Development and validation of a prediction model for axial length elongation in myopic children treated with overnight orthokeratology.

PURPOSE To develop and validate a standardized prediction model aiming at 1-year axial length elongation and to guide the orthokeratology lens practice. METHODS This retrospective study was based on medical records of myopic children treated with orthokeratology. Individuals aged 8-15 years (n = 1261) were included and divided into the primary cohort (n = 757) and validation cohort (n = 504). Feature selection was primarily performed to sort out influential predictors by high-throughput extraction. Then, the prediction model was developed using multivariable linear regression analysis completed by backward stepwise selection. Finally, the validation of the prediction model was performed by evaluation metrics (mean-square error, root-mean-square error, mean absolute error and Rad2 ). RESULTS No significant difference was found between primary and validation cohort (all p > 0.05). After the feature selection, the crude model was adjusted by demographic information in multivariable linear regression analysis, and five final predictors were identified (all p < 0.01). The interaction effect of age with 1-month change of zone-3 mm flat K was detected (p < 0.01); hence, two final prediction models were developed based on two age subgroups. The validation proved an acceptable performance. CONCLUSION An effective multivariable prediction model aiming at 1-year axial length elongation was developed and validated. It can potentially help clinicians to predict orthokeratology efficacy and make valid adjustments. The influential variables revealed in this model can also provide designers directions to optimize the design of lens to improve the efficacy of myopia control.