Enhancing Ophthalmic Triage: identification of new clinical features to support healthcare professionals in triage.

OBJECTIVE: To investigate which features from a patient's history are either high or low risk that could support healthcare professionals in ophthalmic emergency triage. METHODS: Prospective, 12,584 visits from 11,733 adult patients attending an Accident and Emergency department at a single tertiary centre were analysed. Data were collected by ophthalmic nurses working in triage, using an online form from August 2021 to April 2022. Multivariate analysis (MVA) was conducted to identify which features from the patients' history would be associated with emergency care. RESULTS: This study found that 45.5% (5731 patient visits (PV)) required a same day eye emergency examination (SDEE), 11.3% (1416 PV) needed urgent care, and 43.2% (5437 PV) were appropriate for elective consultations with a GP or optometrist. The MVA top ten features that were statistically significant (p < 0.05) that would warrant SDEE with odds ratio (95% CI) were: bilateral eye injury 36.5 [15.6-85.5], unilateral eye injury 25.8 [20.9-31.7], vision loss 4.8 [2.9-7.8], post-operative ophthalmic ( < 4 weeks) 4.6 [3.8-5.7], contact lens wearer 3.9 [3.3-4.7], history of uveitis 3.9 [3.3-4.7], photophobia 2.9 [2.4-3.6], unilateral dark shadow/curtain in vision 2.4 [1.8-3.0], unilateral injected red eye 2.0 [1.8-2.2] and rapid change in visual acuity 1.8 [1.5-2.2]. CONCLUSION: This study characterises presenting features covering almost 100 ophthalmic acute presentations that are commonly seen in emergency and elective care. This information could supplement current red flag indicators and support healthcare professionals in ophthalmic triage. Further research is required to evaluate the cost effectivity and safety of our findings for triaging acute presentations.

A machine learning system to optimise triage in an adult ophthalmic emergency department: a model development and validation study.

Background: A substantial proportion of attendances to ophthalmic emergency departments are for non-urgent presentations. We developed and evaluated a machine learning system (DemDx Ophthalmology Triage System: DOTS) to optimise triage, with the aim of reducing inappropriate emergency attendances and streamlining case referral when necessary. Methods: DOTS was built using retrospective tabular data from 11,315 attendances between July 1st, 2021, to June 15th, 2022 at Moorfields Eye Hospital Emergency Department (MEH) in London, UK. Demographic and clinical features were used as inputs and a triage recommendation was given ("see immediately", "see within a week", or "see electively"). DOTS was validated temporally and compared with triage nurses' performance (1269 attendances at MEH) and validated externally (761 attendances at the Federal University of Minas Gerais - UFMG, Brazil). It was also tested for biases and robustness to variations in disease incidences. All attendances from patients aged at least 18 years with at least one confirmed diagnosis were included in the study. Findings: For identifying ophthalmic emergency attendances, on temporal validation, DOTS had a sensitivity of 94.5% [95% CI 92.3-96.1] and a specificity of 42.4% [38.8-46.1]. For comparison within the same dataset, triage nurses had a sensitivity of 96.4% [94.5-97.7] and a specificity of 25.1% [22.0-28.5]. On external validation at UFMG, DOTS had a sensitivity of 95.2% [92.5-97.0] and a specificity of 32.2% [27.4-37.0]. In simulated scenarios with varying disease incidences, the sensitivity was ≥92.2% and the specificity was ≥36.8%. No differences in sensitivity were found in subgroups of index of multiple deprivation, but the specificity was higher for Q2 when compared to Q4 (Q4 is less deprived than Q2). Interpretation: At MEH, DOTS had similar sensitivity to triage nurses in determining attendance priority; however, with a specificity of 17.3% higher, DOTS resulted in lower rates of patients triaged to be seen immediately at emergency. DOTS showed consistent performance in temporal and external validation, in social-demographic subgroups and was robust to varying relative disease incidences. Further trials are necessary to validate these findings. This system will be prospectively evaluated, considering human-computer interaction, in a clinical trial. Funding: The Artificial Intelligence in Health and Care Award (AI_AWARD01671) of the NHS AI Lab under National Institute for Health and Care Research (NIHR) and the Accelerated Access Collaborative (AAC).

Usability of an artificially intelligence-powered triage platform for adult ophthalmic emergencies: a mixed methods study.

