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建立和验证长期控制结果的预测模型,以后的正视镜片佩戴:一个为期五年的队列研究
Zixun Wang1, Xiaoling Zhang2, Xiaoxue Hu3
1Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China.
Frontiers in cell and developmental biology
|February 12, 2026
概括
这项研究开发了一种用于正视镜 (Ortho-K) 镜头佩戴的预测模型,识别患有轴长度控制不良风险的儿童. 关键预测因素包括轴长度随时间变化,为近视管理提供早期警告.
科学领域:
- 眼科医生 眼科 眼科
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 儿童近视控制至关重要.
- 视近视疗法 (Ortho-K) 是一种有效的近视控制方法.
- 预测Ortho-K的长期疗效对于个性化治疗至关重要.
研究的目的:
- 开发一个临床预测模型,用于5年轴长 (AL) 控制穿着Ortho-K镜头的儿童.
- 集成传统的回归与可解释的机器学习 (ML) 进行可靠的预测.
- 确定AL控制不良的关键预测因素,以指导干预措施.
主要方法:
- 利用了504名患有基线近视的儿童的数据.
- 使用的特征选择技术 (LASSO,Boruta,物流回归).
- 评估了多个ML算法,并将最佳模型视觉化为使用SHAP分析的nomogram.
主要成果:
- 轴长度变化 (Δ1, Δ3) 和平面E是5年AL控制的一致预测因素.
- 后勤回归显示了最好的整体性能 (AUC = 0.969).
- 使用 Δ1, Δ3 和平面 E 的简化模型保持了强大的预测能力 (AUC = 0.949).
结论:
- 使用Δ1,Δ3和平面E的强大,可解释的模型预测了5年的Ortho-K疗效.
- 在SHAP分析中,确定了一种临床值 (Δ3 = 0.05毫米) 控制近视不理想.
- 在线名图支持个性化风险估计,用于精确的近视管理.
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