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在基于无监督机器学习的眼睛高血压患者中,确定与快速视野进展相关的因素

Xiaoqin Huang¹, Asma Poursoroush¹, Jian Sun²

¹Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA.

|October 9, 2023

概括

无监督机器学习确定了四种眼睛高血压 (OHT) 亚型,揭示了不同的视野 (VF) 进展率. 快速VF进展与特定的人口统计和病史有关,指导个性化OHT治疗.

科学领域:

眼科医生眼科眼科
医学数据科学医学数据科学
临床研究临床研究

背景情况:

眼睛高血压 (OHT) 是青光眼的前体,其特征是眼内压力升高 (IOP).
了解OHT视野 (VF) 损失的异质进展对于及时干预至关重要.
目前的方法可能无法完全捕捉OHT患者中VF变化的多样性轨迹.

研究的目的:

根据不同的视野 (VF) 进展模式,使用无监督机器学习对眼睛高血压 (OHT) 的亚型进行分类.
为了确定与OHT快速VF进展相关的人口,临床和眼部因素.
为OHT管理提供个性化治疗策略的信息.

主要方法:

隐性类混合建模 (LCMM) 用于分析标准自动化周长度 (SAP) 平均偏差 (MD) 轨迹从眼睛高血压治疗研究 (OHTS) 中.
包括至少有五次随访VF测试的参与者 (n=1568名参与者的3133只眼睛).
使用一般估计方程 (GEE) 分析确定了与快速VF进展相关的因素.

主要成果:

确定了四种不同的OHT亚型:改善者 (25%),稳定者 (54%),缓慢进步者 (17%) 和快速进步者 (4%).
快速进展者表现出平均MD下降为-0.45dB/年,与较高的基线年龄,内血压,模式标准偏差 (PSD) 和折射误差 (RE) 相关,但较低的中枢角膜厚度 (CCT).
与快速进展相关的因素包括男性性别,非裔美国人种族,心脏病史,糖尿病,中风,偏头痛和使用通道阻塞剂.

结论:

无监督的聚类客观地识别了OHT亚型,包括那些快速恶化VF的亚型.
在OHT中VF的快速进展与特定的风险因素和人口特征有关.
分类OHT可以指导量身定制的治疗调整,以减轻视力丧失和提高患者的生活质量.

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