There is growing demand for emergency-based eyecare services where the majority of those attending do not require urgent ophthalmic management. The Royal College of Ophthalmologists have recommended upskilling and supporting of allied health professionals to support eyecare delivery, where machine learning algorithms could help. A mixed methods study was conducted to evaluate the usability of an artificial intelligence (AI) powered online triage platform for ophthalmology. The interface, usability, safety and acceptability were investigated using a Think Aloud interview and usability questionnaires. Twenty participants who actively examine patients in ophthalmic triage within a tertiary eye centre or primary care setting completed the interview and questionnaires. 90% or more of participants found the platform easy to use, reflected their triage process and were able to interpret the triage outcome, 85% found it safe to use and 95% felt the processing time was fast. A quarter of clinicians reported that they have experienced some uncertainty when triaging in their career and were unsure of using AI, after this study 95% of clinicians were willing to use the platform in their clinical workflow. This study showed the platform interface was acceptable and usable for clinicians actively working in ophthalmic emergency triage.

Ocular Inflammatory Events Following COVID-19 Vaccination in the Paediatric Population: A Multinational Case Series.

BACKGROUND: Ocular inflammatory events following COVID-19 vaccination have been reported in the adult population. METHODS: Multinational case series of patients under the age of 18 diagnosed with ocular inflammatory events within 28 days of COVID-19 vaccination. RESULTS: Twenty individuals were included. The most common event was anterior uveitis (n = 8, 40.0%), followed by intermediate uveitis (7 patients, 35%), panuveitis (4 patients, 20%), and posterior uveitis (1 patient, 5%). The event was noticed in the first week after vaccination in 11 patients (55.0%). Twelve patients (60.0%) had a previous history of intraocular inflammatory event. Patients were managed with topical corticosteroids (n = 19, 95.0%), oral corticosteroids (n = 10, 50.0%), or increased dose of immunosuppressive treatment (n = 6, 30.0%). Thirteen patients (65.0%) had a complete resolution of the ocular event without complications. All patients had a final visual acuity unaffected or less than three lines of loss. CONCLUSION: Ocular inflammatory events may happen in the paediatric population following COVID-19 vaccination. Most events were successfully treated, and all showed a good visual outcome.

Glaucoma and Telemedicine.

PRCIS: Teleglaucoma is the future, but there is a need for clearer regulation by government agencies and medical entities, as well as for studies to further demonstrate the cost-effectiveness and safety of teleglaucoma strategies at a global scale. ABSTRACT: The coronavirus disease 2019 pandemic drastically impacted global health, forcing institutions to provide alternative models of safe and reliable health care. In this context, telemedicine has been successfully used to overcome distance barriers and improve access to medical services. Teleglaucoma is the application of telemedicine to screen and monitor glaucoma, a chronic and progressive optic neuropathy. Teleglaucoma screening aims to detect the disease at an earlier stage, especially in high-risk populations and underserved areas, also identifying patients who require more urgent treatment. Teleglaucoma monitoring seeks to provide remote management through virtual clinics, where classic in-person visits are replaced by synchronous data collection (clinical measurements) performed by non-ophthalmologists and asynchronous review (decision-making) by ophthalmologists. This may be employed for low-risk patients with early disease, improving health care logistics, reducing the number of face-to-face consultations, and saving time and costs. New technologies may also allow home monitoring of patients in teleglaucoma programs, with the addition of artificial intelligence methods, which are expected to increase the accuracy of remote glaucoma screening/monitoring and support clinical decision-making. However, for the incorporation of teleglaucoma into clinical practice, a complex system for collection, transfer, flow, and interpretation of data is still necessary, in addition to clearer regulatory markers by government agencies and medical entities.

Ocular inflammatory events following COVID-19 vaccination: a multinational case series.

BACKGROUND: Inflammatory adverse events following COVID-19 vaccination are being reported amidst the growing concerns regarding vaccine's immunogenicity and safety, especially in patients with pre-existing inflammatory conditions. METHODS: Multinational case series of patients diagnosed with an ocular inflammatory event within 14 days following COVID-19 vaccination collected from 40 centres over a 3 month period in 2021. RESULTS: Seventy patients presented with ocular inflammatory events within 14 days following COVID-19 vaccination. The mean age was 51 years (range, 19-84 years). The most common events were anterior uveitis (n = 41, 58.6%), followed by posterior uveitis (n = 9, 12.9%) and scleritis (n = 7, 10.0%). The mean time to event was 5 days and 6 days (range, 1-14 days) after the first and second dose of vaccine, respectively. Among all patients, 36 (54.1%) had a previous history of ocular inflammatory event. Most patients (n = 48, 68.6%) were managed with topical corticosteroids. Final vision was not affected in 65 (92.9%), whereas 2 (2.9%) and 3 (4.3%) had reduction in visual acuity reduced by ≤3 lines and > 3 lines, respectively. Reported complications included nummular corneal lesions (n = 1, 1.4%), cystoid macular oedema (n = 2, 2.9%) and macular scarring (n = 2, 2.9%). CONCLUSION: Ocular inflammatory events may occur after COVID-19 vaccination. The findings are based on a temporal association that does not prove causality. Even in the possibility of a causal association, most of the events were mild and had a good visual outcome.

