Annals of medicine · 2025
Tracking myopia development through axial length progression: a retrospective longitudinal study.
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Tao Zhengyang, Wang Jiao, Lv Zongyue, Hu Guorui, Xu Zhixing, Chen Lifei, Deng Hongwei
Аннотация
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.
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.