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Annual myopia progression and subsequent year progression in Singaporean children

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Posterboard#: A0058

Abstract Number: 76 - A0058

AuthorBlock: Noel A. Brennan1, Saiko Matsumara2, Hla Myint Htoon2,3, Biten K Kathrani1, Chuen Seng Tan4, Carla Lanca2, Donald Tan3,5, Charumathi Sabanayagam2,3, Seang-Mei Saw2,3
1Johnson & Johnson Vision, Johnson & Johnson Vision Care, Jacksonville , Florida, United States; 2Singapore Eye Research Institute, , Singapore; 3Duke-NUS Medical School, , Singapore; 4Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore; 5Singapore National Eye Centre, , Singapore;

DisclosureBlock: Noel A. Brennan, Johnson & Johnson Vision Code E (Employment), Saiko Matsumara, None; Hla Myint Htoon, None; Biten K Kathrani, Johnson & Johnson Vision Code E (Employment), Chuen Seng Tan, None; Carla Lanca, None; Donald Tan, None; Charumathi Sabanayagam, None; Seang-Mei Saw, None;

To investigate predictors of myopia progression for subsequent year amongst myopic children in the Singapore Cohort Study of the Risk Factors for Myopia (SCORM).

A total of 674 myopic children (353M, 321F) aged 7 to 10 (mean 8.0 ± 0.9) years from 3 schools at baseline with at least 2 follow-up visits in SCORM were included. Cycloplegic autorefraction (RK5 autokeratorefractometer) and axial length (AL) measurement (US-800 Echo scan) were performed at every visit. Multiple linear regression analysis was performed with annual future myopia progression as the dependent variable. Receiver operating characteristic (ROC) curves from multiple logistic regressions were used to derive prediction scores for future fast myopia progression defined by the median cut at different durations of different follow up years.

Myopia progression in Year 1 correlated with Year 2 progression (r = 0.47; see figure). For every 1 D increase in annual myopia progression in Year 1, Year 2 progression increased by 0.35 D (p < 0.001), in a multivariate linear regression model. Children with slow myopia progression during the first year (Year 1) (>-0.50 D/year) had the slowest mean Year 2 progression (-0.44 ± 0.44 D/Year), while children with fast myopia progression (<-1.25 D) in Year 1 had the fastest mean progression (-1.01 ± 0.39 D/year) in Year 2. There was a dose-response relationship (p for trend < 0.001). Year 1 myopia progression had the highest AUC for predicting fast Year 2 progression [0.76 (95% CI 0.73-0.80)] when compared to baseline SE [0.70 (95% CI 0.66-0.73)] or age of myopia onset [0.70 (95% CI 0.66-0.73)] or parental myopia [0.70 (95% CI 0.66-0.73)], after adjusting for confounders. For Year 1 myopia progression, AUC for predicting fast Year 2 progression was 0.76 [95% CI 0.73-0.80] and higher than those for fast Year 3 [0.69 (95% CI 0.65-0.73)] or Year 4 [0.63 (95% CI 0.57-0.68)] progression.

One-year annual myopia progression correlates with immediate subsequent year myopia progression. However, annual progression as a single factor cannot fully predict subsequent year or long term myopia progression. Strategic management to a given individual should be determined based on multiple patient-specific factors including myopia progression in the previous year, age of myopia onset and parental myopia.

Layman Abstract (optional): Provide a 50-200 word description of your work that non-scientists can understand. Describe the big picture and the implications of your findings, not the study itself and the associated details.
Myopia is the number one eye health threat of the 21st century. Eyecare practitioners worldwide are waking up to this reality and are beginning to intervene to slow myopia progression. When deciding whether to treat, past progression is often used as a yardstick. We examined the validty of this approach. While future progression does correlate with past progression, there is sufficient variation in measurement and progression that many children in need of treatment may be overlooked. Age of onset should also feature in clinical considerations.