Use of machine learning to predict the risk of early morning intraocular pressure peaks in glaucoma patients and suspects / Uso de aprendizado de máquina para predizer o risco de picos de pressão intraocular no início da manhã em pacientes com glaucoma e suspeitos

ABSTRACT Purpose: To use machine learning to predict the risk of intraocular pressure peaks at 6 a.m. in primary open-angle glaucoma patients and suspects. Methods: This cross-sectional observational study included 98 eyes of 98 patients who underwent a 24-hour intraocular pressure curve (including the intraocular pressure measurements at 6 a.m.). The diurnal intraocular pressure curve was defined as a series of three measurements at 8 a.m., 9 a.m., and 11 a.m. from the 24-hour intraocular pressure curve. Two new variables were introduced: slope and concavity. The slope of the curve was calculated as the difference between intraocular pressure measurements at 9 a.m. and 8 a.m. and reflected the intraocular pressure change in the first hour. The concavity of the curve was calculated as the difference between the slopes at 9 a.m. and 8 a.m. and indicated if the curve was bent upward or downward. A classification tree was used to determine a multivariate algorithm from the measurements of the diurnal intraocular pressure curve to predict the risk of elevated intraocular pressure at 6 a.m. Results: Forty-nine (50%) eyes had intraocular pressure measurements at 6 a.m. >21 mmHg, and the median intraocular pressure peak in these eyes at 6 a.m. was 26 mmHg. The best predictors of intraocular pressure measurements >21 mmHg at 6 a.m. were the intraocular pressure measurements at 8 a.m. and concavity. The proposed model achieved a sensitivity of 100% and a specificity of 86%, resulting in an accuracy of 93%. Conclusions: The machine learning approach was able to predict the risk of intraocular pressure peaks at 6 a.m. with good accuracy. This new approach to the diurnal intraocular pressure curve may become a widely used tool in daily practice and the indication of a 24-hour intraocular pressure curve could be rationalized according to risk stratification.

RESUMO Objetivo: Utilizar aprendizado de máquina para predizer o risco de picos de pressão intraocular às 6 AM em pacientes com glaucoma primário de ângulo aberto e suspeitos. Métodos: Esse estudo observacional transversal incluiu 98 olhos de 98 pacientes submetidos à curva de 24 horas de pressão intraocular (incluindo as medidas às 6 AM). A curva diurna de pressão intraocular foi definida como uma série de três medidas da curva de 24 horas de pressão intraocular às 8 AM, às 9 AM e às 11 AM. Duas novas variáveis foram apresentadas: inclinação e concavidade. A inclinação da curva às 8 AM foi calculada como a diferença entre pressão intraocular às 9 AM e 8 AM e reflete a variação da pressão intraocular na primeira hora. A concavidade da curva foi calculada como a diferença entre as inclinações às 9 AM e às 8 AM e pode ser para cima ou para baixo. Uma árvore de classificação foi usada para determinar um algoritmo multivariado a partir das medidas da curva diurna para prever o risco de pressão intraocular elevada às 6 AM. Resultados: Quarenta e nove (50%) olhos apresentaram pressão intraocular às 6 AM >21 mmHg e a mediana do pico de pressão intraocularPIO foi 26 mmHg. Os melhores preditores de pressão intraocular às 6 AM >21 mmHg foram a pressão intraocular às 8 AM e a concavidade. O modelo proposto apresentou uma sensibilidade de 100% e uma especificidade de 86%, com uma acurácia de 93%. Conclusões: A abordagem de aprendizado de máquina foi capaz de prever o risco de picos de pressão intraocular às 6 AM com uma boa acurácia. Essa nova abordagem para a curva diurna de pressão intraocular pode se tornar uma ferramenta amplamente utilizada na prática clínica e a indicação da curva de 24 horas de pressão intraocular pode ser racionalizada de acordo com a estratificação de risco.

