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

Abstract

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.

Predicting the onset of myopia in children by age, sex, and ethnicity: Results from the CLEERE Study

“…10 SCORM found a C statistic of 0.91 for predicting myopia onset in the year following baseline based on cycloplegic refractive error and parental myopia. 30 Refractive error is common to each of the other predictive models, but the number of additional terms varies. WEPrOM uses sex, spherical equivalent, axial length, corneal refractive power, and positive relative accommodation (which requires some clinical training to measure).…”

“…10,29,30 The racial/ethnic composition also varies across these studies. Chinese ancestry is well represented in these datasets, [26][27][28][29][30] as are Australians of European descent, 15 children from Northern Ireland, 10 and children in the United States from several racial/ethnic groups. 17 A more formal comparison of predictive models would help to quantify the effects of these potential sources of variability.…”

“…17 Remarkably, the SCORM cutpoints for myopia onset within 1 year from baseline were almost identical to those from CLEERE despite being conducted in different countries, namely, 0.00 D for baseline ages 6 to 8 years and −0.25 D for children older than 9 years. 30 Similar cutpoints can be seen for CLEERE as the leftmost points in the figure from Zadnik et al 17 The more hyperopic points to the right in the CLEERE figure provided the cutpoints for myopia onset by the eighth grade: less hyperopic than +0.75 D for grade 1 (age 6 years), +0.50 D or less hyperopic for grades 2 and 3 (ages 7 and 8 years), +0.25 D or less hyperopic for grades 4 and 5 (ages 9 and 10 years), and emmetropic or more myopic for grade 6 (age 11 years). 17 The longer time horizon from CLEERE of up to 7 years from baseline is arguably more clinically useful as it provides the opportunity for earlier intervention.…”

“…Numerous predictive factors for myopia onset have been identified, including demographic, biometric, and accommodative variables. 10,15,[26][27][28][29][30] However, the best single predictor of future myopia is a less hyperopic baseline spherical equivalent refractive error. 15,17,27,30 The increase in prediction accuracy from adding other variables as judged by a change in the area under a receiver operating characteristic (ROC) curve is often small, ranging from less than 0.05 up to 0.10 compared with 0.64 to 0.93 for spherical equivalent refractive error alone.…”

Fundus Tessellated Density of Pathologic Myopia

“…Therefore, ex- Indeed, the advancements in artificial intelligence-assisted imaging processing have opened up new possibilities for semiquantitative and quantitative analysis of fundus photographs and OCT scans for various ocular diseases, [16][17][18][19] and even possible to predict the progression and prognosis of myopia based on ocular biometrics. 20 However, there is a gap in the existing literature regarding the specific focus on FTD in patients with PM and the associated pathological changes observed in fundus photographs. One of the challenges in studying FTD in PM patients is the identification and characterization of patchy chorioretinal atrophy, which can significantly impact the final results.…”