1,698 research outputs found

    Glaucoma Screening in the Haitian Afro-Caribbean Population of South Florida

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    Objective: To evaluate the presence of clinical signs consistent with suspected glaucoma in Haitian Afro-Caribbean individuals residing in South Florida who do not receive regular eye examinations. Design: Retrospective, cross-sectional study. Methods: SETTING: Community health center in the Little Haiti district of Miami, Florida. PATIENT POPULATION: We reviewed medical records and screening forms from five health screenings between October 2011 to October 2013 of 939 Afro-Caribbean individuals older than 18 years, who were never diagnosed with glaucoma or had an eye examination within the last ten years. PROCEDURES: Measurements of distance visual acuity (VA), intraocular eye pressure (IOP), central corneal thickness (CCT), cup-to-disc ratio (CDR), frequency doubling technology (FDT) perimeter visual field (VF). Main Outcome Measures: Proportion of glaucoma suspects, based on IOP greater than or equal to 24 mm Hg or CDR greater than or equal to 0.7 in either eye, and determinants of CDR and IOP. Results: One hundred ninety-one (25.5%) of 750 patients were identified as glaucoma suspects. Glaucoma suspects were common in both the youngest and oldest age groups (70 years, 25.0%; 95% CI, 21.8–28.2) and higher in men than women less than 70 years; the reverse was true after 70 years. Among all patients, mean IOP was 19.2±4.5 mmHg, mean CDR was 0.37±0.17, and mean CCT was 532±37.1 µm. In multiple linear stepwise regression analysis, determinates of increased CDR included increasing age (P = 0.004), lack of insurance (P = 0.019), and higher IOP (P<0.001), while increasing CDR (P<0.001) and thicker CCT (P<0.001) were associated with higher IOP. Conclusions: This first glaucoma survey in a U.S. Haitian Afro-Caribbean population indicates glaucoma suspect status is high across all age groups, and suggests glaucoma monitoring in people less than 40 years of age is indicated in this population

    Towards modifying the genetic predisposition for glaucoma: An overview of the contribution and interaction of genetic and environmental factors

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    Glaucoma, the leading cause of irreversible blindness worldwide, is a complex human disease, with both genetic and environmental determinants. The availability of large-scale, population-based cohorts and biobanks, combining genotyping and detailed phenotyping, has greatly accelerated research into the aetiology of glaucoma in recent years. Hypothesis-free genome-wide association studies have furthered our understanding of the complex genetic architecture underpinning the disease, while epidemiological studies have provided advances in the identification and characterisation of environmental risk factors. It is increasingly recognised that the combined effects of genetic and environmental factors may confer a disease risk that reflects a departure from the simple additive effect of the two. These gene-environment interactions have been implicated in a host of complex human diseases, including glaucoma, and have several important diagnostic and therapeutic implications for future clinical practice. Importantly, the ability to modify the risk associated with a particular genetic makeup promises to lead to personalised recommendations for glaucoma prevention, as well as novel treatment approaches in years to come. Here we provide an overview of genetic and environmental risk factors for glaucoma, as well as reviewing the evidence and discussing the implications of gene-environment interactions for the disease

    Reliable intraocular pressure measurement using automated radio-wave telemetry

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    Purpose To present an autonomous intraocular pressure (IOP) measurement technique using a wireless implantable transducer (WIT) and a motion sensor. Methods: The WIT optical aid was implanted within the ciliary sulcus of a normotensive rabbit eye after extracapsular clear lens extraction. An autonomous wireless data system (AWDS) comprising of a WIT and an external antenna aided by a motion sensor provided continuous IOP readings. The sensitivity of the technique was determined by the ability to detect IOP changes resulting from the administration of latanoprost 0.005% or dorzolamide 2%, while the reliability was determined by the agreement between baseline and vehicle (saline) IOP. Results: On average, 12 diurnal and 205 nocturnal IOP measurements were performed with latanoprost, and 26 diurnal and 205 nocturnal measurements with dorzolamide. No difference was found between mean baseline IOP (13.08±2.2 mmHg) and mean vehicle IOP (13.27±2.1 mmHg) (P=0.45), suggesting good measurement reliability. Both antiglaucoma medications caused significant IOP reduction compared to baseline; latanoprost reduced mean IOP by 10% (1.3±3.54 mmHg; P<0.001), and dorzolamide by 5% (0.62±2.22 mmHg; P<0.001). Use of latanoprost resulted in an overall twofold higher IOP reduction compared to dorzolamide (P<0.001). Repeatability was ±1.8 mmHg, assessed by the variability of consecutive IOP measurements performed in a short period of time (≤1 minute), during which the IOP is not expected to change. Conclusion: IOP measurements in conscious rabbits obtained without the need for human interactions using the AWDS are feasible and provide reproducible results

    Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization

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    Fairness (also known as equity interchangeably) in machine learning is important for societal well-being, but limited public datasets hinder its progress. Currently, no dedicated public medical datasets with imaging data for fairness learning are available, though minority groups suffer from more health issues. To address this gap, we introduce Harvard Glaucoma Fairness (Harvard-GF), a retinal nerve disease dataset with both 2D and 3D imaging data and balanced racial groups for glaucoma detection. Glaucoma is the leading cause of irreversible blindness globally with Blacks having doubled glaucoma prevalence than other races. We also propose a fair identity normalization (FIN) approach to equalize the feature importance between different identity groups. Our FIN approach is compared with various the-state-of-the-art fairness learning methods with superior performance in the racial, gender, and ethnicity fairness tasks with 2D and 3D imaging data, which demonstrate the utilities of our dataset Harvard-GF for fairness learning. To facilitate fairness comparisons between different models, we propose an equity-scaled performance measure, which can be flexibly used to compare all kinds of performance metrics in the context of fairness. The dataset and code are publicly accessible via \url{https://ophai.hms.harvard.edu/datasets/harvard-glaucoma-fairness-3300-samples/}
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