Ophthalmology (Eye & Ear Hospital) - Research Publications
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ItemImpact of Visual Impairment and Eye diseases on Mortality: the Singapore Malay Eye Study (SiMES)(NATURE PORTFOLIO, 2015-11-09)We investigated the relationship of visual impairment (VI) and age-related eye diseases with mortality in a prospective, population-based cohort study of 3,280 Malay adults aged 40-80 years between 2004-2006. Participants underwent a full ophthalmic examination and standardized lens and fundus photographic grading. Visual acuity was measured using logMAR chart. VI was defined as presenting (PVA) and best-corrected (BCVA) visual acuity worse than 0.30 logMAR in the better-seeing eye. Participants were linked with mortality records until 2012. During follow-up (median 7.24 years), 398 (12.2%) persons died. In Cox proportional-hazards models adjusting for relevant factors, participants with VI (PVA) had higher all-cause mortality (hazard ratio[HR], 1.57; 95% confidence interval[CI], 1.25-1.96) and cardiovascular (CVD) mortality (HR 1.75; 95% CI, 1.24-2.49) than participants without. Diabetic retinopathy (DR) was associated with increased all-cause (HR 1.70; 95% CI, 1.25-2.36) and CVD mortality (HR 1.57; 95% CI, 1.05-2.43). Retinal vein occlusion (RVO) was associated with increased CVD mortality (HR 3.14; 95% CI, 1.26-7.73). No significant associations were observed between cataract, glaucoma and age-related macular degeneration with mortality. We conclude that persons with VI were more likely to die than persons without. DR and RVO are markers of CVD mortality.
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ItemGenetic variants linked to myopic macular degeneration in persons with high myopia: CREAM Consortium(PUBLIC LIBRARY SCIENCE, 2019-08-15)PURPOSE: To evaluate the roles of known myopia-associated genetic variants for development of myopic macular degeneration (MMD) in individuals with high myopia (HM), using case-control studies from the Consortium of Refractive Error and Myopia (CREAM). METHODS: A candidate gene approach tested 50 myopia-associated loci for association with HM and MMD, using meta-analyses of case-control studies comprising subjects of European and Asian ancestry aged 30 to 80 years from 10 studies. Fifty loci with the strongest associations with myopia were chosen from a previous published GWAS study. Highly myopic (spherical equivalent [SE] ≤ -5.0 diopters [D]) cases with MMD (N = 348), and two sets of controls were enrolled: (1) the first set included 16,275 emmetropes (SE ≤ -0.5 D); and (2) second set included 898 highly myopic subjects (SE ≤ -5.0 D) without MMD. MMD was classified based on the International photographic classification for pathologic myopia (META-PM). RESULTS: In the first analysis, comprising highly myopic cases with MMD (N = 348) versus emmetropic controls without MMD (N = 16,275), two SNPs were significantly associated with high myopia in adults with HM and MMD: (1) rs10824518 (P = 6.20E-07) in KCNMA1, which is highly expressed in human retinal and scleral tissues; and (2) rs524952 (P = 2.32E-16) near GJD2. In the second analysis, comprising highly myopic cases with MMD (N = 348) versus highly myopic controls without MMD (N = 898), none of the SNPs studied reached Bonferroni-corrected significance. CONCLUSIONS: Of the 50 myopia-associated loci, we did not find any variant specifically associated with MMD, but the KCNMA1 and GJD2 loci were significantly associated with HM in highly myopic subjects with MMD, compared to emmetropes.
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ItemReferral for disease-related visual impairment using retinal photograph-based deep learning: a proof-of-concept, model development study(ELSEVIER, 2021-01)BACKGROUND: In current approaches to vision screening in the community, a simple and efficient process is needed to identify individuals who should be referred to tertiary eye care centres for vision loss related to eye diseases. The emergence of deep learning technology offers new opportunities to revolutionise this clinical referral pathway. We aimed to assess the performance of a newly developed deep learning algorithm for detection of disease-related visual impairment. METHODS: In this proof-of-concept study, using retinal fundus images from 15 175 eyes with complete data related to best-corrected visual acuity or pinhole visual acuity from the Singapore Epidemiology of Eye Diseases Study, we first developed a single-modality deep learning algorithm based on retinal photographs alone for detection of any disease-related visual impairment (defined as eyes from patients with major eye diseases and best-corrected visual acuity of <20/40), and moderate or worse disease-related visual impairment (eyes with disease and best-corrected visual acuity of <20/60). After development of the algorithm, we tested it internally, using a new set of 3803 eyes from the Singapore Epidemiology of Eye Diseases Study. We then tested it externally using three population-based studies (the Beijing Eye study [6239 eyes], Central India Eye and Medical study [6526 eyes], and Blue Mountains Eye Study [2002 eyes]), and two clinical studies (the Chinese University of Hong Kong's Sight Threatening Diabetic Retinopathy study [971 eyes] and the Outram Polyclinic Study [1225 eyes]). The algorithm's performance in each dataset was assessed on the basis of the area under the receiver operating characteristic curve (AUC). FINDINGS: In the internal test dataset, the AUC for detection of any disease-related visual impairment was 94·2% (95% CI 93·0-95·3; sensitivity 90·7% [87·0-93·6]; specificity 86·8% [85·6-87·9]). The AUC for moderate or worse disease-related visual impairment was 93·9% (95% CI 92·2-95·6; sensitivity 94·6% [89·6-97·6]; specificity 81·3% [80·0-82·5]). Across the five external test datasets (16 993 eyes), the algorithm achieved AUCs ranging between 86·6% (83·4-89·7; sensitivity 87·5% [80·7-92·5]; specificity 70·0% [66·7-73·1]) and 93·6% (92·4-94·8; sensitivity 87·8% [84·1-90·9]; specificity 87·1% [86·2-88·0]) for any disease-related visual impairment, and the AUCs for moderate or worse disease-related visual impairment ranged between 85·9% (81·8-90·1; sensitivity 84·7% [73·0-92·8]; specificity 74·4% [71·4-77·2]) and 93·5% (91·7-95·3; sensitivity 90·3% [84·2-94·6]; specificity 84·2% [83·2-85·1]). INTERPRETATION: This proof-of-concept study shows the potential of a single-modality, function-focused tool in identifying visual impairment related to major eye diseases, providing more timely and pinpointed referral of patients with disease-related visual impairment from the community to tertiary eye hospitals. FUNDING: National Medical Research Council, Singapore.