Prediction of myopia onset with refractive error measured using non-cycloplegic subjective refraction: the WEPrOM Study

Wong, Yee Ling; Yuan, Yongyi; Su, Binbin; Tufail, Shezad; Ding, Yang; Ye, Yingying; Paillé, Damien; Drobe, Björn; Chen, Hao; Bao, Jinhua

doi:10.1136/bmjophth-2020-000628

Cited by 11 publications

(45 citation statements)

References 44 publications

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“…The refraction was transformed into spherical equivalent (SE = sphere power + 0.5cylinder power). Refractive status was judged according to SE [myopia: SE ≤ −0.5D ( 26 , 27 ); emmetropia: −0.5D < SE < +1.0D; hypertropia: SE ≥ 1.0D].…”

Section: Methodsmentioning

confidence: 99%

Correlation Between Increase of Axial Length and Height Growth in Chinese School-Age Children

Tao¹,

Wang

Peng

et al. 2022

Front. Public Health

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PurposeTo identify the relationship between the increase in axial length (AL) and height in school-age children and explore the influence of refractive status on such a relationship.MethodsIn this 5-year cohort study, 414 Chinese children (237 boys) aged 6–9 years (mean 7.12) underwent measurements annually. AL was measured using the Lenstar; height with the children standing, without shoes; and refraction using subjective refraction without cycloplegia. Participants were divided according to the refractive status: persistent emmetropia, persistent myopia, and newly developed myopia. The measurement time points of the persistent emmetropia and persistent myopia groups were marked as T1, T2, T3, T4, and T5. The time of myopia onset in the newly developed myopia group was marked as t0; the preceding time points were marked as t−1, t−2, and so on, and the succeeding as t1, t2, and so on. The association between increase in AL and height was analyzed using simple correlation analysis.ResultsThe mean changes in AL, height, and refraction were 1.39 mm, 23.60 cm, and −1.69 D, respectively, over 5 years in all children. The increase in AL and height were positively correlated for T1~T2, T1~T3, T1~T4, and T1~T5 (r = 0.262, P < 0.001; r = 0.108, P = 0.034; r = 0.165, P = 0.001; r = 0.174, P = 0.001, respectively). The changes in AL and height in the newly developed myopia group were significantly correlated (r = 0.289, P = 0.009) after myopia onset (t0~t2).ConclusionThe increase in AL and height were positively correlated, especially in the newly developed myopia group after myopia onset. Thus, when children grow quickly, AL elongation should be monitored.

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Section: Methodsmentioning

confidence: 99%

Correlation Between Increase of Axial Length and Height Growth in Chinese School-Age Children

Tao¹,

Wang

Peng

et al. 2022

Front. Public Health

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“…With the aim of improving accessibility of myopia risk assessments, this study developed the first risk score system that used ocular biometry alongside patient characteristics such as age and number of myopic parents, to provide a scalable, population-based clinical tool for premyopia. Without the need for cycloplegic refraction data inputs, the calculator can provide a score range that gives an estimated risk of myopia that will hopefully encourage early referral to eye vision specialists 24,25. It is noteworthy that when cycloplegic refraction is not available, UCVA and noncycloplegic refraction are commonly used as referral criteria for school screening or general physicians.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Model to Predict Who Will Develop Myopia in the Following Year as a Criterion to Define Premyopia

Chen

Tan

Foo

et al. 2023

Asia-Pacific Journal of Ophthalmology

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Purpose: To develop and validate models to predict who will develop myopia in the following year based on cycloplegic refraction or ocular biometry and to identify thresholds of premyopia. Methods: Prospective longitudinal data of nonmyopic children at baseline from the Guangzhou Twins Eye Study and the Guangzhou Outdoor Activity Longitudinal Study were used as the training set, and the Singapore Cohort Study of the Risk factors for Myopia study formed the external validation set. Age, sex, cycloplegic refraction, ocular biometry, uncorrected visual acuity, and parental myopia were integrated into 3 logistic regression models to predict the onset of myopia in the following year. Premyopia cutoffs and an integer risk score system were derived based on the identified risk. Results: In total, 2896 subjects with at least 2 visits were included. Cycloplegic refraction at baseline is a better predictor to identify the children with myopia onset [C-statistic = 0.91, 95% confidence interval (CI), 0.87-0.94; C-statistic = 0.92, 95% CI, 0.92-0.92 for internal and external validation, respectively], comparing to axial length, corneal curvature radius (CR) and anterior chamber depth (C-statistic = 0.81, 95% CI, 0.73-0.88; C-statistic = 0.80, 95% CI, 0.79-0.80, respectively), and axial length/CR (C-statistic = 0.78, 95% CI, 0.71-0.85; C-statistic = 0.76, 95% CI, 0.75-0.76). With a risk of > 70%, the definitions of premyopia indicating approaching myopia onset were 0.00 D for 6-8 years and −0.25 D for ≥ 9 years in children with 2 myopic parents. Conclusions: Either cycloplegic refraction or ocular biometry can predict 1-year risk of myopia. Premyopia can be successfully defined through risk assessments based on children's age and predict who would require more aggressive myopia prophylaxis.

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“…With the clarity of risk factors, the prediction model of myopia has also been widely concerned. Our compact model including only 10 non-ocular features had a high prediction performance even better than some models including ocular biometry and genetic information [ 19 , 43 , 44 ]. Wong et al developed models based non-cycloplegic SE, axial length and positive relative accommodation, with AUC values of 0.64, 0.62, and 0.66, respectively, and their combination with age, gender and parental myopia only achieved an AUC of 0.74 [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

Does multidimensional daily information predict the onset of myopia? A 1-year prospective cohort study

et al. 2023

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Purpose This study aimed to develop an interpretable machine learning model to predict the onset of myopia based on individual daily information. Method This study was a prospective cohort study. At baseline, non-myopia children aged 6–13 years old were recruited, and individual data were collected through interviewing students and parents. One year after baseline, the incidence of myopia was evaluated based on visual acuity test and cycloplegic refraction measurement. Five algorithms, Random Forest, Support Vector Machines, Gradient Boosting Decision Tree, CatBoost and Logistic Regression were utilized to develop different models and their performance was validated by area under curve (AUC). Shapley Additive exPlanations was applied to interpret the model output on the individual and global level. Result Of 2221 children, 260 (11.7%) developed myopia in 1 year. In univariable analysis, 26 features were associated with the myopia incidence. Catboost algorithm had the highest AUC of 0.951 in the model validation. The top 3 features for predicting myopia were parental myopia, grade and frequency of eye fatigue. A compact model using only 10 features was validated with an AUC of 0.891. Conclusion The daily information contributed reliable predictors for childhood’s myopia onset. The interpretable Catboost model presented the best prediction performance. Oversampling technology greatly improved model performance. This model could be a tool in myopia preventing and intervention that can help identify children who are at risk of myopia, and provide personalized prevention strategies based on contributions of risk factors to the individual prediction result.

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Prediction of myopia onset with refractive error measured using non-cycloplegic subjective refraction: the WEPrOM Study

Cited by 11 publications

References 44 publications

Correlation Between Increase of Axial Length and Height Growth in Chinese School-Age Children

Correlation Between Increase of Axial Length and Height Growth in Chinese School-Age Children

Development and Validation of a Model to Predict Who Will Develop Myopia in the Following Year as a Criterion to Define Premyopia

Does multidimensional daily information predict the onset of myopia? A 1-year prospective cohort study

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