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基于电子病历系统数据的青光眼快速进展预测模型

Predictive modeling of rapid glaucoma progression based on systemic data from electronic medical records.

作者信息

Oh Richul, Kim Hyunjoong, Kim Tae-Woo, Lee Eun Ji

机构信息

Department 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.

Department of Applied Statistics, Yonsei University, Seoul, Republic of Korea.

出版信息

Sci Rep. 2025 Apr 16;15(1):13101. doi: 10.1038/s41598-025-97344-1.

Abstract

This study investigated the baseline systemic features that predict rapid thinning of the retinal nerve fiber layer (RNFL) in patients with primary open-angle glaucoma (POAG). A database drawn from electronic medical records (EMRs) was searched for patients diagnosed with POAG between 2009 and 2016 who had been followed up for > 5 years with the annual evaluation of global RNFL thickness using spectral-domain optical coherence tomography. The rate of change in global RNFL thickness for each eye was determined by linear regression analysis over time. Systemic data obtained within 6 months from the time of glaucoma diagnosis were extracted from the EMRs and incorporated into a model to predict the rate of progressive RNFL thinning. The predictive model was trained and tested using a random forest (RF) method and interpreted using Shapley additive explanation plots (SHAP). The features able to explain the rate of progressive RNFL thinning were identified and interpreted. Data from 1256 eyes of 696 patients and 1107 eyes of 607 patients were included in the training and test sets, respectively. The R value for the RF model was 0.88 and mean absolute error of the model was 0.205 μm/year. The prediction model identified higher serum levels of aspartate aminotransferase, lower blood glucose, lower systolic blood pressure, and higher high-density lipoprotein as the four most important systemic features predicting rapid RNFL thinning over 5 years. Among the ophthalmic features, a higher global RNFL thickness and a higher intraocular pressure were the most important factors predicting rapid RNFL thinning. The study revealed baseline systemic features from the EMRs that were of predictive value for progression rate of POAG patients.

摘要

本研究调查了可预测原发性开角型青光眼(POAG)患者视网膜神经纤维层(RNFL)快速变薄的基线全身特征。从电子病历(EMR)数据库中搜索2009年至2016年间被诊断为POAG且随访时间超过5年的患者,这些患者每年使用光谱域光学相干断层扫描评估全视网膜神经纤维层厚度。通过线性回归分析确定每只眼睛全视网膜神经纤维层厚度随时间的变化率。从电子病历中提取青光眼诊断后6个月内获得的全身数据,并将其纳入一个模型,以预测视网膜神经纤维层渐进性变薄的速率。使用随机森林(RF)方法对预测模型进行训练和测试,并使用Shapley加法解释图(SHAP)进行解释。确定并解释了能够解释视网膜神经纤维层渐进性变薄速率的特征。训练集和测试集分别纳入了696例患者的1256只眼和607例患者的1107只眼的数据。RF模型的R值为0.88,模型的平均绝对误差为0.205μm/年。预测模型确定较高的血清天冬氨酸转氨酶水平、较低的血糖、较低的收缩压和较高的高密度脂蛋白是预测5年内视网膜神经纤维层快速变薄的四个最重要的全身特征。在眼科特征中,较高的全视网膜神经纤维层厚度和较高的眼压是预测视网膜神经纤维层快速变薄的最重要因素。该研究揭示了电子病历中的基线全身特征对POAG患者的病情进展速率具有预测价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5842/12003650/9998b0149141/41598_2025_97344_Fig1_HTML.jpg

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