Myopia is a major public health issue and occurs at young ages. Apart from its high prevalence, myopia results in high costs and irreversible blinding diseases. Accurate prediction of the risk of myopia onset is crucial for its precise prevention. We aimed to develop and validate an effective nomogram for predicting myopia onset in schoolchildren.

School-based prospective cohort study.

A total of 1073 schoolchildren were enrolled from November 2014 to May 2019 in China, and were divided into the training and validation cohorts. Myopia was defined as a spherical equivalent refraction (SER) ≤−0.5 diopters. Predictors of myopia were determined through the least absolute shrinkage and selection operator regression and multivariable Cox proportional hazard model based on the training cohort. The predictive performance of the nomogram was validated internally through time-dependent receiver operating characteristic (ROC) curves, calibration plot, decision curve analysis, and Kaplan-Meier curves.

Independent predictors at baseline including gender, SER, axial length, corneal refractive power, and positive relative accommodation were included in the nomogram prediction model. This nomogram demonstrated excellent calibration, clinical net benefit, and discrimination, with all the area under the ROC curves (AUCs) between 0.74 and 0.86 in the training and validation cohorts. The Kaplan-Meier curves showed that 3 distinct risk groups stratified through X-tile analysis were well discriminated and robust among subgroups. The Harrell's C-index and net reclassification improvement demonstrated that the nomogram substantially improved compared with previous models. An online myopia risk calculator was generated for better individual prediction.

The nomogram provides accurate and individual prediction of myopia onset in schoolchildren. External validation is needed to verify the generalizability of this nomogram.