目的：探讨利用极限梯度提升(XGBoost)算法基于角膜地形图参数及临床基础检查指标预测角膜塑形镜订单参数(平K、球镜度数、镜片直径及环曲量)的可行性，为临床智能化验配提供辅助工具。

方法：回顾性收集2019年1月至2024年3月于濮阳市第二人民医院初次验配角膜塑形镜且适配良好的近视患者资料。将左右眼数据作为独立样本进行整合，以患者为单位按7∶3划分为训练集和测试集，整合后的输入特征包括裸眼视力(UCVA)、眼轴长度(AL)、角膜水平直径(HVID)、平K、陡K、水平e值、垂直e值，环曲量预测模型在此基础上增补角膜散光(ΔK)与角膜非对称指数(SAI)，输出标签为最终订单参数(平K、球镜度数、镜片直径、环曲量)。采用XGBoost回归算法分别构建各输出参数的预测模型，通过网格搜索结合5折交叉验证优化超参数。在测试集上以决定系数(R2)、平均绝对误差(MAE)及均方根误差(RMSE)评估模型性能，并分析各特征的重要性。

结果： 本研究共纳入有效样本400例800眼，其中男208例(52.0%)，女 192例(48.0%)，平均年龄10.1±2.3岁。以患者为单位划分后，训练集包含280例560眼，其中男146例，女134例，平均年龄10.1±2.3岁； 测试集包含120例240眼，其中男62例，女58例，平均年龄10.0±2.2岁。两组性别构成及年龄比较均无差异(P>0.05)。训练集280例患者中，使用环曲镜片者140例(50.0%)； 测试集120例患者中，使用环曲镜片者50例(41.7%)(χ2=2.017，P=0.156)。平K预测的MAE为0.34 D，R2为0.84； 球镜度数预测的MAE为0.43 D，R2为0.70； 镜片直径预测的MAE为0.12 mm，R2为0.42； 环曲量预测(Tweedie回归，power=1.5)的MAE为0.44 D，R2为0.38。各参数的MAE均在临床容许误差范围内(平K≤0.50 D、球镜≤0.75 D、镜片直径≤0.25 mm、环曲量≤0.75 D)。散点图与Bland-Altman分析显示预测值与真实值具有良好的一致性。特征重要性分析提示角膜平K(0.70)和陡K(0.11)是平K模型贡献最大的特征。

结论：基于常规临床参数构建的XGBoost模型对平K、球镜度数及镜片直径的预测MAE均在临床可接受范围内，环曲量预测具有参考价值，有望辅助临床验配。未来仍需前瞻性研究验证其提升实际配适成功率的价值。

METHODS:Clinical data were retrospectively collected from myopic patients who underwent initial orthokeratology lens fitting and achieved successful fitting at Puyang Second People's Hospital between January 2019 and March 2024. Data from right and left eyes were treated as independent samples, and the dataset was partitioned at the patient level into training and test sets in a 7∶3 ratio. Input features included uncorrected visual acuity(UCVA), axial length(AL), horizontal visible iris diameter(HVID), flat K, steep K, horizontal eccentricity, and vertical eccentricity. The lens toricity prediction model additionally incorporated keratometric astigmatism(ΔK)and surface asymmetry index(SAI). The output labels were the final ordering parameters(flat K, spherical power, lens diameter, and lens toricity). Separate XGBoost regression models were constructed for each output parameter, with hyperparameters optimized independently through grid search combined with 5-fold cross-validation. Model performance was evaluated on the test set using the coefficient of determination(R2), mean absolute error(MAE), and root mean square error(RMSE). Feature importance analysis was also performed.

RESULTS:Totally 400 patients(800 eyes)were included in the final analysis. The 400 patients comprised 208 male(52.0%)and 192 female(48.0%)patients, with a mean age of 10.1±2.3 y. After partitioning at the patient level, the training set comprised 280 patients(560 eyes; 146 males and 134 females; mean age 10.1±2.3 y), and the test set comprised 120 patients(240 eyes; 62 males and 58 females; mean age 10.0±2.2 y). No statistically significant differences in sex composition or age were found between the two sets(P>0.05). Toric lenses were used by 140 of the 280 patients(50.0%)in the training set and by 50 of the 120 patients(41.7%)in the test set(χ2=2.017, P=0.156). The flat K prediction model achieved an MAE of 0.34 D and an R2 of 0.84. The spherical power model attained an MAE of 0.43 D and an R2 of 0.70. The lens diameter model yielded an MAE of 0.12 mm and an R2 of 0.42. The lens toricity model(Tweedie regression, power=1.5)achieved an MAE of 0.44 D and an R2 of 0.38. The MAE for each parameter fell within the clinically acceptable tolerance limits(flat K≤0.50 D, spherical power ≤0.75 D, lens diameter ≤0.25 mm, lens toricity ≤0.75 D). Scatter plots and Bland-Altman analysis demonstrated strong agreement between predicted values and actual observations. Feature importance analysis revealed that flat K(0.70)and steep K(0.11)were the most influential predictors in the flat K prediction model.

CONCLUSION: The XGBoost model based on routine clinical parameters provides predictions of flat K, spherical power, and lens diameter with MAEs within clinically acceptable ranges, and offers useful reference for cylindrical power prediction. This approach may serve as a useful adjunct to clinical lens fitting. Nevertheless, future prospective studies are warranted to validate its clinical utility in improving actual fitting success rates.