Use of machine learning to predict the risk of early morning intraocular pressure peaks in glaucoma patients and suspects.

PURPOSE: To use machine learning to predict the risk of intraocular pressure peaks at 6 a.m. in primary open-angle glaucoma patients and suspects. METHODS: This cross-sectional observational study included 98 eyes of 98 patients who underwent a 24-hour intraocular pressure curve (including the intraocular pressure measurements at 6 a.m.). The diurnal intraocular pressure curve was defined as a series of three measurements at 8 a.m., 9 a.m., and 11 a.m. from the 24-hour intraocular pressure curve. Two new variables were introduced: slope and concavity. The slope of the curve was calculated as the difference between intraocular pressure measurements at 9 a.m. and 8 a.m. and reflected the intraocular pressure change in the first hour. The concavity of the curve was calculated as the difference between the slopes at 9 a.m. and 8 a.m. and indicated if the curve was bent upward or downward. A classification tree was used to determine a multivariate algorithm from the measurements of the diurnal intraocular pressure curve to predict the risk of elevated intraocular pressure at 6 a.m. RESULTS: Forty-nine (50%) eyes had intraocular pressure measurements at 6 a.m. >21 mmHg, and the median intraocular pressure peak in these eyes at 6 a.m. was 26 mmHg. The best predictors of intraocular pressure measurements >21 mmHg at 6 a.m. were the intraocular pressure measurements at 8 a.m. and concavity. The proposed model achieved a sensitivity of 100% and a specificity of 86%, resulting in an accuracy of 93%. CONCLUSIONS: The machine learning approach was able to predict the risk of intraocular pressure peaks at 6 a.m. with good accuracy. This new approach to the diurnal intraocular pressure curve may become a widely used tool in daily practice and the indication of a 24-hour intraocular pressure curve could be rationalized according to risk stratification.

Cytokine gene functional polymorphisms and phenotypic expression as predictors of evolution from latent to clinical rheumatic heart disease.

INTRODUCTION: Inflammation associated with rheumatic heart disease (RHD) is influenced by gene polymorphisms and inflammatory cytokines. There are currently no immunologic and genetic markers to discriminate latent versus clinical patients, critical to predict disease evolution. Employing machine-learning, we searched for predictors that could discriminate latent versus clinical RHD, and eventually identify latent patients that may progress to clinical disease. METHODS: A total of 212 individuals were included, 77 with latent, 100 with clinical RHD, and 35 healthy controls. Circulating levels of 27 soluble factors were evaluated using Bio-Plex ProTM® Human Cytokine Standard 27-plex assay. Gene polymorphism analyses were performed using RT-PCR for the following genes: IL2, IL4, IL6, IL10, IL17A, TNF and IL23. RESULTS: Serum levels of all cytokines were higher in clinical as compared to latent RHD patients, and in those groups than in controls. IL-4, IL-8, IL-1RA, IL-9, CCL5 and PDGF emerged in the final multivariate model as predictive factors for clinical, compared with latent RHD. IL-4, IL-8 and IL1RA had the greater power to predict clinical RHD. In univariate analysis, polymorphisms in IL2 and IL4 were associated with clinical RHD and in the logistic analysis, IL6 (GG + CG), IL10 (CT + TT), IL2 (CA + AA) and IL4 (CC) genotypes were associated with RHD. CONCLUSION: Despite higher levels of all cytokines in clinical RHD patients, IL-4, IL-8 and IL-1RA were the best predictors of clinical disease. An association of polymorphisms in IL2, IL4, IL6 and IL10 genes and clinical RHD was observed. Gene polymorphism and phenotypic expression of IL-4 accurately discriminate latent versus clinical RHD, potentially instructing clinical management.

SARS-CoV-2 and COVID-19 for the ophthalmologist.

The COVID-19 pandemic has had an unprecedented impact on ophthalmology. This review compiles general aspects of the novel coronavirus and COVID-19, further dissects the most recent data on the role of the eye regarding disease transmission and manifestations, and summarizes preventive measures in the particular context of eye care.

Clinical and Multimodal Imaging Findings and Risk Factors for Ocular Involvement in a Presumed Waterborne Toxoplasmosis Outbreak, Brazil1.

