRD1, MRD2, LCH, and MCH. The mean absolute difference between graders is approximately 1.5-2 mm for LBH and MBH and approximately 2-4 mm for MID and LID. The 95% confidence intervals for all graders overlapped in most cases, demonstrating that the algorithm performs similarly to human graders. The segmentation of a smartphone video demonstrated that MRD1 can be dynamically measured. CONCLUSIONS: We present, to our knowledge, the first open-sourced, artificial intelligence system capable of automating static and dynamic periorbital measurements. A fully automated tool stands to transform the delivery of clinical care and quantification of surgical outcomes.