基于无监督机器学习识别眼压过高症患者视野快速恶化的相关因素

IF 2 4区 医学 Q2 OPHTHALMOLOGY
Journal of Glaucoma Pub Date : 2024-11-01 Epub Date: 2024-08-05 DOI:10.1097/IJG.0000000000002472
Xiaoqin Huang, Asma Poursoroush, Jian Sun, Michael V Boland, Chris A Johnson, Siamak Yousefi
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引用次数: 0

摘要

目的:基于无监督机器学习,识别具有不同视野(VF)进展趋势的眼压过高(OHT)亚型,并发现与VF快速进展相关的因素:设计:横断面和纵向研究:方法:我们采用潜类混合模型对1568名眼压治疗研究(OHTS)参与者的3133只眼睛进行了至少5次随访VF测试:我们使用潜类混合模型(LCMM),利用标准自动验光仪(SAP)的平均偏差(MD)轨迹来识别 OHT 亚型。我们根据基线时的人口、临床、眼部和 VF 因素对亚型进行了特征描述。然后,我们利用广义估计方程(GEE)确定了VF快速进展的驱动因素,并从定性和定量的角度对研究结果进行了论证:主要结果测量:SAP 平均偏差(MD)变化率:LCMM模型发现了MD恶化轨迹不同的四个眼群(亚型)。簇中的眼球数量分别为 794 只(25%)、1675 只(54%)、531 只(17%)和 133 只(4%)。根据MD下降率的平均值(分别为0.08、-0.06、-0.21和-0.45 dB/年),我们将这些群组标记为改善者(群组1)、稳定者(群组2)、缓慢进展者(群组3)和快速进展者(群组4)。VF进展快的眼睛基线年龄、眼压(IOP)、模式标准偏差(PSD)和屈光不正(RE)较高,但中心角膜厚度(CCT)较低。快速进展与男性、心脏病史、糖尿病史、非裔美国人和中风史有关:结论:无监督聚类可以客观地识别 OHT 亚型,包括那些 VF 快速恶化的亚型,而无需人工专家干预。快速 VF 进展与较高的中风、心脏病和糖尿病病史有关。快速进展者多为非裔美国人、男性,青光眼转化的发生率较高。亚型分析可为调整治疗方案提供指导,从而减缓视力丧失,改善进展较快患者的生活质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying Factors Associated With Fast Visual Field Progression in Patients With Ocular Hypertension Based on Unsupervised Machine Learning.

Prcis: We developed unsupervised machine learning models to identify different subtypes of patients with ocular hypertension in terms of visual field (VF) progression and discovered 4 subtypes with different trends of VF worsening. We then identified factors associated with fast VF progression.

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 5 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 (cluster 1), stables (cluster 2), slow progressors (cluster 3), and fast progressors (cluster 4) based on their mean of MD decline rate, 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 being male, heart disease history, diabetes history, African American race, and stroke history.

Conclusions: 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 and diabetes. Fast progressors were more from African American race, 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.

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来源期刊
Journal of Glaucoma
Journal of Glaucoma 医学-眼科学
CiteScore
4.20
自引率
10.00%
发文量
330
审稿时长
4-8 weeks
期刊介绍: The Journal of Glaucoma is a peer reviewed journal addressing the spectrum of issues affecting definition, diagnosis, and management of glaucoma and providing a forum for lively and stimulating discussion of clinical, scientific, and socioeconomic factors affecting care of glaucoma patients.
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