Prediction for Cycloplegic Refractive Error in Chinese School Students: Model Development and Validation

• Published in: Translational vision science & technology 2022-01, Vol.11 (1), p.15-15
• Main Authors: Wang, Jianyong, Wang, Xinyi, Gao, Hans M, Zhang, Huiyan, Yang, Ying, Gu, Fang, Zheng, Xin, Gu, Lei, Huang, Jianyao, Meng, Jia, Li, Juanjuan, Gao, Lei, Zhang, Ronghua, Shen, Jianqin, Ying, Gui-Shuang, Cui, Hongguang
• Format: Article
• Language: English
• Subjects: Adolescent; Child; Child, Preschool; China - epidemiology; Female; Humans; Male; Mydriatics; Prevalence; Refractive Errors - diagnosis; Refractive Errors - epidemiology; Schools; Students
• ISSN: 2164-2591; 2164-2591
• DOI: 10.1167/tvst.11.1.15

To predict cycloplegic refractive error using measurements obtained under noncycloplegic conditions. Refractive error was measured in 5- to 18-year-old Chinese students using a NIDEK autorefractor before and after administration of 0.5% tropicamide. Spherical equivalent (SER) in diopters (D) was calculated as sphere plus half cylinder. A multivariable prediction model for cycloplegic SER was developed using data from students in Jinyun (n = 1938) and was validated using data from students in Hangzhou (n = 1498). The performance of the prediction model was evaluated using R2, mean difference between predicted and measured cycloplegic SER, and sensitivity and specificity for predicting myopia (cycloplegic SER ≤ -0.5 D). Among 3436 students (mean age, 9.7 years; 51% female), the mean (SD) noncycloplegic and cycloplegic SER values were -1.12 (1.97) D and -0.20 (2.19) D, respectively. The prediction model that included demographics, noncycloplegic SER, axial length/corneal curvature radius ratio, uncorrected visual acuity (UCVA), and intraocular pressure predicted cycloplegic SER with R2 of 0.93 in the development dataset and 0.92 in the validation dataset. The mean (SD) differences between predicted and measured cycloplegic SER were 0.0 (0.55) D in the development dataset and 0.06 (0.64) D in the validation dataset. In both the development and validation datasets, the combination of predicted SER and UCVA yielded high sensitivity (91.4% and 91.9%, respectively) and specificity (95.0% and 90.1%, respectively) for detecting myopia. Cycloplegic refractive error can be predicted using measurements obtained under noncycloplegic conditions. The prediction model could potentially be used to correct the myopia prevalence in epidemiological studies in which administering cycloplegic agent on all participants is not feasible. The prediction model may provide a tool for correcting the overestimation of myopia from noncycloplegic refractive error in future epidemiological studies in which administering cycloplegic agent on all participants is not feasible.