机构：[1]Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510120, China [2]Guangzhou National Laboratory, Guangzhou 510000, China [3]Zhuhai International Eye Center of the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Zhuhai 519000, China [4]Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing 100069, China 研究所眼科研究所首都医科大学附属北京同仁医院首都医科大学附属同仁医院[5]Key Laboratory of Myopia, Ministry of Health, Shanghai Key Laboratory of Visual Impairment and Restoration, Department of Ophthalmology, Eye Institute, Eye and Ear, Nose, and Throat Hospital, Fudan University, Shanghai 200031, China [6]DongguanPeople’s Hospital, The First School of Clinical Medicinel, Southern Medical University, Dongguan 523059, China [7]State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China [8]Wenzhou Eye Hospital and Institute for Advanced Study on Eye Health and Diseases, Wenzhou Medical University, Wenzhou 325000, China
Background Myopia is a leading cause of visual impairment in Asia and worldwide. However, accurately predicting the progression of myopia and the high risk of myopia remains a challenge. This study aims to develop a predictive model for the development of myopia.Methods We first retrospectively gathered 612 530 medical records from five independent cohorts, encompassing 227 543 patients ranging from infants to young adults. Subsequently, we developed a multivariate linear regression algorithm model to predict the progression of myopia and the risk of high myopia.Result The model to predict the progression of myopia achieved an R2 value of 0.964 vs a mean absolute error (MAE) of 0.119D [95% confidence interval (CI): 0.119, 1.146] in the internal validation set. It demonstrated strong generalizability, maintaining consistent performance across external validation sets: R2 = 0.950 vs MAE = 0.119D (95% CI: 0.119, 1.136) in validation study 1, R2 = 0.950 vs MAE = 0.121D (95% CI: 0.121, 1.144) in validation study 2, and R2 = 0.806 vs MAE = -0.066D (95% CI: -0.066, 0.569) in the Shanghai Children Myopia Study. In the Beijing Children Eye Study, the model achieved an R2 of 0.749 vs a MAE of 0.178D (95% CI: 0.178, 1.557). The model to predict the risk of high myopia achieved an area under the curve (AUC) of 0.99 in the internal validation set and consistently high area under the curve values of 0.99, 0.99, 0.96 and 0.99 in the respective external validation sets.Conclusion Our study demonstrates accurate prediction of myopia progression and risk of high myopia providing valuable insights for tailoring strategies to personalize and optimize the clinical management of myopia in children.
基金：
This study was supported by the Zhuhai Science and Technology Plan Medical and Health Project (Grant/Award No.
ZH2202200033HJL), Macau Science and Technology Development
Fund, Macao (0007/2020/AFJ, 0070/2020/A2, 0003/2021/AKP). We
thank staffs from the Kang Zhang laboratory for their help and
assistance.).
第一作者机构：[1]Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510120, China [2]Guangzhou National Laboratory, Guangzhou 510000, China
共同第一作者：
通讯作者：
通讯机构：[1]Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510120, China [2]Guangzhou National Laboratory, Guangzhou 510000, China [3]Zhuhai International Eye Center of the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology, Zhuhai 519000, China [8]Wenzhou Eye Hospital and Institute for Advanced Study on Eye Health and Diseases, Wenzhou Medical University, Wenzhou 325000, China
推荐引用方式(GB/T 7714)：
Li Jiahui,Zeng Simiao,Li Zhihuan,et al.Accurate prediction of myopic progression and high myopia by machine learning[J].PRECISION CLINICAL MEDICINE.2024,7(1):doi:10.1093/pcmedi/pbae005.
APA：
Li, Jiahui,Zeng, Simiao,Li, Zhihuan,Xu, Jie,Sun, Zhuo...&Zhang, Kang.(2024).Accurate prediction of myopic progression and high myopia by machine learning.PRECISION CLINICAL MEDICINE,7,(1)
MLA：
Li, Jiahui,et al."Accurate prediction of myopic progression and high myopia by machine learning".PRECISION CLINICAL MEDICINE 7..1(2024)
