Wu Tzu-En, Chen Hsin-An, Jhou Mao-Jhen, Chen Yen-Ning, Chang Ting-Jen, Lu Chi-Jie
Department of Ophthalmology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 11101, Taiwan.
School of Medicine, Fu Jen Catholic University, New Taipei City 242062, Taiwan.
J Clin Med. 2020 Dec 30;10(1):111. doi: 10.3390/jcm10010111.
Atropine is a common treatment used in children with myopia. However, it probably affects intraocular pressure (IOP) under some conditions. Our research aims to analyze clinical data by using machine learning models to evaluate the effect of 19 important factors on intraocular pressure (IOP) in children with myopia treated with topical atropine. The data is collected on 1545 eyes with spherical equivalent (SE) less than -10.0 diopters (D) treated with atropine for myopia control. Four machine learning models, namely multivariate adaptive regression splines (MARS), classification and regression tree (CART), random forest (RF), and eXtreme gradient boosting (XGBoost), were used. Linear regression (LR) was used for benchmarking. The 10-fold cross-validation method was used to estimate the performance of the five methods. The main outcome measure is that the 19 important factors associated with atropine use that may affect IOP are evaluated using machine learning models. Endpoint IOP at the last visit was set as the target variable. The results show that the top five significant variables, including baseline IOP, recruitment duration, age, total duration and previous cumulative dosage, were identified as most significant for evaluating the effect of atropine use for treating myopia on IOP. We can conclude that the use of machine learning methods to evaluate factors that affect IOP in children with myopia treated with topical atropine is promising. XGBoost is the best predictive model, and baseline IOP is the most accurate predictive factor for endpoint IOP among all machine learning approaches.
阿托品是治疗儿童近视的常用方法。然而，在某些情况下它可能会影响眼压（IOP）。我们的研究旨在通过使用机器学习模型分析临床数据，以评估19个重要因素对局部使用阿托品治疗的近视儿童眼压（IOP）的影响。数据收集自1545只等效球镜（SE）小于-10.0屈光度（D）且使用阿托品控制近视的眼睛。使用了四种机器学习模型，即多元自适应回归样条（MARS）、分类与回归树（CART）、随机森林（RF）和极端梯度提升（XGBoost）。使用线性回归（LR）作为基准。采用10折交叉验证法来评估这五种方法的性能。主要结局指标是使用机器学习模型评估与阿托品使用相关的可能影响眼压的19个重要因素。将最后一次就诊时的终点眼压设定为目标变量。结果表明，包括基线眼压、招募持续时间、年龄、总持续时间和既往累积剂量在内的前五个显著变量被确定为评估阿托品治疗近视对眼压影响的最显著变量。我们可以得出结论，使用机器学习方法评估局部使用阿托品治疗的近视儿童中影响眼压的因素是有前景的。在所有机器学习方法中，XGBoost是最佳预测模型，基线眼压是终点眼压最准确的预测因素。