In 2015, an outbreak of presumed waterborne toxoplasmosis occurred in Gouveia, Brazil. We conducted a 3-year prospective study on a cohort of 52 patients from this outbreak, collected clinical and multimodal imaging findings, and determined risk factors for ocular involvement. At baseline examination, 12 (23%) patients had retinochoroiditis; 4 patients had bilateral and 2 had macular lesions. Multimodal imaging revealed 2 distinct retinochoroiditis patterns: necrotizing focal retinochoroiditis and punctate retinochoroiditis. Older age, worse visual acuity, self-reported recent reduction of visual acuity, and presence of floaters were associated with retinochoroiditis. Among patients, persons >40 years of age had 5 times the risk for ocular involvement. Five patients had recurrences during follow-up, a rate of 22% per person-year. Recurrences were associated with binocular involvement. Two patients had late ocular involvement that occurred >34 months after initial diagnosis. Patients with acquired toxoplasmosis should have long-term ophthalmic follow-up, regardless of initial ocular involvement.

Multimodal Imaging in Ocular Toxoplasmosis.

Multimodal imaging relies on combination of multiple imaging modalities to precisely delineate pathological changes in the posterior segment of the eye associated with a wide range of conditions. This combined application of fundus photography, optical coherence tomography, fundus reflectance/autofluorescence and fundus angiography (with fluorescein, indocyanine green and/or optical coherence tomography) is of great utility for assessment of patients with ocular toxoplasmosis. Multimodal imaging is helpful to characterize the typical pattern of toxoplasmic retinochoroiditis, with primary focal inflammatory involvement of the neurosensory retina, and secondary changes at the level of underlying choroid, retinal blood vessels, vitreous and even optic disc. It may also be valuable to document and follow local complications, including macular edema, vascular occlusions, and choroidal neovascularization, among others.

Characterization of Retinopathy Among Patients With Yellow Fever During 2 Outbreaks in Southeastern Brazil.

IMPORTANCE: Yellow fever still threatens people in endemic areas, and besides conjunctival icterus, little is known about the ocular changes that occur in these patients. OBJECTIVE: To characterize retinal changes in patients with confirmed yellow fever during 2 recent outbreaks of the disease in Minas Gerais, Southeastern Brazil. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional, observational study conducted at a single referral center for infectious diseases in Southeastern Brazil collected data between January 2017 and February 2018 from 94 consecutive patients with suspicion of yellow fever who were eligible for the study. MAIN OUTCOMES AND MEASURES: Patients underwent ophthalmic examination. Clinical findings, laboratory results, and occurrence of retinopathy and death during hospitalization were reported, including age, sex, comorbidities, disease severity, serum aspartate aminotransferase level, total bilirubin level, serum creatinine level, arterial lactate level, international normalized ratio, and platelet count at hospital admission. RESULTS: In total, 64 patients were included who had received a confirmed diagnosis of yellow fever, with a median (interquartile range) age of 47 (38-56) years, and 12 patients (19%) were women. Twenty eyes (16%) of 13 patients (20%) had retinopathy at the same time as yellow fever. The most common fundus changes among the 20 eyes were retinal nerve fiber layer infarcts (11 [55%]), superficial hemorrhages (7 [35%]) and grayish deep lesions (6 [30%]), possibly at the level of the outer retina or choroid. Aspartate aminotransferase levels higher than 3000 U/L (odds ratio [OR], 14.2; 95% CI, 3.5-77.8; P < .001), total bilirubin levels higher than 2.3 mg/dL (OR, 20.0; 95% CI, 4.4-159.7; P < .001), serum creatinine levels higher than 2.0 mg/dL (OR, 8.2; 95% CI, 2.1-36.0; P = .003), arterial lactate levels higher than 17.1/mg/dL (OR, 4.6; 95% CI, 1.1-19.0; P = .03), platelet count lower than 94 × 103/µL (OR, 7.8; 95% CI, 1.8-59.9; P = .004), and classification of disease as severe (OR, 11.7; 95% CI, 2.0-301.0; P = .003) were associated with retinopathy. Arterial hypertension, diabetes, international normalized ratio, and death were not associated with retinopathy. CONCLUSIONS AND RELEVANCE: Retinopathy was present in 20% of patients with yellow fever and appeared to be associated with more severe systemic disease. Retinal nerve fiber layer infarcts and superficial hemorrhages, but not the grayish deep lesions, resembled those associated with other flavivirus (eg, dengue virus) infections. The clinical relevance of these findings may warrant further investigation.