Associations between age-related macular degeneration and sleep dysfunction: A systematic review.

BACKGROUND: Age-related macular degeneration, a prevalent degenerative retinal disease, is associated with non-visual and psychosocial impairments that may affect sleep. In this systematic review, we evaluated associations between age-related macular degeneration (AMD) and sleep, highlighted knowledge gaps and provided evidence-based recommendations to clinicians to enable holistic management of AMD patients. METHODS: We searched PubMed, Embase and the Cochrane Central registries for papers published before May 2022. Non-English, qualitative studies and grey literature were excluded. Studies evaluating the association between AMD and sleep (including sleep disorders like insomnia and sleep apnea), and vice versa, were included. The quality of shortlisted studies was evaluated using the Newcastle Ottawa Scale. RESULTS: Six (two case-control studies, three longitudinal cohort studies and one cross-sectional study) of 551 studies were included in this review. Four studies found that AMD was associated with increased rates of sleep apnea and poorer reported sleep quality, while five studies showed that patients with sleep apnea or insomnia were at higher risk of developing AMD. Associations between self-reported sleep quantity and AMD were conflicting. No study evaluated the relationship between AMD and sleep using objective sleep assessment tools. CONCLUSION: Only a limited number of studies investigated associations between AMD and sleep. These studies suggest a bidirectional relationship between AMD and sleep dysfunction yet disagree on the relationship between sleep quantity and the likelihood of AMD. Additional studies, using objective characterisation of sleep in patients with AMD are required to confirm these findings.

Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology.

With the advancement of computational power, refinement of learning algorithms and architectures, and availability of big data, artificial intelligence (AI) technology, particularly with machine learning and deep learning, is paving the way for 'intelligent' healthcare systems. AI-related research in ophthalmology previously focused on the screening and diagnosis of posterior segment diseases, particularly diabetic retinopathy, age-related macular degeneration and glaucoma. There is now emerging evidence demonstrating the application of AI to the diagnosis and management of a variety of anterior segment conditions. In this review, we provide an overview of AI applications to the anterior segment addressing keratoconus, infectious keratitis, refractive surgery, corneal transplant, adult and paediatric cataracts, angle-closure glaucoma and iris tumour, and highlight important clinical considerations for adoption of AI technologies, potential integration with telemedicine and future directions.

Rates and Predictors of Nonadherence to Postophthalmic Screening Tertiary Referrals in Patients with Type 2 Diabetes.

Purpose: To determine the rates and develop an initial risk prediction model for nonadherence to post screening ophthalmic referral (PSOR) in type 2 diabetes mellitus (T2DM) patients attending a national diabetic retinopathy screening program in Singapore. Methods: Data from 2387 patients with T2DM (mean [standard deviation] age: 66.5 [11] years; 52.5% female patients) who underwent teleophthalmic screening between 2010 and 2014 under the Singapore Integrated Diabetic Retinopathy Program were extracted from electronic medical records. All were referred for tertiary ophthalmic management at the Singapore National Eye Centre (SNEC). Nonadherence was defined as not attending the SNEC appointment within 6 months of the assigned appointment date. Regression analysis using traditional modified Poisson and conditional inference models was used to construct and evaluate the discriminative ability of the preliminary risk prediction model to identify nonadherent individuals. Results: Nonadherence rates to PSOR was 12.7% (95% confidence interval, 11.4%-14.1%). In traditional multivariable models adjusted for sociodemographic, lifestyle, and ocular factors, nonadherent individuals had higher triglyceride levels and were less likely to have a referable eye condition (P < 0.05). This model was able to identify nonadherent individuals with an accuracy (area under the curve) of 84%. In contrast, the conditional inference model was able to achieve similar discriminative ability using only participants' ocular health characteristics. Conclusions: The rates of nonadherence to PSOR in Singaporean individuals with T2DM is low, with better ocular health being strongly predictive of nonadherence in our Asian population. Translational Relevance: Our results may inform interventions to decrease nonadherence to PSOR.

Compensation of retinal nerve fibre layer thickness as assessed using optical coherence tomography based on anatomical confounders.

BACKGROUND/AIMS: To compensate the retinal nerve fibre layer (RNFL) thickness assessed by spectral-domain optical coherence tomography (SD-OCT) for anatomical confounders. METHODS: The Singapore Epidemiology of Eye Diseases is a population-based study, where 2698 eyes (1076 Chinese, 704 Malays and 918 Indians) with high-quality SD-OCT images from individuals without eye diseases were identified. Optic disc and macular cube scans were registered to determine the distance between fovea and optic disc centres (fovea distance) and their respective angle (fovea angle). Retinal vessels were segmented in the projection images and used to calculate the circumpapillary retinal vessel density profile. Compensated RNFL thickness was generated based on optic disc (ratio, orientation and area), fovea (distance and angle), retinal vessel density, refractive error and age. Linear regression models were used to investigate the effects of clinical factors on RNFL thickness. RESULTS: Retinal vessel density reduced significantly with increasing age (1487±214 µm in 40-49, 1458±208 µm in 50-59, 1429±223 µm in 60-69 and 1415±233 µm in ≥70). Compensation reduced the variability of RNFL thickness, where the effect was greatest for Chinese (10.9%; p<0.001), followed by Malays (6.6%; p=0.075) and then Indians (4.3%; p=0.192). Compensation reduced the age-related RNFL decline by 55% in all participants (ß=-3.32 µm vs ß=-1.50 µm/10 years; p<0.001). Nearly 62% of the individuals who were initially classified as having abnormally thin RNFL (outside the 99% normal limits) were later reclassified as having normal RNFL. CONCLUSIONS: RNFL thickness compensated for anatomical parameters reduced the variability of measurements and may improve glaucoma detection, which needs to be confirmed in future studies.

Evaluation of timings and outcomes in category-one caesarean sections: A retrospective cohort study.

BACKGROUND AND AIMS: A decision-to-delivery interval (DDI) of 30 min for category-one caesarean section (CS) deliveries is the standard of practice recommended by clinical guidelines. Our institution established a protocol for category-one ('crash') CS to expedite deliveries. The aim of this study is to evaluate DDI, factors that affect DDI and the mode of anaesthesia for category-one CS. METHODS: This retrospective cohort study evaluated 390 women who underwent category-one CS in a tertiary obstetric centre. We analysed the factors associated with DDI, mode of anaesthesia and perinatal outcomes. Summary statistics were performed for the outcomes. The association factors were considered significant at P < 0.05. RESULTS: The mean (standard deviation) DDI was 9.4 (3.2) min with all deliveries achieved within 30 min. The longest factor in the DDI was time taken to transfer patients. A shorter DDI was not significantly associated with improved perinatal outcomes. The majority (88.9%) of women had general anaesthesia (GA) for category-one CS. Of those who had an epidural catheter already in situ (34.4%), 25.6% had successful epidural extension. GA was associated with shorter DDI, but worse perinatal outcomes than regional anaesthesia (RA). CONCLUSIONS: Our 'crash' CS protocol achieved 100% of deliveries within 30 min. The majority (88.9%) of the patients had GA for category-one CS. GA was found to be associated with shorter anaesthesia and operation times, but poorer perinatal outcomes compared to RA.