Yang Lanting, Xu Hui, Jiang Honghu, Zhu Jingyin, Chen Shihao
Department of Ophthalmology, Huadong Hospital, Fudan Universiry, Shanghai, China.
National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
Front Bioeng Biotechnol. 2025 Jul 16;13:1598546. doi: 10.3389/fbioe.2025.1598546. eCollection 2025.
The aim of this study is to develop an index for distinguishing between very asymmetric ectasia with normal topography (VAE-NT) eyes and normal eyes, with good performance in validity, reliability, and predictive values.
In the training dataset, this single-center retrospective study involved 102 healthy eyes and 97 VAE-NT eyes. After propensity score matching (PSM), data from 53 healthy eyes and 53 VAE-NT eyes, including demographic and Corvis ST examination results, were collected. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, intraclass correlation coefficient (ICC), and positive and negative likelihood ratios were calculated for the dynamic corneal response (DCR) parameters of Corvis ST. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model was used to objectively and comprehensively evaluate the Corvis ST DCRs, and logistic regression was used to determine the optimal combination of parameters that can accurately separate VAE-NT from normal corneas. In the validation dataset, 44 VAE-NT eyes and 49 normal eyes were involved. The validity, reliability, and predictive value of the index were further assessed using the validation dataset. The VAE-NT index was compared with the tomographic and biomechanical index (TBI) in both the training and validation datasets.
In the training dataset, the optimal parameter combination forming the VAE-NT index included the following DCRs: SP A1, SP HC, A1 Time, DA Ratio Max (2 mm), DA Ratio Max (1 mm), Integrated Radius, and stress-strain index version 2 (SSI2). The receiver operating characteristic (ROC) curve analysis showed an AUC value of 0.971, with a cut-off value of 0.425, an accuracy of 95.283%, a specificity of 94.340%, and a sensitivity of 96.230%. In the validation dataset, the AUC value of the VAE-NT index was 0.980. The sensitivity and specificity of the VAE-NT index were 93.180% and 95.920%, respectively. The positive and negative likelihood ratios of the VAE-NT index were 22.830 and 0.071, respectively. The ICC of the VAE-NT index was 0.835, and the accuracy was 94.624%. The VAE-NT index outperformed TBI in both the training and validation datasets.
The VAE-NT index was developed, exhibiting high sensitivity, specificity, and AUC, along with favorable likelihood ratios and repeatability, suggesting that the VAE-NT index is a robust and reliable tool for distinguishing VAE-NT eyes from normal eyes. Further validation in broader populations and over longer follow-up periods is needed to support clinical translation.
本研究旨在开发一种用于区分具有正常形态的极不对称扩张（VAE-NT）眼与正常眼的指数，该指数在有效性、可靠性和预测价值方面具有良好表现。
在训练数据集中，这项单中心回顾性研究纳入了102只健康眼和97只VAE-NT眼。经过倾向得分匹配（PSM）后，收集了53只健康眼和53只VAE-NT眼的数据，包括人口统计学数据和Corvis ST检查结果。计算了Corvis ST动态角膜反应（DCR）参数的受试者操作特征曲线下面积（AUC）、敏感性、特异性、组内相关系数（ICC）以及阳性和阴性似然比。采用理想解相似排序法（TOPSIS）模型对Corvis ST DCR进行客观全面评估，并使用逻辑回归确定能够准确区分VAE-NT与正常角膜的最佳参数组合。在验证数据集中，纳入了44只VAE-NT眼和49只正常眼。使用验证数据集进一步评估该指数的有效性、可靠性和预测价值。在训练和验证数据集中将VAE-NT指数与断层扫描和生物力学指数（TBI）进行比较。
在训练数据集中，构成VAE-NT指数的最佳参数组合包括以下DCR：SP A1、SP HC、A1 Time、DA Ratio Max（2mm）、DA Ratio Max（1mm）、综合半径和应力应变指数版本2（SSI2）。受试者操作特征（ROC）曲线分析显示AUC值为0.971，截断值为0.425，准确率为95.283%，特异性为94.340%，敏感性为96.230%。在验证数据集中，VAE-NT指数的AUC值为0.980。VAE-NT指数的敏感性和特异性分别为93.180%和95.920%。VAE-NT指数的阳性和阴性似然比分别为22.830和0.071。VAE-NT指数的ICC为0.835，准确率为94.624%。在训练和验证数据集中，VAE-NT指数均优于TBI。
开发了VAE-NT指数，其具有高敏感性、特异性和AUC，以及良好的似然比和可重复性，表明VAE-NT指数是区分VAE-NT眼与正常眼的强大且可靠的工具。需要在更广泛的人群中进行进一步验证并进行更长时间的随访，以支持临床转化。
