Cheng Shiyu, Zhao Xinyu, Zhao Qing, Meng Lihui, Chen Youxin
Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Beijing, China.
Br J Ophthalmol. 2025 Feb 24;109(3):391-400. doi: 10.1136/bjo-2024-325246.
To construct and validate an optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) image model for predicting the occurrence of short-term vitreous haemorrhage (VH) in polypoidal choroidal vasculopathy (PCV) patients.
We retrospectively collected clinical and imaging information from patients diagnosed with PCV at Peking Union Medical College Hospital, Beijing, China, between January 2015 and October 2022. Six different screening strategies, including univariate analysis, multivariate analysis, least absolute shrinkage and selection operator, stepwise logistic regression, random forest and clinical-data-only approach, were used to select variables and build models. The nomogram was constructed based on the model with the best area under the curve (AUC) and was evaluated using receiver operating characteristic curves, calibration curves, decision curve analysis and clinical impact curves.
A total of 147 PCV patients were included and randomly divided into a training set (103 patients) and a validation set (44 patients), with an average follow-up time of 17.56±14.99 months. The optimal model that achieved higher AUC in both training and validation sets incorporated seven significant variables identified through univariate analysis: male [OR=2.76, p=0.022], central macular thickness [OR=1.003, p=0.002], the presence of haemorrhagic pigment epithelial detachment (HPED) [OR=6.99, p<0.001], the height of HPED [OR=1.002, p<0.001], the area of HPED [OR=1.16, p<0.001], the presence of multiple PEDs [OR=2.94, p=0.016] and the presence of subretinal haemorrhage [OR=3.11, p=0.011]. A predictive nomogram based on these variables yielded an AUC of 0.896 (95% CI 0.827 to 0.965) in the training set and 0.861 (95% CI 0.749 to 0.973) in the validation set, demonstrating good calibration and clinical usefulness.
The proposed OCT/OCTA-based image nomogram, as a novel and non-invasive tool, achieved satisfactory prediction of VH secondary to PCV.
构建并验证一种光学相干断层扫描(OCT)和光学相干断层扫描血管造影(OCTA)图像模型,用于预测息肉状脉络膜血管病变(PCV)患者短期玻璃体积血(VH)的发生。
我们回顾性收集了2015年1月至2022年10月在中国北京协和医院诊断为PCV的患者的临床和影像信息。采用六种不同的筛选策略,包括单因素分析、多因素分析、最小绝对收缩和选择算子、逐步逻辑回归、随机森林和仅临床数据方法,来选择变量并建立模型。基于曲线下面积(AUC)最佳的模型构建列线图,并使用受试者工作特征曲线、校准曲线、决策曲线分析和临床影响曲线进行评估。
共纳入147例PCV患者,随机分为训练集(103例患者)和验证集(44例患者),平均随访时间为17.56±14.99个月。在训练集和验证集中均获得较高AUC的最佳模型纳入了通过单因素分析确定的七个显著变量:男性[比值比(OR)=2.76,p=0.022]、中心黄斑厚度[OR=1.003,p=0.002]、出血性色素上皮脱离(HPED)的存在[OR=6.99,p<0.001]、HPED的高度[OR=1.002,p<0.001]、HPED的面积[OR=1.16,p<0.001]、多个PED的存在[OR=2.94,p=0.016]和视网膜下出血的存在[OR=3.11,p=0.011]。基于这些变量的预测列线图在训练集中的AUC为0.896(95%置信区间0.827至0.965),在验证集中为0.861(95%置信区间0.749至0.973),显示出良好的校准和临床实用性。
所提出的基于OCT/OCTA的图像列线图作为一种新型的非侵入性工具,对PCV继发的VH实现了令人满意的预测。