Department of Ophthalmology and Vision Science, Eye and ENT Hospital, Fudan University, 83 Fenyang Road, Shanghai, 200031, China.
NHC Key Laboratory of Myopia, Fudan University, Shanghai, China.
J Transl Med. 2024 Mar 17;22(1):289. doi: 10.1186/s12967-024-05075-0.
Global myopia prevalence poses a substantial public health burden with vision-threatening complications, necessitating effective prevention and control strategies. Precise prediction of spherical equivalent (SE), myopia, and high myopia onset is vital for proactive clinical interventions.
We reviewed electronic medical records of pediatric and adolescent patients who underwent cycloplegic refraction measurements at the Eye & Ear, Nose, and Throat Hospital of Fudan University between January 2005 and December 2019. Patients aged 3-18 years who met the inclusion criteria were enrolled in this study. To predict the SE and onset of myopia and high myopia in a specific year, two distinct models, random forest (RF) and the gradient boosted tree algorithm (XGBoost), were trained and validated based on variables such as age at baseline, and SE at various intervals. Outputs included SE, the onset of myopia, and high myopia up to 15 years post-initial examination. Age-stratified analyses and feature importance assessments were conducted to augment the clinical significance of the models.
The study enrolled 88,250 individuals with 408,255 refraction records. The XGBoost-based SE prediction model consistently demonstrated robust and better performance than RF over 15 years, maintaining an R exceeding 0.729, and a Mean Absolute Error ranging from 0.078 to 1.802 in the test set. Myopia onset prediction exhibited strong area under the curve (AUC) values between 0.845 and 0.953 over 15 years, and high myopia onset prediction showed robust AUC values (0.807-0.997 over 13 years, with the 14th year at 0.765), emphasizing the models' effectiveness across age groups and temporal dimensions on the test set. Additionally, our classification models exhibited excellent calibration, as evidenced by consistently low brier score values, all falling below 0.25. Moreover, our findings underscore the importance of commencing regular examinations at an early age to predict high myopia.
The XGBoost predictive models exhibited high accuracy in predicting SE, onset of myopia, and high myopia among children and adolescents aged 3-18 years. Our findings emphasize the importance of early and regular examinations at a young age for predicting high myopia, thereby providing valuable insights for clinical practice.
全球近视患病率给视力带来严重威胁，并发症也随之而来，因此需要有效的预防和控制策略。准确预测球镜等效值（SE）、近视和高度近视的发病时间对于主动进行临床干预至关重要。
我们回顾了复旦大学附属眼耳鼻喉科医院 2005 年 1 月至 2019 年 12 月期间接受睫状肌麻痹验光的儿科和青少年患者的电子病历。符合纳入标准的 3-18 岁患者被纳入本研究。为了预测特定年份的 SE 和近视及高度近视的发病时间，我们分别基于年龄和不同时间点 SE 等变量，使用随机森林（RF）和梯度提升树算法（XGBoost）两种不同的模型进行训练和验证。输出结果包括 SE、近视和高度近视发病时间，预测范围最长可达初次就诊后 15 年。我们还进行了年龄分层分析和特征重要性评估，以增强模型的临床意义。
该研究共纳入 88250 名患者，共 408255 份验光记录。在 15 年的预测中，XGBoost 模型在 SE 预测方面始终表现出优于 RF 的稳健和更好的性能，其在测试集中的 R 超过 0.729，平均绝对误差（Mean Absolute Error，MAE）范围为 0.078-1.802。在 15 年的预测中，近视发病时间的预测具有很强的曲线下面积（Area Under the Curve，AUC）值（0.845-0.953），而高度近视发病时间的预测在 13 年的时间内具有稳健的 AUC 值（0.807-0.997，第 14 年为 0.765），这表明模型在测试集中在不同年龄段和时间维度上均具有有效性。此外，我们的分类模型表现出优异的校准能力，这一点从始终低于 0.25 的低布莱尔得分值可以得到证实。此外，我们的研究结果强调了在儿童和青少年时期定期进行早期检查以预测高度近视的重要性。
XGBoost 预测模型在预测 3-18 岁儿童和青少年的 SE、近视和高度近视发病时间方面具有较高的准确性。我们的研究结果强调了在年轻时进行早期和定期检查以预测高度近视的重要性，为临床实践提供了有价值的见解。
