Photodiagnosis and photodynamic therapy · 2025
Early changes in choroidal thickness and ocular biometry in predicting who will achieve full myopia control with repeated low-level red light therapy.
СтатьяМногоцентровое исследованиеРандомизированное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Chen Yanxian, Li Mingge, Shang Xianwen, Li Guangyu, Zhao Ziwei, Li Pengju, Liu Yanjun, Xiong Ruilin, Lai Mengying, Wang Yueye, He Mingguang, Zhu Zhuoting
Аннотация
PURPOSE: To evaluate the early predictors for achieving full myopia control with repeated low-level red light (RLRL) therapy based on two independent randomized clinical trials (RCTs).
METHODS: Myopic children undergoing RLRL therapy from a multi-center RCT (training set) and a single-center RCT (validation set) were included. Full myopia control was defined as axial elongation <0.1mm/year. Variables included age, sex, baseline refraction, ocular parameters at baseline, 1 and 3 months (axial length [AL] and subfoveal choroidal thickness [sChT]), as well as their rates of change over the first 3 months. Four random forest models to predict full myopia control after 1 year and a logistic regression was used to estimate 2-year outcome.
RESULTS: A total of 148 children were analyzed. The proportions of 1-year full myopia control was 54.2 % of eyes in the training set and 55.0 % in the validation set. Random forest models incorporating the rate of change in AL and sChT showed high predictive accuracy (AUC: 0.97 to 0.98) in external validation. The rate of change in AL contributed the most for model accuracy. For 2-year control, the rate of AL change had an AUC of 0.99 while the rate of change in sChT achieved only 0.69.
CONCLUSIONS: The rate of change in AL during the first three months emerged as the most important predictor for treatment outcomes at both 1-year and 2-year, rather than the change in sChT. Early monitoring of AL changes could be a valuable tool for identifying children most likely to benefit from this intervention.
METHODS: Myopic children undergoing RLRL therapy from a multi-center RCT (training set) and a single-center RCT (validation set) were included. Full myopia control was defined as axial elongation <0.1mm/year. Variables included age, sex, baseline refraction, ocular parameters at baseline, 1 and 3 months (axial length [AL] and subfoveal choroidal thickness [sChT]), as well as their rates of change over the first 3 months. Four random forest models to predict full myopia control after 1 year and a logistic regression was used to estimate 2-year outcome.
RESULTS: A total of 148 children were analyzed. The proportions of 1-year full myopia control was 54.2 % of eyes in the training set and 55.0 % in the validation set. Random forest models incorporating the rate of change in AL and sChT showed high predictive accuracy (AUC: 0.97 to 0.98) in external validation. The rate of change in AL contributed the most for model accuracy. For 2-year control, the rate of AL change had an AUC of 0.99 while the rate of change in sChT achieved only 0.69.
CONCLUSIONS: The rate of change in AL during the first three months emerged as the most important predictor for treatment outcomes at both 1-year and 2-year, rather than the change in sChT. Early monitoring of AL changes could be a valuable tool for identifying children most likely to benefit from this intervention.