Incidence and Risk Factors of Second Eye Involvement in Myopic Macular Neovascularization

Purpose: To report the cumulative incidence and risk factors of second eye involvement after diagnosis of myopic macular neovascularization (MNV) in the first eye. Design: Retrospective analysis of longitudinal data from a tertiary hospital in the Netherlands. Participants:Patients with high myopia (spherical equivalent [SE] ≤ − 6 diopters [D]), of European ethnicity, who were diagnosed with active MNV lesion in 1 eye between 2005 and 2018. Fellow eyes were free of MNV or macular atrophy at baseline, and data were collected on the SE, axial length, and presence of diffuse or patchy chorioretinal atrophy and lacquer cracks.Methods:Incidence rate and 2-, 5-, and 10-year cumulative incidences were calculated; hazard ratios (HRs) of second eye involvement were analyzed for potential risk factors using Cox proportional hazard models. Main outcomes measures: Incidence of second eye involvement after onset of myopic MNV in the first eye. Results: We included 88 patients over a period of 13 years with a mean age of 58 ± 15 years, mean axial length of 30 ± 1.7 mm and SE −14 ± 4 D at baseline. Twenty-four fellow eyes (27%) developed a myopic MNV during follow-up. This resulted in an incidence rate of 4.6 (95% confidence interval [CI], 2.9–6.7) per 100 person-years and a cumulative incidence of 8%, 21%, and 38% at 2, 5, and 10 years, respectively. Mean time until MNV development in the fellow eye was 48 ± 37 months. Patients aged &lt; 40 years at the initial presentation had a 3.8 times higher risk of bilateral myopic MNV (HR, 3.8; 95% CI, 1.65–8.69; P = 0.002). The presence of lacquer cracks in the second eye seemed to increase risk, but this did not reach statistical significance (HR, 2.25; 95% CI, 0.94–5.39; P = 0.07). Conclusions: Our study of high myopes of European descent shows very similar incidence rates for second eye myopic MNV compared with Asian studies. Our findings substantiate the importance for clinicians to monitor closely and create awareness, especially in younger patients. Financial Disclosure(s): The authors have no proprietary or commercial interest in any materials discussed in this article.</p

Genetic epidemiologic studies on age-related maculopathy: a population-based approach

The western world is aging rapidly. In the Netherlands, the current mean life expectancy for men and women is 74.6 and 80.4 years, respectively, and those over 65 years of age comprise 13.6% of the total population. This proportion of elderly is expected to increase considerably within the coming years, and this will lead to higher frequencies of diseases. Age-related maculopathy (ARM) is one of those frequent geriatric diseases. It is an eye disease ultimately leading to blindness. The prevalence of the clinical end stages of this disorder range from 1 % in those aged 60 years of age to 10% in those aged 85 years and older. At least 60000 Dutch subjects are severely affected by these end stages, also called age-related macular degeneration (AMD). AMD has a great impact on visual function and the performance of daily tasks, in particular because there are still no means for long term restoration of vision. During the last decade there has been steadily increasing research activity investigating the disease etiology. It became better known that the pathogenesis was complex with a variety of risk factors involved. Family reports and twin studies pointed to a genetic background, and epidemiologic studies suggested environmental influences from vascular and dietmy factors, sunlight and smoking. However, findings were not unequivocal, and the evidence on most of these relations was insufficient and inconclusive. This called for more extensive research into the causes of ARM. This thesis aimed to answer the following questions: Pal'l I: What is the current genetic epidemiologic knowledge on ARM? Pari Jl: What is the incidence of AMD, what is the natural course of the disease, and what is the relation with visual impairment

Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization

Modern high-throughput experiments provide a rich resource to investigate causal determinants of disease risk. Mendelian randomization (MR) is the use of genetic variants as instrumental variables to infer the causal effect of a specific risk factor on an outcome. Multivariable MR is an extension of the standard MR framework to consider multiple potential risk factors in a single model. However, current implementations of multivariable MR use standard linear regression and hence perform poorly with many risk factors. Here, we propose a two-sample multivariable MR approach based on Bayesian model averaging (MR-BMA) that scales to high-throughput experiments. In a realistic simulation study, we show that MR-BMA can detect true causal risk factors even when the candidate risk factors are highly correlated. We illustrate MR-BMA by analysing publicly-available summarized data on metabolites to prioritise likely causal biomarkers for age-related macular degeneration

Patient-reported utilities in bilateral visual impairment from amblyopia and age-related macular degeneration

