Dataset Information

Incidence of and Factors Associated With Myopia and High Myopia in Chinese Children, Based on Refraction Without Cycloplegia.

ABSTRACT:

Importance

Myopia has reached epidemic levels among children in regions of East and Southeast Asia. High myopia is associated with myopic macular degeneration, glaucoma, and retinal detachment.

Objective

To determine the incidence of myopia and high myopia based on refraction without cycloplegia among children in primary and junior high schools in China.

Design, setting, and participants

This observational cohort study was completed in Guangzhou, China. It consisted of a cohort from 19 primary schools, who were followed up from 2010 to 2015, and a cohort from 22 junior high schools, who were followed up from 2010 to 2012. All schools were randomly chosen at rates proportional to the number of schools in each of the city's 11 districts. Students with or without myopia in grade 1 (primary school) or grade 7 (junior high school) were eligible for inclusion. Data analysis occurred from February 2017 to October 2017.

Main outcomes and measures

Myopia was defined as a spherical equivalent refraction (SER) of -0.50 diopters (D) or less, as measured by subjective refraction without cycloplegia; high myopia was defined as a SER of -6.0 D or less. Annual incidences were defined as the proportion of participants each year found to have myopia or high myopia who did not previously have the condition. Height, weight, axial length (AL), corneal radius of curvature (CRC), and AL/CRC ratio were examined to assess if these measures were associated with future myopia or high myopia.

Results

A total of 4741 students with or without myopia in either grade 1 for the primary school cohort (mean [SD] age 7.2 [0.4] years; 932 of 1975 [47.2%] female) or grade 7 for the junior high school cohort (mean [SD] age 13.2 [0.5] years; 1254 of 2670 [47.0%] female) were included. Baseline mean (SD) SER was 0.31 (0.86) D among 1975 students in grade 1 vs -1.60 (2.00) D among 2670 students in grade 7. Baseline prevalence of myopia was 12.0% in grade 1 students (n = 237 of 1969) and 67.4% in grade 7 students (n = 1795 of 2663). The incidence of myopia was 20% to 30% each year throughout both cohorts. The incidence of high myopia was initially less than 1% in the primary school cohort (grade 1: n = 2 of 1825; 0.1% [95% CI, 0.0%-0.3%]), but incidence exceeded 2% in the junior high school cohort (in grade 9: n = 48 of 2044; 2.3% [95% CI, 1.0%-3.7%]).

Conclusions and relevance

The incidence of myopia among Chinese students based on refraction without cycloplegia is among the highest of any cultural or ethnic group. If confirmed with cycloplegic refraction, interventions to prevent myopia onset in Chinese populations should be initiated in primary schools.

SUBMITTER: Wang SK

PROVIDER: S-EPMC6142978 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundElectronic medical records provide large-scale real-world clinical data for use in developing clinical decision systems. However, sophisticated methodology and analytical skills are required to handle the large-scale datasets necessary for the optimisation of prediction accuracy. Myopia is a common cause of vision loss. Current approaches to control myopia progression are effective but have significant side effects. Therefore, identifying those at greatest risk who should undergo targeted therapy is of great clinical importance. The objective of this study was to apply big data and machine learning technology to develop an algorithm that can predict the onset of high myopia, at specific future time points, among Chinese school-aged children.Methods and findingsReal-world clinical refraction data were derived from electronic medical record systems in 8 ophthalmic centres from January 1, 2005, to December 30, 2015. The variables of age, spherical equivalent (SE), and annual progression rate were used to develop an algorithm to predict SE and onset of high myopia (SE ? -6.0 dioptres) up to 10 years in the future. Random forest machine learning was used for algorithm training and validation. Electronic medical records from the Zhongshan Ophthalmic Centre (a major tertiary ophthalmic centre in China) were used as the training set. Ten-fold cross-validation and out-of-bag (OOB) methods were applied for internal validation. The remaining 7 independent datasets were used for external validation. Two population-based datasets, which had no participant overlap with the ophthalmic-centre-based datasets, were used for multi-resource validation testing. The main outcomes and measures were the area under the curve (AUC) values for predicting the onset of high myopia over 10 years and the presence of high myopia at 18 years of age. In total, 687,063 multiple visit records (?3 records) of 129,242 individuals in the ophthalmic-centre-based electronic medical record databases and 17,113 follow-up records of 3,215 participants in population-based cohorts were included in the analysis. Our algorithm accurately predicted the presence of high myopia in internal validation (the AUC ranged from 0.903 to 0.986 for 3 years, 0.875 to 0.901 for 5 years, and 0.852 to 0.888 for 8 years), external validation (the AUC ranged from 0.874 to 0.976 for 3 years, 0.847 to 0.921 for 5 years, and 0.802 to 0.886 for 8 years), and multi-resource testing (the AUC ranged from 0.752 to 0.869 for 4 years). With respect to the prediction of high myopia development by 18 years of age, as a surrogate of high myopia in adulthood, the algorithm provided clinically acceptable accuracy over 3 years (the AUC ranged from 0.940 to 0.985), 5 years (the AUC ranged from 0.856 to 0.901), and even 8 years (the AUC ranged from 0.801 to 0.837). Meanwhile, our algorithm achieved clinically acceptable prediction of the actual refraction values at future time points, which is supported by the regressive performance and calibration curves. Although the algorithm achieved balanced and robust performance, concerns about the compromised quality of real-world clinical data and over-fitting issues should be cautiously considered.ConclusionsTo our knowledge, this study, for the first time, used large-scale data collected from electronic health records to demonstrate the contribution of big data and machine learning approaches to improved prediction of myopia prognosis in Chinese school-aged children. This work provides evidence for transforming clinical practice, health policy-making, and precise individualised interventions regarding the practical control of school-aged myopia.

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