基于电子医疗记录的系统数据,基于快速眼进展的预测建模
Richul Oh1, Hyunjoong Kim2, Tae-Woo Kim1
1Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam, Gyeonggi-do, 13620, Republic of Korea.
Scientific reports
|April 16, 2025
概括
这项研究确定了关键的系统健康指标,预测了初级开角玻璃眼 (POAG) 患者的视网膜神经纤维层更快地变薄. 这些预测因素包括特定的血标记物和血压,有助于对青光眼的进展进行管理.
科学领域:
- 眼科医生 眼科 眼科
- 玻璃眼研究研究 玻璃眼研究
- 医疗数据分析 医学数据分析
背景情况:
- 主要开角青光眼 (POAG) 是不可逆转失明的主要原因.
- 预测视网膜神经纤维层 (RNFL) 稀薄的速度对于管理POAG进展至关重要.
- 系统因素在POAG进展中的作用需要进一步阐明.
研究的目的:
- 为了确定基线系统特征,预测POAG患者的快速RNFL稀释.
- 使用电子病历 (EMR) 开发和验证POAG进展的预测模型.
- 了解系统健康和眼部疾病进展之间的关系.
主要方法:
- 对POAG患者EMR数据的回顾性分析,追踪时间超过5年.
- 全球RNFL厚度每年使用光谱域光学连贯性断层扫描测量.
- 随机森林 (RF) 模型在系统数据上进行训练,以预测RNFL稀释率,使用SHAP值进行解释.
主要成果:
- 射频模型实现了0.88的R2,准确地预测了RNFL稀释.
- 确定了关键的系统预测因素:较高的阿斯巴酸氨基转移酶,较低的血糖,较低的系统血压和较高的HDL.
- 眼科因素,如较高的基线RNFL厚度和眼内压力也是显著的预测因素.
结论:
- 基线系统特征,包括特定的血液检测结果和血压,是POAG进展的有价值预测因素.
- 将系统性健康数据集成到预测模型中可以增强青光眼的治疗策略.
- 这项研究突出了对POAG的系统影响,并为个性化患者护理提供了见解.
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