Background: Utility of visual impairment caused by amblyopia is important for the cost-effectiveness of screening for amblyopia (lazy eye, prevalence 3-3.5 %). We previously measured decrease of utility in 35-year-old persons with unilateral persistent amblyopia. The current observational case-control study aimed to measure loss of utility in patients with amblyopia with recent decrease of vision in their better eye. As these patients are rare, the sample was supplemented by patients with bilateral age-related macular degeneration with similar decrease of vision. Methods: From our out-patient department, two groups of patients with recent deterioration to bilateral visual acuity less than Snellen 0.5 (bilateral visual impairment, BVI) were recruited, with either persistent amblyopia and age-related macular degeneration (AMB + AMD), or with bilateral age-related macular degeneration (BAMD). To measure utility, the time trade-off method and the standard gamble method were applied through interviews. Correlations were sought between utility values and visual acuity, age and Visual Function Questionnaire-25 scores. Results: Seventeen AMB + AMD patients (mean age 72.9 years), and 63 BAMD patients (mean age 79.6 years) were included in the study. Among AMB + AMD, 80 % were willing to trade lifetime in exchange for cure. The overall mean time trade-off utility was 0.925. Among BAMD, 75 % were willing to trade, utility was 0.917. Among AMB + AMD, 38 % accepted risk of death in exchange for cure, overall mean standard gamble utility was 0.999. Among BAMD, 49 % accepted risk of death, utility was 0.998. Utility was not related to visual acuity but it was to age (p = 0.02). Conclusion: Elderly patients with BVI, caused by persistent amblyopia and age-related macular degeneration (AMD) or by bilateral AMD, had an approximately 8 % loss of TTO utility. Notably, the 8 % loss in elderly with BVI differs little from the 3.7 % loss we found previously in 35-year-old persons with unilateral amblyopia with good vision in the other eye. The moderate impact of BVI in senescence could be explained by adaptation, comorbidity, avoidance of risk and a changed percept of cure

Uncertainty-aware multiple-instance learning for reliable classification:Application to optical coherence tomography

Deep learning classification models for medical image analysis often perform well on data from scanners that were used to acquire the training data. However, when these models are applied to data from different vendors, their performance tends to drop substantially. Artifacts that only occur within scans from specific scanners are major causes of this poor generalizability. We aimed to enhance the reliability of deep learning classification models using a novel method called Uncertainty-Based Instance eXclusion (UBIX). UBIX is an inference-time module that can be employed in multiple-instance learning (MIL) settings. MIL is a paradigm in which instances (generally crops or slices) of a bag (generally an image) contribute towards a bag-level output. Instead of assuming equal contribution of all instances to the bag-level output, UBIX detects instances corrupted due to local artifacts on-the-fly using uncertainty estimation, reducing or fully ignoring their contributions before MIL pooling. In our experiments, instances are 2D slices and bags are volumetric images, but alternative definitions are also possible. Although UBIX is generally applicable to diverse classification tasks, we focused on the staging of age-related macular degeneration in optical coherence tomography. Our models were trained on data from a single scanner and tested on external datasets from different vendors, which included vendor-specific artifacts. UBIX showed reliable behavior, with a slight decrease in performance (a decrease of the quadratic weighted kappa (κw) from 0.861 to 0.708), when applied to images from different vendors containing artifacts; while a state-of-the-art 3D neural network without UBIX suffered from a significant detriment of performance (κw from 0.852 to 0.084) on the same test set. We showed that instances with unseen artifacts can be identified with OOD detection. UBIX can reduce their contribution to the bag-level predictions, improving reliability without retraining on new data. This potentially increases the applicability of artificial intelligence models to data from other scanners than the ones for which they were developed. The source code for UBIX, including trained model weights, is publicly available through https://github.com/qurAI-amsterdam/ubix-for-reliable-classification.</p

Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization

Funder: UK Medical Research Council (MC UU 00002/7) and Wellcome Trust and the Royal Society (Grant Number 204623/Z/16/Z)Abstract: Modern high-throughput experiments provide a rich resource to investigate causal determinants of disease risk. Mendelian randomization (MR) is the use of genetic variants as instrumental variables to infer the causal effect of a specific risk factor on an outcome. Multivariable MR is an extension of the standard MR framework to consider multiple potential risk factors in a single model. However, current implementations of multivariable MR use standard linear regression and hence perform poorly with many risk factors. Here, we propose a two-sample multivariable MR approach based on Bayesian model averaging (MR-BMA) that scales to high-throughput experiments. In a realistic simulation study, we show that MR-BMA can detect true causal risk factors even when the candidate risk factors are highly correlated. We illustrate MR-BMA by analysing publicly-available summarized data on metabolites to prioritise likely causal biomarkers for age-related macular degeneration