Tracking myopia development through axial length progression: a retrospective longitudinal study.

BACKGROUND

Current prediction models for myopia progression remain limited in their ability to provide personalized risk assessments.

OBJECTIVE

To examine the predictive role of axial length (AL) progression in progressive myopia.

INTRODUCTION

This retrospective cohort study analysed longitudinal ocular biometric data collected through repeated measurements in a school-based population.

SUBJECTS: The study population comprised a longitudinal cohort of 1697 Chinese students aged 6-14 years, with ocular biometric data collected between December 2017 and May 2019 (18-month follow-up period).

METHOD: The dataset was randomly partitioned into development and validation cohorts at a 2:1 ratio, with two-thirds of the data allocated for nomogram model construction and the remaining one-third reserved for validation. The axial length-to-corneal radius (AL/CR) ratio was defined as the AL divided by the mean CR value measured in 90° meridians and 180° meridians: AL/CR = 2AL/(CR90° + CR180°). The primary outcome is progressive myopia, defined as an annualized spherical equivalent (SE) progression rate ≥0.75 DS/year over 1.5 years. The key predictor, first-visit AL progression, reflects 6-month axial elongation from baseline.

RESULTS

The baseline subjects (N = 1697) were divided into the training set (N = 1132) and the validation set (N = 565). Multivariate logistic regression analysis indicated AL/CR in baseline (OR = 70.414, 95%CI: 22.795-217.511, p < .001) and first-visit AL progression (OR = 12845.569, 95%CI: 2915.219-56602.490, p < .001) significantly contributed to the risk of progressive myopia. Accordingly, baseline AL/CR and first-visit AL progression were treated as the main factor to build the nomogram model. The model showed good predictive performance (AUC = 0.785 in training set/0.771 in validation set) with well-calibrated slopes (approaching 1) and clinically useful thresholds (0.20-0.80).

CONCLUSIONS

This study develops a personalized prediction model for progressive myopia, grounded on factors of the first visit AL progression and baseline AL/CR. The model offers a dynamic and reliable foundation for selecting effective myopia control measures in future stages.