Identifying factors associated with fast visual field progression in patients with ocular hypertension based on unsupervised machine learning
Identifying factors associated with fast visual field progression in patients with ocular hypertension based on unsupervised machine learning
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DOI：
10.48550/arxiv.2309.15867
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发表时间：
2023-09
期刊：
影响因子：
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通讯作者：
Xiaoqin Huang;Asma Poursoroush;Jian Sun;Michael V. Boland;Chris Johnson;Siamak Yousefi
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作者：
Xiaoqin Huang;Asma Poursoroush;Jian Sun;Michael V. Boland;Chris Johnson;Siamak Yousefi
目的：基于无监督机器学习识别具有不同视野（VF）进展趋势的高眼压（OHT）亚型，并发现与快速VF进展相关的因素。设计：横断面和纵向研究。参会人员：本研究共纳入了1568例高眼压治疗研究（OHTS）参与者的3133只眼，至少进行了5次随访VF测试。方法：我们使用潜在类混合模型（LCMM），以确定OHT亚型使用标准自动视野检查（SAP）的平均偏差（MD）轨迹。我们根据基线时的人口统计学、临床、眼部和VF因素对亚型进行了表征。然后，我们使用广义估计方程（GEE）确定了驱动VF快速进展的因素，并定性和定量地证明了结果。主要结果测量：SAP平均偏差（MD）变化率。结果：LCMM模型发现了四个集群（亚型）的眼睛与不同的轨迹MD恶化。群集眼数分别为794眼（25%）、1675眼（54%）、531眼（17%）和133眼（4%）。我们根据MD下降的平均值将这些集群标记为改善者、稳定者、缓慢进展者和快速进展者，分别为0.08、-0.06、-0.21和-0.45 dB/年。VF进展快的眼具有较高的基线年龄、眼压（IOP）、模式标准差（PSD）和屈光不正（RE），但较低的中央角膜厚度（CCT）。快速进展与钙通道阻滞剂、男性、心脏病史、糖尿病史、非裔美国人、中风史和偏头痛相关。结论：无监督聚类可以客观地识别OHT亚型，包括快速VF恶化的亚型，而无需人类专家干预。快速VF进展与较高的卒中、心脏病、糖尿病史以及更多使用钙通道阻滞剂的病史相关。快速进展者更多来自非洲裔美国人种族和男性，青光眼转换的发生率更高。分型可以为调整治疗计划提供指导，以减缓视力丧失并改善进展较快的患者的生活质量。
Purpose: To identify ocular hypertension (OHT) subtypes with different trends of visual field (VF) progression based on unsupervised machine learning and to discover factors associated with fast VF progression. Design: Cross-sectional and longitudinal study. Participants: A total of 3133 eyes of 1568 ocular hypertension treatment study (OHTS) participants with at least five follow-up VF tests were included in the study. Methods: We used a latent class mixed model (LCMM) to identify OHT subtypes using standard automated perimetry (SAP) mean deviation (MD) trajectories. We characterized the subtypes based on demographic, clinical, ocular, and VF factors at the baseline. We then identified factors driving fast VF progression using generalized estimating equation (GEE) and justified findings qualitatively and quantitatively. Main Outcome Measure: Rates of SAP mean deviation (MD) change. Results: The LCMM model discovered four clusters (subtypes) of eyes with different trajectories of MD worsening. The number of eyes in clusters were 794 (25%), 1675 (54%), 531 (17%) and 133 (4%). We labeled the clusters as Improvers, Stables, Slow progressors, and Fast progressors based on their mean of MD decline, which were 0.08, −0.06, −0.21, and −0.45 dB/year, respectively. Eyes with fast VF progression had higher baseline age, intraocular pressure (IOP), pattern standard deviation (PSD) and refractive error (RE), but lower central corneal thickness (CCT). Fast progression was associated with calcium channel blockers, being male, heart disease history, diabetes history, African American race, stroke history, and migraine headaches. Conclusion: Unsupervised clustering can objectively identify OHT subtypes including those with fast VF worsening without human expert intervention. Fast VF progression was associated with higher history of stroke, heart disease, diabetes, and history of more using calcium channel blockers. Fast progressors were more from African American race and males and had higher incidence of glaucoma conversion. Subtyping can provide guidance for adjusting treatment plans to slow vision loss and improve quality of life of patients with a faster progression course.